Renewable energy system design and operational planning for demand fulfillment

Abstract

Renewable energy sources have gained prominence in reducing the dependency on fossil fuels and minimizing their negative environmental impacts. Considering renewables' uncertain and variable nature, an effective design and operational planning of hybrid energy systems is key to success in clean energy transition. We study the optimal design and operational planning problem of hybrid energy systems involving a renewable energy source and a storage unit. We first develop two-stage stochastic mixed-integer programming models to determine the optimal sizing and investment decisions for solar/wind farms co-operated with pumped hydro energy storage facilities in decentralized areas. We then utilize a Markov decision process to find the optimal energy generation and storage decisions for decentralized grid-connected wind farm-battery systems with demand-fulfillment obligations. This is a novel study that compares several pumped hydro energy storage configurations with respect to optimal sizing decisions for system components and allows for uncertainties in electricity price, wind speed, and electricity demand for optimal operational planning. Using real-life data and considering economic benefits, we demonstrate how the renewable energy systems should be designed and managed to mitigate the adverse effects of uncertainties in matching supply with demand.