An Optimal Energy Dispatch Management System for Hybrid Power Plants: PV-Grid-Battery-Diesel Generator-Pumped Hydro Storage

Effective real-time energy management strategies are crucial for optimising hybrid power plants, particularly when challenged with integrating Renewable Energy Sources (RESs) and managing their intermittent nature. This paper presents a comprehensive energy management framework holding real-time optimisation for HPP. The practical implications of this research are significant, as it provides a roadmap for seamlessly integrating RESs with Battery Energy Storage Systems (BESSs) in Hybrid Power Plants (HPPs) to minimise cost while meeting daily household energy demands. Furthermore, it demonstrates how diesel generators (DGs) can be incorporated into the HPP’s energy management system while minimising carbon emissions. An Energy Dispatch Engine (EDE) is introduced to control HPPs that combine PV, BESS, DG and Pumped Hydro Storage (PHS). Two optimisation approaches are used, namely, Mixed-Integer Linear Programming (MILP) and Stochastic Dual Dynamic Programming (SDDP). The system leverages load and RES power data while considering State-of-Charge (SoC) constraints to manage battery health proactively. Optimising discharge and charge profiles of the BESS, with the overarching goal of minimising the total cost of satisfying daily load demand, is an objective. Various tariff schemes were explored to assess the presented EDE. Our testing demonstrates that the SDDP approach consistently results in lower total costs than MILP. The total cost for the MILP method, where the system with PHS incurs higher costs (219.8 ${\$}$ /24h) than the total cost for the SDDP method, where the system with PHS system (180 ${\$}$ /24h). The cost of CO2 emissions was found to be lower in the case of SDDP, amounting to 8.3 ${\$}$ /24h for a total emission of 160 kg. In contrast, the MILP approach resulted in a higher CO2 cost of 10.2 ${\$}$ /24h for a total emission of 200 kg. This suggests that SDDP is more cost-effective in terms of reducing CO2 emissions.