Considering the fluctuation of microgrid output and customer’s demand, an optimal dispatching strategy for the combined cooling, heating, and power supply microgrid is proposed. The fluctuation of energy sources, such as a photovoltaic system and multiple loads, may affect the safety, economics, and stability in combined cooling, heating, and power microgrid operation. Therefore, the extreme learning machine optimized by particle swarm algorithm is used to improve the prediction accuracy of photovoltaic power generation, wind power generation, and load power. The regularization coefficient C and the kernel parameter λ of kernel extreme learning machine are regarded as the optimization targets of the particle swarm algorithm so that the prediction accuracy can be improved. Forecasted value of cooling, heating, and electricity microgrid system and new energy power generation as well as real-time electricity price, fuel unit price, etc. are considered in the operating cost. In order to minimize the operating cost and improve the energy utilization, an improved shuffled frog leaping algorithm is used to solve the cost minimization problem to give the equipment output dispatch strategy. Comparative simulation results can be found that under the same conditions, compared to the kernel extreme learning machine and the kernel extreme learning machine optimized by the genetic algorithm, the kernel extreme learning machine optimized by the particle swarm has faster convergence speed and higher prediction accuracy. Comparative simulations of microgrid dispatching on typical days in summer and winter are carried out. Compared with the cost of distribution, the cooling, heating, and power microgrid based on the improved shuffled frog leaping algorithm has obvious economic benefits and higher energy utilization property.
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