Optimizing day-ahead electricity market prices: increasing the total surplus for energy exchange İstanbul

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buir.contributor.authorTanrısever, Fehmi
dc.citation.epage716en_US
dc.citation.issueNumber4en_US
dc.citation.spage700en_US
dc.citation.volumeNumber22en_US
dc.contributor.authorDerinkuyu, K.
dc.contributor.authorTanrısever, Fehmi
dc.contributor.authorKurt, N.
dc.contributor.authorCeyhan, G.
dc.date.accessioned2021-03-03T11:35:47Z
dc.date.available2021-03-03T11:35:47Z
dc.date.issued2020
dc.departmentDepartment of Managementen_US
dc.description.abstractProblem definition: We design a combinatorial auction to clear the Turkish day-ahead electricity market, and we develop effective tabu search and genetic algorithms to solve the problem of matching bidders and maximizing social welfare within a reasonable amount of time for practical purposes. Academic/practical relevance: A double-sided blind combinatorial auction is used to determine electricity prices for day-ahead markets in Europe. Considering the integer requirements associated with market participants’ bids and the nonlinear social welfare objective, a complicated problem arises. In Turkey, the total number of bids reaches 15,000, and this large problem needs to be solved within minutes every day. Given the practical time limit, solving this problem with standard optimization packages is not guaranteed, and therefore, heuristic algorithms are needed to quickly obtain a high-quality solution. Methodology: We use nonlinear mixed-integer programming and tabu search and genetic algorithms. We analyze the performance of our algorithms by comparing them with solutions commercially available to the market operator. Results: We provide structural results to reduce the problem size and then develop customized heuristics by exploiting the problem structure in the day-ahead market. Our algorithms are guaranteed to generate a feasible solution, and Energy Exchange Istanbul has been using them since June 2016, increasing its surplus by 448,418 Turkish liras (US$128,119) per day and 163,672,570 Turkish liras (US$46,763,591) per year, on average. We also establish that genetic algorithms work better than tabu search for the Turkish day-ahead market. Managerial implications: We deliver a practical tool using innovative optimization techniques to clear the Turkish day-ahead electricity market. We also modify our model to handle similar European day-ahead markets and show that performances of our heuristics are robust under different auction designs.en_US
dc.description.provenanceSubmitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2021-03-03T11:35:47Z No. of bitstreams: 1 Optimizing_day_ahead_electricity_market_prices_increasing_the_total_surplus_for_energy_exchange_İstanbul.pdf: 1005591 bytes, checksum: decab2b1a27281f39b7ea84ebb3df2aa (MD5)en
dc.description.provenanceMade available in DSpace on 2021-03-03T11:35:47Z (GMT). No. of bitstreams: 1 Optimizing_day_ahead_electricity_market_prices_increasing_the_total_surplus_for_energy_exchange_İstanbul.pdf: 1005591 bytes, checksum: decab2b1a27281f39b7ea84ebb3df2aa (MD5) Previous issue date: 2020en
dc.identifier.doi10.1287/msom.2018.0767en_US
dc.identifier.eissn1526-5498
dc.identifier.issn1523-4614
dc.identifier.urihttp://hdl.handle.net/11693/75716
dc.language.isoEnglishen_US
dc.publisherINFORMS: Institute for Operations Research and the Management Sciencesen_US
dc.relation.isversionofhttps://dx.doi.org/10.1287/msom.2018.0767en_US
dc.source.titleManufacturing and Service Operations Managementen_US
dc.subjectAuctions and mechanism designen_US
dc.subjectDay-ahead electricity marketen_US
dc.subjectEnergy-related operationsen_US
dc.subjectOM practiceen_US
dc.titleOptimizing day-ahead electricity market prices: increasing the total surplus for energy exchange İstanbulen_US
dc.typeArticleen_US

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