电力市场竞争中大、小客户多目标最优竞价方法

Manisha Saini, Ajay Bhardwaj, Sarfaraz Nawaz
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引用次数: 0

摘要

在当前的电力能源市场中,发电企业通过优化电力市场投标来寻求收益最大化。在竞争市场中,策略投标使每个参与者都能提高自己的个人利润;然而,这对公共利益产生了不利影响。本研究提出一种在池式能源市场中,电力生产者与使用者策略性竞标的机制。系统被分配到社会福利最大化,每个供应商/大消费者投标一个线性的供给/需求函数。价格接受者需要一个合适的投标结构来确定最佳的投标策略。因此,必须将模型视为一个两级优化问题。价格接受者向下级独立系统运营商(ISO)提交战略报价,而ISO市场出清价格(MCP)用于在日前电力市场中实现上层社会福利最大化,在竞争性电力市场中采用密封竞价支付机制实现上层社会福利最大化。在IEEE-30总线系统上,验证了该方法的有效性。采用NSGA-II、NSGA-III、MOGWO和MOPSO四种不同的进化算法从两个不同的角度来解决所提出的多目标问题。结果部分对总利润和市场出清价格进行了对比分析,表明NSGA-III算法的结果优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Objective Optimal Bidding Approach for both Small & Large Customers in Competitive power Market
In the present scenario of the electricity energy market, power generation firms seek to maximize revenue by optimizing the bid in the electricity market. In a competitive market, Strategic bidding allows each participant to improve his individual profit; however, this has a detrimental effect on public benefit. This study presents a mechanism for developing a strategic bid for electricity producers and users in a pool co-style energy market. The system is dispatched to maximize social welfare, with each supplier/large consumer bidding a linear supply/demand function. Price takers require a proper bidding structure to identify the best bidding tactics. As a result, the model must be thought of as a two-level optimization issue. Price takers submit strategic bids to the Independent System Operator (ISO) at the lower level, while the ISO Market Clearing Price (MCP) is used to maximize social welfare at the upper level in a day-ahead power market to maximize social welfare at the upper level using a pay-as-bid mechanism in a sealed auction in the competitive power market. On the IEEE-30 bus system, the proposed method's efficiency was tested. Four different evolutionary algorithms such as NSGA-II, NSGA-III, MOGWO, and MOPSO were used to address the problem from two separate perspectives for solving proposed multi-objective problems. The result section presents a comparative analysis of the total profit and market clearing price, showing that the NSGA-III algorithm offers superior results than other methods.
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