Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China

Abstract

Hybrid generation of large-scale photovoltaic (PV) power together with hydropower offers a promising option to promote the integration of PV power, because hydro units can complement variable PV generation rapidly at relatively low cost. However, the strong variations in PV generation create uncertainties for the operation of the hydro units. To improve guidelines for large-scale hydro–PV plant operation, a stochastic hydro unit commitment model considering the uncertainty in forecasting PV power is presented. This model seeks robust solutions (i.e., hydro unit status) to minimize the hydro plant’s water consumption when external load demands are imposed onto the hybrid system. A two-layer nested optimization framework is proposed to solve the model in a hierarchical structure. In the outer layer, a cuckoo search algorithm, combined with a novel encoding strategy, optimizes the number of online units that can meet the load demand under all PV generation processes. In the inner layer, load dispatch schemes for the given number of online units are determined by dynamic programming. China’s Longyangxia hydro–PV plant was selected as a case study. Operational results were compared for three scenarios: actual operation, deterministic operation without consideration of PV forecast errors, and stochastic operation with consideration of PV forecast errors. The results indicate that: (1) the encoding strategy can handle the minimum online and offline time constraints, and can decrease optimization dimensionality; (2) the two-layer nested optimizer can make robust and effective decisions within an acceptable period when using the pre-stored optimization results provided by dynamic programming; and (3) water consumption in deterministic and stochastic operation scenarios decreased by 1.5% and 1.0%, respectively, compared to that of actual operation. These findings verify the applicability and effectiveness of the proposed methods, and also highlight the importance of decreasing uncertainty in the PV power forecast.