{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:36:07Z","timestamp":1760240167873,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T00:00:00Z","timestamp":1554163200000},"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>Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price\u2013elastic demand curve that relies on several factors such as estimation of a customer\u2019s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price\u2013elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer\u2019s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.<\/jats:p>","DOI":"10.3390\/en12071261","type":"journal-article","created":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T03:39:28Z","timestamp":1554262768000},"page":"1261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme"],"prefix":"10.3390","volume":"12","author":[{"given":"Neda","family":"Hajibandeh","sequence":"first","affiliation":[{"name":"C-MAST, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-5355","authenticated-orcid":false,"given":"Miadreza","family":"Shafie-khah","sequence":"additional","affiliation":[{"name":"School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland"}]},{"given":"Sobhan","family":"Badakhshan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Sharif University of Technology, Tehran 11365-11155, Iran"}]},{"given":"Jamshid","family":"Aghaei","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 71557-13876, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6102-5872","authenticated-orcid":false,"given":"S\u00edlvio J. P. S.","family":"Mariano","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, 6201-001 Covilh\u00e3, Portugal"},{"name":"University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]},{"given":"Jo\u00e3o P. S.","family":"Catal\u00e3o","sequence":"additional","affiliation":[{"name":"Faculty of Engineering of the University of Porto and INESC TEC, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1016\/j.epsr.2008.04.002","article-title":"A summary of demand response in electricity markets","volume":"78","author":"Albadi","year":"2008","journal-title":"Elecr. Power Syst. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hajibandeh, N., Ehsan, M., Soleymani, S., Shafie-khah, M., and Catalao, J.P. (2017). The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey. 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