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Optimizing Day-Ahead Electricity Market Prices: Increasing the Total Surplus for Energy Exchange Istanbul

机译:优化日前电力市场价格:增加能源交换伊斯坦布尔的总盈余

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Problem 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 dear 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.
机译:问题定义:我们设计一个组合拍卖,清除土耳其日前电力市场,我们开发了有效的禁忌搜索和遗传算法,以解决竞标者在合理的时间内匹配竞标者和最大化社会福利的问题。学术/实际相关性:双面盲馆组合拍卖用于确定欧洲前方市场的电价。考虑到与市场参与者出价和非线性社会福利目标相关的整数要求,出现了复杂的问题。在土耳其,投标总数达到15,000,并且需要在每天几分钟内解决这个问题。鉴于实用的时间限制,无法保证使用标准优化包的解决这个问题,因此,需要启发式算法来快速获得高质量解决方案。方法论:我们使用非线性混合整数编程和禁忌搜索和遗传算法。我们通过将其与市场运营商可商购的解决方案进行比较来分析我们的算法的性能。结果:我们提供了结构性结果,以减少问题规模,然后通过利用日前市场的问题结构来开发定制启发式。我们的算法得到保证,以自2016年6月以来,能源交换伊斯坦布尔一直在使用它们,每天增加448,418美元的土耳其拉拉(128,119美元),平均每年为163,672,570美元(每年46,763,591美元)。我们还规定了遗传算法优于禁忌搜索土耳其日前市场。管理含义:我们利用创新优化技术提供了一种实用的工具,使土耳其前方电力市场亲爱的。我们还修改了我们的模型来处理类似的欧洲前方市场,并显示我们的启发式的表演在不同的拍卖设计下是强大的。

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