{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T19:29:40Z","timestamp":1775849380743,"version":"3.50.1"},"reference-count":155,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["768559"],"award-info":[{"award-number":["768559"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (\u201cbaseline\u201d) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.<\/jats:p>","DOI":"10.3390\/en15145259","type":"journal-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T11:22:24Z","timestamp":1658316144000},"page":"5259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load"],"prefix":"10.3390","volume":"15","author":[{"given":"Ottavia","family":"Valentini","sequence":"first","affiliation":[{"name":"European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy"},{"name":"Department of Science, Technology and Society, University School for Advanced Studies IUSS, 27100 Pavia, Italy"},{"name":"Department of Economics, University of Insubria, Via Monte Generoso 71, 21100 Varese, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3114-8994","authenticated-orcid":false,"given":"Nikoleta","family":"Andreadou","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy"}]},{"given":"Paolo","family":"Bertoldi","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6381-965X","authenticated-orcid":false,"given":"Alexandre","family":"Lucas","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy"},{"name":"Institute for Systems and Computer Engineering, Technology and Science\u2014INESC TEC, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0576-0282","authenticated-orcid":false,"given":"Iolanda","family":"Saviuc","sequence":"additional","affiliation":[{"name":"Department of Engineering Management, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium"}]},{"given":"Evangelos","family":"Kotsakis","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112075","DOI":"10.1016\/j.enbuild.2022.112075","article-title":"Policies for energy conservation and sufficiency: Review of existing policies and recommendations for new and effective policies in OECD countries","volume":"264","author":"Bertoldi","year":"2022","journal-title":"Energy Build."},{"key":"ref_2","unstructured":"Directorate-General for Energy (European Commission) (2020). EU Energy in Figures. Statistical Pocketbook 2020, Publications Office of the European Union."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rser.2019.05.005","article-title":"Conflicting values in the smart electricity grid a comprehensive overview","volume":"111","author":"Chappin","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.enpol.2018.06.018","article-title":"Technology, business model, and market design adaptation toward smart electricity distribution: Insights for policy making","volume":"121","author":"Pereira","year":"2018","journal-title":"Energy Policy"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zangheri, P., Serrenho, T., and Bertoldi, P. (2019). Energy Savings from Feedback Systems: A Meta-Studies\u2019 Review. Energies, 12.","DOI":"10.3390\/en12193788"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e368","DOI":"10.1002\/wene.368","article-title":"The future of power systems: Challenges, trends, and upcoming paradigms","volume":"9","author":"Lopes","year":"2020","journal-title":"WIREs Energy Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.rser.2012.09.024","article-title":"Life cycle assessment of the air emissions during building construction process: A case study in Hong Kong","volume":"17","author":"Zhang","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","unstructured":"Bertoldi, P., Zancanella, P., and Boza-Kiss, B. (2020, March 30). Demand Response Status in EU Member States, Available online: https:\/\/publications.jrc.ec.europa.eu\/repository\/bitstream\/JRC101191\/ldna27998enn.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TII.2019.2909276","article-title":"Comprehensive Review of the Recent Advances in Industrial and Commercial DR","volume":"15","author":"Siano","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/COMST.2014.2341586","article-title":"A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms","volume":"17","author":"Vardakas","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"96851","DOI":"10.1109\/ACCESS.2021.3094090","article-title":"An Overview of Demand Response: From its Origins to the Smart Energy Community","volume":"9","author":"Honarmand","year":"2021","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77555","DOI":"10.1109\/ACCESS.2021.3082430","article-title":"A Review on Communication Aspects of Demand Response Management for Future 5G IoT- Based Smart Grids","volume":"9","author":"Ahmadzadeh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106811","DOI":"10.1016\/j.ijepes.2021.106811","article-title":"Blockchain technology in the future smart grids: A comprehensive review and frameworks","volume":"129","author":"Hasankhani","year":"2021","journal-title":"Electr. Power Energy Syst."},{"key":"ref_14","unstructured":"European Commission (2021, May 12). The EU\u2019s 2021\u20132027 Long-Term Budget and Next Generation EU: Facts and Figures. 29 April 2021, Available online: http:\/\/op.europa.eu\/en\/publication-detail\/-\/publication\/d3e77637-a963-11eb-9585-01aa75ed71a1\/language-it."},{"key":"ref_15","unstructured":"(2019). Directive 2012\/27\/EU of the European Parliament and of the Council of 25 October 2012 on Energy Efficiency, Amending Directives 2009\/125\/EC and 2010\/30\/EU and Repealing Directives 2004\/8\/EC and 2006\/32\/ECText with EEA Relevance, European Parliament."},{"key":"ref_16","unstructured":"(2012). Directive (EU) 2019\/44 of the European Parliament and of the Council\u2014of 5 June 2019\u2014on Common Rules for the Internal Market for Electricity and Amending Directive 2012\/27\/EU, European Parliament."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1016\/j.apenergy.2019.02.020","article-title":"Demand response algorithms for smart-grid ready residential buildings using machine learning models","volume":"239","author":"Pallonetto","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_18","unstructured":"European Commission (2021, January 02). Communication from the Commission. Delivering the Internal Electricity Market and Making the Most of Public Intervention, Available online: https:\/\/ec.europa.eu\/energy\/sites\/ener\/files\/documents\/com_2013_public_intervention_en_0.pdf."},{"key":"ref_19","unstructured":"Goldman, C., Hopper, N., Bharvirkar, R., Neenan, B., and Cappers, P. (2021, January 03). Estimating Large-Customer Demand Response Market Potential: Integrating Price and Customer Behavior. June 2007. Available online: https:\/\/escholarship.org\/uc\/item\/4p48j22n."},{"key":"ref_20","unstructured":"North American Energy Standards Board (2009). Wholesale and Retail Demand Response Definition of Terms."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.apenergy.2017.03.034","article-title":"Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings","volume":"195","author":"Chen","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jup.2016.04.001","article-title":"Time-based pricing and electricity demand response: Existing barriers and next steps","volume":"40","author":"Eid","year":"2016","journal-title":"Util. Policy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106073","DOI":"10.1016\/j.epsr.2019.106073","article-title":"Load demand forecasting of residential buildings using a deep learning model","volume":"179","author":"Wen","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116652","DOI":"10.1016\/j.apenergy.2021.116652","article-title":"Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation","volume":"289","author":"Sha","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_25","unstructured":"Rossetto, N. (2021, April 26). Measuring the Intangible: An Overview of the Methodologies for Calculating Customer Baseline Load in PJM. Available online: https:\/\/cadmus.eui.eu\/\/handle\/1814\/54744."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/TIA.2016.2613985","article-title":"Error Analysis of Customer Baseline Load (CBL) Calculation Methods for Residential Customers","volume":"53","author":"Mohajeryami","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1109\/TSG.2014.2309053","article-title":"When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers","volume":"5","author":"Wijaya","year":"2014","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mathieu, J., Callaway, D., and Kiliccote, S. (2011, January 12). Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing. Proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, USA.","DOI":"10.1109\/CDC.2011.6160628"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rser.2017.01.043","article-title":"Review and classification of barriers and enablers of demand response in the smart grid","volume":"72","author":"Good","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_30","unstructured":"(2014). Energy Management Systems\u2014Measuring Energy Performance Using Energy Baselines (EnB) and Energy Performance Indicators (EnPI)\u2014General Principles and Guidance. Standard No. ISO 50006."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TST.2015.7085625","article-title":"Load profiling and its application to demand response: A review","volume":"20","author":"Wang","year":"2015","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cappers, P., MacDonald, J., Page, J., Potter, J., and Stewart, E. (2016). Future Opportunities and Challenges with Using Demand Response as a Resource in Distribution System Operation and Planning Activities, University of California. LBNL\u20141003951.","DOI":"10.2172\/1333622"},{"key":"ref_33","unstructured":"Pinto, T., Vale, Z., and Widergren, S. (2021). Local Electricity Markets, Academic Press."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111890","DOI":"10.1016\/j.rser.2021.111890","article-title":"Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques","volume":"156","author":"Tsaousoglou","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Li, J. (2012, January 10\u201312). Demand response in electricity markets: A review. Proceedings of the IEEE 2012 9th International Conference on the European Energy Market, Florence, Italy.","DOI":"10.1109\/EEM.2012.6254817"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Albadi, M.H., and El-Saadany, E.F. (2007, January 24\u201328). Demand Response in Electricity Markets: An Overview. Proceedings of the 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA.","DOI":"10.1109\/PES.2007.385728"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rser.2015.01.062","article-title":"Demand response for sustainable energy systems: A review, application and implementation strategy","volume":"45","author":"Shariatzadeh","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.enpol.2012.09.039","article-title":"A review of the costs and benefits of demand response for electricity in the UK","volume":"52","author":"Bradley","year":"2013","journal-title":"Energy Policy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.jup.2013.10.001","article-title":"How to engage consumers in demand response: A contract perspective","volume":"27","author":"He","year":"2013","journal-title":"Util. Policy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3570","DOI":"10.1016\/j.rser.2017.10.103","article-title":"What makes consumers adopt to innovative energy services in the energy market? A review of incentives and barriers","volume":"82","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.renene.2015.03.016","article-title":"UK smart grid development: An expert assessment of the benefits, pitfalls and functions","volume":"81","author":"Xenias","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1016\/j.rser.2016.06.021","article-title":"Smart grid customers\u2019 acceptance and engagement: An overview","volume":"65","author":"Ellabban","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.enpol.2014.11.015","article-title":"Barriers to electricity load shift in companies: A survey-based exploration of the end-user perspective","volume":"76","author":"Olsthoorn","year":"2015","journal-title":"Energy Policy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1016\/j.energy.2010.12.027","article-title":"Common failures of demand response","volume":"36","author":"Kim","year":"2011","journal-title":"Energy"},{"key":"ref_45","unstructured":"Gouveia, C., Alves, E., Villar, J., Ferreira, R., Silva, R., Chaves, J.P., G\u00f3mez, T., Herding, L., Morell, L., and Rivier, M. (2022, July 12). Observatory of Research and Demonstration Initiatives on Future Electricity Grids and Markets. Deliverable 1.2 of EUniversal Project, Available online: https:\/\/euniversal.eu\/deliverable-1-2-observatory-of-research-and-demonstration-initiatives-on-future-electricity-grids-and-markets\/."},{"key":"ref_46","unstructured":"Falc\u00e3o, J., Louro, M., Pereira, N., Corujas, J., Sancho, A., \u00c1guas, A., Carvalho, D., Marques, P., Staudt, M., and Brummund, D. (2022, July 12). Grid Flexibility Services Definition. Deliverable 1.2 of EUniversal Project. Available online: https:\/\/euniversal.eu\/wp-content\/uploads\/2021\/02\/EUniversal__D2.1.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"140881","DOI":"10.1109\/ACCESS.2021.3120099","article-title":"Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response","volume":"9","author":"Lee","year":"2021","journal-title":"IEEE Access"},{"key":"ref_48","unstructured":"Reif, V., Nouicer, A., Schittekatte, T., Deschamps, V.N.A., and Meeus, L. (2021, September 23). INTERRFACE D9.12 Report on the Foundations for the Adoptions of New Network Codes 1. Available online: www.interrface.eu."},{"key":"ref_49","unstructured":"McAnany, J. (2022, July 12). PJM\u20142020 Demand Response Operations Markets Activity Report: March 2021. Available online: https:\/\/www.pjm.com\/-\/media\/markets-ops\/dsr\/2020-demand-response-activity-report.ashx."},{"key":"ref_50","unstructured":"Charles River Associates (2021, September 23). An Assessment of the Economic Value of Demand-Side Participation in the Balancing Mechanism and an Evaluation of Options to Improve Access, Available online: https:\/\/www.ofgem.gov.uk\/sites\/default\/files\/docs\/2017\/07\/an_assessment_of_the_economic_value_of_demand-side_participation_in_the_balancing_mechanism_and_an_evaluation_of_options_to_improve_access.pdf."},{"key":"ref_51","unstructured":"Smart Energy Demand Coalition (SEDC) (2020, March 31). Explicit Demand Response in Europe Mapping the Markets 2017. Available online: https:\/\/www.smarten.eu\/wp-content\/uploads\/2017\/04\/SEDC-Explicit-Demand-Response-in-Europe-Mapping-the-Markets-2017.pdf."},{"key":"ref_52","unstructured":"(2022, July 12). Final Report: Demand Side Flexibility Perceived Barriers and Proposed Recommendations, Smart Grid Task Force Expert Group 3 for the Deployment of Demand Response, Available online: https:\/\/ec.europa.eu\/energy\/sites\/ener\/files\/documents\/eg3_final_report_demand_side_flexiblity_2019.04.15.pdf."},{"key":"ref_53","unstructured":"European Commission, Brussels (2021, April 06). A Renovation Wave for Europe\u2014Greening Our Buildings, Creating Jobs, Improving Lives, Available online: https:\/\/eur-lex.europa.eu\/resource.html?uri=cellar:0638aa1d-0f02-11eb-bc07-01aa75ed71a1.0003.02\/DOC_1&format=PDF."},{"key":"ref_54","unstructured":"Gangale, F., Vasiljevska, J., Covrig, C.F., Mengolini, A.M., and Fulli, G. (2020, March 30). Smart Grid Projects Outlook 2017: Facts, Figures and Trends in Europe, Available online: https:\/\/publications.jrc.ec.europa.eu\/repository\/bitstream\/JRC106796\/sgp_outlook_2017-online.pdf."},{"key":"ref_55","unstructured":"XENERGY for California Energy Commission Sacramento, California (2022, July 12). Protocol Development for Demand Response Calculation\u2014Draft Findings and Recommendations. Available online: http:\/\/www.calmac.org\/publications\/2002-08-02_XENERGY_REPORT.pdf."},{"key":"ref_56","unstructured":"Kaneshiro, B. (2022, July 12). Baselines for Retail Demand Response Programs. 2009. p. 11. Available online: https:\/\/www.caiso.com\/Documents\/Presentation-Baselines_RetailDemandResponsePrograms.pdf."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Coughlin, K., Piette, M.A., Goldman, C., and Kiliccote, S. (2008). Estimating Demand Response Load Impacts: Evaluation of BaselineLoad Models for Non-Residential Buildings in California, LBNL\u201463728; Lawrence Berkeley National Laboratory.","DOI":"10.2172\/928452"},{"key":"ref_58","unstructured":"Quantum Consulting Inc., and Summit Blue Consulting, LLC (2006). Evaluation of 2005 Statewide Large Nonresidential Day-Ahead and Reliability Demand Response Programs, Southern California Edison Company. Prepared for Southern California Edison Company and Working Group 2 Measurement and Evaluation Committee."},{"key":"ref_59","unstructured":"EnerNOC Utility Solutions (2022, July 12). Energy Baseline Methodologies for Industrial Facilities. E13-265. Available online: https:\/\/neea.org\/img\/uploads\/energy-baseline-methodologies-for-industrial-facilities.pdf."},{"key":"ref_60","unstructured":"The CADMUS Group LLC (2020, May 16). Demand Response ProgramAnnual Evaluation, Phase III of Act 129 Program Year 9 (1 June 2017\u201431 May 2018) for Pennsylvania Act 129 of 2008 Energy Efficiency and Conservation Plan. Prepared by Cadmus for PPL Electric Utilities. Phase III of Act 129. Available online: https:\/\/www.pplelectric.com\/-\/media\/PPLElectric\/Save-Energy-and-Money\/Docs\/Act129_Phase3\/PPLPY9ChapterDRProgram20180115.pdf?la=en."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TSG.2011.2145010","article-title":"Quantifying Changes in Building Electricity Use, With Application to Demand Response","volume":"2","author":"Mathieu","year":"2011","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_62","first-page":"133","article-title":"Understanding the Effect of Baseline Modeling Implementation Choices on Analysis of Demand Response Performance","volume":"Volume 45264","author":"Addy","year":"2012","journal-title":"ASME International Mechanical Engineering Congress and Exposition"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111221","DOI":"10.1016\/j.enpol.2019.111221","article-title":"A systematic review of motivations, enablers and barriers for consumer engagement with residential demand response","volume":"138","author":"Parrish","year":"2020","journal-title":"Energy Policy"},{"key":"ref_64","unstructured":"AEIC Load Research Committee (2021, April 06). Demand Response Measurement & Verification Applications for Load Research. Available online: https:\/\/www.naesb.org\/\/pdf4\/dsmee_group2_040909w5.pdf."},{"key":"ref_65","unstructured":"Reiss, P., and White, M. (2022, July 12). Demand and Pricing in Electricity Markets: Evidence from San Diego during California\u2019s Energy Crisis. Available online: https:\/\/www.nber.org\/system\/files\/working_papers\/w9986\/w9986.pdf."},{"key":"ref_66","unstructured":"California Public Utilities Commission Energy Division (2020, March 31). Attachment A\u2014Load Impact Estimation for Demand Response: Protocols and Regulatory Guidance, Available online: http:\/\/www.calmac.org\/events\/FinalDecision_AttachementA.pdf."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.apenergy.2019.05.050","article-title":"Spillover as a cause of bias in baseline evaluation methods for demand response programs","volume":"250","author":"Todd","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_68","unstructured":"Lake, A., and PJM Empirical Analysis of Demand Response Baseline Methods (2022, July 12). April 2011. Available online: https:\/\/www.pjm.com\/-\/media\/committees-groups\/subcommittees\/drs\/20110613\/20110613-item-03b-cbl-analysis-report.ashx."},{"key":"ref_69","unstructured":"Australian Energy Market Operator (2022, July 12). AEMO Virtual Power Plant Demonstrations\u2014Knowledge Sharing Report #2. Available online: https:\/\/aemo.com.au\/-\/media\/files\/electricity\/der\/2020\/vpp-knowledge-sharing-stage-2.pdf."},{"key":"ref_70","unstructured":"Zhou, X., Yu, N., Yao, W., and Johnson, R. (2016, January 17\u201321). Forecast load impact from demand response resources. Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s11149-012-9208-1","article-title":"Incentive effects of paying demand response in wholesale electricity markets","volume":"43","author":"Chao","year":"2013","journal-title":"J. Regul. Econ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"201","DOI":"10.5547\/01956574.37.2.xche","article-title":"Money for nothing? Why FERC order 745 should have died","volume":"37","author":"Chen","year":"2016","journal-title":"Energy J."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1109\/TSG.2020.3024208","article-title":"Incentives to Manipulate Demand Response Baselines with Uncertain Event Schedules","volume":"12","author":"Ellman","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1109\/TSG.2021.3137098","article-title":"Modeling and Analysis of Baseline Manipulation in Demand Response Programs","volume":"13","author":"Wang","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.tej.2009.12.007","article-title":"Price-Responsive Demand Management for a Smart Grid World","volume":"23","author":"Chao","year":"2010","journal-title":"Electr. J."},{"key":"ref_76","unstructured":"Ruff, L. (2002). Economic Principles of Demand Response in Electricity, Edison Electric Institute."},{"key":"ref_77","unstructured":"FERC (2022, May 23). Demand Response Compensation in Organized Wholesale Energy Markets, Available online: https:\/\/www.ferc.gov\/sites\/default\/files\/2020-06\/Order-745.pdf."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1007\/s11149-010-9135-y","article-title":"Demand response in wholesale electricity markets: The choice of customer baseline","volume":"39","author":"Chao","year":"2011","journal-title":"J. Regul. Econ."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wang, X., and Tang, W. (2018, January 9\u201311). To Overconsume or Underconsume: Baseline Manipulation in Demand Response Programs. Proceedings of the 2018 North. American Power Symposium (NAPS), Fargo, ND, USA.","DOI":"10.1109\/NAPS.2018.8600558"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"110357","DOI":"10.1016\/j.rser.2020.110357","article-title":"Why baselines are not suited for local flexibility markets","volume":"135","author":"Ziras","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_81","unstructured":"Energy Independence and Security Act (EISA) (2021, September 14). Public Law 110-140, US, Available online: https:\/\/www.govinfo.gov\/content\/pkg\/BILLS-110hr6enr\/pdf\/BILLS-110hr6enr.pdf."},{"key":"ref_82","unstructured":"Federal Energy Regulatory Commission (2022, July 12). A National Assessment of Demand Response Potential. p. 254, Available online: https:\/\/www.ferc.gov\/sites\/default\/files\/2020-05\/06-09-demand-response_1.pdf."},{"key":"ref_83","unstructured":"Goldberg, M., and Agnew, G.K. (2013). Measurement and Verification for Demand Response."},{"key":"ref_84","unstructured":"EnerNOC Utility Solutions (2021, April 07). The Demand Response Baseline. Available online: https:\/\/library.cee1.org\/sites\/default\/files\/library\/10774\/CEE_EvalDRBaseline_2011.pdf."},{"key":"ref_85","unstructured":"Eric Winkler (2021, September 23). Measurement and Verification Standards Wholesale Electric Demand Response Recommendation Summary. IRC ISP\/RTO Council. Available online: https:\/\/www.naesb.org\/\/pdf3\/dsmee100308w7.pdf."},{"key":"ref_86","unstructured":"PJM (2021, September 23). PJM Manual 11: Energy & Ancillary Services Market Operations. Available online: https:\/\/www.pjm.com\/-\/media\/documents\/manuals\/archive\/m11\/m11v95-energy-and-ancillary-services-market-operations-06-01-2018.ashx."},{"key":"ref_87","unstructured":"(2016). CORE CONCEPTS\u2014IPMVP International Performance Measurement and Verification Protocol, EVO\u2014Efficiency Valuation Organization."},{"key":"ref_88","unstructured":"(2020, April 08). DRIMPAC H2020 Project. Available online: https:\/\/www.drimpac-h2020.eu\/."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485128","article-title":"Tackling Climate Change with Machine Learning","volume":"55","author":"Rolnick","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Lucas, A., Jansen, L., Andreadou, N., Kotsakis, E., and Masera, M. (2019). Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector. Energies, 12.","DOI":"10.3390\/en12142725"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.apenergy.2012.02.027","article-title":"Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting","volume":"96","author":"Javed","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Atef, S., and Eltawil, A.B. (2019, January 23\u201326). Real-Time Load Consumption Prediction and Demand Response Scheme Using Deep Learning in Smart Grids. Proceedings of the 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), Paris, France.","DOI":"10.1109\/CoDIT.2019.8820363"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rser.2016.04.030","article-title":"Bayesian networks in renewable energy systems: A bibliographical survey","volume":"62","author":"Borunda","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Feng, C., and Zhang, J. (2019, January 18\u201321). Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting. Proceedings of the 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA.","DOI":"10.1109\/ISGT.2019.8791671"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1109\/TSG.2016.2517211","article-title":"Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning","volume":"8","author":"Ruelens","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3698","DOI":"10.1109\/TSG.2018.2834219","article-title":"On-Line Building Energy Optimization Using Deep Reinforcement Learning","volume":"10","author":"Mocanu","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1016\/j.apenergy.2017.08.166","article-title":"Foresee: A user-centric home energy management system for energy efficiency and demand response","volume":"205","author":"Jin","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.apenergy.2018.03.036","article-title":"Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm","volume":"219","author":"Hu","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Noy\u00e9, S., Saralegui, U., Rey, R., Anton, M.A., and Romero, A. (2019). Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings. E3S Web of Conferences, EDP Sciences location.","DOI":"10.1051\/e3sconf\/201911105025"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1016\/j.apenergy.2018.11.014","article-title":"Drivers of domestic electricity users\u2019 price responsiveness: A novel machine learning approach","volume":"235","author":"Guo","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"O\u2019Neill, D., Levorato, M., Goldsmith, A., and Mitra, U. (2010, January 4\u20136). Residential Demand Response Using Reinforcement Learning. Proceedings of the 2010 First IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA.","DOI":"10.1109\/SMARTGRID.2010.5622078"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.apenergy.2018.11.002","article-title":"Reinforcement learning for demand response: A review of algorithms and modeling techniques","volume":"235","author":"Nagy","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"6629","DOI":"10.1109\/TSG.2019.2909266","article-title":"Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network","volume":"10","author":"Lu","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.rser.2014.08.035","article-title":"Demand side management using artificial neural networks in a smart grid environment","volume":"41","author":"Macedo","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.enbuild.2011.08.008","article-title":"New artificial neural network prediction method for electrical consumption forecasting based on building end-uses","volume":"43","year":"2011","journal-title":"Energy Build."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Ruberto, S., Terragni, V., and Moore, J.H. (2020). SGP-DT: Semantic Genetic Programming Based on Dynamic Targets. Genetic Programming, Springer.","DOI":"10.1145\/3377929.3397486"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Jazaeri, J., Alpcan, T., Gordon, R., Brandao, M., Hoban, T., and Seeling, C. (December, January 28). Baseline methodologies for small scale residential demand response. Proceedings of the 2016 IEEE Innovative Smart Grid Technologies\u2014Asia (ISGT-Asia), Melbourne, Australia.","DOI":"10.1109\/ISGT-Asia.2016.7796478"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Oyedokun, J., Bu, S., Han, Z., and Liu, X. (October2019, January 29). Customer Baseline Load Estimation for Incentive-Based Demand Response Using Long Short-Term Memory Recurrent Neural Network. Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania.","DOI":"10.1109\/ISGTEurope.2019.8905582"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Arunaun, A., and Pora, W. (2018, January 24\u201326). Baseline Calculation of Industrial Factories for Demand Response Application. Proceedings of the 2018 IEEE International Conference on Consumer Electronics\u2014Asia (ICCE-Asia), JeJu, Korea.","DOI":"10.1109\/ICCE-ASIA.2018.8552114"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Weng, Y., and Rajagopal, R. (2015, January 26\u201330). Probabilistic baseline estimation via Gaussian process. Proceedings of the 2015 IEEE Power Energy Society General Meeting, Denver, CO, USA.","DOI":"10.1109\/PESGM.2015.7285756"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.ijepes.2018.02.049","article-title":"Probabilistic baseline estimation based on load patterns for better residential customer rewards","volume":"100","author":"Weng","year":"2018","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Tehrani, N.H., Khan, U.T., and Crawford, C. (2016, January 15\u201318). Baseline load forecasting using a Bayesian approach. Proceedings of the 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, BC, Canada.","DOI":"10.1109\/CCECE.2016.7726749"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Schwarz, P., Mohajeryami, S., and Cecchi, V. (2020). Building a Better Baseline for Residential Demand Response Programs: Mitigating the Effects of Customer Heterogeneity and Random Variations. Electronics, 9.","DOI":"10.3390\/electronics9040570"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"4876","DOI":"10.1109\/TSG.2021.3105747","article-title":"Two-stage decoupled estimation approach of aggregated baseline load under high penetration of behind-the-meter PV system","volume":"12","author":"Li","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"6128","DOI":"10.1109\/TIA.2020.3014575","article-title":"PV-Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer with Distributed PV System","volume":"56","author":"Xuan","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"2042","DOI":"10.1016\/j.egypro.2017.12.408","article-title":"A baseline load estimation approach for residential customer based on load pattern clustering","volume":"142","author":"Li","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"109861","DOI":"10.1016\/j.rser.2020.109861","article-title":"On the assessment and control optimisation of demand response programs in residential buildings","volume":"127","author":"Pallonetto","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.rser.2014.05.056","article-title":"Review of building energy modeling for control and operation","volume":"37","author":"Li","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Park, S., Ryu, S., Choi, Y., and Kim, H. (2014, January 3\u20136). A framework for baseline load estimation in demand response: Data mining approach. Proceedings of the 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy.","DOI":"10.1109\/SmartGridComm.2014.7007719"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"10239","DOI":"10.3390\/en80910239","article-title":"Data-Driven Baseline Estimation of Residential Buildings for Demand Response","volume":"8","author":"Park","year":"2015","journal-title":"Energies"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"101213","DOI":"10.1016\/j.jup.2021.101213","article-title":"Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models","volume":"70","year":"2021","journal-title":"Util. Policy"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"6972","DOI":"10.1109\/TSG.2018.2824842","article-title":"Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description","volume":"9","author":"Wang","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, K., Gao, X., Wang, F., and Mi, Z. (2018, January 20\u201322). Customer Baseline Load Bias Estimation Method of Incentive-Based Demand Response Based on CONTROL Group Matching. Proceedings of the 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China.","DOI":"10.1109\/EI2.2018.8582122"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/TPWRS.2015.2453889","article-title":"Statistical Estimation of the Residential Baseline","volume":"31","author":"Hatton","year":"2016","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1109\/TSG.2015.2463755","article-title":"A Cluster-Based Method for Calculating Baselines for Residential Loads","volume":"7","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/TSG.2021.3112611","article-title":"Closed-Loop Aggregated Baseline Load Estimation Using Contextual Bandit with Policy Gradient","volume":"13","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_127","unstructured":"Vrettos, E., Kara, E.C., MacDonald, J., Andersson, G., and Callaway, D.S. (2016). Experimental demonstration of frequency regulation by commercial buildings\u2014Part I: Modeling and Hierarchical Control Design. arXiv."},{"key":"ref_128","unstructured":"Vrettos, E., Kara, E.C., MacDonald, J., Andersson, G., and Callaway, D.S. (2016). Experimental Demonstration of Frequency Regulation by Commercial Buildings\u2014Part II: Results and Performance Evaluation. arXiv."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TSG.2019.2917396","article-title":"Mechanism Design for Demand Response Programs","volume":"11","author":"Muthirayan","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.1109\/TSG.2019.2949263","article-title":"A Minimal Incentive-Based Demand Response Program with Self Reported Baseline Mechanism","volume":"11","author":"Muthirayan","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Xue, W., Yao, L., Shan, B., and Yang, Y. (2020, January 1\u20134). A Market Clearing Model with Demand Response Program of Self-Reported Baseline Mechanism. Proceedings of the 2020 IEEE 1st China International Youth Conference on Electrical Engineering (CIYCEE), Wuhan, China.","DOI":"10.1109\/CIYCEE49808.2020.9332579"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.apenergy.2018.05.050","article-title":"Limiting gaming opportunities on incentive-based demand response programs","volume":"225","author":"Vuelvas","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_133","unstructured":"Caujolle, M., Glorieux, L., Eyrolles, P., Le Baut, J., Irhly, R., Toledo, F.-X., Belhomme, R., Naso, F., Morozova, O., and Valtorta, G. (2021, April 07). ADDRESS D6.2\u2014Prototype Field Tests. Test Results. Available online: http:\/\/www.addressfp7.org\/config\/files\/ADD-WP6-T6.3-DEL-Iberdrola-D6.2-PrototypeFieldTests.TestResults.pdf."},{"key":"ref_134","unstructured":"AnyPLACE Project (2020, May 17). Adaptable Platform for Active Services Exchange. Available online: https:\/\/www.anyplace2020.org\/."},{"key":"ref_135","unstructured":"Santinelli, G., Siilin, K., Hoang, H., Garc\u00eda, N.P., Marguerite, C., Monteverdi, I., Decorme, R., and Synthesis of CITYOPT Demonstrations (2020, May 17). CITYOPT, Co-funded by the European Commission, Deliverable D 3.5. Available online: http:\/\/www.cityopt.eu\/Deliverables\/D35.pdf."},{"key":"ref_136","unstructured":"Araghi, R.Y., van der Stoep, A., Koch-Mathian, S., and van der Lei, T. (2020, May 17). Common Monitoring Strategy. AIM, Amsterdam, Deliverable 7.1. Available online: http:\/\/www.cityzen-smartcity.eu\/wp-content\/uploads\/2016\/01\/Cityzen-D7_1-Common_monitoring_strategy_FINAL.pdf."},{"key":"ref_137","unstructured":"Andreadou, N., Poursanidis, I., Marinopoulos, A., Lucas, A., Kotsakis, E., Anagnostopoulos, S., Cole, I., Venizelou, V., and Therapontos, P. (2021, April 07). DELTA D1.4\u2014Performance Measurement & Verification Methodology Report. Joint Research Centre, European Commission. Available online: https:\/\/www.delta-h2020.eu\/wp-content\/uploads\/2019\/11\/DELTA_D1.4_PMV_v1.pdf."},{"key":"ref_138","unstructured":"Boisson, P., Thebault, S., Rodriguez, S., Breukers, S., Charlesworth, R., Bull, S., Perevozchikov, I., Sissini, M., Noris, F., and Ceclan, A. (2019). Deliverable 5.1. Monitoring and Validation Strategies, EU."},{"key":"ref_139","unstructured":"(2021, April 07). DRIvE H2020 Project\u2014Grant Agreement: 774431. Available online: https:\/\/www.h2020-drive.eu\/."},{"key":"ref_140","unstructured":"Lund, P., Nyeng, P., Grandal, R.D., S\u00f8rensen, S.H., Bendtsen, M.F., le Ray, G., Larsen, E.M., Mastop, J., Judex, F., and Leimgruber, F. (2020, May 17). Overall Evaluation and Conclusion. Energinet.dk, Deliverable 6.7. Available online: http:\/\/www.eu-ecogrid.net\/images\/Documents\/D6.7_160121_Final.pdf."},{"key":"ref_141","unstructured":"Leon, E.J.S., and Hunter, B. (2020, May 17). Recommendations for Baseline Load Calculations inDR Programs V1. Teesside University, Deliverable 3.2. Available online: https:\/\/edream-h2020.eu\/wp-content\/uploads\/2019\/05\/eDREAM.D3.2.TU_.WP3_.V1.0.pdf."},{"key":"ref_142","unstructured":"(2020, May 17). EnergyLab Nordhavn\u2014New Urban Energy Infrastructure. EnergyLab Nordhavn. Available online: http:\/\/www.energylabnordhavn.com\/."},{"key":"ref_143","unstructured":"Kos, A., Kiljander, J., Horvat, U., Elmasllari, E., Selmke, P., Gabrijel\u010di\u010d, D., Stepan\u010di\u010d, Z., and Mueller, H. (2022, July 12). Flex4Grid\u2014Final Pilot Deployment. Deliverable 6.5. Available online: https:\/\/ec.europa.eu\/research\/participants\/documents\/downloadPublic\/YzN5OGlPUWc1TUh5TE45QURtVUlHcTBFYW1DVkZLVkk5dE1pQ3JVOGxqU2dQbGZVamUxTTZ3PT0=\/attachment\/VFEyQTQ4M3ptUWVIM0hPb3ZYRzZmdlNjK0dvMmdhUGE=."},{"key":"ref_144","unstructured":"Conserva, J., Aranda, L., Morcillo, A., Azar, G., and Tual, R. (2020, May 17). FLEXCoop PMV Methodology Specifications\u2014Preliminary Version. CIRCE, Deliverable 2.5. Available online: https:\/\/uploads.strikinglycdn.com\/files\/5ef9aef5-53ff-44bb-a983-858669777bb3\/FLEXCoop-D2.5%20PMV%20Methodology%20Specifications%20-%20Preliminary%20Version-final.pdf."},{"key":"ref_145","unstructured":"IndustRE (2016). Adapted Methodology for Optimal Valorization of Flexible Industrial Electricity Demand. Deliverable 3.2, IndustRE."},{"key":"ref_146","unstructured":"(2015). Distribution Grid and Retail Market Scenarios and Use Case Definition. Deliverable 1.2."},{"key":"ref_147","unstructured":"Porras, E., Feliu, J., Lalaguna, I., Gomez, J., and Pouttu, A. (2022, July 12). Certification Mechanisms to Measure the Confidence and Reliability of the Energy Transactions. Deliverable 4.1. Available online: https:\/\/www.p2psmartest-h2020.eu\/."},{"key":"ref_148","unstructured":"Diez, F.J., Cruz, M., Mart\u00ednez, L., Seri, F., Berbakov, L., Tomasevic, N., and Batic, M. (2020, May 17). RESPOND\u2014System Reference Architecture. Deliverable 2.1. Available online: https:\/\/get.dexma.com\/hubfs\/RESPOND%20Deliverables\/RESPOND_2-1.pdf?utm_campaign=RESPOND&utm_source=RESPONDPublicationsWeb."},{"key":"ref_149","unstructured":"Kolhe, M. (2022, July 12). Algorithms for Demand Response and Load Control. Deliverable 5.1. Available online: https:\/\/projects.au.dk\/semiah\/."},{"key":"ref_150","unstructured":"Fischer, D., Casotti, M., D\u2019Alonzo, V., Grutsch, S., Hilber, S., Kleewein, K., Mautner, P., Pernetti, R., Pezzutto, S., and Pfeifer, D. (2014). Deliverable 2.2\u2014Good Practice District Stimulator, Refinement of Local Master Plans for Smart Energy Cities Transition: The Experience of Bolzano and Innsbruck, Sinfonia."},{"key":"ref_151","unstructured":"(2022, July 12). Conceptual Design of SmarterEMC2 Architecture. INTRACOM TELECOM, Deliverable 2.4. Available online: www.smarterEMC2.eu."},{"key":"ref_152","unstructured":"Nolay, P. (2020, May 17). Smart-UP Final-report. Deliverable 6.4. Available online: https:\/\/www.smartup-project.eu\/wp-content\/uploads\/2019\/02\/D6.4-Final-report-WP6.pdf."},{"key":"ref_153","unstructured":"Pascual, H., D\u00edez, I., Garc\u00eda, E., and Report of Societal Research (2022, July 12). Socioeconomic Impact of Smart Grid. Report of Transfer Replication Strategy and Communication. Deliverable 9.3. Available online: https:\/\/ec.europa.eu\/research\/participants\/documents\/downloadPublic\/RVFDd3d6bkh3c0s5MnpRM2RhVUVoUjFaTEJ6QTVXanBBRFV2M3ZHRksyTXRIdXcxclM1L3R3PT0=\/attachment\/VFEyQTQ4M3ptUWVRcFU0bHgzd0VrSWFDVWpud2RHZm8=."},{"key":"ref_154","unstructured":"(2020, May 17). Energywise-The Final Energy Saving Trial Report (also Known as Vulnerable Customers and Energy Efficiency). Ukpowernetworks. Available online: https:\/\/innovation.ukpowernetworks.co.uk\/wp-content\/uploads\/2019\/05\/Energywise-The-Final-Energy-Saving-Trial-Report.pdf."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"116254","DOI":"10.1016\/j.apenergy.2020.116254","article-title":"Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects","volume":"283","author":"Zheng","year":"2020","journal-title":"Appl. Energy"}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/15\/14\/5259\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:54:44Z","timestamp":1760140484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/15\/14\/5259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,20]]},"references-count":155,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["en15145259"],"URL":"https:\/\/doi.org\/10.3390\/en15145259","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,20]]}}}