CVaR-Constrained Customers’ Discomfort-Minimization Scheduling Strategy of a Demand Response Aggregator Considering PV Uncertainties

A demand response aggregator (DRA) designed to fully exploit the capability of incentive-responsive loads in the residential sector is responsible for both the optimal bidding in different electricity market externally and the optimal scheduling of multitudinous flexible resources internally, while the latter remains a hard task because of the diversity of demand-side resources. To this end, this paper devises an optimal scheduling scheme for the DRA with the objective of maximizing customers' comfort level, considering the risk posed by the distributed PV generation. In consideration of each customers' unique discomfort parameters and DR participation willingness, the optimal scheduling model is established for the DRA while preserving the comfort level of residential customers. To hedge against profit loss brought up by PV, the Latin hypercube sampling based Monte Carlo simulation method is firstly introduced to settle the variability of distributed PV generation and the Conditional Value at Risk (CVaR) method is then integrated into the scheduling problem to yield an optimal dispatching plan. Case studies using the dataset from the Ausgrid company verified the validity of the proposed method as a practical decision support tool for DRA to formulate a reliable and profitable scheduling scheme.