Research On Energy Optimal Scheduling For Microgrid

Microgrid is composed of renewable energy,the combined cooling and heating power system(CCHP)and battery system(BS),which has good technical,economic and environmental benefits.However,there are many kinds of distributed generation in microgrid,and the generation of renewable generators and load demand have great uncertainty and volatility.It is difficult for microgrid to ensure economic and reliable operation under the influence of these factors.Therefore,in order to realize the microgrid economic and reliable operation,it is very important to optimize the scheduling of microgrid.In this paper,the optimal scheduling of microgrid is studied from the following two aspects:First,the economic optimal scheduling of microgrid is studied.First of all,in order to reduce the impact of wind and solar power generation and load demand fluctuations on the operation of microgrid,a stochastic optimization model of wind,solar and load was established.The stochastic optimization model of wind,solar and load takes into account the influence of the day-ahead scheduling forecasting error on the optimal scheduling of microgrid.Taking prediction error as random variable,then scenario generation strategy is adopted to reduce the impact of wind,solar and load uncertainty on microgrid,and the reverse reduction algorithm is used to reduce the number of scenes to reduce the computational burden.Then,a stochastic optimization model for daily dispatching of microgrid is established.This optimization model takes the output power of each controllable distributed generation and the power purchased from the large power grid as the control variables,and takes the lowest operating cost of the system as the objective function to establish the optimal scheduling model of microgrid.Because the optimal scheduling of microgrid is a nonlinear problem with many variables and constraints,the improved particle swarm optimization algorithm(IPSO)is used to solve the optimal scheduling model.The generalized opposition-based learning(GOBL)strategy is used in the early stage of the improved algorithm to make the algorithm quickly close to the potential search area and improve the convergence speed.In the later stage of the search,the mutation and crossover strategies of differential evolution(DE)algorithm are used to update the optimal particles of the current population to improve the diversity of the population,and then to obtain the global optimal solution.The basic test function is used to verify the excellent performance of IPSO.Finally,an example is given to verify the correctness of the proposed model and the effectiveness of IPSO.Second,the optimal scheduling of the microgrid combined cooling,heating and power system is studied.The CCHP system can realize the joint supply of electric energy,cold energy and heat energy,which is conducive to energy cascade,efficient utilization and environmental protection.In order to improve the utilization rate of energy,micro gas turbine is used as the main power and power generation equipment.The lithium bromide refrigerator uses the high-temperature flue gas waste heat generated by the micro gas turbine for refrigeration / heating to meet the user's demand for cooling and heating loads,and the electric air conditioner meets the insufficient cooling and heating loads.In order to minimize the economic cost,the objective function including operation cost,fuel cost and environmental pollution cost is established.And the operation constraints of each equipment are considered.The optimal output power of each device is obtained by optimization and simulation.The load demand of two typical days in summer and winter is used in the example simulation to verify the optimal scheduling model of CCHP system.The optimization results verify the correctness of the model,as well as the goal of the best economy and better energy utilization.