Control and optimal management of hybrid pv-wind integrated charging station for electric vehicles

Abstract

Electric vehicles (EVs) are rapidly gaining popularity worldwide due to significant advancements in their technology; however, the development of their charging infrastructure remains a topic of interest. Historically, most EV charging requirements were fulfilled by the conventional alternating current grid. Extensive research has, however, been conducted over the years to explore the integration of renewable energies into electric vehicle charging stations (EVCSs). Most research studies focus on management, optimal control, charging schemes, charging strategies, and prosumer trade options. To date, limited research has been conducted on charging stations that incorporate a grid-integrated hybrid renewable energy system that utilises sources like wind and solar power. These charging stations typically utilise a central battery bank (CBB) and explore the implementation of a peer-to-peer (P2P) energy-trading model. The objective of this model is to enhance profitability for both the charging station and prosumers, who are consumers that also produce electricity. The aim of this research was to address the challenges posed by inadequate power systems in the country and to develop sustainable charging solutions for EVs. By leveraging renewable energy sources and facilitating energy trading, these charging stations aim to promote vast adaptation of EVs in a country with ailing energy-generation infrastructure while promoting a more sustainable approach to EV charging. This work developed a well-coordinated charging scheme of EVs in a charging station while optimally managing the charging and discharging of the CBB (used for energy storage), as well as the power dispatch of a grid, wind, and photovoltaic (PV) charging station. The model maximises EV user satisfaction, meets all the charging requirements of the charging station, and offers cost-effective charging schemes such as the P2P energy-sharing model. The first model was based on an optimisation approach and a modelling framework for a PV-grid-integrated EVCS with battery storage and P2P vehicle-charging strategies. The main objective was to optimise the system for reliability and profitability while minimising operational costs. The model considered factors such as energy demand, grid stability, battery health, and charging costs to efficiently utilise resources. A notable aspect of the proposed model is the introduction of a P2P energy-sharing mechanism. Prosumers are allowed to trade energy with one another, which enables efficient utilisation of resources and fosters a collaborative energy ecosystem. The dynamic power demand and pricing environment facilitate fair and optimised energy exchanges. It outlines the development of a mathematical algorithm formulation for the proposed model and validates its effectiveness and performance using a real-world case study conducted at Nelson Mandela University in South Africa. This comprehensive approach considered daily varying weather and load conditions to ensure a realistic analysis of system performance and economic feasibility. The simulation results demonstrate significant energy demand cost savings, with a 38.14% reduction considering time-of-use (ToU) tariffs, and a 30.5% reduction in maximum demand through optimal control in comparison to the baseline model and it further conducts a detailed techno-economic analysis over a 20-year period. The results indicate substantial reductions in grid costs and a breakeven point within eight years, which highlight the economic advantages of the optimisation model. Furthermore, the results demonstrate significant cost savings, including energy demand cost savings and ToU savings. The second model was based on the model predictive control (MPC) optimisation of a hybrid smart grid with PV-wind and a CBB incorporating demand-side management, a P2P charging scheme, as well as EVCS profitability. The aforementioned model maximised EV user satisfaction that meets all the charging requirements of the EVCS while offering cost-effective charging schemes such as the P2P energy-sharing model. The optimal power scheduling was modelled into a control problem to benefit from feedback advantages and predictions. The programming framework of the MPC model was linearized to accommodate two scenarios under scrutiny (with and without the occurrence of power cuts). The closed-loop MPC model further enhanced the open-loop optimal control with an additional 18.04%, which translated to an overall cost savings of 43.29% when compared to the baseline model. The lifecycle cost also saw a reduction of two years, which translated to reaching the breakeven point after six years. The results proved the effectiveness and benefits of each optimal-control model, while displaying the robustness of the MPC closed-loop control model. This may assist in advancing the vast adaptation of EVs while displaying the profitability and reliability of the EVCS under the proposed scenarios.