Research On Optimal Energy Management Of Microgrid Cluster Based On Model Predictive Control

Microgrid cluster is the product of the development of microgrid to a certain stage.Interconnecting multiple microgrids and operating in the form of a cluster is conducive to improving the reliability of power supply.The microgrid cluster can increase the permeability of distributed generation and realize the efficient utilization of renewable energy through coordinated operation and energy complementation among internal sub-microgrids.Optimal energy management of microgrid cluster is the basis for ensuring the efficient and orderly operation of microgrid cluster.Compared with the traditional single microgrid,there is power exchange in the microgrid cluster.The energy optimization of microgrid cluster should not only consider the internal economic operation and voltage stability of the microgrid,but also consider the power coordination among microgrids,so as to realize the overall optimal operation of microgrid cluster.On the one hand,the output fluctuation of distributed generation will adversely affect the energy optimization of the microgrid cluster;on the other hand,the traditional centralized optimization has the disadvantages of being difficult to guarantee privacy and security,and requiring high communication.In this paper,model predictive control(MPC)theory and synchronous alternating direction method of multipliers(ADMM)are introduced to study the optimal energy management of microgrid and microgrid cluster.With the lowest total operating cost as the optimization objective,power coordination optimization model of the microgrid cluster is established.Using the exchange power as the coupling variable,the centralized optimization problem is decoupled into the sub-problems of each sub-microgrids,and the iterative form based on synchronous ADMM for solving the microgrid cluster optimization problem is derived.The asynchronous iterative process of sub-problems is converted into a synchronous iterative process.In each iteration,the correction value of the calculation result of the expected exchange power of the previous round is selected as the reference value of this iteration.At the same time,the finite-time consistency algorithm is used to transfer the expected exchange power information required by the optimization process,so as to realize completely distributed computing.The idea of MPC rolling optimization is introduced,and the distributed solution process of microgrid cluster coordination optimization problem is given.An example is given to verify the convergence of the distributed optimization algorithm based on synchronous ADMM and the rationality of the optimization results.Compared with the optimization results of the centralized optimization,the optimality of the distributed optimization results is verified.Finally,the reliability of information exchange based on the finite-time consistency algorithm is verified in the case of communication failure.Combining active power economic optimization and reactive power voltage optimization of microgrid under cluster operation,a two-layer optimal energy management method based on MPC was proposed.Active and reactive optimization adopt different time scales.The upper-layer optimization considers the power exchange between microgrids,with the lowest operating cost as the optimization objective,and optimizes the active power output of all controllable power sources.The reactive power margin of the controllable power sources is calculated with the results of active power optimization.The lower layer optimizes the reactive power output of all controllable power sources and reactive power compensation equipment to maintain the local reactive power balance and maintain the microgrid voltage within the allowable range.Both active power optimization and reactive power optimization adopt the mode of rolling optimization and feedback correction of MPC to deal with the fluctuation of power output.The effectiveness and advantages of the two-layer optimal energy management method based on MPC are verified through the comparison and analysis of an example.