Dissertations / Theses on the topic 'Smart energy demand'

ENABLING DEMAND RESPONSE ON ENERGY MANAGEMENT IN SMART HOME With the penetration of the Internet of Things (IoT) paradigm into the household scenario, an increasing number of smart appliances have been deployed to improve the comfort of living in the household. At present, most smart home devices are adopting the Cloud-based paradigm. The increasing electricity overhead from these smart appliances, however, has caused issues, as existing home energy management systems are unable to reduce electricity consumption effectively. To address this issue, we propose the use of an Edge-based computing platform with lightweight computing devices. In our experiments, this Edge-based platform has proven to be more energy efficient when compared to the traditional Cloud-based platform. To further reduce energy tariffs for households, we propose an energy management framework, namely Edge-based energy management System (EEMS), to be used with the Edge-based system that was designed in the first stage of our research. The EEMS is a low infrastructure investment system. A small-scale solar energy harvesting system has also been integrated into this system. The non-intrusive load monitoring (NILM) algorithm has been implemented in appliances monitoring. Regarding to energy management function, the scheduling strategy can also conform to user preference. We have conducted a realistic experiment with several smart appliances and Raspberry Pi. The experiment resulted in the electricity tariff being reduced by 82.3%. The last part of research addresses demand response (DR) technology. With the development of DR, energy management systems such as EEMS are better able to be implemented. We propose the use of an electricity business trading model, integrated with user-side demand response resources. The business trading model can be adopted to manage risks, increase profit and improve user satisfaction. Users will also benefit from tariffs reduction with the use of this model.

Abstract The global energy system is undergoing a slow but massive change, initiated by environmental concerns but it is increasingly driven also by the zero-marginal cost of renewable energy. This change includes an increase in the effort to make the electric power system the main transport path for energy in the future. A massive research and development effort has henceforth been put into modernizing the electricity grid towards a so-called Smart Grid, by combining the power grid with communication networks and automation, as well as modernized market systems and structures. This work contributes to this process by introducing two unique models. The first provides a tool for better understanding the impact of combined infrastructure networks with a simple yet complex model of a combined energy, communication and decision model. The second model provides a detailed agent-based environment of an electricity market, supporting various independent entities inside the market, as well as a high time resolution and the often-neglected aspect of coupled market stages. That is, all mis-predictions of the first market stage (day-ahead) have to be settled at the second (balancing) stage. Both models are then used to assess the problem of demand side management, in which the traditional practice of power production being adjusted to the demand is at least partially dropped and flexibility in the demand is used to match the supply – as such technologies are deemed crucial to integrate the unsteady supply from renewable resources, like wind and solar power. We find that complicated scaling effects can be found even in the simplified model, hinting at insufficient consideration of the complexities involved in the real world. We then go to show such unfavorable scaling effects also exist in the current market environment as modeled in our second model. Finally, we show how to circumvent these problems within the current environment as well as introduce a framework to analyze cyber-physical systems and better handle their complexity
Tiivistelmä Globaali energiajärjestelmä on hitaan, mutta massiivisen muutoksen edessä. Tämän muutostarpeen on käynnistänyt ympäristöämme koskevat huolet, mutta lisääntyvässä määrin tähän vaikuttaa nykyään uusiutuvan energian marginaalikustannusten nollataso. Tähän muutokseen liittyy selkeä sähköverkkojen roolin korostuminen, ja pyrkimyksenä näyttää olevan muutos, jossa sähköverkot siirtävät suurimman osan käyttämästämme energiasta. Tämän seurauksena on käynnistetty merkittäviä tutkimus-ja tuotekehityspanostuksia, joiden tavoitteena on nykyaikaistaa sähköverkot älysähköverkoiksi. Älysähköverkoissa yhdistyvät sähköverkkoon integroidut tietoliikenneverkot ja automaatio sekä modernit sähkömarkkinajärjestelmät ja -rakenteet. Tämä työ tuottaa lisää ymmärrystä uudistumisprosessiin tuottamalla kaksi uutta malliratkaisua. Ensimmäisessä mallissa kehitetään työkalu, jonka avulla pystytään paremmin ymmärtämään toisiinsa yhdistettyjen infrastruktuuriverkkojen toimintaa yksinkertaisella, mutta kompleksisella mallilla, jossa energia- ja tietoliikenneverkot sekä tarvittava päätöksenteko yhdistetään. Toinen malli tuottaa yksityiskohtaisen agenttipohjaisen ympäristön sähkömarkkinasta. Malli tukee erilaisten itsenäisten sähkömarkkinaosapuolten mallintamista korkealla aikaresoluutiolla ja erityisesti usein huomiotta jätettyjen toisiinsa kytkeytyvien eri markkinavaiheiden mallintamista. Malli antaa eväitä vastata kysymykseen, miten ensimmäisessä markkinavaiheessa (vuorokausimarkkina) syntyvä ero tuotannon ja kulutuksen välillä tasapainotetaan toisessa markkinavaiheessa (tasapainotusmarkkina). Kumpaakin luotua mallia hyödynnetään arvioitaessa kulutushallintaa, jossa sähköverkkojen perinteisestä tuotannon ja kulutuksen tasapainosta ainakin osittain luovutaan ja kysyntä- eli kulutusjoustoa käytetään tasaamaan kulutus tuotannon suuruiseksi. Tämänkaltaiset mekanismit ovat oleellisia ja kriittisiä, kun sähköverkkoon liitetään suuria määriä vaihtelevatuottoista uusiutuvaa energiaa, kuten aurinko- ja tuulienergiaa. Tutkimuksessa huomasimme merkittäviä ja monimutkaisia skaalautuvuusilmiöitä, jotka kertovat, että sähköverkkojen tutkimuksessa reaalimaailman kompleksisuuden huomioiminen on ollut riittämätöntä. Sähkömarkkinamallia hyödyntämällä huomasimme vastaavia epätoivottuja skaalausilmiöitä esiintyvän myös nykyisessä sähkömarkkinassa. Erityisesti loimme keinoja, joilla skaalausilmiöistä on mahdollista päästä eroon nykyistä sähkömarkkinarakennetta käytettäessä. Tässä työssä luotuja malleja ja niiden viitekehystä hyödyntämällä pystymme paremmin analysoimaan kyberfysikaalisia järjestelmiä ja hallitsemaan niiden kompleksisuutta

Mitigating climate change lies to a large part within the Energy System. In order to make it sustainable and efficient, policies have to be framed accordingly. This study focuses on formulation of policies based on future projections of the energy demand in Oskarshamn municipality of Sweden. Oskarshamn is a former industrial municipality, whose economic activity is in decline and it requires policies that accelerates its growth. It is also stereo-typical of much of Europe, as industrial activities are transferred elsewhere and regions are left to re-invent themselves. Questions such as “how to make the existing system more efficient” and “what is the best energy saving alternative”, have to be answered. For which, Long range Energy Alternatives Planning (LEAP) tool is used to create scenarios based on different pathways and to project the energy demand in the future. The business as usual scenario is compared with mitigation scenario considering various energy efficiency measures. The measures mainly focus on Demand Side Management and improving energy lifestyle interactions. Examples include the impact of electric vehicles (EV) in the transport sector and effects of better insulation in residential buildings, etc. Nuclear is currently the main source and would possibly be phased out in the horizon and thus creating a need for alternative and sustainable sources of energy. The renewable energy scenario focuses on proposals for mixing renewable fuels in the energy supply side. These are not without costs and opportunities which are discussed in the study. The outcomes work a clear delineation of Greenhouse gas mitigation options, which in collaboration with the municipality would form the basis for a policy action plan.

To mitigate the technical challenges faced by the next-generation smart power grid, in this thesis, novel frameworks are developed for optimizing energy management and trading between power companies and grid consumers, who own renewable energy generators and storage units. The proposed frameworks explicitly account for the effect on demand-side energy management of various consumer-centric grid factors such as the stochastic renewable energy forecast, as well as the varying future valuation of stored energy. In addition, a novel approach is proposed to enhance the resilience of consumer-centric energy trading scenarios by analyzing how a power company can encourage its consumers to store energy, in order to supply the grid’s critical loads, in case of an emergency. The developed energy management mechanisms advance novel analytical tools from game theory, to capture the coupled actions and objectives of the grid actors and from the framework of prospect theory (PT), to capture the irrational behavior of consumers when faced with decision uncertainties. The studied PT and game-based solutions, obtained through analytical and algorithmic characterization, provide grid designers with key insights on the main drivers of each actor’s energy management decision. The ensuing results primarily characterize the difference in trading decisions between rational and irrational consumers, and its impact on energy management. The outcomes of this thesis will therefore allow power companies to design consumer-centric energy management programs that support the sustainable and resilient development of the smart grid by continuously matching supply and demand, and providing emergency energy reserves for critical infrastructure.
Master of Science

This research aims to develop solutions to relieve system stress conditions in electric grids. The approach adopted in this research is based on a new concept in the Smart Grid, namely, demand response optimization. A number of demand response programs with energy storage systems are designed to enable a community to achieve optimal demand side energy management. The proposed models aim to improve the utilization of the demand side energy through load management programs including peak shaving, load shifting, and valley lling. First, a model is proposed to nd the optimal capacity of the battery energy storage system (BESS) to be installed in a power system. This model also aims to design optimal switchable loads programs for a community. The penetration of the switchable loads versus the size of the BESS is investigated. Another model is developed to design an optimal load operation scheduling of a residential heating ventilation and air-conditioning system (HVACs). This model investigates the ability of HVACs to provide optimal demand response. The model also proposes a comfort/cost trade-os formulation for end users. A third model is proposed to incorporate the uncertainty of the photovoltaic power in a residential model. The model would nd the optimal utilization of the PV-output to supply the residential loads. In the first part of this research, mixed integer programming (MIP) formulations are proposed to obtain the optimal capacity of the (BESS) in a power system. Two optimization problems are investigated: (i) When the BESS is owned by a utility, the operation cost of generators and cost of battery will be minimized. Generator on/o states, dispatch level and battery power dispatch level will be determined for a 24-hour period. (ii) When the BESS is owned by a community for peak shaving, the objective function will have a penalty component for the deviation of the importing power from the scheduled power. MIP problems are formulated and solved by CPLEX.The simulation results present the effect of switchable load penetration level on battery sizing parameters. In the second part, a mixed integer programming (MIP) based operation is proposed in this part for residential HVACs. The objective is to minimize the total cost of the HVAC energy consumption under varying electricity prices. A simplied model of a space cooling system considering thermal dynamics is adopted. The optimization problems consider 24-hour operation of HVAC. Comfort/cost trade-o is modeled by introducing a binary variable. The big-M technique is adopted to obtain linear constraints while considering this binary variable. The MIP problems are solved by CPLEX. Simulation results demonstrate the effectiveness of HVAC's ability to respond to varying electricity price. Then, in the final part of this research, two Benders Decomposition strategies are applied to solve a stochastic mixed integer programming (MIP) formulation to obtain the optimal sizing of a photovoltaic system (PV) and battery energy storage system (BESS) to power a residential HVACs. The uncertainty of PV output is modeled using stochastic scenarios with the probability of their occurrence. Total cost including HVAC energy consumption cost and PV/battery installation cost is to be minimized with the system at grid-connected mode over eight hours subject to a varying electricity price. The optimization problem will nd the optimal battery energy capacity, power limit, a number of PV to be installed, and expected HVAC on/o states and BESS charging/discharging states for the next eight hours. This optimization problem is a large-scale MIP problem with expensive computing cost.

Secured provision of energy is vital for the sustainable development in all aspects. In this regard, smart grids are considered as a solution towards the sustainable energy provision as they enable efficient and reliable production, distribution, transmission, and consumption of electricity. However, smart electricity grid system is a complex system of systems that requires sophisticated collaboration tools and intelligent techniques along with active participations from all its connected users. Dynamic role and proactive participation of consumers through the integration of distributed renewable energy resources are highly anticipated for the long-term sustenance of these grids. By understanding the value and the necessity of consumers’ active participations, this research work focuses on the demand-supply collaboration among proactive participants for effectively managing the energy demands. The main goal of this research study is to analyze the impacts of integrating the various renewable energy resources in the collaborative network. Along with this goal, the main objective is to explore the role of different participants in the collaborative network under the domestic/residential environment. A quantitative research methodology is adopted to demonstrate and numerically explain the impact of consumers’ engagements and their demand flexibility towards energy demand management. The key results highlight that consumers should be induced to change their consumption patterns in conjunction with the dimensions of smart energy demand. In addition to this, they should be provided with properly designed collaboration platforms that can yield mutual benefits (financial, personal, behavioral), and provision of such platforms would assist them to create higher demand flexibility. Accordingly, active participation of prosumers and consumers would create a positive impact on locally managing the energy supply and demand. This active participation would also allow them to exchange or sell their demand flexibility among the connected members in the energy networks.

The efficiency, security and resiliency are very important factors for the operation of a distribution power system. Taking into account customer demand and energy resource constraints, electric utilities not only need to provide reliable services but also need to operate a power grid as efficiently as possible. The objective of this dissertation is to design, develop and deploy the Multi-Agent Systems (MAS) - together with control algorithms - that enable demand response (DR) implementation at the customer level, focusing on both residential and commercial customers. For residential applications, the main objective is to propose an approach for a smart distribution transformer management. The DR objective at a distribution transformer is to ensure that the instantaneous power demand at a distribution transformer is kept below a certain demand limit while impacts of demand restrike are minimized. The DR objectives at residential homes are to secure critical loads, mitigate occupant comfort violation, and minimize appliance run-time after a DR event. For commercial applications, the goal is to propose a MAS architecture and platform that help facilitate the implementation of a Critical Peak Pricing (CPP) program. Main objectives of the proposed DR algorithm are to minimize power demand and energy consumption during a period that a CPP event is called out, to minimize occupant comfort violation, to minimize impacts of demand restrike after a CPP event, as well as to control the device operation to avoid restrikes. Overall, this study provides an insight into the design and implementation of MAS, together with associated control algorithms for DR implementation in smart buildings. The proposed approaches can serve as alternative solutions to the current practices of electric utilities to engage end-use customers to participate in DR programs where occupancy level, tenant comfort condition and preference, as well as controllable devices and sensors are taken into account in both simulated and real-world environments. Research findings show that the proposed DR algorithms can perform effectively and efficiently during a DR event in residential homes and during the CPP event in commercial buildings.
Ph. D.

Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 54-61).
Energy efficiency and demand response are critical resources for the transition to a cleaner electricity grid. Demand-side management programs can reduce electricity use during peak times when power is scarce and expensive, and they can help to integrate intermittent renewable energy resources by balancing real-time supply and demand for electricity. These programs are more cost-effective than large-scale energy storage technologies and are particularly important in cities and states with strong climate change and energy goals. Since 2000, Austin Energy has managed a residential demand response program that enables it to reduce air conditioning usage by remotely adjusting thermostat settings at tens of thousands of homes. The utility distributed free thermostats to households that participated in this program; however, by 2012, it determined that only one third of them were working as intended. During the summer of 2013, Austin Energy decided to implement a new program utilizing new technology, Wi-Fi connected "smart" thermostats. Instead of providing free thermostats to reduce peak demand, the utility encouraged residents to bring their own device and receive a one-time $85 enrollment incentive. This thesis analyzes these two approaches to residential demand response as measured by program enrollment rates and participant performance during demand response events. In addition, it assesses the smart thermostats' ability to reduce energy consumption (i.e. improve energy efficiency) over the course of the summer. My analysis indicates that smart thermostats were more effective at reducing peak demand than the free thermostats employed in the previous program. However, homes with smart thermostats used more energy for air conditioning over the course of the summer than homes without, indicating limited energy efficiency potential from smart thermostats among the study population.
by Brian Bowen.
M.C.P.

he conditions of energy availability from different resources is depending on myriad factors like technological innovation, geographical prerequisites and market dynamics. In, microgrids, where often multiple energy resources are combined, managing only the generation side of the energy system is not always the most cost effective and sustainable approach. In this light, demand side management (DSM) has shown to bear great potential for additional improvement of microgrid (MG) performance. In this master thesis, three DSM strategies for the MG at Tezpur University (TU) are developed and evaluated regarding their potential for technical, financial and sustainability improvement. During a two-week visit to TU campus in the North-East of India, extensive studies (interviews, surveys, etc.) were conducted to establish the required knowledge, based on which DSM potential could be identified. The MG at TU consists of a grid connection, a PV installation and Diesel generators (DG). The development of DSM strategies is focused on the operation of DGs as they are subject to high operational cost. Major barriers for DSM applicability are the absence of detailed operational data, the complexity of required control systems and load prioritization. Office air conditioning (AC) units, the student hostels and the water treatment plants (WTP) have been found to provide suitable environments for implementation of DSM measures. The air conditioning outage energy control (AC-OEC) is a strategy for automatic AC unit shut-down during grid outages using wireless switches. The hostel daytime energy control (H-DEC) is a time-based approach to manage hostel loads during the absence of students. The water pumping energy control (WP-EC) establishes an alternative pumping schedule which allows the deferral of pump operation from DG to grid supplied time periods. By applying all three strategies, annual energy savings of 26.7% (49.3 MWh) of the DGs can be achieved. This corresponds to an equal share of CO2 emissions reduction (175.4 t) and reduction of operational cost (11,200 €). The total annual energy consumption of TU can be reduced by 4.4% (213 MWh), what corresponds to a reduction of 5.3% (24,600 €) of total annual spending on electricity and 4.3% of greenhouse gas emissions reduction. Over a lifetime of 25 years, accumulated monetary savings of 493,200 € at a payback period of the investment of 0.9 years are expected.

A smart home energy management system has been used to reshape the electricity demand of the residential buildings widely. It normally requires understanding the capability of residential buildings’ thermal mass which revisits to the temperature flatirons and providing enough energy buffers. In this project, phase change material (PCM) was used as the virtual thermal energy storage. Basically, two parts were included: thermal modelling of residential building with PCM layer. Secondly thermal behaviour of models under different conditions (heating, ventilation and air conditioning system, fenestration, solar radiation) is discussed. Some numerical methods for thermal modelling with EnergyPlus are also presented. A conduction finite difference algorithm in EnergyPlus are applied to calculate heat transfer between ambient and zone. The results indicate that PCM layers shift and decreased the indoor temperature during peak period. Also, solar radiation and fenestration can influence its performance. A model that is easily scalable in one thermal zone and convex as a function of the control inputs is derived based on energy balance equations. The indoor temperatures are treated as control inputs together with the cooling energy exchange with the virtual thermal storage. This simplifies the enforcement of comfort, which can be imposed through appropriate constraints on the control inputs. A convex constrained optimization program was formulated to address the optimal energy management, in order to minimize the electricity cost caused by Heating, Ventilation and Air Conditioning unit.

La modernisation des réseaux électriques via ce que l'appelle aujourd'hui les réseaux intelligents (ou smart grids) promet des avancées pour permettre de faire face à une augmentation de la demande mondiale ainsi que pour faciliter l'intégration des ressources décentralisées. Grâce à des moyens de communication et de calcul avancés, les smart grids offrent de nouvelles possibilités pour la gestion des ressources des consommateurs finaux, y compris pour de petits éléments comme de l'électroménager. Cependant, ce type de gestion basée sur des décisions prises indépendamment peuvent causer des perturbations tels qu'un rebond de consommation, ou des instabilités sur le réseau. La prise en compte des interactions entre les décisions de gestion énergétique de différentes maisons intelligentes est donc une problématique naissante dans les smart grids. Cette thèse vise à évaluer l'impact potentiel de mécanismes de coordination entre consommateurs résidentiels au niveau de quartiers, et ce à travers trois études complémentaires. Tout d'abord, une première stratégie pour la gestion coordonnée de maisons est proposée avec l'objectif d'augmenter l'utilisation locale d'énergie renouvelable à travers la mise en place d'échanges d'énergie électrique entre voisins. Les participants reçoivent en échange une compensation financière. L'algorithme de gestion est étudié dans une configuration centralisée et une configuration décentralisée en faisant appel au concept de système multi-agents, chaque maison étant représentée par un agent. Les résultats de simulation montrent que les deux approches sont efficaces pour augmenter la consommation locale d'énergie renouvelable et réduire les coûts énergétiques journaliers des consommateurs. Bien que l'approche décentralisée retourne des résultats plus rapidement, l'approche centralisée a une meilleure performance concernant les coûts. Dans une seconde étude, deux algorithmes de gestion énergétiques à J-1 sont proposés pour un quartier résidentiel. Un modèle de tarification dynamique est utilisé, où le prix dépend de la consommation agrégée du quartier ainsi que d'une forme de tarification heures creuses-heures pleines. L'objectif est ici de concevoir un mécanisme de coordination plus avancé (par rapport au précédent), en permettant des échanges d'énergie renouvelable résiduelle au sein du quartier. La performance des algorithmes est étudiée sur une période d'une journée puis d'une année, en prenant ou non en compte les erreurs de prévision. D'après les résultats de simulation, les deux algorithmes proposés montrent de meilleurs performances que les méthodes de référence (sans contrôle, et algorithme égoïste), même en considérant les erreurs de prévision. Enfin, dans une troisième étude, l'impact de l'introduction de production photovoltaïque résidentielle sur la performance d'un agrégateur est évaluée, dans une configuration centralisée. L'agrégateur interagit avec le marché spot et le gestionnaire de réseau, de façon à proposer un nouveau modèle de tarification permettant d'influencer les consommateurs à agir sur leur consommation. Les résultats de simulation montrent quand le taux de pénétration de photovoltaïque résidentiel augmente, le profit de l'agrégateur diminue, du fait de l'autoconsommation dans le quartier
Grid modernization through philosophies as the Smart Grid has the potential to help meet the expected world increasing demand and integrate new distributed generation resources at the same time. Using advanced communication and computing capabilities, the Smart Grid offers a new avenue of controlling end-user assets, including small units such as home appliances. However, with such strategies, decisions taken independently can cause undesired effects such as rebound peaks, contingencies, and instabilities in the network. Therefore, the interaction between the energy management actions of multiple smart homes is a challenging issue in the Smart Grid. Under this purpose, in this work, the potential of coordination mechanisms established among residential customers at the neighborhood level is evaluated through three studies. Firstly, coordinative home energy management is presented, with the aim to increase local renewable energy usage in the neighborhood area by establishing energy trading among smart homes, which are compensated by incentives. The control algorithm is realized in both centralized and decentralized manners by deploying a multi-agent system, where neighborhood entities are modeled as agents. Simulations results show that both methods are effective on increasing local renewable energy usage and decreasing the daily electricity bills of customers. However, while the decentralized approach gives results in shorter time, the centralized approach shows a better performance regarding costs. Secondly, two decentralized energy management algorithms are proposed for day-ahead energy management in the neighborhood area. A dynamic pricing model is used, where price is associated to the aggregated consumption and grid time-of-use scheme. The objective of the study is to establish a more advanced coordination mechanism (compared to previous work) with residual renewable energy is shared among smart homes. In this study, the performance of the algorithms is investigated with daily and annual analyses, with and without considering forecasting errors. According to simulations results, both coordinative control models show better performance compared to baseline and selfish (no coordination) control cases, even when considering forecasting errors. Lastly, the impact of photovoltaic systems on a residential aggregator performance (in a centralized approach) is investigated in a neighborhood area. In the proposed model, the aggregator interacts with the spot market and the utility, and proposes a novel pricing scheme to influence customers to control their loads. Simulation results show that when the penetration level of residential photovoltaics (PV) is increased, the aggregator profit decreases due to self-consumption ability with PV in the neighborhood

Demand response (DR) is one of the integral mechanisms of today's smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector.

In the future, residential energy users will seize the full potential of demand response schemes by using an automated smart home energy management system (SHEMS) to schedule their distributed energy resources. The underlying optimisation problem facing a SHEMS is a sequential decision making problem under uncertainty because the states of the devices depend on the past state. There are two major challenges to optimisation in this domain; namely, handling uncertainty, and planning over suitably long decision horizons. In more detail, in order to generate high quality schedules, a SHEMS should consider the stochastic nature of the photovoltaic (PV) generation and energy consumption. In addition, the SHEMS should accommodate predictable inter-daily variations over several days. Ideally, the SHEMS should also be able to integrate into an existing smart meter or a similar device with low computational power. However, extending the decision horizon of existing solution techniques for sequential stochastic decision making problems is computationally difficult and moreover, these approaches are only computationally feasible with a limited number of storage devices and a daily decision horizon. Given this, the research investigates, proposes and develops fast solution techniques for implementing efficient SHEMSs. Specifically, three novel methods for overcoming these challenges: a two-stage lookahead stochastic optimisation framework; an approximate dynamic programming (ADP) approach with temporal difference learning; and a policy function approximation (PFA) algorithm using extreme learning machines (ELM) are presented. Throughout the thesis, the performance of these solution techniques are benchmarked against dynamic programming (DP) and stochastic mixed-integer linear programming (MILP) using a range of residential PV-storage (thermal and battery) systems. We use empirical data collected during the Smart Grid Smart City project in New South Wales, Australia, to estimate the parameters of a Markov chain model of PV output and electrical demand using an hierarchical approach, which first cluster empirical data and then learns probability density functions using kernel regression (Chapter 2). The two-stage lookahead method uses deterministic MILP to solve a longer decision horizon, while its end-of-day battery state of charge is used as a constraint for a daily DP approach (Chapter 4). Here DP is used for the daily horizon as it is shown to provide close-to-optimal solutions when the state, decision and outcome spaces are finely discretised (Chapter 3). However, DP is computationally difficult because of the dimensionalities of state, decision and outcome spaces, so we resort to MILP to solve the longer decision horizon. The two-stage lookahead results in significant financial benefits compared to daily DP and stochastic MILP approaches (8.54% electricity cost savings for a very suitable house), however, the benefits decreases as the actual PV output and demand deviates from their forecast values. Building on this, ADP is proposed in Chapter 5 to implement a computationally efficient SHEMS. Here we obtain policies from value function approximations (VFAs) by stepping forward in time, compared to the value functions obtained by backward induction in DP. Similar to DP, we can use VFAs generated during the offline planning phase to generate fast real-time solutions using the Bellman optimality condition, which is computationally efficient compared to having to solve the entire stochastic MILP problem. The decisions obtained from VFAs at a given time-step are optimal regardless of what happened in the previous time-steps. Our results show that ADP computes a solution much faster than both DP and stochastic MILP, and provides only a slight reduction in quality compared to the optimal DP solution. In addition, incorporating a thermal energy storage unit using the proposed ADP-based SHEMS reduces the daily electricity cost by up to 57.27% for a most suitable home, with low computational burden. Moreover, ADP with a two-day decision horizon reduces the average yearly electricity cost by a 4.6% over a daily DP method, yet requires less than half of the computational effort. However, ADP still takes a considerable amount of time to generate VFAs in the off-line planning phase and require us to estimate PV and demand models. Given this, a PFA algorithm that uses ELM is proposed in Chapter 6 to overcome these difficulties. Here ELM is used to learn models that map input states and output decisions within seconds, without solving an optimisation problem. This off-line planning process requires a training data set, which has to be generated by solving the deterministic SHEMS problem over couple of years. Here we can use a powerful cloud or home computer as it is only needed once. PFA models can be used to make fast real-time decisions and can easily be embedded in an existing smart meter or a similar low power device. Moreover, we can use PFA models over a long period of time without updating the model and still obtain similar quality solutions. Collectively, ADP and PFA using ELM can overcome challenges of considering the stochastic variables, extending the decision horizon and integrating multiple controllable devices using existing smart meters or a device with low computational power, and represent a significant advancement to the state of the art in this domain.

Energy consumption by buildings, due to various factors such as temperature regulation, lighting, poses a threat to our environment and energy resources. In the United States, statistics reveal that commercial and residential buildings combined contribute about 40 percent of the overall energy consumption, and this figure is expected to increase. In order to manage the growing demand for energy, there is a need for energy system optimization, which would require a realistic, high-resolution energy-demand model. In this work, we investigate and model the energy consumption of buildings by taking into account physical, structural, economic, and social factors that influence energy use. We propose a novel activity based modeling framework that generates an energy demand profile on a regular basis for a given nominal day.  We use this information to generate a building-level energy demand profile at highly dis-aggregated level. We then investigate the different possible uses of generated demand profiles in different What-if scenarios like urban-area planning, demand-side management, demand sensitive pricing, etc. We also provide a novel way to resolve correlational and consistency problems in the generation of individual-level and building-level "shared" activities which occur due to individuals\' interactions.
Master of Science

Fuel supply issues are a major concern in remote island communities and this is an engineering field that needs to be analyzed in detail for transition to sustainable energy systems. Power generation in remote communities such as the islands of the Maldives relies on power generation systems primarily dependent on diesel generators. As a consequence, power generation is easily disrupted by factors such as the delay in transportation of diesel or rises in fuel price, which limits shipment quantity. People living in remote communities experience power outages often, but find them just as disruptive as people who are connected to national power grids. The use of renewable energy sources could help to improve this situation, however, such systems require huge initial investments. Remote power systems often operate with the help of financial support from profit-making private agencies and government funding. Therefore, investing in such hybrid systems is uncommon. Current electrical power generation systems operating in remote communities adopt an open loop control system, where the power supplier generates power according to customer demand. In the event of generation constraints, the supplier has no choice but to limit the power supplied and this often results in power cuts. Most smart grids that are being established in developed grids adopt a closed loop feedback control system. The smart grids integrated with demand side management tools enable the power supplier to keep customers informed about their daily energy consumption. Electric utility companies use different demand response techniques to achieve peak energy demand reduction by eliciting behavior change. Their feedback information is commonly based on factors such as cost of energy, environmental concerns (carbon dioxide intensity) and the risk of black-outs due to peak loads. However, there is no information available on the significant link between the constraints in resources and the feedback to the customers. In resource-constrained power grids such as those in remote areas, there is a critical relationship between customer demand and the availability of power generation resources. This thesis develops a feedback control strategy that can be adopted by the electrical power suppliers to manage a resource-constrained remote electric power grid such that the most essential load requirements of the customers are always met. The control design introduces a new concept of demand response called participatory demand response (PDR). PDR technique involves cooperative behavior of the entire community to achieve quality of life objectives. It proposes the idea that if customers understand the level of constraint faced by the supplier, they will voluntarily participate in managing their loads, rather than just responding to a rise in the cost of energy. Implementation of the PDR design in a mini-grid consists of four main steps. First, the end-use loads have to be characterized using energy audits, and then they have to be classified further into three different levels of essentiality. Second, the utility records have to be obtained and the hourly variation factors for the appliances have to be calculated. Third, the reference demand curves have to be generated. Finally, the operator control system has to be designed and applied to train the utility operators. A PDR case study was conducted in the Maldives, on the island of Fenfushi. The results show that a significant reduction in energy use was achieved by implementing the PDR design on the island. The overall results from five different constraint scenarios practiced on the island showed that during medium constrained situations, load reductions varied between 4.5kW (5.8%) and 7.7kW (11.3%). A reduction of as much as 10.7kW (15%) was achieved from the community during a severely constrained situation.

To combat global climate change, the reduction of carbon emissions in different industries, particularly the power industry, has been gradually moving towards a low-carbon profile to alleviate any irreversible damage to the planet and our future generations. Traditional fossil-fuel-based generation is slowly replaced by more renewable energy generation while it can be harnessed. However, renewables such as solar and wind are stochastic in nature and difficult to predict accurately. With the increasing content of renewables, there is also an increasing challenge to the planning and operation of the grid. With the rapid deployment of smart meters and advanced metering infrastructure (AMI), an emerging approach is to schedule controllable end-use devices to improve energy efficiency. Real-time pricing signals combined with this approach can potentially deliver more economic and environmental advantages compared with the existing common flat tariffs. Motivated by this, the thesis presents an automatic and optimal load scheduling framework to help balance intermittent renewables via the demand side. A bi-level consumer-utility optimization model is proposed to take marginal price signals and wind power into account. The impact of wind uncertainty is formulated in three different ways, namely deterministic value, scenario analysis, and cumulative distributions function, to provide a comprehensive modeling of unpredictable wind energy. To solve the problem in off-the-shelf optimization software, the proposed non-linear bi-level model is converted into an equivalent single-level mixed integer linear programming problem using the Karush-Kuhn-Tucker optimality conditions and linearization techniques. Numerical examples show that the proposed model is able to achieve the dual goals of minimizing the consumer payment as well as improving system conditions. The ultimate goal of this work is to provide a tool for utilities to consider the demand response model into their market-clearing procedure. As high penetration of distributed renewable energy resources are most likely applied to remote or stand-alone systems, planning such systems with uncertainties in both generation and demand sides is needed. As such, a three-level probabilistic sizing methodology is developed to obtain a practical sizing result for a stand-alone photovoltaic (PV) system. The first-level consists of three modules: 1) load demand, 2) renewable resources, and 3) system components, which comprise the fundamental elements of sizing the system. The second-level consists of various models, such as a Markov chain solar radiation model and a stochastic load simulator. The third-level combines reliability indices with an annualized cost of system to form a new objective function, which can simultaneously consider both system cost and reliability based on a chronological Monte Carlo simulation and particle swamp optimization approach. The simulation results are then tested and verified in a smart grid laboratory at the University of Hong Kong to demonstrate the feasibility of the proposed model. In summary, this thesis has developed a comprehensive framework of demand response on variable end-use consumptions with stochastic generation from renewables while optimizing both reliability and cost. Smart grid technologies, such as renewables, microgrid, storage, load signature, and demand response, have been extensively studied and interactively modeled to provide more intelligent planning and management for the smart grid.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy

Sweden has developed ambitious goals regarding energy and climate politics. One major goal is to change the entire electricity production from fossil fuels to sustainable energy sources, this will contribute to Sweden being one of the first countries in the world with non-fossil fuel in the electricity sector. To manage this, major changes need to be implemented and difficulties on the existing grid will occur with the expansion of digitalization, electrification and urbanization. By using smart grids, it is possible to deal with these problems and change the existing electricity grid to use more distributed power generation, contributing to flexibility, stability and controllability. The goal with smart grids is to have a sustainable electricity grid with low losses, security of supply, environmental-friendly generation and also have choices and affordable electricity for customers. The purpose of this project is to identify and evaluate several indicators for a smart grid, how they relate and are affected when different scenarios with different technologies are implemented in a test system. Smart grid indicators are quantified metrics that measure the smartness of an electrical grid. There are five scenarios where all are based on possible changes in the society and electricity consumption, these scenarios are; Scenario A – Solar power integration, Scenario B – Energy storage integration, Scenario C – Electric vehicles integration, Scenario D – Demand response and Scenario E – Solar power, Energy storage, Electric vehicles and Demand response integration. A model is implemented in MATLAB and with Monte Carlo simulations expected values, standard deviation and confidence interval were gained. Four selected indicators (Efficiency, capacity factor, load factor and relative utilization) was then analyzed. The results show that progress on indicators related to all smart grid characteristics is needed for the successful development of a smart grid. In scenario C, all four selected indicators improved. This shows that these indicators could be useful for promoting the integration of electric vehicles in an electricity grid. In Scenario A, solar power integration contributed to all indicators deteriorate, this means that, technical solutions that can stabilize the grid are necessary to implement when integrating photovoltaic systems. The load factor is a good indicator for evaluating smart grids. This indicator can incentivize for an even load and minimize the peak loads which contributes to a flexible and efficient grid. With the capacity factor, the utilization and free capacity can be measured in the grid, but it can counteract renewable energy integration if the indicator is used in regulation.

Cette thèse propose un concept d'optimisation de la consommation d'énergie dans les maisons intelligentes basées sur la gestion de la demande qui repose sur l'utilisation de système d e gestion de l'énergie à la maison (HEMS) qui est en mesure de contrôler les appareils ménagers. L'avantage de ce concept est l'optimisation de la consommation d'énergie sans réduire les utilisateurs vivant confort. Un mécanisme adaptatif pour une croissance intelligente système de gestion de l'énergie de la maison qui a composé des algorithmes qui régissent l'utilisation des différents types de charges par ordre de priorité pré-sélectionné dans la maison intelligente est proposé. En outre, une méthode pourl'optimisation de la puissance générée à partir d'un hybride de systèmes d'énergie renouvelables (HRES) afin d'obtenir la demande de charge. particules technique d'optimisation essaim (PSO) est utilisé comme l'optimisation algorithme de recherche en raison de ses avantages par rapport à d'autres techniques pour réduire le coût moyen actualisé de l'énergie (LCE) avec une plage acceptable de la production en tenant compte des pertes entre la production et la demande. Le problème est défini et la fonction objective est introduite en tenant compte des valeurs de remise en forme de sensibilité dans le processus d’essaim de particules. La structure de l'algorithme a été construite en utilisant un logiciel MATLAB et Arduino 1.0.5 du logiciel.Ce travail atteint le but de réduire la charge de l'électricité et la coupure du rapport pic-moyenne (PAR)
This thesis proposes a concept of power consumption optimization in smart homes based on demand side management that reposes on using Home Energy Management System (HEMS) that is able to control home appliances. The advantage of the concept is optimizing power consumption without reducing the users living comfort. An adaptive mechanism for smart home energy management system which composed of algorithms that govern the use of different types of loads in order of pre-selected priority in smart home is proposed. In addition a method for the optimization of the power generated from a Hybrid Renewable Energy Systems (HRES) in order to achieve the load demand. Particle Swarm Optimization Technique (PSO) is used as optimization searching algorithm due to its advantages over other techniques for reducing the Levelized Cost of Energy (LCE) with an acceptable range of the production taking into consideration the losses between production and demand sides. The problem is defined and the objective function is introduced taking into consideration fitness values sensitivity in particle swarm process. The algorithm structure was built using MATLAB software and Arduino 1.0.5 Software. This work achieves the purpose of reducing electricity expense and clipping the Peak-toAverage Ratio (PAR). The experimental setup for the smart meter implementing HEMS is built relying on the Arduino Mega 2560 board as a main controller and a web application of URL http://www.smarthome-em.com to interface with the proposed smart meter using the Arduino WIFI Shield

The 2050 national energy scenarios as planned by the DECC, academia and industry specify a range of different decarbonised supply side technologies combined with the electrification of transportation and heating. Little attention is paid to the household demand side; indeed within many scenarios a high degree of domestic Demand Side Management (DSM) is implicit if the National Grid is to maintain supply-demand balance. A top-down, bottom-up hybrid model named Shed-able Household Energy Demand (SHED) has been developed and the results of which presented within this thesis. SHED models six published national energy scenarios, including three from the Department for Energy and Climate Change, in order to provide a broad coverage of the possible energy scenario landscape. The objective of which is to quantify the required changes in current household energy demand patterns via DSM, as are implicit under these highly electricity dominated scenarios, in order to maintain electrical supply-demand balance at the national level. The frequency and magnitude of these required household DSM responses is quantified. SHED performs this by modelling eleven years of supply-demand dynamics on the hourly time step, based on the assumptions of the published energy scenarios as well as weather data from around 150 weather stations around the UK and National Grid historic electricity demand data. The bottom-up component of SHED is populated by 1,000 households hourly gas and electricity demand data from a recently released dataset from a smart metering trial in Ireland. This aggregate pool of households enables national domestic DSM dynamics to be disaggregated to the aggregate household level. Using household classifications developed by the Office for National Statistics three typical ' households are identified within the aggregate pool and algorithms developed to investigate the possible required responses from these three households. SHED is the first model of its kind to connect national energy scenarios to the implications these scenarios may have on households consumption of energy at a high temporal resolution. The analysis of the top-down scenario modelling shows significant periods where electrical demand exceeds supply within all scenarios, within many scenarios instances exist where the deficit is unserviceable due to lack of sufficient spare capacity either side of the deficit period. Considering the level of participation required within the modelled scenarios in order to balance the electricity system and the current lack in understanding of smart metering and Time-Of-Use (TOU) tariffs within households, it would seem there is a disconnect between the electricity system being planned, the role this system expects of households and the role households are willing to play.

This PhD thesis proposes a bottom-up approach that accurately addresses the operational flexibility embedded in each part of a multi-energy system (MES). Several models which cover the simulations from replicating domestic electrified demands to power system scheduling are proposed. More specifically, a domes-tic multi-energy consumption model is firstly developed to simulate one minute resolution energy profiles of individual dwellings with the installation of prospec-tive technologies (i.e., electric heat pumps (EHPs), electric vehicles (EVs)). After-wards, a fast linear programming (LP) unit commitment (UC) model is devel-oped with the consideration of characteristics of generators and a full set of ancil-lary services (i.e., frequency response and reserves). More importantly, the fre-quency response requirements in low inertia systems are assessed with the con-sideration of three grid frequency regulations (i.e., rate of change of frequency, Nadir and quasi-steady state). Furthermore, the UC model has integrated vari-ous flexibility contributors in MES to provide ancillary and flexibility services, which include pumped hydro storages (PHSs), interconnectors, batteries and demand side resources (i.e., individual EHPs, heat networks, electrolysers). More importantly, the fast frequency response (FFR) provision from nonsynchronous resources is implemented and the demand response application of electrolysers is taken as an example to provide FFR in the UC model. By using the integrated UC model with the consideration of flexibility services provided by resources in the MES, the advantages of multi-energy operation can be clearly identified which can be used to inform system operators and policy makers to design and operate energy systems in a more economic and environment-friendly way.

The global energy demand in residential sector is increasing steadily every year due to advancement in technologies. The present electricity grid is designed to support peak demand rather than Peak to Average (PAR) demand. Utilities are investigating the residential Demand Response (DR) to lower the (PAR) ratio and eliminate the need of building new power infrastructure. This requires Home Energy Management System (HEMS) at grid edge to manage and control the energy demand. In this thesis, we presented an MDPSO based DR enabled HEMS model for optimal allocation of energy resources in a smart dwelling. The algorithm is designed to lower peak energy demand as well as encourage the active participation of customers by offering a reward to comply with DR request. We categorized appliances as elastic non-deferrable loads and inelastic deferrable loads based on their DR potential and operating characteristics. The scheduling of elastic and inelastic class of appliances is performed separately using canonical and binary version of PSO given how we expressed out load categories. We performed use case simulation to validate the performance of MDPSO for combination of different tariffs: Time of Use (TOU), TOU and Critical peak rebate signal (CPR), TOU and upper demand limit. Simulation results show that algorithm can reduce the electricity cost in range of 28% to 7% under increasing comfort conditions in response to TOU prices and Peak demand reduction of about 24% under TOU pricing and medium comfort conditions for single household. Under CPR DR requests, with respect to TOU pricing, there is effectively no change in the peak under the minimum comfort scenario. Furthermore, algorithm is able to suppress the peak upto 25% under combination of TOU and hard constraint on maximum power withdrawn from grid with no change in the electricity cost. Scheduling of multiple houses under TOU pricing results in peak reduction of 7 % as compared to baseline state. Under combination of TOU and CPR the aggregate peak energy demand of multiple households during DR activation time intervals is reduced by 32 %. The algorithm can suppress the peak demand by 27% under TOU and hard constraint on maximum power withdrawn from grid by multiple houses.

Smart grid has been under continuous development since 2008. It requires the re-construction of traditional power systems. As an important component of a smart grid, load management has been diffusely known as a bright solution to enhance the demand side energy efficiency and optimize energy consumption. In this project, load management techniques on the demand side are studied at two levels in a smart grid: the smart home level and the load aggregator level. At the smart home level, this project studies the development of home energy management systems (HEMSs), which optimally schedule home energy resources to minimize home electricity costs. The potential of plug-in electric vehicles (EVs) is harnessed by the developed HEMS to supply power to the home and absorb surplus residential renewable power output. At the load aggregator level, this project studies the feasibility of aggregating thermostatically controlled loads (TCLs) in multiple buildings to bid in the wholesale power market. An optimal scheduling model for TCLs is proposed in this project to minimize imbalance costs of the load aggregators in the power market. To address the uncertainties in imbalance penalty prices, this project introduces the rolling horizon optimization (RHO) technique to continuously update TCL ON/OFF plans with the realization of imbalance prices. This research also includes techniques for solving load management optimization models. A new heuristic optimization method, Natural Aggregation Algorithm (NAA), is used to solve the aforementioned HEMSs and TCLs scheduling models. The encoding scheme and constraint handling strategies are studied, and the efficiency of NAA in solving the models is also investigated.

The 2008 Climate Change Act set legally-binding carbon reduction targets. Demand side management (DSM) includes energy use reduction and peak shaving and offers significant potential to reduce the amount of carbon used by the electricity grid. The demand side management (DSM) schemes that have tried to meet this challenge have been dominated by engineering-based approaches and so favour tools like automation (which aims to make shifting invisible) and pricing (which requires customer response) to shift demand. These approaches tend to focus on the tools for change and take little account of people and energy-use practices. This thesis argues that these approaches are limited and therefore unlikely to produce the level of response that will be needed in future. The thesis therefore investigates the potential for time-shifting domestic energy demand but takes a different angle by trying to understand how people use energy in their daily lives, whether this use can be shifted and some of the implications of shifting it. The centrepiece of the work is an empirical study of eleven households energy-use practices. The interdisciplinary methodology involved in-house observations, interviews, photographs, metered energy data and disruptive interventions. The data was collected in two phases. Initially, a twenty-four hour observation was carried out in each household to find out how energy was implicated in everyday practices. Next, a series of three challenges were carried out, aimed at assessing the implications of disrupting practices by time-shifting food preparation, laundry and work/ leisure. A practice theory approach is used to shift the focus of attention from appliances, tools for change, behaviour or even people, to practices. The central finding of this work is that practices were flexible. This finding is nuanced, in the light of the empirical research, by an extended discussion on the nature of practices; in particular, the relationship between practices and agency and the temporal-spatial locatedness of practices. The findings demonstrate that, in this study at least, expanding the range of demand response options was possible. The research suggests numerous possibilities for extending the potential of practices to shift in time and space, shift the energy used in practices or substitute practices for other non-energy-using practices, though there are no simple technological or behavioural fixes . More profoundly, however, the thesis concludes that infrastructures of provision , such as the electricity grid and the companies that run it, underpin and facilitate energy-use practices irrespective of the time of day and year. In this context technology-led demand response schemes may ultimately contribute to the problem they purport to solve. A more fundamental interrogation of demand and the infrastructures that serve it is therefore necessary and is almost entirely absent from the demand response debate.

The objective of the proposed research is to analyze automated residential energy management technology using primary energy source utilization. A residential energy management system (REMS) is an amalgamation of hardware and software that performs residential energy usage monitoring, planning, and control. Primary energy source utilization quantifies power system levels impacts on power generation cost, fuel utilization, and environmental air pollution; based on power system generating constraints and electric load. Automated residential energy management technology performance is quantified through a physically-based REMS simulation. This simulation includes individual appliance operation and accounts for consumer behavior by stochastically varying appliance usage and repeating multiple simulation iterations for each simulated scenario. The effect of the automated REMS under varying levels of control will be considered. Aggregate REMS power system impacts are quantified using primary energy source utilization. This analysis uses a probabilistic economic dispatch algorithm. The economic dispatch algorithm quantifies: fuel usage and subsequent environmental air pollution (EAP) generated; based on power system generating constraints and electric load (no transmission constraints are considered). The analysis will comprehensively explore multiple residential energy management options to achieve demand response. The physically-based REMS simulation will consider the following control options: programmable thermostat, direct load control, smart appliance scheduling, and smart appliance scheduling with a stationary battery. The ability to compare multiple automated residential energy management technology options on an equal basis will guide utility technology investment strategies.

[EN] The electric power industry is being shaken by a new idea that is taking shape: smart grids. Three aspects are considered keys to reach smart grids: a) The structure of the network must meet the smart grid concept, i.e. it must be resistant to failures, e.g. causing the automatic separation of any broken element without affecting the operation of the other components; it must be flexible to allow the connection or disconnection of loads and distributed generators, it must maintain efficient operation under various load conditions, and so on. b) The network should open the possibilities of participation of large and small generators as well as users, enabling new business opportunities and active participation, so that "intelligent" generation or consumption may benefit. c) All participants must have easy access to the information needed to choose the best operating strategy in each case. In regard to the first condition (a) there are significant challenges to solve: network automation, optimal design, development of new protection and control equipment, etc. It will be necessary to develop equipment adapted to new problems and new needs that will be generated in these networks. Those items of equipment should be standardised, it will be necessary to define tests to take into account issues that currently are not usually needed, such as the presence of disturbances in voltage, or others. In this sense, marginally though, the research team in which the author works has collaborated with a laboratory for electrical testing, the Flex Power Grid Lab Research Infrastructure DNV KEMA in the Netherlands, in the definition and implementation of some tests, as described in Chapter 3. Smart grids. In the second aspect (b), deep social changes are needed and, above all, regulation changes are crucial. In any case, the first step is to know how the consumption of loads is, how can demand be modified, how can small generation (mainly renewable) and energy storage influence generation, and so on. Having accurate models that provide this information is a key factor for network agents to establish their best strategies. This dissertation discusses many aspects of energy demand and the problem of controlling several resources and agents in the system operation is addressed and Chapter 3. Smart grids shows the management and control software (in which the author has collaborated during the design and development stages) of a small smart grid that exists in LabDER laboratory at UPV, where various resources are integrated according to the needs of demand, energy prices, and so on. In the third condition (c) there are also major challenges to be solved, such as mass information management and the increasing volume of data traffic that it can involve. This dissertation proposes several algorithms to facilitate treatment of the available data to optimise the management of the resources in a smart grid or to make decisions about the participation in demand response programs, as shown in Chapter 8. Energy Management Systems for Smart Customers.
[ES] La industria eléctrica de potencia está siendo sacudida por una idea que va tomando forma: las smart grids. Tres aspectos pueden considerarse claves para llegar a las smart grids: a) La estructura de la red debe responder al concepto de red inteligente, es decir, ser resistente a fallos, por ejemplo provocando la separación automática de cualquier elemento averiado sin afectar al funcionamiento del resto de la red; ser flexible para permitir la conexión o desconexión de cargas y generadores distribuidos, mantener un funcionamiento eficiente bajo diversos estados de carga, etc. b) La red debe abrir las posibilidades de participación de grandes y pequeños generadores así como de los usuarios, permitiendo nuevas posibilidades de negocio y de participación activa, de manera que la generación o el consumo "inteligentes" se vean beneficiados. c) Todos los participantes deben tener acceso fácil a la información necesaria para poder elegir la mejor estrategia de funcionamiento en cada caso. En lo que respecta a la primera condición (a) hay importantes retos por resolver: automatización de la red, diseño óptimo, desarrollo de nuevas protecciones y equipos de control, etc. Será necesario desarrollar equipos adaptados a los nuevos problemas y nuevas necesidades que se generarán en estas redes. Esos equipos deberán ser normalizados, para lo cual será necesario definir ensayos que tengan en cuenta aspectos que actualmente no suelen ser necesarios, como la presencia de perturbaciones en la tensión, u otros. En este sentido, aunque de forma marginal, se ha colaborado con un laboratorio para ensayos eléctricos, la Flex Power Grid Lab Research Infrastructure del DNV KEMA en los Países Bajos, en la definición y realización de algunos ensayos, como se indica en el Capítulo 3. Smart grids. En el aspecto segundo (b), son necesarios profundos cambios sociales y, sobre todo, legislativos. En cualquier caso, el primer paso consiste en saber cómo es el consumo de los receptores, de qué manera puede variarse la demanda, qué influencia puede tener la pequeña generación (renovable principalmente) y el almacenamiento de energía, etc. Disponer de modelos precisos que proporcionen esta información es clave para que los actores de la red puedan establecer sus mejores estrategias. En la tesis se analizan muchos aspectos relacionados con la demanda de energía y se aborda el problema del control de la participación de diversos recursos y diversos agentes en el funcionamiento del sistema y en el Capítulo 3. Smart grids se muestra el software de gestión y control (en cuyo diseño y desarrollo se ha colaborado) de una pequeña smart grid que existe en el laboratorio LabDER de la UPV, donde se integran diversos recursos en función de las necesidades de la demanda, los precios de la energía, etc. En la tercera condición (c) hay, también, grandes retos por resolver, como la gestión masiva de información y el incremento en el volumen de tránsito de datos que puede representar. En la tesis se proponen diferentes algoritmos para facilitar el tratamiento de los datos disponibles a la hora de optimizar la gestión de los recursos de una smart grid o tomar decisiones de cara a participar en programas de respuesta de la demanda, tal como puede verse en el Capítulo 8. Sistemas de Gestión Energética para Smart Customers.
[CAT] La indústria elèctrica de potència està sent sacsada per una idea que va prenent forma: les smart grids. Tres aspectes poden considerar-se claus per a arribar a les smart grids: a) L'estructura de la xarxa ha de respondre al concepte de xarxa intel·ligent, és a dir, ser resistent a fallades, per exemple amb la separació automàtica de qualsevol element avariat sense afectar el funcionament de la resta de la xarxa; ser flexible per a permetre la connexió o desconnexió de càrregues i generadors distribuïts; mantindre un funcionament eficient davall diversos estats de càrrega, etc. b) La xarxa ha d'obrir les possibilitats de participació de grans i xicotets generadors així com dels usuaris. Així, ha de permetre noves possibilitats de negoci i de participació activa, de manera que la generació o el consum "intel·ligents" es vegen beneficiats. c) Tots els participants han de tindre accés fàcil a la informació necessària per a poder triar la millor estratègia de funcionament en cada cas. Pel que fa a la primera condició (a) hi ha importants reptes per resoldre: automatització de la xarxa, disseny òptim, desenrotllament de noves proteccions i equips de control, etc. Serà necessari desenrotllar equips adaptats als nous problemes i noves necessitats que es generaran en aquestes xarxes. Aqueixos equips hauran de ser normalitzats, per a la qual cosa serà necessari definir assajos que tinguen en compte aspectes que actualment no solen ser necessaris, com la presència de pertorbacions en la tensió, o altres. En aquest sentit, encara que de forma marginal, s'ha col·laborat amb un laboratori per a assajos elèctrics, la Flex Power Grid Lab Research Infrastructure del DNV KEMA en els Països Baixos, en la definició i realització d'alguns assajos, com s'indica en el Capítol 3. Smart grids. En l'aspecte segon (b), són necessaris profunds canvis socials i, sobretot, legislatius. En qualsevol cas, el primer pas consisteix a saber com és el consum dels receptors, de quina manera pot variar-se la demanda, quina influència pot tindre la xicoteta generació (renovable principalment) i l'emmagatzemament d'energia, etc. Disposar de models precisos que proporcionen aquesta informació és clau perquè els actors de la xarxa puguen establir les seues millors estratègies. En la tesi s'analitzen molts aspectes relacionats amb la demanda d'energia i s'aborda el problema del control de la participació de diversos recursos i diversos agents en el funcionament del sistema i en el Capítol 3. Smart grids es mostra el programari de gestió i control (en el disseny i desenrotllament del qual s'ha col·laborat) d'una xicoteta smart grid que existeix en el laboratori LabDER de la UPV, on s'integren diversos recursos en funció de les necessitats de la demanda, els preus de l'energia, etc. En la tercera condició (c) hi ha, també, grans reptes per resoldre, com ara la gestió massiva d'informació i l'increment en el volum de trànsit de dades que pot representar. En la tesi es proposen diferents algoritmes per a facilitar el tractament de les dades disponibles a l'hora d'optimitzar la gestió dels recursos d'una smart grid o prendre decisions de cara a participar en programes de resposta de la demanda, tal com pot veure's en el Capítol 8. Sistemes de Gestió Energètica per a Smart Customers.
Roldán Blay, C. (2016). Avances en Verificación y Medida de la Respuesta de la Demanda y Aplicación a su integración en Smart Grids [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61302
TESIS

Residential buildings are one of the main stakeholders to electricity consumption. As there is fast-paced technological advancement in electricity conservation, the residential buildings infrastructure has become very electricity-efficient in Sweden. However, there is still room for improvement with regards to electricity conservation via behavioral change. Meaning, residents have the potential to reduce household electricity consumption by developing a conservative behavior. The road to such a behavioral development is not straightforward. According to literature, behavioral change is influenced by different beliefs and norms. There also exists a global trend that fewer and fewer people are able to name a single neighbor, and it is aptly called “Globally Connected yet Locally Isolated”. In this master thesis the strategy to achieve electricity conservation is based upon local social cohesion, and the awareness of electricity, at Malvinas – a student residence at the campus of KTH Royal Institute of Technology. The study is carried out at LocalLife – a local social networking service for sustainable communities – implementing a mixed methodology of surveys and interviews. 8 LocalLife users are studied in detail. The result incorporates the most suitable features from the relevant topics that could enable long term change and retainment of users. The results showed that the participants: 1) indicated an increased energy awareness; 2) reported an improvement in local community-life; 3) felt motivated to change behavior to facilitate saving electricity.
I detta examensarbete görs en omfattande analys med hjälp av kvalitativa och kvantitativa metoder för att undersöka ifall användandet av LocalLife – en lokal social nätverkstjänst som pilot-testas vid Kungliga Tekniska Högskolan – har förbättrat användarnas beteende och attityder kring hushållselsförbrukning samt hjälpt till att förbättra det lokala samhällslivet. Bostadshus ger upphov till en betydande del av världens energiförbrukning. På grund av den snabba tekniska utvecklingen har byggnaderna blivit mycket mer energieffektiva i Sverige på senare tid. Det finns dock fortfarande förbättringspotential när det gäller att spara el genom att ändra de boendes konsumtionsbeteende. Att få till en sådan beteendeförändring är dock inte enkelt. Enligt litteraturen påverkas beteendet av uppfattningar och normer. Det finns dessutom en global trend där allt färre människor lär känna sina grannar, kallad “globalt uppkopplad men lokalt isolerad”. Denna uppsats studerar därför en strategi att spara el som går ut på lokal sammanhållning och ökad kunskap om elanvändning hos de boende i studentlägenheterna på Malvinas väg på KTH campus. Studien utförs bland användare av LocalLife, ett lokalt socialt nätverk för hållbara stadsdelar – genom att blanda metoder såsom enkäter och intervjuer. Åtta LocalLife- användare studeras i detalj. Resultatet presenterar de mest lämpliga delarna från de relevanta ämnena som kan möjliggöra en bestående beteendeförändring öka chansen för att behålla användarna. Resultaten visar att deltagarna: 1) visade på en ökad energimedvetenhet; 2) upplevde en förbättring av det lokala samhällslivet; 3) kände sig motiverade att ändra sitt beteende för att spara el.

Electricity networks are undergoing a period of rapid change and transformation, with increased penetration levels of renewable-based distributed generation, and new influences on electricity end-use patterns from demand-manageable loads and micro-generation. This creates a number of new challenges for the delivery of a reliable supply of electrical energy. The main aim of this PhD research is to provide a methodology for a more detailed and accurate assessment of the effects of wind-based distributed generation (DG) and demand side management (DSM) on transmission network operation. In addition, the work investigates the potential for co-ordinated implementation and control of DG and DSM to improve overall system performance. A significant amount of previous literature on network integration of DG and DSM resources has focused on the effects at the distribution level, where their impact is direct and often easily observed. However, as penetration levels increase, DG and DSM will have a growing influence on the operation and management of the bulk transmission system. Modelling and analysis of the impact of embedded and highly-dispersed DG and DSM resources at transmission voltage levels will present a significant challenge for transmission network operators in the future. Accordingly, this thesis presents a number of new approaches and methodologies allowing for a more accurate modelling and aggregation of DG and DSM resources in power system studies. The correct representation of input wind energy resources is essential for accurate estimation of power and energy outputs of wind-based DG. A novel modelling approach for a simple and accurate representation of the statistical and temporal characteristics of the wind energy resources is presented in the thesis. An "all-scale" approach to modelling and aggregation of wind-based generation is proposed, which is specifically intended for assessing the impact of embedded wind generation on the steady state performance of transmission systems. The methodology allows to include in the analysis wind-based generation at all scales and all levels of implementation, from micro and small LV-connected units, through medium-size wind plants connected at MV, up to large HV-connected wind farms. The thesis also presents an assessment of the potential for DSM in the UK residential and commercial sectors, based on the analysis and decomposition of measured demands at system bulk supply points into the corresponding load types. Using a section of the Scottish transmission network as a case study, a number of DG and DSM scenarios are investigated in detail. These results demonstrate the importance of accurately modelling the interactions between the supply system and various DG and DSM schemes, and show that the aggregated effects of highly-distributed DG and DSM resources can have significant impacts on the operation of the bulk transmission system.

Thesis advisor: Scott Fulford
Can smart metering program and time-of-use (TOU) prices help reduce energy consumption in New York City? Being able to track electricity consumption levels and to modify consumer usage patterns are important for policy makers to efficiently manage the energy markets. Unfortunately, no reliable and up-to-date data have been brought to bear on this question. I study the effects of time-of-use (TOU) prices and smart metering for the residents of Shanghai and I investigate further what can policy makers do in order to adapt and transfer this successful DSM experience from Shanghai to the residential sector in New York City. The primary objective of my study is to characterize the realistic short-term and long-term potential for the smart metering program in New York City given my empirical findings that the smart metering program has had brought great benefits to the residents of Shanghai. People respond to incentives; if electricity is charged at different prices throughout a day, consumers are likely to shift their usage to when it is cheaper. My findings suggest that policy makers should think harder about designing a pricing scheme that can optimize the social plus
Thesis (BA) — Boston College, 2011
Submitted to: Boston College. College of Arts and Sciences
Discipline: College Honors Program
Discipline: Economics Honors Program
Discipline: Economics

Thesis: S.M. in Management Studies, Massachusetts Institute of Technology, Sloan School of Management, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 74-78).
This thesis evaluates the potential impact of smart thermostats on the residential energy efficiency and demand response in North America. Smart thermostats are rapidly gaining popularity, and our estimations indicate that today there are more than nine million units already installed in North America. Electric utilities have recently started pilot programs known as Bring Your Own Thermostat (BYOT) through which they subsidize part of the smart thermostat that their customers install in their homes in exchange for taking command of the settings certain hours per day during for a few summer days. Currently, there are only about 50,000 homeowners enrolled in BYOT programs in the USA, but the expectation that smart thermostats can impact energy efficiency and change the residential demand response (DR) landscape is high. Using System Dynamics, this thesis has examined this potential, and the results show that the smart thermostats, in the business as usual case, can save about 60 TWh/year of electricity (or the continuous production of about fifteen 500MW coal plants - or Rosenfelds by 2025). If programs such as BYOT, where part of the thermostat is subsidized, were going to be popularized, this number can almost double. And additionally, this technology is creating an important potential in the residential demand response space, which is also studied in this thesis.
by Diego Ariza Muñoz.
S.M. in Management Studies

The electric power industry and society are facing the challenges and opportunities of transforming the present power grid into a smart grid. To meet these challenges, new types of control systems are connected over IT infrastructures. While this is done to meet highly set economical and environmental goals, it also introduces new sources of uncertainty in the control loops. In this thesis, we consider control design taking some of these uncertainties into account. In Part I of the thesis, some economical and environmental concerns in smart grids are taken into account, and a scheduling framework for static loads (e.g., smart appliances in residential areas) and dynamic loads (e.g., energy storage systems) in the distribution level is investigated. A robust formulation is proposed taking the user behavior uncertainty into account, so that the optimal scheduling cost is less sensitive to unpredictable changes in user preferences. In addition, a novel distributed algorithm for the studied scheduling framework is proposed, which aims at minimizing the aggregated electricity cost of a network of apartments sharing an energy storage system. We point out that the proposed scheduling framework is applicable to various uncertainty sources, storage technologies, and programmable electrical loads. In Part II of the thesis, we study smart grid uncertainty resulting from possible security threats. Smart grids are one of the most complex cyber-physical systems considered, and are vulnerable to various cyber and physical attacks. The attack scenarios consider cyber adversaries that may corrupt a few measurements and reference signals, which may degrade the system’s reliability and even destabilize the voltage magnitudes. In addition, a practical attack-resilient framework for networked control systems is proposed. This framework includes security information analytics to detect attacks and a resiliency policy to improve the performance of the system running under the attack. Stability and optimal performance of the networked control system under attack and by applying the proposed framework, is proved here. The framework has been applied to an energy management system and its efficiency is demonstrated on a critical attack scenario.

QC 20160830

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households.

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources.

Energy systems are evolving into structures in which the role of the consumer is more and more significant. Consumers are no longer the passive entities that in the past had to be supplied in an unidirectional way (from the network to the customer), but can also supply power to the grid through renewable resources, storage capacity through the batteries of their electric vehicles or operating services through the use of their flexibility. However, when discussing on smart grids, electricity supply and consump-tion are the only considered side on many occasions, neglecting other dimensions such as natural gas, sanitary hot water or transport. In this context, this dissertation represents a novel approach to the role of consumers in the natural gas sector. While it is true that electricity consumers have been involved for years in different operation services related to the use of their flexibility (especial-ly in countries such as the United States and more recently in the European Union), the use of demand response resources in the gas sector has been so far non-existent. However, the success of demand response initiatives in electricity systems and their similarity to the gas sector, where their regulatory and technological development has been carried out in parallel in recent years, allows us to expect similar successful re-sults when implementing equivalent programs to gas networks. This dissertation highlights the huge potential that remains unexplored on the demand side of natural gas, which could be used by gas network operators for the solution of technical constraints, balance services or optimization of programming of under-ground storage. This potential is especially interesting at the moment, when the mas-sive installation of smart gas meters has started in some European countries, an infra-structure that would facilitate the use of demand response resources for the better op-eration of gas networks. The dissertation presents, firstly, an exhaustive analysis of the demand response pro-grams currently used in electrical systems around the world, identifying those services that could be equally applicable to the gas sector. The traditional structure based on which gas systems have been developed in different countries is analyzed below. In order to make better use of resources and to optimize its operation, an architecture based on the concept of smart grid is then proposed, identifying the agents that would participate in this structure and emphasizing the role that consumers would play, not only as energy demanders, but also as providers of network services. This active role of demand requires the use of adequate measurement, control and communication technologies, aspect that is also properly analyzed. Based on the results of the analysis mentioned above, this thesis proposes a new meth-odology for the development and evaluation of demand response mechanisms that allow a greater participation of gas consumers in the provision of operating services to the manager of the network, increasing the joint efficiency of the system and reducing the costs associated with such services. The proposed methodology has been successfully applied to the gas network in Italy, where the analyzed operation services have been evaluated in a town of 16,000 inhab-itants located in the central north-Italian area. In that town, consumers have been grouped by end-use, sector and size, which evidences the need to enhance the role of the aggregator for the proper use of the potential of smaller consumers, whether they receive a gas supply directly or through a distributed heat network. The results presented in this dissertation should encourage regulators to empower the use of the consumers' flexibility in order to increase the efficiency of the natural gas system, as it reduces operating costs while favoring the participation of customers in a more dynamic energy structure.
Los sistemas energéticos están evolucionando hacia estructuras en las que el papel desempeñado por el consumidor es cada vez más importante. Hoy en día, los consumidores ya no son los entes pasivos de antaño a los que había que suministrar energía de forma unidireccional (de la red al cliente), sino que también pueden suministrar energía a la red a través de recursos renovables, capacidad de almacenamiento mediante las baterías de sus vehículos eléctricos o servicios de operación a través de la utilización de su flexibilidad. Sin embargo, al hablar de redes inteligentes, en muchas ocasiones se sobreentiende únicamente lo relativo al suministro y consumo de electricidad, obviando otras dimensiones como pueden ser el gas natural, el agua caliente sanitaria o el transporte. En este marco, esta tesis supone un enfoque novedoso en lo que se refiere al papel de los consumidores en el sector del gas natural. Si bien es cierto que los consumidores de electricidad han participado desde hace años en diferentes servicios relacionados con el uso de su flexibilidad, la utilización de la respuesta de la demanda en el sector gasista ha sido hasta ahora inexistente. Sin embargo, el éxito de iniciativas de respuesta de la demanda en los sistemas eléctricos y su similitud con el sector gasista, cuyo desarrollo normativo y tecnológico se ha realizado en paralelo en los últimos años, permite esperar resultados igualmente exitosos al aplicar programas equivalentes a las redes de gas. Esta tesis pone de manifiesto el enorme potencial que permanece inexplorado en el lado de la demanda de gas natural, el cual podría ser utilizado para la solución de restricciones técnicas, servicios de balance u optimización de la programación de los almacenamientos subterráneos. Este potencial resulta especialmente interesante en estos momentos, cuando en algunos países europeos se ha comenzado la instalación masiva de contadores inteligentes de gas. La tesis presenta un análisis exhaustivo de los programas de respuesta de la demanda utilizados en la actualidad en sistemas eléctricos alrededor del mundo, identificándose aquellos servicios que podrían ser aplicables al sector gasista. A continuación se analiza la estructura tradicional en base a la que los sistemas gasistas se han desarrollado en diversos países, proponiéndose a continuación una arquitectura basada en el concepto de red inteligente, donde se identifican los agentes que participarían en esta estructura y se enfatiza el rol que los consumidores desempeñarían no sólo como demandantes de energía, sino también como proveedores de servicios de red. Este papel activo de la demanda necesita de la utilización de tecnologías de medición, control y comunicación adecuadas, aspecto que también se analiza en detalle. En base a los resultados del análisis mencionado, esta tesis propone una nueva metodología para el desarrollo y evaluación de mecanismos de respuesta de la demanda que permitan una mayor participación de los consumidores de gas en la provisión de servicios de operación al gestor de la red, aumentando la eficiencia conjunta del sistema y reduciendo los costes asociados a dichos servicios. La metodología propuesta ha sido aplicada con éxito a la red gasista de Italia, donde los servicios de operación analizados han sido evaluados en una ciudad de 16.000 habitantes, donde los consumidores han sido agrupados por uso final, sector y tamaño. Esto ha puesto de manifiesto la necesidad de potenciar el papel del agregador para valorizar el potencial de los consumidores más pequeños, tanto si reciben un suministro de gas directo o a través de una red de calor distribuido. Los resultados expuestos en esta tesis deberían impulsar a los reguladores a incentivar la utilización de la flexibilidad de los consumidores a fin de incrementar la eficiencia del sistema de gas natural, ya que reduce los costes de operación al tiempo que favorece la particip
Els sistemes energètics estan evolucionant cap a estructures en què el paper exercit pel consumidor és cada vegada més important. Avui dia, els consumidors ja no són els ens passius d'antany als quals calia subministrar energia de forma unidireccional (de la xarxa al client), sinó que també poden subministrar energia a la xarxa a través de recursos renovables, capacitat d'emmagatzematge mitjançant les bateries dels seus vehicles elèctrics o serveis d'operació a través de la utilització de la seva flexibilitat. No obstant això, en parlar de xarxes intel·ligents, en moltes ocasions se sobreentén únicament quant al subministrament i consum d'electricitat, obviant altres dimensions com poden ser el gas natural, l'aigua calenta sanitària o el transport. En aquest marc, aquesta tesi suposa un enfocament nou pel que fa al paper dels consumidors en el sector del gas natural. Si bé és cert que els consumidors d'electricitat han participat des de fa anys en diferents serveis d'operació relacionats amb l'ús de la seva flexibilitat, la utilització de la resposta de la demanda en el sector gasista ha estat fins ara inexistent. No obstant això, l'èxit d'iniciatives de resposta de la demanda en els sistemes elèctrics i la seva similitud amb el sector gasista, el desenvolupament normatiu i tecnològic s'ha realitzat en paral·lel en els últims anys, permet esperar resultats igualment reeixits en aplicar programes equivalents a les xarxes de gas. Aquesta tesi posa de manifest l'enorme potencial que roman inexplorat en el costat de la demanda de gas natural, el qual podria ser utilitzat per a la solució de restriccions tècniques, serveis de balanç o optimització de la programació dels emmagatzematges subterranis. Aquest potencial és especialment interessant en aquests moments, quan en alguns països europeus s'ha començat la instal·lació massiva de comptadors intel·ligents de gas. La tesi presenta una anàlisi exhaustiva dels programes de resposta de la demanda utilitzats en l'actualitat en sistemes elèctrics voltant del món, identificant-se aquells serveis que podrien ser aplicables al sector gasista. A continuació s'analitza l'estructura tradicional sobre la base de la qual els sistemes gasistes s'han desenvolupat en diversos països, proposant-se a continuació una arquitectura basada en el concepte de xarxa intel·ligent, on s'identifiquen els agents que participarien en aquesta estructura i s'emfatitza el paper que els consumidors exercirien no només com a demandants d'energia, sinó també com a proveïdors de serveis de xarxa. Aquest paper actiu de la demanda necessita de la utilització de tecnologies de mesurament, control i comunicació adequades, aspecte que també s'analitza en detall. En base als resultats de l'anàlisi esmentat, aquesta tesi proposa una nova metodologia per al desenvolupament i avaluació de mecanismes de resposta de la demanda que permetin una major participació dels consumidors de gas a la provisió de serveis d'operació al gestor de la xarxa, augmentant l'eficiència conjunta del sistema i reduint els costos associats a aquests serveis. La metodologia proposada ha estat aplicada amb èxit a la xarxa gasista d'Itàlia, on els serveis d'operació analitzats han estat avaluats en una ciutat de 16.000 habitants, on els consumidors han estat agrupats per ús final, sector i grandària. Això ha posat de manifest la necessitat de potenciar el paper de l'agregador per valoritzar el potencial dels consumidors més petits, tant si reben un subministrament de gas directe o mitjançant una xarxa de calor distribuïda. Els resultats exposats en aquesta tesi haurien d'impulsar els reguladors a incentivar la utilització de la flexibilitat dels consumidors a fi d'incrementar l'eficiència del sistema de gas natural, ja que redueix els costos d'operació i alhora afavoreix la participació dels clients en una estructura més dinàmica.
Montuori, L. (2017). Application of demand response strategies for the management of natural gas systems under the smart grid configuration: development of a methodology for technical, economic and environmental evaluation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90407
TESIS

Recent technological developments are bringing about substantial changes that are converting traditional distribution networks into "smart" distribution networks. In particular, it is possible to observe seamless integration of Information and Communication Technologies (ICTs), including the widespread installation of automatic equipment, smart meters, etc. The increased automation facilitates active network management, interaction between market actors and demand side participation. If we also consider the increasing penetration of distributed generation, renewables and various emerging technologies such as storage and dynamic rating, it can be argued that the capacity of distribution networks should not only depend on conventional asset. In this context, taking into account uncertain load growth and ageing infrastructure, which trigger network investments, the above-mentioned advancements could alter and be used to improve the network design philosophy adopted so far. Hitherto, in fact, networks have been planned according to deterministic and conservative standards, being typically underutilised, in order for capacity to be available during emergencies. This practice could be replaced by a corrective philosophy, where existing infrastructure could be fully unlocked for normal conditions and distributed energy resources could be used for post fault capacity services. Nonetheless, to thoroughly evaluate the contribution of the resources and also to properly model emergency conditions, a probabilistic analysis should be carried out, which captures the stochasticity of some technologies, the randomness of faults and, thus, the risk profile of smart distribution networks. The research work in this thesis proposes a variety of post fault capacity services to increase distribution network utilisation but also to provide reliability support during emergency conditions. In particular, a demand response (DR) scheme is proposed where DR customers are optimally disconnected during contingencies from the operator depending on their cost of interruption. Additionally, time-limited thermal ratings have been used to increase network utilisation and support higher loading levels. Besides that, a collaborative operation of wind farms and electrical energy storage is proposed and evaluated, and their capacity contribution is calculated through the effective load carrying capability. Furthermore, the microgrid concept is examined, where multi-generation technologies collaborate to provide capacity services to internal customers but also to the remaining network. Finally, a distributed software infrastructure is examined which could be effectively used to support services in smart grids. The underlying framework for the reliability analysis is based on Sequential Monte Carlo Simulations, capturing inter-temporal constraints of the resources (payback effects, dynamic rating, DR profile, storage remaining available capacity) and the stochasticity of electrical and ICT equipment. The comprehensive distribution network reliability analysis includes network reconfiguration, restoration process, and ac power flow calculations, supporting a full risk analysis and building the risk profile for the arising smart distribution networks. Real case studies from ongoing project in England North West demonstrate the concepts and tools developed and provide noteworthy conclusions to network planners, including to inform design of DR contracts.

Electric Vehicles (EVs) are active loads as they increase the demand for electricity and introduce several challenges to electrical distribution feeders during charging. Demand Response (DR) or performing load control in commercial buildings along with the deployment of solar photovoltaic (PV) and ice storage systems at the building level can improve the efficiency of electricity grids and mitigate expensive peak demand/energy charges for buildings. This research aims to provide such a solution to make EV penetration transparent to the grid. Firstly, this research contributes to the development of an integrated control of major loads, i.e., Heating Ventilation and Air Conditioning (HVAC), lighting and plug loads while maintaining occupant environmental preferences in small- and medium-sized commercial buildings which are an untapped DR resource. Secondly, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-minute resolution data set at the individual plug load level. The research evaluates total building power consumption performance taking into account interactions among lighting, plug load, HVAC and control systems in a realistic manner. Third, this research presents a model to study integrated control of PV and ice storage on improving building operation in demand responsive buildings. The research presents the impact of deploying various combinations of PV and ice storage to generate additional benefits, including clean energy generation from PV and valley filling from ice storage, in commercial buildings. Fourth, this research presents a coordinated load control strategy, among participating commercial buildings in a distribution feeder to optimally control buildings' major loads without sacrificing occupant comfort and ice storage discharge, along with strategically deployed PV to absorb EV penetration. Demand responsive commercial building load profiles and field recorded EV charging profiles have been added to a real world distribution circuit to analyze the effects of EV penetration, together with real-world PV output profiles. Instead of focusing on individual building's economic benefits, the developed approach considers both technical and economic benefits of the whole distribution feeder, including maintaining distribution-level load factor within acceptable ranges and reducing feeder losses.
Ph. D.

The UK’s decarbonisation strategy to increasingly electrify heating and transport will change the demand requirement on the electricity system. Additionally, under a climate change future, it is projected that the decarbonised grid will need to be able to operate under higher average temperatures in the UK, increasing the need for comfort cooling during summer and leading to additional electricity demand. These new demands will result in greater variation between minimum and peak demand as well as a significant increase in overall demand. Concurrently, supply-side decarbonisation programmes may lead to more intermittent renewables such as wind, PV, tidal and wave, elevating variability in electricity generation. Coupled with the anticipated higher variation in demand this brings on several challenges in operating the electricity grid. In order to characterise these challenges this research develops a bespoke electricity dispatch model which builds on hourly models of demand and generation. The hourly demand profiles are based on a high electrification of heating, transport and cooling coupled with future temperatures premised on the UKCP09 high emission scenario climate projections. The demand profiles show a significant increase in peak demand by 2050 reaching 194 GW, mainly due to summer cooling loads which contribute 70% of the demand. The cumulative CO2 emissions budgets of the GB power sector that are consistent with avoiding global climate change to 2°C are used to develop two low carbon generation scenarios distinguished by the amount of intermittent renewable generation technologies. The dispatch model tests the capability of generation scenarios with the use of hourly generation models in meeting future demand profiles out to 2050.The outputs from dispatch model indicate that there are shortages and excesses of generation relative to demand from 2030 onwards. The variability analysis outlines low and high generation periods from intermittent technologies along with the pace at which intermittent generation increases or decreases within an hour. The characterisation of variability analysis reveals the type of reserve capacity or smart solutions that are required to maintain the security of electricity supply. The solutions that could address the challenges quantified from the model outputs in operating a decarbonised GB electricity grid are explored using expert interviews. The analysis of the stakeholder interviews suggests smart grid solutions that include technologies as well as changes in operational procedures in order to enhance the operational resilience of the grid. Active Network Management through monitoring and control, demand management, storage systems and interconnectors are proposed to address challenges arising from varying demand and generation variability.

Smart metering residential demand management programs, such as consumption feedback, variable pricing regimes and the remote control of appliances, are being used to respond to the resource management problems of peak electricity demand, climate change and water shortages. Like other demand management programs, these strategies fail to account for (and respond to) the reasons why people consume resources in their homes, namely to carry out everyday practices such as bathing, laundering, heating and cooling. In particular, comfort and cleanliness practices together constitute most of Australia's potable water consumption in urban centres, and represent most of household energy consumption. In addition, new household cooling practices involving air-conditioning appliances are the major contributor to the nation's rising peak electricity demand, which overloads the electricity system on hot days, costing consumers millions of dollars each year. The oversight of comf ort and cleanliness practices in smart metering demand management programs is concerning because these practices are continuing to shift and change, often in more resource-consuming directions, potentially negating the resource savings achieved through demand management programs. This thesis aims to bridge the problematic divide between the policies and strategies of demand managers, and the day-to-day practices which constitute everyday life. Using the empirical 'hook' of smart metering demand management programs and the everyday practices of comfort and cleanliness, this thesis develops a practice-based conceptual framework to study, understand and analyse these practices and the ways in which smart metering demand management programs reconfigure or further entrench them. A series of qualitative methods were employed in studying 65 households across four research groups, focusing specifically on the household practices of heating, cooling, bathing, laundering, toilet flushing and house cleaning. In addition, 27 interviews were conducted with smart metering industry stakeholders involved or implicated in delivering demand management strategies. Together, these lines of inquiry are used to analyse householders' existing and changing comfort and cleanliness practices, the role of several smart metering demand management strategies in reconfiguring these practices, and potential avenues and opportunities for further practice change in less resource-intensive directions. In particular, this thesis highlights the inherent contradictions and problems in accounting for everyday practices within the dominant demand management paradigm, and offers an alternative paradigm termed the co-management of everyday practices. The thesis concludes by briefly identifying the ways in which smart metering could potentially constrain or catalyse a transition towards this new paradigm.

Since the last United Nations Climate Change Conference in 2015 in Paris (the COP 21), world leaders acknowledged climate change. There is no need any more to justify the switch from fossil fuel-based to renewable energy sources. Nevertheless, this transition is far from being straightforward. Besides technologies that are not yet mature -- or at least not always financially viable in today's economy -- the power grid is currently not ready for a rapid and massive integration of renewable energy sources. A main challenge for the power grid is the inadequacy between electric production and consumption that will rise along with the integration of such sources. Indeed, due to their dependence on weather, renewable energy sources are intermittent and difficult to forecast with today's tools. As a commodity, electricity is a quite distinct good for which there must be perfect adequacy of production and consumption at all time and characterized by a very inelastic demand. High shares of renewable energy sources lead to high price volatility and a higher risk to jeopardize the security of supply. Additionally, the switch to renewable energy sources will lead to an electrification of loads and transportation, and thus the emergence of new higher-consumption loads such as electric vehicles and heat pumps. These new and higher-consumption loads, combined with the population growth, will cause over-rated power load increases with less predictable load patterns in the future.This work focuses on issues specific to the distribution power grid in the context of the current energy transition. Traditional low-voltage grids are perhaps the most passive circuits in power grids. Indeed, they are designed primarily using a fit and forget approach where power flows go from the distribution transformer to the consumers and no element has to be operated or regularly managed. In fact, low-voltage networks completely lack observability due to very low monitoring. The distribution grid will especially undergo drastic changes from this energy transition. Distributed sources and new high-consumption -- and uncoordinated -- loads result in new power flow patterns, as well as exacerbated evening peaks for which it is not designed. The consequences are power overloads and voltage imbalances that deteriorate grid components, such as a main asset like the medium-to-low voltage transformer. Additionally, the distribution grid is characterized by end-users that pay a price for electricity that does not reflect the grid situation -- that is, mostly constant over a year -- and allow little to no actions on their consumption.These issues have motivated authorities to propose a global approach to ensure security of electricity supply at short and medium-term. The latter requires, among others, the development of demand response programs that encourage users to take advantage of load flexibility. First, we propose adequate electricity pricing structures that will allow users to unlock the potential of such demand response programs; namely, dynamic pricings combined with a prosumer structure. Second, we propose a fast and robust two-level optimization, formulated as a mixed-integer linear program, that coordinates flexible loads. We focus on two types of loads; electric vehicles and heat pumps, in an environment with solar PV panels. The lower level aims at minimizing individual electricity bills while, at the second level, we optimize the power load curve, either to maximize self-consumption, or to smoothen the total power load of the transformer. We propose a parametric study on the trade-off between only minimizing the individual bills versus only optimizing power load curves, which have proven to be antagonist objectives. Additionally, we assess the impact of the rising share of flexible loads and renewable energy sources for scenarios from today until 2050. A macro-analysis of the results allows us to assess the benefits of load flexibility for every actor of the distribution grid, and depending on the choice of a pricing structure. Our optimization has proved to prevent evening peaks, which increases the lifetime of the distribution transformer by up to 200%, while individual earnings up to 25% can be made using adequate pricings. Consequently, the optimization significantly increases the power demand elasticity and increases the overall welfare by 10%, allowing the high shares of renewable energy sources that are foreseen.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

I dagsläget har världen en stadigt växande befolkning och där igenom en stadigt växande energiförbrukning. Med en växande energiförbrukning har det under de senaste åren uppenbarats diskussioner rörande samhällets hållbarhet och miljöpåverkan.  Samtidigt sker det en kontinuerlig teknikutveckling och människan är mer beroende av konstant elförsörjning än någonsin tidigare. Teknologiska framsteg, tillsammans med önskan att sträva mot ett mer hållbart samhälle med hög elleveranssäkerhet, har mynnat ett begrepp kallat smarta elnät. Till följd av att elnätet involverar en stor bransch råder det delad mening över vad som utgör ett smart elnät. Detta har lett till uppkomsten av olika definitioner och modeller av konceptet. I syfte att skapa en övergripande uppfattning har en litteraturstudie utförts för att sammanställa de huvudsakliga områden som utgör det smarta elnätet. För att skapa denna överblick har ett förslag på en övergripande definition framtagits enligt följande: Ett smart elnät är nästa steg i elnätets fortgående utveckling som sker till följd av samhällets ökande förlitlighet på konstant elförsörjning och önskan att begränsa människans miljöpåverkan. Målet är att med hjälp av kostnadseffektiva tekniska lösningar, effektiv teknik och ekonomiska drivkrafter främja införandet av ytterligare förnyelsebar elproduktion, en ökad elanvändning och ett effektivare utnyttjande av elnätet – ett elnät med låga förluster, hög elkvalitet och leveranssäkerhet med elkunder som är mer medvetna och delaktiga i sin elförbrukning än förr. Utifrån denna definition kan man summera smarta elnät till att omfatta två huvudsakliga intressen för samhället – hållbarhet och en ökad leveranssäkerhet. I framtiden förväntas därför elnätet hantera vidare utbredning av förnyelsebar elproduktion och en ökad elanvändning. För att möta denna förväntan har det dels konstaterats att nätkapaciteten behöver öka. Det har visats att en ökad nätkapacitet kan nås genom både tekniska lösningar som energilagring och effektivare komponenter men också icke-tekniska lösningar som politiska drivkrafter och incitament för elkunder att sänka sin maxförbrukning och elförbrukning i överlag i form av efterfrågeflexibilitet. I dagsläget finns inga uppenbara incitament för detta och det anses att reformer på vissa delar av elmarknaden kommer att krävas för att främja utvecklingen mot ett hållbart smart elnät. Samtidigt förväntas elnätet förse kunder med högre elkvalitet och leveranssäkerhet. Dagens elnät utgörs av många långlivade och, i många fall, gamla komponenter och investeringar kommer att behöva göras i moderna skyddssystem och kommunikationsnätverk i sinom tid ifall man vill uppnå nya förväntningar. Därtill förväntas det smarta elnätet omfatta olika typer av kommunikationsnätverk inom skyddssystem, övervakning och mätning. Därför har också information rörande relevanta kommunikationsprotokoll, -medier och -nätverk summerats där olika egenskaper lämpar sig för olika tillämpningar.
Currently the world has a steadily growing population and therefore steadily growing need of energy. With a growing need of energy, discussions regarding society’s sustainability and environmental impact have risen. At the same time modern technology has resulted in society being more dependent on a constant power supply than ever before. Technological advances, together with the desire to become a more sustainable society with high availability of power, have yielded a concept known as the smart grid. Due to the power grid being a huge industry there’s a divided perception regarding what a smart grid constitutes. This has resulted in the appearance of different definitions and models of the concept. Therefore a literary study was done with the purpose of creating an overall perception of the main aspects of the smart grid. To create this overview a proposed definition has been developed that describes the smart grid as mainly sustainable and available. The smart grid is the next step of the power grid’s ongoing development in response to society’s increasing reliability of a constant power supply and the wish for decreasing man’s environmental impact. With cost efficient technical solutions, efficient technology and economic forces the goal is to promote introduction of additional renewable electricity production, increased electricity utilization and a more efficient use of the power grid – a power grid with low losses, high power quality and availability with end-users that are more aware and involved in their power consumption than before. Based on this definition the smart grid can be summarized as two main interests for society – sustainability and a higher reliability. In the future the power grid is expected to cope with an increased introduction of renewable electricity production and an increased use of electrical applications. It has been concluded that the grid capacity has to increase in order to meet these expectations. It’s been shown that an increase in grid capacity can be achieved through technical solutions as energy storage and more efficient electrical components but also through non-technical solutions as political forces and incentives for end-users to lower their peak consumption and overall electricity consumption through demand response. At present there are no clear incentives for this and it’s considered that there is a need for reform of certain parts of the electricity market to promote the development towards a sustainable smart grid. The power grid is also expected to supply end-users with a higher power quality and reliability. The power grid of today consists of long lived and, in many cases, old components and investments in modern protection systems and communication networks are required in due time to meet new expectations. In addition, the smart grid is expected to include different types of communication network within protection systems, monitoring and metering. Information was therefore summarized regarding relevant communication protocols, media and networks where different properties are suitable for different applications.

This thesis focuses on the design and implementation of a software tool able to achieve electricity grid optimal management in a dynamic pricing environment, at the residential level, and from a customer's perspective. The main drivers encouraging a development of energy management at the home level are analyzed, and a system architecture modeling power, thermodynamic and economic subsystems is proposed. The user behavior is also considered. A mathematical formulation of the related energy management optimization problem is proposed based on the linear programming theory. Several cases involving controllable and non-controllable domestic loads as well as renewable energy sources are presented and simulation scenarios illustrate the proposed optimization strategy in each case. The performance of the controller and the changes in energy use are analyzed, and ideas for possible future work are discussed.

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The electricity market has many participants performing various roles. In the context of Smart Grids, it expands the number of participating agents and, consequently, the number, the competition and complexity of the interactions between them. The competition allows consumersto discover and explore energy sources of low cost but requires the consumer other interactions beyond the traditional relationship with the distribution company (distco). With the redefinition of the role of the consumer as a result of his active participation in the balance between supply and demand of energy, it becomes necessary to identify their interactions with the other participants, tasks, systems, subsystems and functions important to actively contribute to this balance. These interactions, called interfaces, are characterized by information that actors communicate itself to perform its functions of information collection and control tasks related to market equilibrium. The determination of these interfaces is not a trivial task because of the various alternatives for integration of the demand side. Different types and combinations of interactions between the supplier and its customers are possible. In this work will be used as a basis for discussion a concept of balance between generation and demand called homeostatic control, initially designed for a scenario of a vertically integrated monopoly. It will be studied the interactions of the consumer using the concept of homeostatic control expanded to consider the functions of demand control and integration of distributed generation and a market model that allows for consumer empowerment and within the paradigm of smart grids.
O mercado de energia elétrica possui diversos participantes exercendo vários papéis. No contexto das redes elétricas inteligentes, amplia-se o número de agentes participantes e, consequentemente, o número, a competição e a complexidade das interações entre eles. A competição permite aos consumidores descobrir e explorar fontes de baixo custo, mas requer do consumidor outras interações além da tradicional relação com a empresa distribuidora. Com a redefinição do papel do consumidor, resultado da participação ativa no processo de equilíbrio entre suprimento e demanda de energia, torna-se necessário identificar suas interações com os demais participantes, tarefas, sistemas, subsistemas e funções, importantes para que contribua ativamente com esse equilíbrio. Estas interações, denominadas interfaces, são caracterizadas pelas informações que os atores comunicam em si para executar suas funções de coleta de informações e tarefas de controle relacionadas ao equilíbrio do mercado. A determinação destas interfaces não é uma tarefa trivial em razão das diversas alternativas de integração do lado da demanda. São possíveis diferentes tipos e combinações de interações entre o fornecedor e seus clientes. Neste trabalho será utilizado como base de discussão um conceito de equilíbrio entre geração e demanda denominado controle homeostático, desenhado inicialmente para um cenário de monopólio verticalmente integrado. Serão estudadas as interações do consumidor utilizando o conceito de controle homeostático ampliado para considerar as funções de controle da demanda e a integração da geração distribuída em um modelo de mercado que permite o empoderamento do consumidor dentro do paradigma das redes elétricas inteligentes.

It is a dilemma problem to achieve both these two goals: a) to maintain a best indoor air quality and b) to use a most efficient energy for a house at the same time. One of the outstanding components involving these goals is a smart ventilation system in the house. Smart ventilation strategies, including demand-controlled ventilation (DCV), have been of great interests and some studies believe that DCV strategies have the potential for energy reductions for all ventilation systems. This research aims to improve smart ventilation system, in aspects of energy consumption, indoor CO2 concentrations and living comfortness, by analyzing long-term sensor data. Based on a case study on an experimental house -- Dalarnas Villa, this research investigates how the current two ventilations modes work in the house and improves its ventilation system by developing customized ventilation schedules. A variety of data analysis methods were used in this research. Clustering analysis is used to identify the CO2 patterns and hence determine the residents living patterns; correlation analysis and regression analysis are used to quantify a model to estimate fan energy consumption; a mathematical model is built to simulation the CO2 decreasing when the house is under 0 occupancy. And finally, two customized schedules are created for a typical workday and holiday, respectively, which show advantages in all aspects of energy consumption, CO2 concentrations and living comfortness, compared with the current ventilation modes.

L’essor des technologies renouvelable à apport variable pose des nombreuses difficultés dans le fonctionnement du système électrique. Ce système doit garantir l’équilibre offre-demande à tout moment, ainsi que d’assurer des hauts niveaux de fiabilité du service. Donc, la variabilité accroît les besoins de flexibilité et des services système. Ils existent plusieurs options capables de fournir ceux services, dont : le renforcement des interconnections, le pilotage intelligent de la demande, le renforcement des capacités de réponse rapide des unités de production, mais aussi, le mis en œuvre des technologies de stockage de l’électricité. Cependant, les marchés électriques actuels sont basés sur la rémunération de l’énergie. Donc, la valorisation intégrale des services qui peut fournir le stockage semble difficile, ce qui restreint le « business case » des options de flexibilité.Cette thèse s’inscrit autour des propos suivants : (1) modéliser et évaluer les interrelations entre variabilité, besoins de flexibilité et objectifs de décarbonation du parc électrique, (2) analyser le rôle, ainsi que la valeur, des différents technologies du stockage à travers le cas Français aux horizons 2020, 2030 et 2050, et (3) discuter sur les aspects de régulation de la flexibilité, ainsi que proposer des politique énergétiques concrètes permettant la réussite des objectifs de transition énergétique et de décarbonation du mix électrique français
The increasing variability of electricity production in Europe, which is mainly due to the intermittent production of renewables such as wind and photovoltaic (VRE), will require significant efforts to reconcile demand and supply at all times. Thus, increasing shares of variability imply increasing amounts of system services. In addition to upgraded interconnections, demand-side management (DSM) and dispatchable backup capacity, electric energy storage (EES) technologies will have a major role to play in this context.However, due to the peculiar price formation mechanism prevailing in energy-only electricity markets, the commercial case for EES is being eroded by the very forces that create the need for its increased deployment at the system level. The private incentives of EES are thus diminishing while its social value, which is determined by the multiple system services these technologies can supply, is increasing.This thesis sets out to (1) model and assess the interplays between variability, flexibility needs and decarbonization objectives, (2) analyze the role and the value of EES technologies in view of the French official objectives by 2020, 2030 and 2050, and (3) discuss regulatory aspects, and propose a set of energy policies allowing to succeed in the energy transition and decarbonization goals

Les futurs réseaux de distribution d’électricité devront héberger une part importante et croissante de sources d’énergies renouvelables intermittentes. De plus, ils devront faire face à une part croissante de véhicules électriques. Ces tendances induisent le besoin de nouveaux paradigmes et architectures d’exploitation du réseau de distribution, afin de fiabiliser les réseaux et d'assurer la qualité de fourniture d’électricité. Dans cette thèse nous proposons une nouvelle architecture capable de favoriser la collaboration entre les acteurs du marché de gros, les gestionnaires de réseau de distribution et les clients finaux, afin de tirer parti des ressources énergétiques distribuées tout en prenant en compte les contraintes des réseaux de distribution. L’architecture est conçue pour fournir des services innovants de gestion de la demande résidentielle, dans le cadre de l'autoconsommation individuelle et collective (à l'échelle d'un quartier). La thèse apporte trois contributions principales. D'abord, sur la base de l'internet des objets et de la technologie blockchain, la thèse fournit les éléments de base pour les futures architectures de gestion de l'énergie au niveau du réseau de distribution. Ensuite, en focalisant sur les services rendus par de telles architectures, nous proposons un marché intra-journalier au pas horaire pour l'échange local de l'énergie renouvelable entre maisons, associé à un mécanisme d'allocation dynamique des phases afin d'améliorer la qualité de fourniture. Finalement, nous proposons un mécanisme de contrôle en temps réel pour l'ajustement des transactions du marché vers des échanges finaux d'électricité qui respectent les restrictions posées par le gestionnaire du réseau électrique
Future electricity distribution grids will host an important and growing share of variable renewable energy sources and local storage resources. Moreover, they will face new load structures due for example to the growth of the electric vehicle market. These trends raise the need for new distribution grid architecture and operation paradigms to keep the grid stable and to ensure quality of supply. In addition, these new paradigms will enable the provision of advanced new services. In this thesis we propose a novel architecture capable of fostering collaboration among wholesale market actors, distribution system operators and end customers, to leverage flexible distributed energy resources while respecting distribution system constrains. The architecture is designed for providing innovative residential demand side management services, with a special focus on services enabled by self-consumption at the household and neighborhood level. Following these general objectives, the thesis provides three main contributions. First, based on internet of things and blockchain technology, we propose the building blocks for future distribution grid energy management architectures. Then, focusing on the services enabled by such architectures, we propose hour-ahead markets for the local exchange of renewable energy among households together with dynamic phase allocation mechanism to improve the quality of electricity supply. Finally, we propose a real time control mechanism for the adjustment of market decisions to satisfy distribution system operator constraints