VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

Abstract

This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncertainty sources of electricity prices, wind generations, and loads. Multi-horizon information-gap decision theory (MH-IGDT) as a non-deterministic and non-probabilistic uncertainty modeling framework is proposed here to specifically model the uncertainty sources considering their various uncertainty horizons. Since each uncertain parameter tends to optimize its uncertainty horizon competitively for a particular value of the uncertainty budget, the proposed MH-IGDT model is formulated as a multi-objective optimization problem. To solve this multi-objective problem, enhanced normalized normal constraint (ENNC) method is presented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented normalized normal constraint method and lexicographic optimization technique to enhance the search performance in the objective space. To address the unsolved issue of being risk-averse or risk-seeker for a VPP in the market, a bi-directional decision-making approach is presented. This decision maker comprises an ex-ante performance evaluation method and a forward-backward dynamic programming approach to hourly find the best Pareto solution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strategy are presented for a VPP including dispatchable/non-dispatchable units, storages, and loads.