基于多种群引力粒子群算法的金沙江下游—三峡梯级水库群优化调度

汪涛; 徐杨; 刘亚新; 卢佳; 马皓宇

doi:10.11988/ckyyb.20221439

长江科学院院报 >

2023 , Vol. 40 >Issue 12: 30 - 36,58

DOI: https://doi.org/10.11988/ckyyb.20221439

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基于多种群引力粒子群算法的金沙江下游—三峡梯级水库群优化调度

汪涛 ,
徐杨 ,
刘亚新 ,
卢佳 ,
马皓宇

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1.中国长江电力股份有限公司智慧长江与水电科学湖北省重点实验室,湖北宜昌 443000;
2.中国长江电力股份有限公司三峡水利枢纽梯级调度通信中心,湖北宜昌 443000

汪涛(1997-),男,安徽安庆人,助理工程师,硕士,主要从事水库调度技术研究。E-mail:1355124078@qq.com

收稿日期: 2022-10-28

修回日期: 2023-03-21

网络出版日期: 2023-10-12

基金资助

国家重点研发计划项目(2019YFC0409000)

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Optimal Operation of Cascade Reservoirs in the Lower Reaches of Jinsha River to the Three Gorges Based on Multi-group Gravitational Particle Swarm Algorithm

WANG Tao ,
XU Yang ,
LIU Ya-xin ,
LU Jia ,
MA Hao-yu

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1. Hubei Provincial Key Laboratory of Smart Yangtze River and Hydropower Science of China Changjiang Electric Power Co., Ltd., Yichang 443000, China;
2. Cascade Dispatching Communication Center of the Three Gorges Water Control Project of China Changjiang Electric Power Co., Ltd., Yichang 443000, China

Received date: 2022-10-28

Revised date: 2023-03-21

Online published: 2023-10-12

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摘要

金沙江下游—三峡已形成六库联合调度格局,调度维数增多,约束庞杂交织,优化目标多样,给调度方案制定带来极大困难。针对传统粒子群算法求解调度模型寻优能力不足的难题,提出多种群引力粒子群算法,建立优化调度模型并应用改进算法求解。算法测试和应用结果表明,多种群引力粒子群算法寻优性能更加先进,更适用于求解梯级水库优化调度问题。实例表明,上游龙头电站通过减少自身发电量可以使下游电站和梯级发电量增加。

关键词： 水库群优化调度; 粒子群算法; 算法改进; 梯级水库; 金沙江下游; 三峡

本文引用格式

汪涛 , 徐杨 , 刘亚新 , 卢佳 , 马皓宇 . 基于多种群引力粒子群算法的金沙江下游—三峡梯级水库群优化调度[J]. 长江科学院院报, 2023 , 40(12) : 30 -36,58 . DOI: 10.11988/ckyyb.20221439

Abstract

From the lower reaches of the Jinsha River to the Three Gorges, a complex joint scheduling pattern comprising six reservoirs has emerged. This pattern is characterized by an expanded scope of scheduling, numerous and diverse constraints, and a range of optimization objectives. Consequently, formulating appropriate scheduling schemes has become particularly challenging. Recognizing the limitations of traditional particle swarm algorithms in addressing this scheduling model, we propose a multi-group gravitational particle swarm algorithm to enhance the optimization capabilities of the scheduling model. To this end, a multi-scale and multi-objective nested scheduling model is established, and the improved algorithm is applied to solve it. The test and application results demonstrate that the multi-group gravitational particle swarm algorithm exhibits superior optimization performance compared to other approaches. Moreover, it is more suitable for achieving optimal operation of cascade reservoirs. A case study further illustrates that the upstream leading power station can enhance the generation of downstream power stations and cascade stations by reducing its own power generation capacity.

Key words： optimal operation of reservior groups; particle swarm optimization; algorithm improvement; cascade reservoir; lower Jinsha River; Three Gorges

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