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Development of bidding strategies using genetic algorithm in deregulated electricity market

机译：电力市场解除管制中基于遗传算法的竞价策略开发

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摘要

For past recent years, electric power companies are shifting towards the major restructuring process and deregulated market scenario. Competition has been introduced in the power system all over the world with the aim of increasing the efficiency of the industrial sector and reducing the cost of electricity to the customers. In the competitive electricity markets, the power producers are required to submit their MW outputs and associated prices. The bids of all the suppliers are collected by Independent System Operator who determines the power output of every unit. ISO then solves the welfare maximization problem. When no consumer bidding is considered, ISO solves the total system cost minimization problem where it solves the optimal power flow algorithm that determines prices and quantities. The maximization of individual profit forms a two level optimization problem. In first level, individual maximizes its profit and in second level, the total system cost is minimized based on the bids in the market. This paper proposed conversion of bi-level bidding problem into a single level bidding minimization problem. The new single level minimization problem incorporating transmission constraints, operating limits and ISO market clearing function is solved using GA (Genetic Algorithm). Experimental investigation is carried out on IEEE 14 bus system and simulation results shows that the profits of strategic producer using proposed methodology are higher than that of non-strategic producer.
机译：在过去的几年中，电力公司正朝着主要的重组过程和放松市场管制的方向发展。为了提高工业部门的效率并降低客户的用电成本，全世界的电力系统都引入了竞争。在竞争激烈的电力市场中，电力生产商必须提交其兆瓦输出量和相关价格。所有供应商的投标均由独立系统操作员收集，独立系统操作员确定每个单元的功率输出。然后，ISO解决福利最大化问题。当不考虑消费者竞标时，ISO解决总系统成本最小化的问题，并解决确定价格和数量的最佳潮流算法。个人利润的最大化形成了两个层次的优化问题。在第一层中，个人最大化其利润，在第二层中，根据市场竞标将总系统成本最小化。本文提出将二级投标问题转化为单级投标最小化问题。使用遗传算法（GA）解决了包含传输限制，操作限制和ISO市场清算功能的新的单级最小化问题。在IEEE 14总线系统上进行了实验研究，仿真结果表明，采用所提出的方法，战略生产者的利润要高于非战略生产者的利润。

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