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Journal articles on the topic 'Demand response'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Barreda-Tarrazona, Iván, Aurora García-Gallego, Marina Pavan, and Gerardo Sabater-Grande. "Demand response in experimental electricity markets." Revista Internacional de Sociología 70, Extra_1 (2012): 127–65. http://dx.doi.org/10.3989/ris.2011.10.30.

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2

Elkasrawy, Ayman, and Bala Venkatesh. "Demand Response Cooperative and Demand Charge." IEEE Transactions on Smart Grid 11, no. 5 (2020): 4167–75. http://dx.doi.org/10.1109/tsg.2020.2979435.

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3

Jung, Koeun, Yoonki Min, and Suk Won Han. "Response of multiple demand network to visual search demands." NeuroImage 229 (April 2021): 117755. http://dx.doi.org/10.1016/j.neuroimage.2021.117755.

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4

Blanke, Julia, Christian Beder, Emily Twomey, Sezen Aladag Ozdemir, and Martin Klepal. "E2District: Behaviour Demand Response." Proceedings 1, no. 7 (2017): 691. http://dx.doi.org/10.3390/proceedings1070691.

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5

CRAWFORD, M. "Response: Demand for Electricity." Science 243, no. 4888 (1989): 152. http://dx.doi.org/10.1126/science.243.4888.152-a.

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6

Samarakoon, Kamalanath, Janaka Ekanayake, and Nick Jenkins. "Reporting Available Demand Response." IEEE Transactions on Smart Grid 4, no. 4 (2013): 1842–51. http://dx.doi.org/10.1109/tsg.2013.2258045.

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7

Liu, Zhenhua, Adam Wierman, Yuan Chen, Benjamin Razon, and Niangjun Chen. "Data center demand response." ACM SIGMETRICS Performance Evaluation Review 41, no. 1 (2013): 341–42. http://dx.doi.org/10.1145/2494232.2465740.

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8

Wang, Jianhui, Cary N. Bloyd, Zhaoguang Hu, and Zhongfu Tan. "Demand response in China." Energy 35, no. 4 (2010): 1592–97. http://dx.doi.org/10.1016/j.energy.2009.06.020.

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9

Priyanka, Y. "Load Demand Response Controller." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3810–15. http://dx.doi.org/10.22214/ijraset.2023.54198.

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Abstract: This project aims to design a system which helps electricity consumers to monitor and control their electrical loads. In this system, the energy meter is connected to an optocoupler which provides optical isolation and transfers electrical signals between the energy meter and Arduino UNO. This system has a step-down transformer and Regulated Power Supply (RPS) to provide supply to Arduino UNO and to the loads through electromagnetic relays. The electromagnetic relays act like a switch for the operation of the loads. The system is enabled with an Arduino UNO which acts like a control

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10

Ju, Yun, Weixing Gao, and Xiaoqi Shao. "Electric Energy Demand Response Algorithm based on Deep Reinforcement Learning." International Journal of Scientific Engineering and Research 12, no. 3 (2024): 1–8. https://doi.org/10.70729/se24308203253.

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11

Mieth, Robert, Samrat Acharya, Ali Hassan, and Yury Dvorkin. "Learning-Enabled Residential Demand Response: Automation and Security of Cyberphysical Demand Response Systems." IEEE Electrification Magazine 9, no. 1 (2021): 36–44. http://dx.doi.org/10.1109/mele.2020.3047470.

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12

Loureiro, Tatiana, Raymond Sterling, and Meritxell Vinyals. "Demand Response Integration tEchnologies: Unlocking the Demand Response Potential in the Distribution Grid." Proceedings 2, no. 15 (2018): 1129. http://dx.doi.org/10.3390/proceedings2151129.

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DRIvE is a 36 months Horizon 2020 project which main objective is to unlock the Demand Response (DR) potential of residential and tertiary buildings in the distribution grid. DRIvE comprehensive platform can be seamlessly integrated with existing assets and buildings to achieve optimal operations in the next generation of Smart Grids, paving the way to a fully deployed DR market in the distribution network.

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13

Mohammad, Mirzaei Talabari. "Optimization of grid-connected MicroGrid demand considering demand response." National Security and Strategic Planning 2024, no. 1 (2024): 60–65. http://dx.doi.org/10.37468/2307-1400-2024-1-60-65.

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Electricity grid is a product of urbanization expansion and rapid development of various infrastructures worldwide and over the past centuries. Although power companies are located in diverse regions, they typically use the same technologies to generate and distribute electricity. Proper implementation of the demand response (DR) program should be provided with some equipment to make subscribers aware of electricity price at any time and accordingly provide a proper response to the grid to reduce costs. This, in turn, reduces demand during peak hours. The intelligent grid, using the two-way co

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14

Rochlin, Cliff. "The Alchemy of Demand Response: Turning Demand into Supply." Electricity Journal 22, no. 9 (2009): 10–25. http://dx.doi.org/10.1016/j.tej.2009.09.004.

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15

van Treeck, Christoph, Jerome Frisch, Ben Krämer, and Elisabeth Hirt. "Demand-Response- Management in Wohngebäuden." HLH 71, no. 02 (2020): 14–19. http://dx.doi.org/10.37544/1436-5103-2020-02-14.

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In den Wirtschaftswissenschaften spricht man vom Marktgleichgewicht, wenn Angebot und Nachfrage in einem gesunden Verhältnis zueinander stehen. Auf die Energieversorgung von Wohngebäude übertragen, könnte dieses Prinzip maßgeblich zur Erreichung der Klimaziele beitragen, wie die Ergebnisse einer Masterarbeit an der RWTH Aachen belegen. Diese wurde von der VDI-Gesellschaft Bauen und Gebäudetechnik mit dem Albert-Tichelmann-Preis 2019 ausgezeichnet. Im folgenden werden die Chancen aufgezeigt, die der Betrieb einer Wärmepumpenheizung mittels thermischer Lastverschiebung anhand variabler Stromtari

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16

Zenkin, E., S. Nozhko, I. Soboleva, and S. Datsyura. "Ecological prospects for demand response." Энергетическая политика, no. 2 (2022): 18–25. http://dx.doi.org/10.46920/2409-5516_2022_2168_18.

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17

Herath, Pramod, and Ganesh Kumar Venayagamoorthy. "Scalable Residential Demand Response Management." IEEE Access 9 (2021): 159133–45. http://dx.doi.org/10.1109/access.2021.3119270.

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18

Yamaguchi, Nobuyuki. "Saving Electricity and Demand Response." IEEJ Transactions on Power and Energy 131, no. 9 (2011): 719–23. http://dx.doi.org/10.1541/ieejpes.131.719.

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19

Qi, Junjian, Youngjin Kim, Chen Chen, Xiaonan Lu, and Jianhui Wang. "Demand Response and Smart Buildings." ACM Transactions on Cyber-Physical Systems 1, no. 4 (2017): 1–25. http://dx.doi.org/10.1145/3009972.

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20

Barrett, Larry B. "Demand Response Grows More Resourceful." Strategic Planning for Energy and the Environment 25, no. 3 (2005): 69–76. http://dx.doi.org/10.1080/10485230509509692.

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21

Liu, Zhenhua, Iris Liu, Steven Low, and Adam Wierman. "Pricing data center demand response." ACM SIGMETRICS Performance Evaluation Review 42, no. 1 (2014): 111–23. http://dx.doi.org/10.1145/2637364.2592004.

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22

Conejo, Antonio J., Juan M. Morales, and Luis Baringo. "Real-Time Demand Response Model." IEEE Transactions on Smart Grid 1, no. 3 (2010): 236–42. http://dx.doi.org/10.1109/tsg.2010.2078843.

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23

Ma, Ookie, Nasr Alkadi, Peter Cappers, et al. "Demand Response for Ancillary Services." IEEE Transactions on Smart Grid 4, no. 4 (2013): 1988–95. http://dx.doi.org/10.1109/tsg.2013.2258049.

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24

Taylor, Joshua A., and Johanna L. Mathieu. "Index Policies for Demand Response." IEEE Transactions on Power Systems 29, no. 3 (2014): 1287–95. http://dx.doi.org/10.1109/tpwrs.2013.2289972.

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25

Ruff, Larry E. "Demand Response: Reality versus “Resource”." Electricity Journal 15, no. 10 (2002): 10–23. http://dx.doi.org/10.1016/s1040-6190(02)00401-3.

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26

Allman, Andrew, and Qi Zhang. "Distributed cooperative industrial demand response." Journal of Process Control 86 (February 2020): 81–93. http://dx.doi.org/10.1016/j.jprocont.2019.12.011.

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27

Kim, Jin-Ho, and Anastasia Shcherbakova. "Common failures of demand response." Energy 36, no. 2 (2011): 873–80. http://dx.doi.org/10.1016/j.energy.2010.12.027.

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28

Latiers, Arnaud. "Demand Response Perspectives for Belgium." Reflets et perspectives de la vie économique LIV, no. 1 (2015): 185. http://dx.doi.org/10.3917/rpve.541.0185.

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29

Paciello, Vincenzo, Antonio Pietrosanto, and Paolo Sommella. "Smart Sensors for Demand Response." IEEE Sensors Journal 17, no. 23 (2017): 7611–20. http://dx.doi.org/10.1109/jsen.2017.2728611.

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30

Li, Hepeng, Zhiqiang Wan, and Haibo He. "Real-Time Residential Demand Response." IEEE Transactions on Smart Grid 11, no. 5 (2020): 4144–54. http://dx.doi.org/10.1109/tsg.2020.2978061.

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31

Muthirayan, Deepan, Dileep Kalathil, Sen Li, Kameshwar Poolla, and Pravin Varaiya. "Selling Demand Response Using Options." IEEE Transactions on Smart Grid 12, no. 1 (2021): 279–88. http://dx.doi.org/10.1109/tsg.2020.3011382.

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32

Faria, Pedro, and Zita Vale. "Demand Response in Smart Grids." Energies 16, no. 2 (2023): 863. http://dx.doi.org/10.3390/en16020863.

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33

Hall, Peter H., and Malcolm L. Treadgold. "Aggregate demand curves: A response." Journal of Macroeconomics 11, no. 1 (1989): 141–43. http://dx.doi.org/10.1016/0164-0704(89)90023-2.

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34

Karwe, Markus, and Günter Müller. "Transaktionspseudonymität für Demand-Response-Anwendungen." HMD Praxis der Wirtschaftsinformatik 50, no. 3 (2013): 52–59. http://dx.doi.org/10.1007/bf03340815.

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35

Neumann, Scott, Fereidoon Sioshansi, Ali Vojdani, and Gaymond Yee. "How to Get More Response from Demand Response." Electricity Journal 19, no. 8 (2006): 24–31. http://dx.doi.org/10.1016/j.tej.2006.09.001.

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36

Wang, Kang, Chengfu Wang, Wenliang Yao, et al. "Embedding P2P transaction into demand response exchange: A cooperative demand response management framework for IES." Applied Energy 367 (August 2024): 123319. http://dx.doi.org/10.1016/j.apenergy.2024.123319.

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37

Haroonabadi, Hossein. "Unit Commitment in Power Market Considering Demand Response and Stochastic Wind Generation." International Journal of Engineering Research 3, no. 10 (2014): 564–69. http://dx.doi.org/10.17950/ijer/v3s10/1003.

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38

Xu, Songyuan, Yuqi Liang, and Jing Zuo. "A Travel Demand Response Model in MaaS Based on Spatiotemporal Preference Clustering." Journal of Advanced Transportation 2022 (December 19, 2022): 1–15. http://dx.doi.org/10.1155/2022/2000835.

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To respond to travel demand in the MaaS system, improve transport efficiency, and optimize the framework of MaaS, we propose a travel demand response model based on a spatiotemporal preference clustering algorithm that considers the impact of travel preferences and features of the MaaS system to improve travel demand response and achieve full coverage of travel demands. Specifically, in the MaaS system, the time preference hierarchical clustering algorithm is optimized with travel preference as the perception factor and preference priority order as the iteration index. Then, we cluster the dep

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39

Lamprinos, Ilias, Nikos Hatziargyriou, Isidoros Kokos, and Aris Dimeas. "Making Demand Response a Reality in Europe: Policy, Regulations, and Deployment Status." IEEE Communications Magazine 54, no. 12 (2016): 108–13. https://doi.org/10.1109/MCOM.2016.1600323CM.

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Power systems undergo massive operational and technological changes amid increasing demand for environmental sustainability and energy efficiency. The traditional, supplydriven approach, relying on large-scale generation plants, which has dominated old utilities, is reconsidered to incorporate the increased penetration of variable renewable energy sources, distributed generation and storage. Demand Response is an important instrument for improving energy efficiency, since it increases consumers' engagement and provides a mechanism to reduce or shift

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40

Lempereur, Alain, and Michele Pekar. "The distributive knot: negotiators’ responsibility to untie complex demands." Journal of Business & Industrial Marketing 32, no. 4 (2017): 535–40. http://dx.doi.org/10.1108/jbim-11-2015-0229.

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Purpose This article aims to explore the fundamental negotiation structure as a demand/response dynamic. It tests it in a complex business system, where a manager as a negotiator is confronted with multiple demands or pressures at different levels from a variety of stakeholders, both external and internal. Design/methodology/approach Based on concrete examples from the automotive industry, it presents an analytical framework to tackle all negotiation interactions. Findings This article suggests that it is possible to describe all negotiation interactions, whether they are simple or complex, th

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41

Jalili, Hassan, and Pierluigi Siano. "Modeling of unforced demand response programs." International Journal of Emerging Electric Power Systems 22, no. 2 (2021): 233–41. http://dx.doi.org/10.1515/ijeeps-2020-0208.

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Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering prov

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42

Crawford, Mark. "Response : Demand for Electricity." Science 243, no. 4888 (1989): 152. http://dx.doi.org/10.1126/science.243.4888.152.b.

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43

Hernandez Arias, Victor Andres, Luis Alejandro Arias-Barragán, and Edwin Rivas-Trujillo. "Incentive-based demand response: Case study." Scientia et Technica 25, no. 2 (2020): 216–22. http://dx.doi.org/10.22517/23447214.22701.

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The present article discusses the development of a demand response system on a residential setting given that the proper management of energy resources by users can solve scarcity and energy reliability issues. The behavior of 1000 residential users is modelled through a peak control program based on economic and energy-related incentives. A demand response (DR) application is used where remuneration depends on scarcity pricing. The case study is shown including a mathematic algorithm and the application. The results obtained with the application exhibit a reduction of energy peaks, which tran

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44

Sfeir, Ricardo A., Sandra A. Chow, Bryan W. Hackett, and Ahmad R. Ganji. "Demand Response Opportunities in Northern California." Energy Engineering 107, no. 1 (2009): 6–20. http://dx.doi.org/10.1080/01998591009595080.

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45

Sills, Kyle, Konstantinos Papamichael, and Sila Kiliccote. "In Pursuit of Automated! Demand Response." Lighting Design + Application 43, no. 11 (2013): 84–87. https://doi.org/10.1177/036063251304301112.

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46

Houwing, Michiel, Rudy R. Negenborn, and Bart De Schutter. "Demand Response With Micro-CHP Systems." Proceedings of the IEEE 99, no. 1 (2011): 200–213. http://dx.doi.org/10.1109/jproc.2010.2053831.

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47

Parvania, Masood, and Mahmud Fotuhi-Firuzabad. "Demand Response Scheduling by Stochastic SCUC." IEEE Transactions on Smart Grid 1, no. 1 (2010): 89–98. http://dx.doi.org/10.1109/tsg.2010.2046430.

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48

Eksin, Ceyhun, Hakan Delic, and Alejandro Ribeiro. "Demand Response With Communicating Rational Consumers." IEEE Transactions on Smart Grid 9, no. 1 (2018): 469–82. http://dx.doi.org/10.1109/tsg.2016.2613993.

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49

Carus, Felicity. "What next for demand side response?" Renewable Energy Focus 17, no. 1 (2016): 28–30. http://dx.doi.org/10.1016/j.ref.2015.12.001.

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50

Levy, Roger. "A Vision of Demand Response – 2016." Electricity Journal 19, no. 8 (2006): 12–23. http://dx.doi.org/10.1016/j.tej.2006.08.008.

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