Development of a genetic algorithm and its application to a bi-level problem of system cost optimal electricity price zone configurations

• Published in: Energy economics 2021-09, Vol.101, p.105422, Article 105422
• Main Author: Felling, Tim
• Format: Article
• Language: English
• Subjects: Algorithms; Bidding zone configuration; Borders; Cluster algorithm; Cluster analysis; Clustering; Configurations; Cost analysis; Costs; Delayed; Electricity; Electricity pricing; Energy economics; Energy sources; Evolutionary algorithm; Genetic algorithm; Genetic algorithms; Heuristic; Hierarchies; Locational marginal prices; Optimization; Price zone configuration; Prices; Renewable energy; Renewable energy sources; Trading; Welfare
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2021.105422

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. •Development of genetic algorithm to identify improved price zones.•Based on bi-level optimization problem of system cost optimal price zones.•Identified zones achieve significant cost and redispatch savings.•Hierarchical Algorithms based on LMPs are not the best choice.•Minimizing redispatch does not always correspond to minimizing system costs.