Metaheuristic Optimization for Transmission Network Expansion Planning: Testebed 2 of the Competition on Evolutionary Computation in the Energy Domain

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI:10.1145/3583133.3596347

José Almeida, F. Lezama, J. Soares, Leonardo H. Macedo, Z. Vale, Rubén R. Romero

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Abstract

The complexity of the transmission network expansion planning (TNEP) problem has been increasing due to the new constraints given by renewable generation uncertainty, new market rules and players, and the continuous demand growth with the introduction of electric vehicles and energy storage systems. The problem consists of finding the optimal number and location of new transmission lines to support the demand, which can be extremely hard to optimize. As such, in this paper, we focus on metaheuristic optimization to solve a TENP problem proposed in testbed 2 of the 2023 competition on evolutionary computation in the energy domain. The 87-bus north-northeast Brazilian transmission system is considered for the case study, and different DE metaheuristics are used for the optimization process. Results show that the HyDE algorithm presents the overall best performance when compared to other DE strategies. HyDE is able to achieve the overall lowest costs with a reduction of around 67% compared to L-SHADE.

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输电网扩展规划的元启发式优化:能量域演化计算竞争的实验平台2

由于可再生能源发电的不确定性、新的市场规则和参与者以及随着电动汽车和储能系统的引入而不断增长的需求，输电网络扩展规划(TNEP)问题的复杂性不断增加。这个问题包括找到新输电线路的最佳数量和位置来支持需求，这可能是非常难以优化的。因此，在本文中，我们将重点放在元启发式优化上，以解决2023年能源领域进化计算竞赛测试平台2中提出的TENP问题。以87总线的巴西北-东北输电系统为例，采用不同的DE元启发式方法进行优化。结果表明，与其他DE策略相比，HyDE算法具有最佳的总体性能。与L-SHADE相比，HyDE能够实现整体成本最低，降低约67%。

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Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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