省间电力中长期交易出清问题多解顺次寻优的高效计算方法

doi:10.19595/j.cnki.1000-6753.tces.240774

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摘要

随着全国统一电力市场建设的推进,我国市场规模逐步增长。以省间电力交易为例,当前市场主体已达上千个。由于大量市场主体报价具有相似性,可能导致市场出现多组具有相同社会福利的购售匹配对,使得最大化社会福利的出清问题存在多个最优解。为保障出清有效性与公平性,多解场景下需开展多层目标顺次寻优,但现有基于多目标优化的方法难以兼顾出清效果和效率。对此,本文以存在多解场景的省间电力交易问题为研究对象,提出多层目标顺次寻优的高效出清方法：构建多层目标顺次寻优出清模型,前序模型的最优目标函数将作为后续模型的约束,以确保高优先级目标函数的最优性;提出基于最优解达界约束的出清问题多解判定模型,以帮助了解出清问题的多解性,同时,将其嵌入顺次寻优出清流程,通过避免无效寻优实现出清进程加速;提出有效利用前序模型求解信息的出清无损加速方法,以满足计算效率要求。基于我国实际交易数据的算例仿真表明,所提方法可在保障首要目标最优性的前提下,有效提升面向次要目标的出清效果;所提加速策略可将典型场景的出清计算效率无损提高37倍。

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关键词 ：省间电力中长期交易, 出清问题多解, 多层目标顺次寻优, 多解判定, 出清加速

Abstract：

With the development of the national unified electricity market, the market scale has gradually increased. Take inter-provincial medium- and long-term power transaction as an example, the number of market participants is over a thousand. The similarity of the bid prices of numerous market participants is likely to lead to multiple purchasing and selling pairs having the same social welfare. As a result, the market clearing problem that maximizes social welfare may have multiple optimal solutions. To ensure the effectiveness and fairness of market clearing, the multi-level objective sequential optimization should be implemented under multiple-solution scenarios. However, the existing methods based on multi-objective optimization cannot balance effectiveness and efficiency. To address this issue, take inter-provincial medium- and long-term power transaction that may have multiple solutions as a research objective, an efficient multi-level objective sequential optimization method is proposed in this paper. The main contributions are illustrated as follows:
First, the market clearing model with multi-level objective sequential optimization is established. Four objective functions are considered according to the industrial practices, including maximizing social welfare, maximizing transaction volume of renewable energy, maximizing total transaction volume, and equally distributing tradable power among purchasing and selling pairs with the same social welfare. Market clearing models considering the aforementioned four objective functions are separately established. The optimal objective functions of the preorder model are used as the operating constraints of the subsequent model to ensure the optimality of the objective functions with high priorities. By sequential solving these four market clearing models, the market clearing effectiveness and fairness can be guaranteed even under multiple-solution scenarios.
Second, the multiple-solution judgment auxiliary optimization model for the market clearing problem is established based on the bound constraints of the optimal solution, according to which the multiple-solution characteristics of market clearing problems can be recognized. The recognized multiple-solution characteristics can provide support for market operators to design the measure for handling multiple-solution scenarios. For instance, more objective functions can be introduced if multiple-solution scenarios cannot be effectively avoided after the sequential optimization of four objective functions. Besides, regarding the computational burden caused by the solution to four market clearing models, the multiple-solution judgment auxiliary optimization model is embedded into the sequential optimization process to simplify the clearing process by avoiding unnecessary optimization.
Third, to meet the calculation efficiency demand, the lossless acceleration method for market clearing based on solution information of the preorder model is proposed. For the market clearing models with multi-level objective functions, the optimal solution of the preorder model is used as the high-quality initial feasible solution of the subsequent model, which can guide the warm-start accelerating process of the subsequent model without the loss of accuracy. For the multiple-solution judgment auxiliary optimization model, the optimal solution of the preorder model is used as the initial feasible solution. Based on this, the termination criterion for the calculation process is established according to the comparison between the initial objective function and the current objective function. In this way, the judgment process can be accelerated without affecting judgment accuracy.
Finally, case studies based on practical inter-provincial medium- and long-term transaction data in China demonstrate that the proposed method can greatly improve the market clearing effect for the subordinate objectives while ensuring the optimality of the primary objective. In addition, benefiting from the proposed model solution acceleration strategy and the sequential optimization process simplification strategy, the market clearing efficiency can be improved by 37 times without the loss of accuracy under the typical scenario.

Key words： Inter-provincial medium- and long-term power transactions multiple solutions of market clearing multi-level objective sequential optimization multiple solution judgment market clearing acceleration

收稿日期: 2024-05-14

PACS:	TM73
	F426.61

基金资助:

国家电网有限公司科技项目资助（5108-202218280A-2-284-XG）

通讯作者: 杨知方男,1992年生,博士,教授,研究方向为电力系统优化、电力市场等。E-mail：zfyang@cqu.edu.cn

作者简介: 向明旭男,1994年生,博士,助理研究员,研究方向为电力市场、电力优化调度等。

引用本文:

向明旭, 陈泓霏, 曹晓峻, 杨知方, 金一丁. 省间电力中长期交易出清问题多解顺次寻优的高效计算方法[J]. 电工技术学报, 0, (): 2492943-2492943. Xiang Mingxu, Chen Hongfei, Cao Xiaojun, Yang Zhifang, Jin Yiding. Efficient Sequential Optimization Method for Inter-Provincial Medium- and Long-Term Power Transaction Clearing Problem Under Multiple-Solution Scenarios. Transactions of China Electrotechnical Society, 0, (): 2492943-2492943.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.240774 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/2492943

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