Multi-time scale optimal dispatching of microgrid with electric vehicles based on CVaR-IGDT
There are many uncertain factors in the dispatch and operation of electric vehicles connected to microgrid,which have a certain impact on the safety and stability of the system.In this study,the conditional value-at-risk(CVaR)theory and the information gap decision theory(IGDT)were combined to establish a multi-time scale dispatching model for microgrid with electric vehicles.In the day-ahead stage,with the minimum daily operation cost of the microgrid as the optimization objective,Monte Carlo simulation and attractor propagation clustering algorithm were used to model the uncertainty of electricity price and electric vehicle behavior and CVaR was used to quantify the uncertainty risk.Then,to solve the problem of low prediction accuracy of wind and photovoltaic output,IGDT was used to deal with the uncertainty of wind and photovoltaic output,maximizing the fluctuation range of wind and photovoltaic output while ensuring that the optimization target value was within an acceptable range.In the intra-day stage,with the day-ahead dispatching planning as a reference,the model predictive control was used to optimize the microgrid including electric vehicles in the intra-day rolling,and the error coefficient was introduced for feedback correction to correct the day-ahead dispatching deviation.Finally,the effectiveness of the proposed model was verified by an example,and the effects of the income deviation coefficient and the risk preference coefficient on the optimization results were analyzed.
microgridelectric vehicleinformation gap decision theoryconditional value-at-riskmodel predictive control
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