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浙江大学学报(工学版)
浙江大学学报(工学版)  2019, Vol. 53 Issue (4): 801-810    DOI: 10.3785/j.issn.1008-973X.2019.04.022
电子工程、电气工程     
基于粗糙集理论的风光蓄互补系统优化模型
郭洪武1(),蒲雷1,张予燮1,吴静1,赵蕊1,谭忠富1,2,*()
1. 华北电力大学 经济与管理学院，北京 102206
2. 延安大学 经济与管理学院，陕西 延安 716000
Optimization model for integrated complementary system of wind-PV-pump storage based on rough set theory
Hong-wu GUO1(),Lei PU1,Yu-xie ZHANG1,Jing WU1,Rui ZHAO1,Zhong-fu TAN1,2,*()
1. School of Economics and Management, North China Electric Power University, Beijing 102206, China
2. School of Economics and Management, Yan’an University, Yan’an 716000, China
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摘要：

为了解决风光波动性对系统安全调度和稳定运行的影响，以系统运行成本最小和系统污染排放量最小为目标，构建风光蓄集成互补系统. 基于粗糙集理论和模糊C均值聚类算法，分别确定多目标调度中经济目标和环境目标的权重；提出基于粒子群变异策略和计及约束边界的信息共享方法的改进粒子群优化(PSO)算法，求解多目标调度优化问题；以我国西南地区某省风光蓄集成互补系统为例开展算例仿真，验证所提模型的科学性和实用性. 研究结果表明，与单目标调度相比，多目标调度兼顾经济性和环境性，所提混合粗糙集-改进粒子群算法的收敛精度更优，提高了系统的经济效益和环境效益. 引入抽水蓄能机组，对于实现系统多能源协同互补运行具有重要的意义.
关键词： 风光蓄互补系统;  多目标调度优化;  粗糙集理论;  改进粒子群算法;  权重设计    
Abstract:

The integrated complementary system of wind - PV - pump-storage considering minimizing the operation cost and pollutant emissions was constructed in order to solve the influence of the wind power and PV generation fluctuation on system operation safety and stability. The weights of the economic targets and the environmental targets were determined respectively based on rough set theory and fuzzy C mean clustering algorithm. An improved particle swarm optimization (PSO) algorithm based on particle swarm optimization and constrained boundary information sharing was proposed in order to solve the multi-objective scheduling optimization problem. The case study was based on an integrated system in southwest China to verify the scientificity and practicability of the proposed model. Results show that the multi-objective scheduling considers both economic and environmental benefits of the system compared with single-objective scheduling. The accuracy of the proposed hybrid rough set-improved particle swarm optimization algorithm is better with an improvement in the economic and environmental benefits of the system. Introducing the pumped storage power station is significant for the cooperative and complementary operation of the multi-energy system.
Key words: integrated complementary system of wind-PV-pump storage    multi-objective scheduling optimization    rough set theory    improved particle swarm optimization algorithm    indicator weight design
收稿日期: 2018-06-25 出版日期: 2019-03-28
CLC:  TM 734  
通讯作者: 谭忠富     E-mail: 549618607@qq.com;tanzhongfu@sina.com
作者简介: 郭洪武（1963—），男，博士生，从事电力系统优化研究. orcid.org/0000-0001-8987-5791. E-mail： 549618607@qq.com
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引用本文:

郭洪武,蒲雷,张予燮,吴静,赵蕊,谭忠富. 基于粗糙集理论的风光蓄互补系统优化模型[J]. 浙江大学学报(工学版), 2019, 53(4): 801-810.

Hong-wu GUO,Lei PU,Yu-xie ZHANG,Jing WU,Rui ZHAO,Zhong-fu TAN. Optimization model for integrated complementary system of wind-PV-pump storage based on rough set theory. Journal of ZheJiang University (Engineering Science), 2019, 53(4): 801-810.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.04.022    或    http://www.zjujournals.com/eng/CN/Y2019/V53/I4/801

图 1  风光蓄集成互补系统结构
图 2  改进粒子群算法求解流程
机组 ${\alpha _k}$ ${\beta _k}$ ${\gamma _k}$ $T_{k,t}^{{\rm{on}}}$ $T_{k,t}^{{\rm{off}}}$ $g_k^{\max }$ $g_k^{\min }$ $g_k^{{\rm{up}}}$ $g_k^{{\rm{down}}}$
TP1 80 30.15 0.038 8 8 220 80 90 ?90
TP2 330 34.73 0.040 8 8 150 50 70 ?70
TP3 170 32.50 0.174 6 6 100 30 50 ?50
TP4 1 180 34.61 0.082 7 7 120 40 60 ?60
TP5 450 39.75 0.015 5 5 70 15 35 ?35
TP6 460 34.90 0.083 4 4 80 20 40 ?40
表 1  燃煤机组运行参数
时刻 WPP/
MW 		PV/
MW 		PS/
MW 		负荷/
MW 		时刻 WPP/
MW 		PV/
MW 		PS/
MW 		负荷/
MW
1 155 0 192.5 476 13 220 95 100 994
2 140 0 232.5 456 14 220 95 55 1 010
3 125 0 250 433 15 190 95 67.5 987
4 125 0 222.5 418 16 180 70 100 947
5 145 0 200 445 17 180 57.5 105 920
6 170 5 190 483 18 145 45 135 879
7 180 12.5 185 700 19 150 20 150 859
8 200 35 140 836 20 160 0 162.5 819
9 190 52.5 185 903 21 160 0 170 779
10 185 60 175 911 22 130 0 175 559
11 195 72.5 135 962 23 165 0 182.5 476
12 185 102.5 107.5 987 24 220 0 195 994
表 2  风电、光伏发电、抽水蓄能出力及负荷需求
风光蓄互补系统 ${f_1}/$元 ${f_2}/$元
A 4 867 862 2 739 944
B 4 902 633 2 594 708
C 4 674 141 2 583 536
D 4 917 534 2 759 495
E 4 475 453 2 457 850
F 4 882 764 2 745 530
G 4 276 765 2 575 157
H 4 937 403 2 758 657
I 4 907 600 2 773 460
J 4 078 076 2 452 264
表 3  风光蓄集成互补系统的指标数据
图 3  经济调度优化结果
图 4  环境调度优化结果
指标 重要度 权重
${f_1}$ 0.10 0.555
${f_2}$ 0.08 0.445
表 4  指标重要度及权重
图 5  综合调度优化结果
方案 目标值/元
${f_2}$ ${f_2}$ $F$
经济调度 4 967 207 2 793 012 3 999 690
环境调度 5 306 633 2 647 129 4 123 153
综合调度 5 094 368 2 681 012 4 020 424
表 5  经济调度、环境调度和综合调度的优化结果
图 6  系统运行成本收敛曲线
图 7  污染物排放成本收敛曲线
图 8  2种算法下的经济调度优化结果
图 9  2种算法下的环境调度优化结果
算法 ${f_1}/$元 ${f_2}/$元
粒子群算法 5 108 694 2 687 248
改进粒子群算法 5 094 368 2 681 012
表 6  2种算法下的目标值对比
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