Relevant bibliographies by topics / Smart energy demand

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Smart energy demand'

Author: Grafiati
Published: 21 February 2023

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Journal articles on the topic "Smart energy demand"

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Lai, Jingang, Hong Zhou, Wenshan Hu, Dongguo Zhou, and Liang Zhong. "Smart Demand Response Based on Smart Homes." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/912535.

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Smart homes (SHs) are crucial parts for demand response management (DRM) of smart grid (SG). The aim of SHs based demand response (DR) is to provide a flexible two-way energy feedback whilst (or shortly after) the consumption occurs. It can potentially persuade end-users to achieve energy saving and cooperate with the electricity producer or supplier to maintain balance between the electricity supply and demand through the method of peak shaving and valley filling. However, existing solutions are challenged by the lack of consideration between the wide application of fiber power cable to the home (FPCTTH) and related users’ behaviors. Based on the new network infrastructure, the design and development of smart DR systems based on SHs are related with not only functionalities as security, convenience, and comfort, but also energy savings. A new multirouting protocol based on Kruskal’s algorithm is designed for the reliability and safety of the SHs distribution network. The benefits of FPCTTH-based SHs are summarized at the end of the paper.
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Borges, Yulle G. F., Rafael C. S. Schouery, Flávio K. Miyazawa, Fabrizio Granelli, Nelson L. S. da Fonseca, and Lucas P. Melo. "Smart energy pricing for demand‐side management in renewable energy smart grids." International Transactions in Operational Research 27, no. 6 (November 5, 2019): 2760–84. http://dx.doi.org/10.1111/itor.12747.

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Nambi, S. N. Akshay Uttama, R. Venkatesha Prasad, and Antonio R. Lua. "Decentralized Energy Demand Regulation in Smart Homes." IEEE Transactions on Green Communications and Networking 1, no. 3 (September 2017): 372–80. http://dx.doi.org/10.1109/tgcn.2017.2721818.

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Islam, M. A., M. Hasanuzzaman, N. A. Rahim, A. Nahar, and M. Hosenuzzaman. "Global Renewable Energy-Based Electricity Generation and Smart Grid System for Energy Security." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/197136.

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Energy is an indispensable factor for the economic growth and development of a country. Energy consumption is rapidly increasing worldwide. To fulfill this energy demand, alternative energy sources and efficient utilization are being explored. Various sources of renewable energy and their efficient utilization are comprehensively reviewed and presented in this paper. Also the trend in research and development for the technological advancement of energy utilization and smart grid system for future energy security is presented. Results show that renewable energy resources are becoming more prevalent as more electricity generation becomes necessary and could provide half of the total energy demands by 2050. To satisfy the future energy demand, the smart grid system can be used as an efficient system for energy security. The smart grid also delivers significant environmental benefits by conservation and renewable generation integration.
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Dwijendra, Ngakan Ketut Acwin, Oriza Candra, Ihsan Ali Mubarak, Hassan Taher Braiber, Muneam Hussein Ali, Iskandar Muda, R. Sivaraman, and A. Heri Iswanto. "Optimal Energy-Saving in Smart Energy Hub Considering Demand Management." Environmental and Climate Technologies 26, no. 1 (January 1, 2022): 1244–56. http://dx.doi.org/10.2478/rtuect-2022-0094.

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Abstract This study focused on energy saving in energy hub system using smart grid technologies and management of the energy demand. The two-layer energy management is proposed for implementing energy saving. In first layer, energy demand such as electrical, thermal and natural gas are optimized subject to optimal level of the demand at day-ahead. Then, optimized energy demand is applied in second layer to reduction energy generation costs. The optimization of the proposed approach is done by shuffled frog leaping algorithm (SFLA), and results at several case studies to confirmation of the proposed approach are investigated.
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Palensky, Peter, and Dietmar Dietrich. "Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads." IEEE Transactions on Industrial Informatics 7, no. 3 (August 2011): 381–88. http://dx.doi.org/10.1109/tii.2011.2158841.

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Ahmed, Ajaz. "Energy Smart Buildings: Potential for Conservation and Efficiency of Energy." Pakistan Development Review 53, no. 4II (December 1, 2014): 371–81. http://dx.doi.org/10.30541/v53i4iipp.371-381.

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Energy is the basic ingredient for economic growth and development [Lorde, et al. (2010)]. Presently demand for energy has significantly increased due to the overall expansion of economic and industrial activity in all important economic sectors e.g. industry, agriculture, and services. In addition to the expansion of economic activity and subsequent increase in energy demand at industrial level, population growth and increased consumption are also adding to the demand for energy [OECD (2011)]. In other words, modern economy has become highly dependent on energy resources. In order to meet the increased energy demand and ensure its sustainable supply, there is a need to have strong and robust plans with all options to consider at various levels.
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Wang, Xiaonan, Wentao Yang, Sana Noor, Chang Chen, Miao Guo, and Koen H. van Dam. "Blockchain-based smart contract for energy demand management." Energy Procedia 158 (February 2019): 2719–24. http://dx.doi.org/10.1016/j.egypro.2019.02.028.

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Pan, Feng, Guoying Lin, Yuyao Yang, Sijian Zhang, Jucheng Xiao, and Shuai Fan. "Data-driven demand-side energy management approaches based on the smart energy network." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261989161. http://dx.doi.org/10.1177/1748302619891611.

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The energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach employs the internet, Internet of Things (IoT), data mining, and other advanced technologies. The network aims to share energy information to realize distributed smart energy management, which can, for example, save energy while ensuring the reliability and quality of electricity for customers. Based on the network, a series of smart energy management theories are proposed to support the smart energy network. The core idea of these theories is to allow the network to teach itself in order to learn from the massive amounts of collected energy data, by using machine learning algorithms. The concept of power utility is proposed to quantitatively assess load energy efficiency. Then, a data-driven consumer energy activity recognition method is proposed based on a hidden Markov model (HMM). A test system is generated using field data from a pilot project in Guangdong Province, China. The energy saving rate in our test is 37.9%, which means that the smart energy network and the proposed algorithms perform well for automatic and intelligent energy efficiency management.
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Faria, Pedro, and Zita Vale. "Demand Response in Smart Grids." Energies 16, no. 2 (January 12, 2023): 863. http://dx.doi.org/10.3390/en16020863.

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Dissertations / Theses on the topic "Smart energy demand"

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Xia, Chunqiu. "Energy Demand Response Management in Smart Home Environments." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20182.

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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.
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Kühnlenz, F. (Florian). "Analyzing flexible demand in smart grids." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526223889.

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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
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Ramaswamy, Vivek. "Oskarshamn - A Smart Energy Island Assessment." Thesis, KTH, Energisystemanalys, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-182669.

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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.
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El, Rahi Georges. "Demand-Side Energy Management in the Smart Grid: Games and Prospects." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78266.

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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
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Alhaider, Mohemmed Masooud. "Optimal Demand Response Models with Energy Storage Systems in Smart Grids." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6451.

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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.
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TARIQ, Zaheer. "Smart energy demand management. A collaborative approach towards consumers' active participation." Doctoral thesis, Università degli studi di Bergamo, 2014. http://hdl.handle.net/10446/30769.

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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.
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Khamphanchai, Warodom. "An Agent-based Platform for Demand Response Implementation in Smart Buildings." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/70869.

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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.
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Bowen, Brian (Brian Richard). "Climate control : smart thermostats, demand response, and energy efficiency in Austin, Texas." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97346.

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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.
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Sippel, Felix. "Tezpur University Smart Campus: Performance Optimization during Grid Outages using Demand Side Management." Thesis, KTH, Kraft- och värmeteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263691.

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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.
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Zhao, Zhiheng. "Thermal Inertia In Residential Buildings For Demand Response." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16018.

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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.
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Books on the topic "Smart energy demand"

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The smart grid: Enabling energy efficiency and demand response. Lilburn, GA: Fairmont Press, 2009.

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Song, Meng, and Ciwei Gao. Integration of Distributed Resources in Smart Grids for Demand Response and Transactive Energy. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7170-8.

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Planning communities for a changing climate: Smart growth, public demand and private opportunity : hearing before the Select Committee on Energy Independence and Global Warming, House of Representatives, One Hundred Tenth Congress, second session, June 18, 2008. Washington: U.S. G.P.O., 2010.

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Gellings, Clark W. Smart Grid: Enabling Energy Efficiency and Demand Response. River Publishers, 2020.

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Gellings, Clark W. Smart Grid: Enabling Energy Efficiency and Demand Response. River Publishers, 2020.

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Gellings, Clark W. Smart Grid: Enabling Energy Efficiency and Demand Response. River Publishers, 2020.

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Gellings, Clark W. Smart Grid: Enabling Energy Efficiency and Demand Response. River Publishers, 2020.

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Gellings, Clark W. Smart Grid: Enabling Energy Efficiency and Demand Response. River Publishers, 2020.

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Gellings, Clark W. Smart Grid: Enabling Energy Efficiency and Demand Response. River Publishers, 2020.

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Advanced Applications for Smart Energy Systems Considering Grid-Interactive Demand Response. MDPI, 2019. http://dx.doi.org/10.3390/books978-3-03921-999-5.

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Book chapters on the topic "Smart energy demand"

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Zhou, Kaile, and Lulu Wen. "Integrated Energy Services Based on Integrated Demand Response." In Smart Energy Management, 203–21. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9360-1_9.

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Spiegel, Stephan. "Optimization of In-House Energy Demand." In Smart Information Systems, 271–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14178-7_10.

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Takano, Willian Y., and Eduardo N. Asada. "Developing Energy Demand Forecasting Methods." In Handbook of Smart Energy Systems, 1–19. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72322-4_47-1.

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Matsuyama, Takashi. "i-Energy: Smart Demand-Side Energy Management." In Green Energy and Technology, 141–63. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6281-0_8.

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Zhou, Kaile, and Lulu Wen. "Power Demand and Probability Density Forecasting Based on Deep Learning." In Smart Energy Management, 101–34. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9360-1_5.

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Zhou, Kaile, and Lulu Wen. "Incentive-Based Demand Response with Deep Learning and Reinforcement Learning." In Smart Energy Management, 155–82. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9360-1_7.

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Sajjad, Intisar Ali, Haroon Farooq, Waqas Ali, and Rehan Liaqat. "Demand-Side Management in Smart Grids." In Handbook of Energy Transitions, 237–55. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003315353-14.

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Mohammad, Nur, and Yateendra Mishra. "Demand-Side Management and Demand Response for Smart Grid." In Energy Systems in Electrical Engineering, 197–231. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1768-2_6.

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Du, Pengwei, Ning Lu, and Haiwang Zhong. "Integrated Demand Response in the Multi-Energy System." In Demand Response in Smart Grids, 121–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19769-8_5.

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Manojpraphakar, T., and Soundarrajan A. "Energy Demand Prediction Using Linear Regression." In Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications, 407–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24051-6_40.

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Conference papers on the topic "Smart energy demand"

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Kiliccote, Sila, Mary Ann Piette, Girish Ghatikar, David Hafemeister, Daniel Kammen, Barbara Goss Levi, and Peter Schwartz. "Smart Buildings and Demand Response." In PHYSICS OF SUSTAINABLE ENERGY II: USING ENERGY EFFICIENTLY AND PRODUCING IT RENEWABLY. AIP, 2011. http://dx.doi.org/10.1063/1.3653861.

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Schellong, Wolfgang, and Sarah Gerngross. "Energy demand analysis in smart grids." In 2015 International Energy and Sustainability Conference (IESC). IEEE, 2015. http://dx.doi.org/10.1109/iesc.2015.7384385.

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Taqqali, Wasim M., and Nidhal Abdulaziz. "Smart Grid and demand response technology." In 2010 IEEE International Energy Conference (ENERGYCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/energycon.2010.5771773.

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Antolic, Mladen, Boris Fazo, Srete Nikolovski, and Zoran Baus. "Active Demand Energy Services Decomposition." In 2018 International Conference on Smart Systems and Technologies (SST). IEEE, 2018. http://dx.doi.org/10.1109/sst.2018.8564584.

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Bjornfors, M. "Energy Demand Research Project overview." In IET Seminar on Smart Metering 2009: Making it Happen. IET, 2009. http://dx.doi.org/10.1049/ic.2009.0054.

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Hasan, R., Y. Tanveer, M. M. Aman, W. A. Qureshi, M. S. Khan, A. Abbas, and J. A. Qureshi. "Smart Electricity Demand Side Controller." In 3rd IET International Conference on Clean Energy and Technology (CEAT) 2014. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.1452.

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Schien, Daniel, Paul Shabajee, John Brenton, Chris Jones, and Chris Preist. "IODiCUS - Smart Balancing of Local Energy Demand." In ICT for Sustainability 2016. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/ict4s-16.2016.32.

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Nambi S. N., Akshay Uttama, Antonio R. Lua, and Venkatesha Prasad R. "Decentralized Energy Demand Regulation in Smart Homes." In 2016 IEEE Global Communications Conference (GLOBECOM). IEEE, 2016. http://dx.doi.org/10.1109/glocom.2016.7841718.

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Oviedo, Raul J. Martinez, Zhong Fan, Sedat Gormus, Parag Kulkarni, and Dritan Kaleshi. "Residential energy demand management in smart grids." In 2012 IEEE/PES Transmission and Distribution Conference and Exposition (T&D). IEEE, 2012. http://dx.doi.org/10.1109/tdc.2012.6281573.

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Hemapala, Kullappu T. M. U., and Asitha L. Kulasekera. "Demand Side Management for Microgrids through Smart Meters." In Power and Energy Systems. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.768-060.

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Reports on the topic "Smart energy demand"

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Aryal, Jeetendra Prakash. Contribution of Agriculture to Climate Change and Low-Emission Agricultural Development in Asia and the Pacific. Asian Development Bank Institute, October 2022. http://dx.doi.org/10.56506/vaoy9373.

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The agriculture sector in Asia and the Pacific region contributes massively to climate change, as the region has the largest share of greenhouse gas (GHG) emissions from agriculture. The region is the largest producer of rice, a major source of methane emissions. Further, to achieve food security for the increasing population, there has been a massive increase in the use of synthetic fertilizer and energy in agricultural production in the region over the last few decades. This has led to an enormous rise in nitrous oxide (N2O; mostly from fertilizer-N use) and carbon dioxide (mostly from energy use for irrigation) emissions from agriculture. Besides this, a substantial increase in livestock production for meat and dairy products has increased methane emissions, along with other environmental problems. In this context, this study conducts a systematic review of strategies that can reduce emissions from the agriculture sector using a multidimensional approach, looking at supply-side, demand-side, and cross-cutting measures. The review found that though there are huge potentials to reduce GHG emissions from agriculture, significant challenges exist in monitoring and verification of GHG emissions from supply-side measures, shifting to sustainable consumption behavior with regard to food consumption and use, and the design and implementation of regulatory and incentive mechanisms. On the supply side, policies should focus on the upscaling of climate-smart agriculture primarily through expanding knowledge and improving input use efficiency in agriculture, while on the demand side, there is a need to launch a drive to reduce food loss and waste and also to move towards sustainable consumption. Therefore, appropriate integration of policies at multiple levels, as well as application of multiple measures simultaneously, can increase mitigation potential as desired by the Paris Agreement and also help to achieve several of the United Nations’ SDGs.
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Aryal, Jeetendra P. Contribution of Agriculture to Climate Change and Low-Emission Agricultural Development in Asia and the Pacific. Asian Development Bank Institute, October 2022. http://dx.doi.org/10.56506/wdbc4659.

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The agriculture sector in the Asia and Pacific region contributes massively to climate change, as the region has the largest share of greenhouse gas (GHG) emissions from agriculture. The region is the largest producer of rice, a major source of methane emissions. Further, to achieve food security for the increasing population, there has been a massive increase in the use of synthetic fertilizer and energy in agricultural production in the region over the last few decades. This has led to an enormous rise in nitrous oxide (N2O) (mostly from fertilizer-N use) and carbon dioxide (mostly from energy use for irrigation) emissions from agriculture. Besides this, a substantial increase in livestock production for meat and dairy products has increased methane emissions, along with other environmental problems. In this context, we conduct a systematic review of strategies that can reduce emissions from the agriculture sector using a multidimensional approach, looking at supply-side, demand-side, and cross-cutting measures. The review found that though there is a huge potential to reduce GHG emissions from agriculture, significant challenges exist in monitoring and verification of GHG emissions from supply-side measures, shifting to sustainable consumption behavior with regard to food consumption and use, and the design and implementation of regulatory and incentive mechanisms. On the supply side, policies should focus on the upscaling of climate-smart agriculture primarily through expanding knowledge and improving input use efficiency in agriculture, while on the demand side, there is a need to launch a drive to reduce food loss and waste and also to move toward sustainable consumption. Therefore, appropriate integration of policies at multiple levels, as well as application of multiple measures simultaneously, can increase mitigation potential as desired by the Paris Agreement and also help to achieve several of the United Nations’ Sustainable Development Goals.
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