Optimal Energy Management Solutions Using Artificial Intelligence Techniques for Photovoltaic Empowered Water Desalination Plants Under Cost Function Uncertainties

Abstract

Two modern methods of the energy management system (EMS) based on a modified cost function are addressed in this paper. Fuzzy logic (FL) and Harris Hawks Optimization (HHO) is implemented to achieve the optimal performance of seawater desalination plants (SWDP) within the minimum feed-in tariff (FiT). The technical difficulties involved in the variation of energy price from one time to another and the system parameters uncertainties. For example, the price of energy is higher at the peak time and the price is lower at normal times. Also, the peak time can change from one day to another day. The proposed management system can deal with these variations and uncertainty cases. The suggested EMS is achieved through a bidirectional electrical energy interchange approach (π-EEIA). The main concept behind the proposed π-EEIA is how and when to inject the excess generated energy of renewable energy into the utility grid or charge the battery depending on the minimum dynamic cost criterion and vice versa. To accomplish this study a 700 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /day SWDP located in Egypt fed on solar energy and a utility network has been constructed and analyzed. The system includes SWDP fed from a photovoltaic (PV) array as well as the utility grid in addition to a battery energy storage system (BESS). The main objective of this study is the management and coordination between the energy exchange process from the solar energy, the utility network, and BESS to provide sufficient electrical energy for SWDP within the minimum FiT. The system is constructed and validated using the MATLAB/SIMULINK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> software package. The proposed FL and HHO-based EMSs are investigated in the presence of the system uncertainties such as the change in the energy (excess or shortage) as well as the change in the energy price in the utility network (high or low) with (normal or peak) time. The attained results demonstrate that the proposed FL and HHO-based EMSs provide high dynamic performance and accurate coordination between various energy resources and BESS. The results show that FL-based EMS achieves a profit of 10.28 $ but the HHO-based EMS achieves a profit of 10.11 $ in the same period.