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Development of a genetic algorithm and its application to a bi-level problem of system cost optimal electricity price zone configurations
Energy Economics ( IF 12.8 ) Pub Date : 2021-06-29 , DOI: 10.1016/j.eneco.2021.105422
Tim Felling

The topic of alternative price zone configurations is frequently discussed in Central Western Europe where – so far – national borders coincide with borders of price zones. Reconfiguring these price zones is one option in order to improve congestion management, foster trading across borders of price zones and, thus, to increase welfare. In view of the significant increase in redispatch volumes and costs over the last years due to increasing feed-in from renewable energy sources in conjunction with delayed grid expansion, this topic has gained in importance. To determine these improved price zone configurations for a large-scale system like Central Western Europe, often either configurations based on expert guesses are considered or heuristics using approximate criteria like locational marginal prices are used to obtain price zones through clustering. In contrast, the present paper formulates a bi-level optimization problem of how to determine optimal configurations in terms of system costs and – given the size and nature of the problem – solves it with a specially developed genetic algorithm. Resulting price zone configurations are compared to both exogenously given, expert-based price zone configurations from the Entso-E bidding zone study and endogenously assessed configurations from a hierarchical cluster algorithm. Results show that the genetic algorithm achieves best results in terms of system costs. Moreover, the comparison with results from a hierarchical cluster analysis reveals important drawbacks of the latter methodology.



中文翻译:

遗传算法的开发及其在系统成本最优电价区配置双层问题中的应用

中西欧经常讨论替代价格区配置的话题,到目前为止,那里的国界与价格区的边界重合。重新配置这些价格区是改善拥堵管理、促进跨价格区贸易并从而提高福利的一种选择。鉴于可再生能源的馈入增加以及电网扩张延迟,再调度量和成本在过去几年显着增加,因此该主题变得越来越重要。为了确定像中西欧这样的大型系统的这些改进的价格区配置,通常,要么考虑基于专家猜测的配置,要么使用使用近似标准(如位置边际价格)的启发式方法通过聚类获得价格区域。相比之下,本文阐述了如何根据系统成本确定最佳配置的双层优化问题,并且——给定问题的规模和性质——用专门开发的遗传算法解决它。由此产生的价格区域配置与来自 Entso-E 投标区域研究的外生给定的、基于专家的价格区域配置和来自分层聚类算法的内生评估配置进行比较。结果表明,遗传算法在系统成本方面取得了最好的结果。而且,

更新日期:2021-07-08
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