基于遗传算法的微电网负荷优化分配

中国管理科学 ›› 2014, Vol. 22 ›› Issue (3): 68-73.

基于遗传算法的微电网负荷优化分配

周开乐^1,2, 沈超^1,2, 丁帅^1,2, 杨善林^1,2

1. 合肥工业大学管理学院, 安徽合肥 230009;
2. 过程优化与智能决策教育部重点实验室, 安徽合肥 230009

收稿日期:2012-01-19 修回日期:2012-11-04 出版日期:2014-03-20 发布日期:2014-03-19
作者简介:周开乐（1987- ），男（汉族），江苏铜山人，合肥工业大学管理学院，博士研究生，研究方向：优化与决策.
基金资助:
国家高技术研究发展计划（863）（2011AA05A116）；国家自然科学基金资助项目（71131002，71201042）

Power Economic Dispatch of Microgrid Based on Genetic Algorithm

ZHOU Kai-le^1,2, SHEN Chao^1,2, DING Shuai^1,2, YANG Shan-lin^1,2

1. School of Management, Hefei University of Technology, Hefei 230009, China;
2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China

Received:2012-01-19 Revised:2012-11-04 Online:2014-03-20 Published:2014-03-19

摘要/Abstract

摘要： 负荷优化分配是电力系统中的一类重要优化问题，即在满足各类系统约束条件下，实现发电总成本最低。为了促进微电网的优化运行，本文研究了包含柴油发电机、微型燃气轮机、光伏发电机和风力发电机组成的微电网的负荷优化分配问题。首先简要分析了各个微电源的发电特征和成本函数，然后分别建立了孤岛模式和并网模式下的微电网负荷优化分配模型，孤岛模式下优化模型的目标函数是包含燃料成本和运行维护成本的总成本，约束条件包括发电能力约束和系统功率平衡约束，并网模式下的优化模型则在此基础上，在目标函数中增加了其与大电网交易的收入和支出，在约束条件中增加了电力交易约束。最后，通过遗传算法分别对两种模式下的优化模型进行仿真求解。结果表明，本文提出的负荷优化分配方法可以有效降低微电网的运行成本，促进微电网的优化运行。

关键词: 负荷优化分配, 优化模型, 遗传算法, 微电网

Abstract: Power economic dispatch is an important kind of optimization problem in the power systems, that is, achieving the lowest tatal cost of power generation while meet many kinds of system constraints. In order to promote the optimal operation of microgrid, the power economic dispatch problem is studied in this paper of the microgrid with diesel generator, microturbine, photovoltaic power generator and wind turbine. At first, the generation characteristics and cost functions of the micro power generators are analyzed. Then the power economic dispatch models under islanded mode and grid-connected mode are constructed respectively. The objective function of the optimization model under islanded mode is the total costs which include the fuel costs and operation maintance costs, and the constraint conditions include the constraint of power generation and the constraint of system power balance. While the income and expenses of the transaction between microgrid and the large power grids are added to the objective function of the optimization model under grid-connected model based on the model under islanded mode, and the electricity transaction constraint are added to the constraint conditions. Finally, the optimization models under both modes are solved by genetic algorithm. The results show that the power economic dispatch method proposed in this paper can effectively reduce the operating costs and promote the optimal operation of microgrid.

Key words: economic load dispatch, optimization models, genetic algorithm, microgrid

中图分类号:

F407.6

周开乐, 沈超, 丁帅, 杨善林. 基于遗传算法的微电网负荷优化分配[J]. 中国管理科学, 2014, 22(3): 68-73.

ZHOU Kai-le, SHEN Chao, DING Shuai, YANG Shan-lin. Power Economic Dispatch of Microgrid Based on Genetic Algorithm[J]. Chinese Journal of Management Science, 2014, 22(3): 68-73.

参考文献

[1] 朱继忠, 徐国禹. 用网流法求解电力系统动态经济调度[J]. 系统工程学报, 1991, 6(1):33-40.

[2] 朱继忠, 徐国禹. 用网流法求解水火电力系统有功负荷分配, 系统工程理论与实践, 1995, 1:69-73.

[3] Basu M. Bee colony optimization for combined heat and power economic dispatch[J]. Expert Systems with Applications, 2011, 38(11):13527-13531.

[4] Kavrakoglu K, Kiziltan G. Multiobjective strategies in power systems planning[J]. European Journal of Operational Research, 1983, 12(2):159-170.

[5] Makkonen S, Lahdelma R. Non-convex power plant modelling in energy optimization[J]. European Journal of Operational Research, 2006, 171(3):1113-1126.

[6] Gardner D T, Rogers J S. Joint planning of combined heat and power and electricpower systems: An efficient model formulation[J]. European Journal of Operational Research, 1997, 102(1):58-72.

[7] Nowak M P, Rmisch W. Stochastic lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty[J]. Annals of Operations Research, 2000, 100(1-4):251-272.

[8] Thompson M, Davison M, Rasmussen H. Valuation and optimal operation of electric power plants in competitive markets[J]. Operations Research, 2004, 52(4):546-562.

[9] Li F. A comparison of genetic algorithms with conventional techniques on aspectrum of powere conomic dispatch problems[J]. Expert Systems with Applications, 1998, 15(2):133-142.

[10] Li F, Aggarwal R K. Fast and accurate power dispatch using a relaxed genetical gorithm and a local gradient technique[J]. Expert Systems with Applications, 2000, 19(3):159-165.

[11] 李翔, 牛东晓, 杨尚东. 改进粒子群优化算法在电源规划中的应用[J]. 中国管理科学, 2006, 14(6):113-118.

[12] Lasster R H, Paigi P. Microgrid:A conceptual solution.Proceedings of IEEE 35th Annual Power Electronics Specialists Conference, Aachen, June 20-25, 2004.

[13] Mohamed F A, Koivo H N. System modeling and online management of MicroGrid using multiobjective optimization. Proceedings of International Conference on Clean Electrical Power, Capri, May 21-23, 2007.

[14] Mohamed F A, Koivo H N. System modelling and online optimal management of MicroGrid using Mesh Adaptive Direct Search[J]. Electrical Power and Energy Systems, 2010, 32(5):398-407.

[15] Esram T, Chapman P L. Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques[J]. IEEE Transactions on Energy Conversion, 2007, 22 (2):439-449.

[16] Chedid R, Akiki H, Rahman S. A decision support technique for the design of hybrid solar-wind power systems[J]. IEEE Transactions on Energy Conversation, 1998, 13(1):76-83.