An Intelligent Two-Stage Energy Dispatch Management System for Hybrid Power Plants: Impact of Machine Learning Deployment

Abstract

The utilization of renewable energy sources such as PV and wind power has become imperative due to the increase of carbon dioxide emissions, which leads to the increase in global temperature and the negative consequences of climate change. As a result, renewable energy sources are constantly gaining popularity to be integrated in power systems to create hybrid power plants (HPPs). However, HPPs come with great complications due to the uncertainty in renewable energy output, which has given rise to the need for a reliable and effective energy dispatch management system for HPPs. In this paper, a two-stage machine learning (ML) based energy dispatch management system for HPPs is designed to control renewable energy sources (PV and wind power), reserve energy sources (energy storage systems) and backup energy sources (diesel, fuel cells, auxiliary loads, etc.). The system aims to minimize the power variance in the HPPs to achieve peak shaving and valley filling. The first stage aims to forecast the power output of renewable energy sources, as well as the load demand. The second stage aims to coordinate the energy output of the reserve and backup sources to achieve the required objective. Different ML techniques were compared to find the highest performing ML algorithm to achieve the required objective of the system, where long short-term memory (LSTM) provided the highest results with an average mean squared error of 0.005 and an average explained variance score of 0.9. The results of the management system verify the effectiveness of the system for the management of the energy dispatch in HPPs, through the successful flattening of the load curve of the HPP, which increases the reliability of the power system with the integration of renewable energy sources. Also, the system was shown to be robust against the uncertainty of the PV and wind power output, and the load demand.