Multiobjective Mid‐Term Scheduling of the Hydro–Photovoltaic–Pumped Storage System Considering Uncertainties of Natural Water Inflow and Photovoltaic

This paper proposes a multiobjective medium‐term optimal scheduling model of the cascade hydro–photovoltaic (PV)–pumped storage system to increase the renewable energy accommodation capacity of the system. The uncertainties of natural water inflow and PV power output have been formulated using the information gap decision theory (IGDT), and the proposed multiobjective model is solved with the ε constraint method. A case study of a test system including 410 MW cascade hydro, 70 MW pumped storage, and 60 MW PV shows that the proposed model reduced solar curtailment rate from 22.65% to 0.23% compared to the conventional hydro–PV system, and the IGDT‐based model avoids risk from the uncertainties of natural water inflow and PV power output effectively.