Deep learning-based energy management of a hybrid photovoltaic-reverse osmosis-pressure retarded osmosis system

Abstract

Abstract This paper investigates the energy management of a hybrid grid-connected reverse osmosis (RO) desalination process consisting of photovoltaic (PV), pressure retarded osmosis (PRO), and energy storage system. The developed intelligent energy management system (IEMS) aims to maximize the total water production and contaminant removal efficiency while keeping the grid’s supplied power as low as possible. To promote the performance of the IEMS, the prediction of PV solar power is performed by three deep neural networks based on convolutional neural networks and long short-term memory neural networks. These networks are designed to perform 5-hour-ahead PV power forecasting, and the model with the smallest error is selected. The IEMS employs the particle swarm optimization (PSO) algorithm to find the optimum solutions of the system for each time step. Four performance indices are defined through which the IEMS is evaluated. The results of the proposed technique are compared with two benchmark methods, one of which is similar to the IEMS; however, it does not incorporate the PV power predictions. The superiority of the IEMS over the first benchmark is demonstrated by studying three scenarios: two successive sunny days, two successive cloudy days, and 10\xa0days of operation. Moreover, the simulations are executed for different forecast horizons to investigate the effects of this parameter on the optimization results. The impacts of the best-found forecaster errors are also explored by repeating the simulations with the actual PV power. Finally, the optimization is performed by two other stochastic algorithms: grey wolf optimizer (GWO) and genetic algorithm (GA). It is found that PSO outperforms GWO and GA for solving this optimization problem.