Rolling optimization control method for hydro-photovoltaic-storage microgrid based on stochastic chance constraints

Hydro-photovoltaic-storage (HPS) microgrid has gradually become an important measure to optimize the energy structure and ensure the reliability of regional power supply. However, due to the strong randomness and spatiotemporal correlations of hydropower and photovoltaic (PV) output, traditional deterministic optimization methods are difficult to support the accurate regulation and reliable operation of microgrid with a high proportion of renewable energy integration. On this basis, a rolling optimization control method for HPS microgrid based on stochastic chance constraints is proposed. A novel multivariate scenario reduction method considering hydro-PV correlations is presented to characterize the uncertainty of renewable energy output, and a day-ahead stochastic optimal scheduling model based on chance-constrained programming is constructed. Combined with stochastic model predictive control strategies, the day-ahead scheduling plan can be adjusted at multiple time scales, both intraday power compensation and real-time adjustments, to suppress the intraday power fluctuations induced by day-ahead scenario errors and reduce the influence of the uncertainty of hydro-PV power output on microgrid operation. Experimental results show that compared with the traditional deterministic scheduling method, the proposed method can effectively improve the stability and economy of HPS microgrid operation under complex uncertain conditions.