Energy Management Optimization Of Community Microgrid With Plug-in Electric Vehicle

Renewable energy vehicles represented by plug-in electric vehicles(PEV)are conducive to reducing environmental pollution and improving energy structure.However,due to the random volatility of PEV charging load,its high penetration rate access to community microgrids will make it difficult to adapt to typical load forecasting models and deterministic energy management methods.There is an urgent need for charging load modeling methods that can adapt to its high penetration rate access and the corresponding energy management methods.Therefore,this paper discusses the random charging load modeling method and energy management strategy of high-penetration PEV connected to the community microgrid.First of all,this paper employs the normal distribution model to fit the PEV initial charging time and the lognormal distribution model to fit the daily mileage,respectively,and establishes a PEV random access to the grid charging model considering the habits of multiple types of vehicles.On this basis,the Monte Carlo random sampling technology is used to simulate the charging time of each PEV to form a PEV random charging simulation visualization curve considering the habits of multiple types of vehicles.This part will lay the foundation for the subsequent optimization of community microgrid energy management considering PEV grid connection.Secondly,in order to cope with the risks brought by PEV random charging load to the optimization of community microgrid energy management,this paper first uses principal component analysis to reduce the data dimension of PEV random charging scenarios,and proposes PEV charging load aggregation based on hierarchical clustering.Furthermore,the paper establishes the class model and the method for determining the optimal charging load clustering class number.On this basis,this paper fully considers the power generation physical characteristics of units and system constraints of community microgrids,and establishes a multi-community microgrid energy management optimization model that considers key extreme scenarios.In order to effectively solve the built nonlinear mixed integer programming model,this paper proposes a linearization method based,transforms the proposed model into a linear mixed integer programming problem,and calls the Cplex solver in GAMS to solve it effectively.Finally,this paper verifies the effectiveness and superiority of the proposed model through three interconnected community microgrids.Finally,this chapter uses a constant approximation method to reasonably evaluate the comprehensive loss cost of PEV power batteries.On this basis,a multi-community microgrid energy management optimization model considering the cost of PEV charging and discharging is proposed.The model considers the controllable intelligent loads such as air conditioners,washing machines,and dishwashers in the community microgrids,and also takes into account the two modes of PEV disordered random charging and PEV intelligent charging and discharging,which is more suitable for practical applications.Finally,through the analysis of examples of three interconnected community microgrids,the results show that the established multi-community microgrid energy management model considering PEV smart charging and discharging and controllable smart load response can achieve peak-shaving and valley-filling,and effectively improve the efficiency of the energy management strategy.Besides,the proposed method for determining the cost of PEV discharge loss can comprehensively consider the different states of charge of the power battery to avoid the influence of subjective factors.