Optimal Operation Scheduling of a Combined Wind-Hydro System for Peak Load Shaving

Power demand has increased in recent years, posing a significant challenge to power systems. The use of clean energy generated by wind farms (WF) for the operation of pumped hydroelectric energy storage (PHES) is often advocated as a hybrid renewable energy system for peak load reduction, defined as WF-PHES. The objective of this paper is to present a comprehensive optimal operational scheduling strategy-based algorithm for dynamically shaving or reducing peak power loads. For this purpose, a finite horizon scheduling optimization problem has been formulated to optimally control the real-time operation of the WF-PHES that incorporates both predictions of the power load and winds. The main aim of the proposed framework is the scheduling of the whole system operation based on predicted wind speeds and power load profile, while respecting the operational constraints. A three-layered ANN model has been used to forecast the wind speeds and the power load based on historical data. A mathematical decision model considering the power load-shaving and reduction needs of the network for the summer and winter seasons has been developed. The proposed model has been implemented and applied to a case study. The proposed framework has been compared to two distinct non-linear optimization methods: Pyomo with IPOPT solver and Genetic Algorithm (GA) to prove its performance and effectiveness over extensive numerical simulations. Note to Practitioners—This paper is motivated by the problem of reducing the long-term peak load on the electricity grid. A hybrid efficiency system was developed by combining a renewable energy source (wind) with a large-scale storage system. The optimal system operation strategy given by the optimization model shows important advantages in providing a clear view for those making decisions regarding this type of energy system. The developed decision algorithm may be taken as a practical solution to address the development challenges of wind energy and support utilities and power grid managers to increase the penetration of renewable generators as well as reduce peak power loads.