Energy Management Strategy Based On Model Predictive Control For Microgrid

In order to solve the increasingly prominent contradiction between energy shortage and environmental protection brought about by social development,research on green,safe,lowcarbon and high-efficient renewable energy generation technology is an urgent need for our country to achieve sustainable development.However,renewable energy generation is distributed,intermittent,volatile,and stochastic.The higher the penetration of renewable energy generation,the greater the impact on power quality and reliability of the power system.Microgrid,as an effective way to connect renewable distributed generation to large power grid,can guarantee the reliability and flexibility of power supply,which has attracted widespread attention.Energy management is one of the focuses of microgrid technology research.Energy management of microgrid can make effective use of renewable energy and enable microgrid to operate economically,environmentally,efficiently,and reliably.This paper aims at two kinds of microgrid and studies the microgrid energy management strategy based on model predictive control.The research in this paper mainly includes the following aspects:1)This paper analyzes the characteristics of distributed generation,energy storage and load in microgrid,classifies them according to their physical attributes and control types,and establishes basic models.Secondly,the basic principle of model predictive control applied in this paper is introduced,and an adaptive improvement is proposed to solve the problems existing in current model predictive control in the microgrid energy management.Preprocessing or softening of the constraint with long optimization period is applied;meanwhile,adaptive model for the time parameters in the model predictive control optimization is established according to the penetration of the renewable distributed generation;at the same time,a feedback model is established based on the deviation between the historical output predicted value and the actual value.2)An energy management strategy for household microgrid based on model predictive control is proposed.With the goal of optimizing the operating economy of the household microgrid,energy optimization of the two-layer and multi-time scales is designed and the model predictive control is applied to the real-time energy optimization layer to dispatch the controllable unit in the microgrid.At the same time,the adaptive improvement method of model predictive control proposed in Chapter 2 is applied to the applicable basic model,which adjusts energy storage soft constraint relaxation factor,and adjusts the parameters of the domain,and feedbacks the indoor temperature error.The simulation compares the adaptable environment of adaptive improvement,and verifies that compared to the model predictive control without combining multiple time scales or before application adaptive improvement it verifies that the strategy compared to the model predictive control without combining multiple time scales or application improvement,the strategy can formulate a more reasonable purchase and sale of electricity from the large grid and more fully utilize the energy storage and renewable energy,and then makes global optimization goal better.3)An energy management strategy for combined heat and power industrial park microgrid based on model predictive control is proposed.It considers social benefits and park benefits comprehensively,taking into account operating costs,environmental protection and user comfort in the optimization goal.Energy optimization of the two-layer and multitime scales is designed and the model predictive control is applied to the real-time energy optimization layer to dispatch the controllable unit in the microgrid.At the same time,a method for dealing with the problem of high thermoelectric coupling in the traditional combined heat and power microgrid is proposed,meanwhile according to the improved methods mentioned in Chapter 2,soften the constraints of energy storage and heat storage,and design the heat loss feedback correction link.Simulations have verified that this strategy configured thermal decoupled equipment can play a role in reducing the degree of thermoelectric coupling.At the same time,compared to the model predictive control without combining multiple time scales or before application adaptive improvement,this strategy can play a better role in peak shaving and valley filling for energy storage and heat storage,and improving the accommodation rate of renewable distributed generation,and ensuring user comfort,and then makes global optimization goal better.