This paper considers the operation of hydro plants, tackles the problem of daily hydro-generation scheduling (DHGS), and obtains the optimal hourly operation of cascade hydropower reservoirs. To meet the practical operational demands of power grid, an improved benefit-maximization model in which the peak shaving demands are taken as constraints is proposed for DHGS, and the joint peak load regulation (JPLR) method is applied to the proposed model. The model can enhance the power-generation efficiency and reduce the water spillage significantly under the condition of peak load regulation. Meanwhile, a hybrid method that combines discrete differential dynamic programming with progressive optimality algorithm is proposed to solve the JPLR problem. The complicated constraints can be handled by the proposed algorithm effectively. Moreover, the hydro-unit-commitment problem is solved by a rapid searching algorithm based on equal incremental principle and empirical methods. With this unit-commitment stra...

The increasing integration of wind and photovoltaic energy into power systems brings about large fluctuations and significant challenges for power absorption. Wind–solar–hydro–storage multi-energy complementary systems, especially joint dispatching strategies, have attracted wide attention due to their ability to coordinate the advantages of different resources and enhance both flexibility and economic efficiency. This paper develops a capacity optimization model for a wind–solar–hydro–storage multi-energy complementary system. The objectives are to improve net system income, reduce wind and solar curtailment, and mitigate intraday fluctuations. We adopt the quantum particle swarm algorithm (QPSO) for outer-layer global optimization, combined with an inner-layer stepwise simulation to maximize life cycle benefits under multi-dimensional constraints. The simulation is based on the output and load data of typical wind, solar, water, and storage in Yunnan Province, and verifies the effectiveness of the proposed model. The results show that after the wind–solar–hydro–storage multi-energy complementary system is optimized, the utilization rate of new energy and the system economy are significantly improved, which has a wide range of engineering promotion value. The research results of this paper have important reference significance for the construction of new power systems and the engineering design of multi-energy complementary projects.

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems.

This work formulates a stochastic dynamic programming (SDP) model that incorporates seasonal electricity prices and can handle a constraint on power yield, which is assumed to be satisfied at any time it is possible, thus allowing for an analysis of their impacts on the operational performances of cascaded reservoirs. The model is applied to the Lancang Cascade, specifically its two largest reservoirs, Xiaowan and Nuozhadu. The results show that increasing the power yield of the cascade will reduce energy production unfavorably but will impact water spillage favorably, with a power yield of 2000 MW and with a 91% reliability suggested as being a satisfactory operational target. The case study also suggests that using seasonal electricity prices makes the power generation very unstable during weeks 12–20, which is a period of time that is critical to transferring from dry to flooding seasons.