基于改进杂交粒子群算法的农村微能网多能流优化调度

发布时间：2018-09-08 19:33

【摘要】：西部农村地区电网薄弱,光伏和风电扶贫投资未考虑配套输配电设施,用以处理生物质废弃物的沼气受季节性温度变化影响运行经济性不佳,为解决上述问题,该文提出利用沼气作为气源含可再生能源的冷-热-电-气多能流农村微能网供能架构,建立相应的多能流微能网调度模型,针对粒子群算法早熟、容易陷入局部最优的问题,提出采用动态调整惯性权重的杂交粒子群算法进行求解,算例结果表明,通过对系统内各设备的调度,有效降低系统日运行成本,在冬季,采用改进型杂交粒子群算法所得日运行费用相比采用基本型粒子群算法降低7.6%,其相比系统未优化所得日运行费用降低79.1%;在夏季,相比基本型粒子群算法与未优化分别降低17.0%、71.2%,实现微能网的经济运行,证明了本模型和算法的正确性。
[Abstract]:In the western rural areas, the power grid is weak, the investment in photovoltaic and wind power poverty alleviation does not consider supporting transmission and distribution facilities, and the biogas used to deal with biomass waste is affected by seasonal temperature changes and its operation economy is not good. In order to solve the above problems, Using biogas as the gas source, the energy supply structure of the cold, heat, electricity and gas multi-energy flow rural micro-energy network is proposed, and the corresponding scheduling model of the multi-energy flow micro-energy network is established. The particle swarm optimization algorithm is premature and prone to fall into local optimum. A hybrid particle swarm optimization algorithm with dynamically adjusting inertia weight is proposed to solve the problem. The results of an example show that the daily operating cost of the system can be effectively reduced by scheduling the equipment in the system, and the daily operating cost of the system can be effectively reduced in winter. The daily operating cost of the improved hybrid particle swarm optimization algorithm is 7.6 lower than that of the basic particle swarm optimization algorithm, and the daily operating cost of the improved hybrid particle swarm optimization algorithm is 79.1 percent lower than that of the system without optimization. Compared with the basic PSO algorithm and the unoptimized PSO algorithm, the efficiency of this model and the algorithm is proved to be correct by reducing by 17.0 and 71.2, respectively, and realizing the economic operation of the micro-energy network.
【作者单位】：中国农业大学信息与电气工程学院;内蒙古科技大学信息工程学院;国网甘肃省电力公司电力科学研究院;
【基金】：国家重点研发计划项目课题(2016YFB0900101) 内蒙古自然科学基金项目(2016MS0515)
【分类号】：TM73

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