Distribution Networks Congestion Management Based on Dynamic Tariff and Temporal-spatial Flexibility of Electric Vehicles Under Uncertainty
Electric vehicles(EVs)are an important means of reducing energy consumption and carbon emissions in the transportation sector,but their large-scale disorderly charging may cause congestion in distribution networks.Unlike conventional loads,EV loads have temporal and spatial translatability.By guiding the temporal-spatial distribution of EVs through incentive signals such as prices,the load distribution of the distribution network can be optimized,effective congestion management can be achieved,and the safe and stable operation of the distribution network can be ensured.At the same time,the randomness and fluctuation of the distributed energy resources and loads in distribution networks make the congestion management of the distribution network more complex,thereby affecting the safe and stable operation of the distribution network.In response to the congestion problem of distribution networks containing electric vehicles in uncertain environments,a robust optimization theory is adopted to establish a distribution network congestion management robust optimization model that considers the temporal-spatial coordination of electric vehicles.In the case of fluctuations in uncertain factors in the distribution network,the dynamic tariff is used to guide the temporal-spatial distribution of electric vehicles,fully tapping into the spatiotemporal flexibility of electric vehicles,optimizing the load of the distribution network,and achieving congestion management of the distribution network.The effectiveness of using dynamic tariffs to achieve distribution network congestion management considering the temporal-spatial flexibility of electric vehicles in uncertain environments was verified through the simulation of a set of networks.
electric vehiclescongestion managementrobust optimizationdynamic tariff
万方数据
维普
