Mind the gap between PV generation and residential load curves: Maximizing the roof-top PV usage for prosumers with an IoT-based adaptive optimization and control module

Abstract

The necessity to diversification in terms of energy sources, especially for residential consumers, becomes more stringent as the prices of electricity are going up. There are numerous incentives to generate electricity using roof-top Photovoltaic (PV) panels as it reduces the costs, grid dependency and partially replaces gas or other heating source. However, solutions to optimally use the local solar energy for off-grid and on-grid prosumers are incomplete and do not provide an efficient ready-to-use implementation schema. Thus, in this paper, we propose an Adaptive Optimization and Control (AOC) module for residential appliances using Internet of Things (IoT). It consists of forecasting the PV output, optimizing the schedule and controlling the prosumers’ appliances in real-time to maximize the PV usage. This solution provides the layout concept and practical implementation in a real environment. For seasonal simulation, various prosumer profiles (on– and off-grid), with or without storage and different generation capacities are considered. The optimization process brings clear benefits for all types of prosumers, but the real-time monitoring and control algorithm is more valuable as it considers the current PV output and adapts the load in real-time so that to further enhance the Self-Sustainable Ratio (SSR). Furthermore, it protects the battery and prolongs its lifetime by reducing the number of charge/discharge cycles. On average, the SSR increases significantly by 12 or 13%. The SSR goes up to 93%, meaning that the consumption from grid is considerably reduced. More significantly, the impact on the environment and life quality will be consistent as the residential sector represents 27% of the total energy consumption, out of which 62.8% for heating.