Stochastic Scheduling of Parking Lot Operator in Energy and Regulation Markets amalgamating PBDR

Parking lot operator (PLO) can provide V2G regulation up/down services to System Operator (SO) for grid stability. Nevertheless, PLO faces multiple uncertainties in market prices viz. energy and regulation prices and mobility behavior dynamics, affecting severely its V2G operational behavior. Proposed work models integration of Price-based Demand Response Program (PBDRP) by PLO, to utilize the flexibility of EV owners, deal with the uncertainties, improve its market operations and maximize its expected profit. Proposed stochastic programming problem is formulated by modelling these uncertainties using Monte Carlo Simulation and Kantorovich Distance-based backward reduction algorithm. TOU price design by PLO from EV owners' perspective minimizes charging cost. Conditional-Value-at-Risk (CVaR) is employed as a coherent risk measure for risk-management. The results from realistic case studies illustrate that decisions based on the proposed approach provide better trade-off in terms of expected profit and risk measure.