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

This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncer-tainty 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 op-timization problem. To solve this multi-objective problem, en-hanced normalized normal constraint (ENNC) method is pre-sented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented nor-malized normal constraint method and lexicographic optimiza-tion 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 dy-namic programming approach to hourly find the best Pareto so-lution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strat-egy are presented for a VPP including dispatchable/non-dispatch-able units, storages, and loads.