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The coordinated operation and comprehensive utilization of multi-energy sources require systematic research. A multi-energy microgrid (MEMG) is a coupling system with multiple inputs and outputs. In this paper, a system model based on unified energy flows is proposed to describe the static relationship, and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes. Then, the optimal dispatching model of an MEMG is established as a mixed-integer linear programming (MILP) problem using piecewise linear approximation and convex relaxation. Finally, the system model and optimal dispatching method are validated in an MEMG, including district electricity, natural gas and heat supply, and renewable generation. The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.

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China is launching a campaign to replace fossil fuels with renewable energy. However, the vast majority of renewable energy needs to be converted into electricity before it can be supplied to consumers, therefore, the importance of power grid which is the transmission and distribution carrier of power will become more and more significant. Give that the developed countries have established a complete system of power grid, China can learn from their development experience of power grid to plan the development of power grid. In order to explore the experience of power grid development in developed countries, this paper fist combines the qualitative and quantitative analysis methods to summarize the development rule of power grid in developed countries based on the characteristics quantity of power grid development. Secondly, this paper makes clear the current development stage and the future development trend of China power grid according to the rule of power grid development in developed countries and the development characteristics of China power grid. Finally, some policy recommendations are provided to the future development of China power grid.

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.