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A new enhanced bat-inspired algorithm for finding linear supply function equilibrium of GENCOs in the competitive electricity market

机译：一种新型的蝙蝠启发式算法，用于在竞争激烈的电力市场中寻找GENCO的线性供给函数均衡

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摘要

This paper proposes a new enhanced bat-inspired algorithm to find out linear supply function equilibrium of Generating Companies (GENCOs) in a network-constrained electricity market where they have incomplete information about other rivals. The model enables a GENCO to link its bidding price with the bidding quantity of its product. In this regard, the social welfare maximization is applied to clearing the market and nodal pricing mechanism is utilized to calculate the GENCO's profit. It is formulated as a bi level optimization problem, where the higher level problem maximizes GENCO's payoff and the lower level problem solves the independent system operator's market clearing problem based on the maximization of social welfare. Due to non-convexity nature of the proposed bi level optimization problem, the mathematical-based optimization approach is incapable to solve the problem and obtain the nearly global optima. In order to overcome the obstacle of the conventional approaches, this study suggests a new meta-heuristic Bat-inspired Algorithm (BA) to achieve the nearly global solution of the bi level optimization problem. In addition a novel self-adaptive learning mechanism is utilized on the original BA to improve the population diversity and global searching capability. Numerical examples are applied to three test systems in order to evaluate the performances of the presented framework.
机译：本文提出了一种新的，受蝙蝠启发的增强算法，用于在网络受限的电力市场中，发电公司（GENCO）的线性供应函数均衡，这些电网对其他竞争对手的信息不完整。该模型使GENCO可以将其投标价格与其产品的投标数量关联起来。在这方面，社会福利最大化被用于清理市场，节点定价机制被用来计算GENCO的利润。它被表述为双层次优化问题，其中较高层次的问题最大化了GENCO的收益，而较低层次的问题则基于社会福利的最大化解决了独立系统运营商的市场清算问题。由于所提出的双层优化问题的非凸性，基于数学的优化方法无法解决该问题并获得接近全局的最优值。为了克服传统方法的障碍，本研究提出了一种新的启发式蝙蝠启发算法（BA），以实现双层优化问题的几乎全局解决方案。另外，在原始BA上使用了一种新颖的自适应学习机制，以提高人口多样性和全局搜索能力。数值示例被应用于三个测试系统，以评估所提出框架的性能。

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