Thermostatically Controlled Loads Participating In Microgrid Regulation Strategy Based On Model Prediction

In order to solve the safety,cleanliness,reliability and diversified power supply requirements brought about by social development,researching flexible and reliable microgrid technology is an urgent need for my country to realize the diversified development of power supply.When regulating the microgrid,the demand side response can also provide auxiliary control services besides regulating the output of the generator unit.The thermostatically controlled loads is very important in the research of the demand side response.Thermostatically controlled loads consumption accounts for a large proportion of peak load in China.Therefore,if the thermostatically controlled loads can be controlled to achieve source-grid-load co-regulation,the peak load pressure can be effectively alleviated.Starting from the thermostatically controlled loads,this paper studies the strategy of thermostatically controlled loads participating in microgrid regulation based on model prediction.The research in this paper mainly includes the following aspects:(1)This article analyses the characteristics of thermostatically controlled loads,human comfort and microgrid,and establishes related models.In this paper,the first-order and second-order equivalent thermal parameters models are compared and analyzed,and the basis for selecting the thermostatically controlled loads model in this paper is given.Secondly,this paper puts forward the influencing factors of human comfort index,and simplifies the model of human comfort according to the research focus.(2)A model predictive control based thermostatically controlled loads participating in the microgrid auxiliary regulation strategy is proposed,and the microgrid model predictive controller is designed with the stability of the microgrid as the control objective.At the same time,based on the model established in Chapter two,a microgrid system model with thermostatically controlled loads is established.The parameters and control constraints of the controller are designed according to the system characteristics of the model.The simulation verifies that compared with the model predictive control and traditional control algorithms without thermostatically controlled loads,the proposed strategy can not only ensure the comfort of the user’s side,but also give full play to the advantage of the thermostatically controlled loads being able to quickly respond to external power changes.stability of the microgrid.(3)A nonlinear thermostatically controlled loads participation microgrid regulation strategy based on finite set model and control is proposed.Starting from the working characteristics of the thermostatically controlled loads in reality,a finite-set model predictive controller is designed with the goal of tracking the external power change by thermostatically controlled loads.In addition,an optimization algorithm is designed in the controller to reduce the computational complexity during operation.At the same time,aiming at the weight adjustment problem in the objective function.The DDPG algorithm in reinforcement learning is used to adjust the parameters,and the weight adjustment problem of the objective function is solved on the premise of satisfying the control objective.The simulation verifies the thermostatically controlled loads tracking power changes under different predicted steps and different groups.The results show that the proposed strategy can make the thermostatically controlled loads better track the external power changes,which can improve the stability of the microgrid.The proposed algorithm can improve the service life of the thermostatically controlled loads.Finally,this paper summarizes the research content of the full text,explains the effectiveness of the algorithm proposed in this paper,and looks forward to the possible research direction of temperature control load participating in microgrid regulation strategy based on model prediction in model establishment,intelligent algorithm selection and distributed control.