Simulation on Multi-Objective Wind Power Integration Using Genetic Algorithm with Adaptive Weight

Abstract:

Aiming at problems which were brought by large-scale wind power integration, and the problem of multi-objective reactive power optimization considering the coexistence of discrete variables and continuous variables, a method of simulation based on genetic algorithm with adaptive weight is brought out. A solving thinking presents that capacitor switching and transformer tap adjusting and other discrete equipments are first, and the action sequence of generator and dynamic reactive power compensation (DRPC) devices and other continuous equipments setting follows, which is presented that optimization problem is decomposed into continuous variable optimization and discrete variable optimization, then they are solved respectively and cross iteration until convergence. In view of the optimization complexity and the coexistence of discrete variables and continuous variables, genetic algorithm with adaptive weight is presented for finding global optimal solution. Case studies show that the proposed thinking and algorithm for solving multi-objective reactive power optimization are reasonable.

• Periodical: Advanced Materials Research (Volumes 986-987)
• Pages: 529-532
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.986-987.529
• Online since: July 2014
• Authors: Jie Ren*, Jian She Tian
• Keywords: Adaptive Weight, Genetic Algorithm (GA), Multi-Objective Reactive Power Optimization, Wind Power Integration

References

[1] MA Yan-hong, WANG Ning-bo, HE Shi-en, et al. The Current Situation and Prospect for Jiuquan 10GW Wind Power Base[J]. Power System and Clean Energy, 2009, 25(11): 76-79.

[2] CHI Yong-ning, LIU Yan-hua, WANG Wei-sheng, et al. Study on Impact of Wind Power Integration on Power System[J]. Power System Technology, 2007, 31(3).

[3] SHI Wei, WEI Hua, BAI Xiao-qing. Reactive Power Optimization in Large-scale Power Systems with Discrete Variables[J]. Electric Power Automation Equipment, 2007, 27(3): 41-45.

[4] CHENG Ying, LIU Ming-bo. Reactive-power Optimization of Large-scale Power Systems with Discrete Control Variables[J]. Proceedings of the CSEE, 2002, 22(5): 54-60.

[5] Hsiao Y T, Liu C C, Chiang H D, et al. A New Approach for Optimal VAR Sources Planning in Large Scale eElectric Power Systems[J]. IEEE Trans on Power Systems, 1993, 8(3): 988-996.

DOI: 10.1109/59.260902

[6] WANG Qin, FANG Ge-fei. Multi-objective Reactive Power Optimization Considering Voltage Stability[J]. Automation of Electric Power Systems, 2001, 23(3): 31-34.