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.