基于改进杂交粒子群算法的农村微能网多能流优化调度

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。

Scheduling optimization for rural micro energy grid multi-energy flow based on improved crossbreeding particle swarm algorithm

Abstract: There is poor infrastructure and weak power grid in Chinese western rural areas. Photovoltaic (PV) and wind power pro-poor investments do not consider supporting transmission and distribution facilities. The economy of biogas from biomass waste is not good, due to that it is affected by seasonal variations in temperature. Utilizing PV and wind power to supply energy for biogas can improve biomass energy utilization and solve the problem of environmental pollution, while the absorptive capacity of the PV and wind power is increased, and the comprehensive utilization of biomass and renewable energy in place can be achieved. It has important significance for development of new countryside. Based on national PV and wind power poverty relief policy, this paper proposed rural micro energy grid architecture that combines PV system, wind power system, micro turbine, biogas fired boilers, heat recovery boiler, lithium-bromide absorption-type refrigerator, battery storage, heat and cooling storage, air-source heat pumps for cooling exchange, air-source heat pumps for heating exchange, and so on. Mathematical models of micro turbine CCHP (combined cooling heating and power) system, air-source heat pumps system, heat and cooling storage system and battery storage system were built up. With micro energy grid cost in a single day as an objective function, considering electric power balance, heating power balance, cooling power balance, power exchange with electricity grid and the other constraints, the micro energy grid optimal model was established. Because of premature and local optimization problem for particle swarm algorithm, this paper uses dynamic inertia weight crossbreeding particle swarm optimization algorithm for solving. Taking Chinese west village as an example, according to the actual situation, electric and heating power were supplied in the winter, but electric and cooling power were supplied in the summer. Electricity price applied the time of use price issued by the National Development and Reform Commission. Parameters of energy supply equipment and energy storage equipment, time of use price, and equipment maintenance cost per unit power were determined. Forecasted data were given, which combine PV and wind power outputs, electricity heating and cooling load for typical day. Simulation platform was built in MATLAB 2014a. Electric heating and cooling balance curve of typical day was acquired. System running cost comparison of typical day based on improved and basic algorithm was performed. In addition, according to forecasted curve referred to above, parameters of various devices, time of use price and equipment maintenance cost, the un-optimized system running cost was calculated. Results showed that, through the dispatch of each device in the system, the outputs of energy supplying devices were more reasonable, and energy storage devices played a role of load shifting. The daily running cost based on dynamic inertia weight crossbreeding particle swarm optimization algorithm was less than that based on basic particle swarm and un-optimized cost. To sum up, the proposed algorithm is adopted to dispatch various devices in micro energy grid, it can reduce system running cost effectively, and micro energy grid can be operated economically; the correctness of the models and algorithms can be proved.