Optimal dispatch schedule for the coordinated hydro-wind-photovoltaic system with non-priority output utilizing combined meta-heuristic

With rising power demand and stringent carbon emission regulations, renewable energy is gaining traction in the power grid. However, its acceptance is lower than that of fossil energy due to its inherent intermittency. This study is motivated by such challenges and seeks to overcome it by enabling electricity consumption through coordinated operation strategies for the hydro-wind-photovoltaic systems. To match electricity demand and maximize system power output, non-priority output scheduling should be used. This study presents a systematic nonlinear programming strategy coupled with a combined meta-heuristic approach. The programming aims to increase total power generation, control wasted power, and balance the fluctuation of fossil energy output. The programming makes use of the corresponding constraints for hydro, wind, and photovoltaic power generation. A scenario-based approach also considers the effects of seasonal meteorological factors on electricity output. To address NP-hard issues in complicated nonlinear programming, a hybrid cuckoo search technique and multiple objective particle swarm optimization are used. The combined meta-heuristic technique includes a flight and elimination mechanism to increase search capacity and accelerate convergence. The case study of a hydro-wind-photovoltaic system is then performed over a four-season scheduling horizon. The case supports the study's viability and efficacy. The findings demonstrate the developed methodology's ability to balance the three objectives. The optimal dispatch schedule is shown to reduce intermittency, ensure renewable energy acceptance, and then adjust the power source's installed capacity. This study's coordinated operation strategies promote a more efficient method of establishing MECS and help to reduce power grid risk.