Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load
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- Todd, Annika & Cappers, Peter & Spurlock, C. Anna & Jin, Ling, 2019. "Spillover as a cause of bias in baseline evaluation methods for demand response programs," Applied Energy, Elsevier, vol. 250(C), pages 344-357.
- Pereira, Guillermo Ivan & Specht, Jan Martin & Silva, Patrícia Pereira & Madlener, Reinhard, 2018.
"Technology, business model, and market design adaptation toward smart electricity distribution: Insights for policy making,"
Energy Policy, Elsevier, vol. 121(C), pages 426-440.
- Pereira, Guillermo Ivan & Specht, Jan Martin & Pereira da Silva, Patrícia & Madlener, Reinhard, 2018. "Technology, business model, and market design adaptation toward smart electricity distribution: Insights for policy making," FCN Working Papers 3/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
- Xu Chen and Andrew N. Kleit, 2016. "Money for Nothing? Why FERC Order 745 Should have Died," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
- Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
- Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
- Saehong Park & Seunghyoung Ryu & Yohwan Choi & Jihyo Kim & Hongseok Kim, 2015. "Data-Driven Baseline Estimation of Residential Buildings for Demand Response," Energies, MDPI, vol. 8(9), pages 1-21, September.
- Ziras, Charalampos & Heinrich, Carsten & Bindner, Henrik W., 2021. "Why baselines are not suited for local flexibility markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
- Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
- João Abel Peças Lopes & André Guimarães Madureira & Manuel Matos & Ricardo Jorge Bessa & Vítor Monteiro & João Luiz Afonso & Sérgio F. Santos & João P. S. Catalão & Carlos Henggeler Antunes & Pedro Ma, 2020. "The future of power systems: Challenges, trends, and upcoming paradigms," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(3), May.
- Alexandre Lucas & Luca Jansen & Nikoleta Andreadou & Evangelos Kotsakis & Marcelo Masera, 2019. "Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector," Energies, MDPI, vol. 12(14), pages 1-19, July.
- Hung-po Chao, 2011. "Demand response in wholesale electricity markets: the choice of customer baseline," Journal of Regulatory Economics, Springer, vol. 39(1), pages 68-88, February.
- Gabaldón, A. & García-Garre, A. & Ruiz-Abellón, M.C. & Guillamón, A. & Álvarez-Bel, C. & Fernandez-Jimenez, L.A., 2021. "Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models," Utilities Policy, Elsevier, vol. 70(C).
- Bradley, Peter & Leach, Matthew & Torriti, Jacopo, 2013. "A review of the costs and benefits of demand response for electricity in the UK," Energy Policy, Elsevier, vol. 52(C), pages 312-327.
- Olsthoorn, Mark & Schleich, Joachim & Klobasa, Marian, 2015.
"Barriers to electricity load shift in companies: A survey-based exploration of the end-user perspective,"
Energy Policy, Elsevier, vol. 76(C), pages 32-42.
- Mark Olsthoorn & Joachim Schleich & Marian Klobasa D, 2015. "Barriers to electricity load shift in companies: A survey-based exploration of the end-user perspective," Post-Print hal-01104611, HAL.
- Mark Olsthoorn & Joachim Schleich & Marian Klobasa D, 2015. "Barriers to electricity load shift in companies: A survey-based exploration of the end-user perspective," Grenoble Ecole de Management (Post-Print) hal-01104611, HAL.
- Parrish, Bryony & Heptonstall, Phil & Gross, Rob & Sovacool, Benjamin K., 2020. "A systematic review of motivations, enablers and barriers for consumer engagement with residential demand response," Energy Policy, Elsevier, vol. 138(C).
- Chao, Hung-po, 2010. "Price-Responsive Demand Management for a Smart Grid World," The Electricity Journal, Elsevier, vol. 23(1), pages 7-20, January.
- Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
- Xenias, Dimitrios & Axon, Colin J. & Whitmarsh, Lorraine & Connor, Peter M. & Balta-Ozkan, Nazmiye & Spence, Alexa, 2015. "UK smart grid development: An expert assessment of the benefits, pitfalls and functions," Renewable Energy, Elsevier, vol. 81(C), pages 89-102.
- He, Xian & Keyaerts, Nico & Azevedo, Isabel & Meeus, Leonardo & Hancher, Leigh & Glachant, Jean-Michel, 2013.
"How to engage consumers in demand response: A contract perspective,"
Utilities Policy, Elsevier, vol. 27(C), pages 108-122.
- Xian He & Nico Keyaerts & Isabel Azevedo, Leonardo Meeus, Leigh Hancher, Jean-Michel Glachant, 2013. "How to engage consumers in demand response: a contract perspective," RSCAS Working Papers 2013/76, European University Institute.
- Reiss, Peter C. & White, Matthew W., 2003. "Demand and Pricing in Electricity Markets: Evidence from San Diego During California's Energy Crisis," Research Papers 1829, Stanford University, Graduate School of Business.
- Vuelvas, José & Ruiz, Fredy & Gruosso, Giambattista, 2018. "Limiting gaming opportunities on incentive-based demand response programs," Applied Energy, Elsevier, vol. 225(C), pages 668-681.
- Peter C. Reiss & Matthew W. White, 2003. "Demand and Pricing in Electricity Markets: Evidence from San Diego During California's Energy Crisis," NBER Working Papers 9986, National Bureau of Economic Research, Inc.
- Kim, Jin-Ho & Shcherbakova, Anastasia, 2011. "Common failures of demand response," Energy, Elsevier, vol. 36(2), pages 873-880.
- Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
- Sha, Huajing & Xu, Peng & Lin, Meishun & Peng, Chen & Dou, Qiang, 2021. "Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation," Applied Energy, Elsevier, vol. 289(C).
- Jin, Xin & Baker, Kyri & Christensen, Dane & Isley, Steven, 2017. "Foresee: A user-centric home energy management system for energy efficiency and demand response," Applied Energy, Elsevier, vol. 205(C), pages 1583-1595.
- Hung-po Chao & Mario DePillis, 2013. "Incentive effects of paying demand response in wholesale electricity markets," Journal of Regulatory Economics, Springer, vol. 43(3), pages 265-283, June.
- Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
- Ellabban, Omar & Abu-Rub, Haitham, 2016. "Smart grid customers' acceptance and engagement: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1285-1298.
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- Lind, Leandro & Chaves-Ávila, José Pablo & Valarezo, Orlando & Sanjab, Anibal & Olmos, Luis, 2024. "Baseline methods for distributed flexibility in power systems considering resource, market, and product characteristics," Utilities Policy, Elsevier, vol. 86(C).
- Nikoleta Andreadou & Dimitrios Thomas & Antonio De Paola & Evangelos Kotsakis & Gianluca Fulli, 2023. "Holistic Evaluation of Demand Response Events in Real Pilot Sites: From Baseline Calculation to Evaluation of Key Performance Indicators," Energies, MDPI, vol. 16(16), pages 1-28, August.
- Jaka Rober & Leon Maruša & Miloš Beković, 2023. "A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers," Energies, MDPI, vol. 16(17), pages 1-20, August.
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Keywords
demand response; smart grids; baselines; flexibility: decarbonisation; H2020 projects;All these keywords.
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