{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:24:58Z","timestamp":1760243098381,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2015,6,23]],"date-time":"2015-06-23T00:00:00Z","timestamp":1435017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>Further improvements in demand response programs implementation are needed in order to take full advantage of this resource, namely for the participation in energy and reserve market products, requiring adequate aggregation and remuneration of small size resources. The present paper focuses on SPIDER, a demand response simulation that has been improved in order to simulate demand response, including realistic power system simulation. For illustration of the simulator\u2019s capabilities, the present paper is proposes a methodology focusing on the aggregation of consumers and generators, providing adequate tolls for the demand response program\u2019s adoption by evolved players. The methodology proposed in the present paper focuses on a Virtual Power Player that manages and aggregates the available demand response and distributed generation resources in order to satisfy the required electrical energy demand and reserve. The aggregation of resources is addressed by the use of clustering algorithms, and operation costs for the VPP are minimized. The presented case study is based on a set of 32 consumers and 66 distributed generation units, running on 180 distinct operation scenarios.<\/jats:p>","DOI":"10.3390\/en8066230","type":"journal-article","created":{"date-parts":[[2015,6,23]],"date-time":"2015-06-23T10:19:06Z","timestamp":1435054746000},"page":"6230-6246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5982-8342","authenticated-orcid":false,"given":"Pedro","family":"Faria","sequence":"first","affiliation":[{"name":"GECAD - Knowledge Engineering and Decision Support Research Center,  IPP - Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431,  4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-9544","authenticated-orcid":false,"given":"Zita","family":"Vale","sequence":"additional","affiliation":[{"name":"GECAD - Knowledge Engineering and Decision Support Research Center,  IPP - Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431,  4200-072 Porto, Portugal"}]},{"given":"Jos\u00e9","family":"Baptista","sequence":"additional","affiliation":[{"name":"INESC Technology and Science - INESC-TEC - Associate Laboratory, UTAD University,  5001-801Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1109\/TPWRS.2012.2193490","article-title":"Long-Term Market Equilibrium Model with Strategic, Competitive, and Inflexible Generation","volume":"27","author":"MacCormack","year":"2012","journal-title":"IEEE Trans. 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