Nodal pricing of renewable-oriented distribution network in the presence of EV charging stations: A hybrid robust/stochastic optimization

Abstract

The ever more increased usage of renewable resources and electric vehicles (EVs) with subsequent usage of charging stations (CSs) create new problems such as pricing and market clearing in their presence. Also, the possible uncertainties lead to huge problems in optimal operation of the distribution grid. The introduced framework in this paper addresses the possible uncertainties and risk conditions in the local market-clearing problem with the high penetration of CSs. The proposed model utilizes power tracing (PT) and loss allocation (LA) in its dynamic pricing scheme. Because of the CSs' role in the market and their different behavior with prosumers, the proposed bi-level model is a single leader and multi follower type. The distribution system operator (DSO) is the upper-level (leader) and the prosumer and CSs are each different lower-level (follower). The hybrid stochastic-robust local market clearing method is used in the proposed model. In the stochastic model, different scenarios for probabilistic parameters such as renewable energy sources (RES), loads, and electric vehicles (EVs) are generated to be used in the simulation. In the robust model, a wide range of errors from expected values are considered, which are used in the robust optimization model to solve the problem. The operation of CSs under stochastic-robust conditions and consideration of the EV owner's utility individually are investigated. The robust model's risk-averse conditions increase the network's prices and reduce electricity buying from the upstream grid. In order to hedge against the uncertainties, the distribution system operation costs only increases by 10.96 % for more 20 % robustness in optimal under-uncertainty operation.