The Key Research and Application in Grid Planning Using Improved Genetic Algorithm

The Key Research and Application in Grid Planning Using Improved Genetic Algorithm

Fan Yina (Beijing Normal University, Zhuhai, China), Lang Zixi (South China University of Technology, Guangzhou, China), Ren Yuan (School of Electronics and Information, Shanghai Dianji University, Shanghai, China) and Dong Ruiwen (Beijing Normal University, Zhuhai, China)
DOI: 10.4018/IJORIS.2019070105
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In order to guarantee the power grid operation under the premise of reliability and stability, acquire relative economic investment and operating cost, and adaptable to all kinds of change flexibly, this article improves the traditional generic algorithm by considering the various objective function and constraint condition. The improved algorithm can search and optimize according to mechanism for the survival of the fittest. It is especially suited for the optimization solution of integer variables. The application of the algorithm proposed to fifteen nodes system of a certain city and comparative experiments show that the algorithm has fast convergence speed and optimizing result. A comparative analysis of the optimizing project using improved generic algorithm and computational result using engineering computational method in practical grid planning yield the same results, this shows that the improved algorithm has better adaptability.
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Grid planning can optimize network structure, determine the direction of investment, reduce network running cost and improve the efficiency of electricity transmission, distribution and using in the whole society, which has a considerable social and economic significance (Wang & Wang, 2015; Zhang & Yu, 2011). Based on current study, the focus of grid planning is to look for an optimized network structure from the whole, which includes genetic algorithm, immune algorithm, tabu search algorithm, particle swarm optimization, ant colony algorithm, simulated annealing algorithm etc. (Liang & Zhang, 1998; Jiang, 2006; Ge, Liu, & Yu, 2004; Chen & Chen, 2005; Huan & Huang, 2008; Wang, Zhang, Shu, & Wang, 2011). Genetic algorithm can process any form of objective function and restriction, which has strong universality, internal parallelism and stronger global searching ability. Roulette selection method is adopted to choose crossover parental bodies in conventional genetic algorithm, which cannot reflect individual competitiveness and realize the survival of the fittest of the genetic algorithm. Although traditional genetic algorithm can converge to globally optimal solution theoretically, it still has the problems of premature convergence, convergence to local optimal solution and low convergence speed in practical application (He, Zhu, & Luo, 2011; Wang, Gao, & Li, 2013).

The large-scale integration of renewable energy has radically changed the basic form and operational characteristics of power system. In order to ensure reliable operation of power system and promote capacity of renewable energy consumption, it is of great importance to evaluate the adaptability of power system to strong volatility and uncertainty of renewable energy at the initial stage of power system planning. The paper consider that the adaptability of power system is difficult to quantify, and it analyses the characteristics and actual operation sate of the high-penetration renewable energy system and proposes an index series of grid-structure and generator-capacity adaptability bases on operation safety, efficiency, stability and supply and demand coordination. On the basis of the adaptability indexes, a grid-source coordinated transmission multi-objective planning model is put forward. Finally, the simulation of Gaver-18 bus system shows the feasibility and effectiveness of the adaptability indexes and planning model (Fan, Li, & Liu, 2018).

The distributed generation spread rapidly, the study of the distributed network planning with distributed generation is also increasing. Based on model of micro-grid system, the study established the model of substation locating and sizing and network planning respectively. The foregoing multi-objective nonlinear models are solved by using the improved particle swarm algorithm. In addition, IEEE 50-node example is used to verify that the proposed model is in favor of improving the reliability and economy of the system. And it is good for structure of network (Yang, Zhou, & Xia, 2018).

The paper proposes a method of transmission network expansion planning based on improved quantum genetic algorithm (Zhou, Lin, & Wen, 2012). On the basis of the quantum genetic algorithm, the method proposed a strategy that the quantum chromosomes are directly compared with the current best solution to determine rotating angle of the rotating rate. This improved strategy is targeted and can effectively improve the convergence function of quantum genetic algorithm in the planning for transmission network.

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