Academic literature 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<br>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.<br>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.<br>Ph. D.

Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 54-61).<br>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.<br>by Brian Bowen.<br>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.