Recently, the appeal of Hybrid Energy Storage Systems (HESSs) has been growing in multiple application fields, such as charging stations, grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance, e.g., efficiency and lifespan. Most recent studies on HESS mainly focus on power management and coupling between the different ESSs without a particular interest in a specific type of ESS. Over the last decades, Redox-Flow Batteries (RFBs) have received significant attention due to their attractive features, especially for stationary storage applications, and hybridization can improve certain characteristics with respect to short-term duration and peak power availability. Presented in this paper is a comprehensive overview of the main concepts of HESSs based on RFBs. Starting with a brief description and a specification of the Key Performance Indicators (KPIs) of common electrochemical storage technologies suitable for hybridization with RFBs, HESS are classified based on battery-oriented and application-oriented KPIs. Furthermore, an optimal coupling architecture of HESS comprising the combination of an RFB and a Supercapacitor (SC) is proposed and evaluated via numerical simulation. Finally, an in-depth study of Energy Management Systems (EMS) is conducted. The general structure of an EMS as well as possible application scenarios are provided to identify commonly used control and optimization parameters. Therefore, the differentiation in system-oriented and application-oriented parameters is applied to literature data. Afterwards, state-of-the-art EMS optimization techniques are discussed. As an optimal EMS is characterized by the prediction of the system’s future behavior and the use of the suitable control technique, a detailed analysis of the previous implemented EMS prediction algorithms and control techniques is carried out. The study summarizes the key aspects and challenges of the electrical hybridization of RFBs and thus gives future perspectives on newly needed optimization and control algorithms for management systems.

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.

The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.

The applicability of Hybrid Energy Storage Systems (HESSs) has been shown in multiple application fields, such as Charging Stations (CSs), grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance. In this work, we propose a novel power management controller called the Hybrid Controller for the efficient HESS’s charging and discharging, considering the State of Charge (SoC) of the HESS and the dynamic supply and load. The Hybrid Controller optimises the use of the HESS, i.e., minimises the amount of energy drawn from and discharged to the grid, thus utilising and prioritising the provided Photovoltaic (PV) power. The performance of our proposal was assessed via simulation using various evaluation metrics, i.e., Autarky, charge/discharge cycle, and Self-Consumption (SC), where we defined 24 scenarios in different locations in Germany.

Vanadium redox-flow batteries (VRFBs) have played a significant role in hybrid energy storage systems (HESSs) over the last few decades owing to their unique characteristics and advantages. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as they are indispensable for incorporating the distinctive features of energy storage systems and control algorithms within embedded energy architectures. In this work, we propose a novel approach that combines model-based and data-driven techniques to predict battery state variables, i.e., the state of charge (SoC), voltage, and current. Our proposal leverages enhanced deep reinforcement learning techniques, specifically deep q-learning (DQN), by combining q-learning with neural networks to optimize the VRFB-specific parameters, ensuring a robust fit between the real and simulated data. Our proposed method outperforms the existing approach in voltage prediction. Subsequently, we enhance the proposed approach by incorporating a second deep RL algorithm—dueling DQN—which is an improvement of DQN, resulting in a 10% improvement in the results, especially in terms of voltage prediction. The proposed approach results in an accurate VFRB model that can be generalized to several types of redox-flow batteries.

The transition towards sustainable energy systems necessitates effective management of renewable energy sources alongside conventional grid infrastructure. This paper presents a comprehensive approach to optimizing grid management by integrating Photovoltaic (PV), wind, and grid energies to minimize costs and enhance sustainability. A key focus lies in developing an accurate scheduling algorithm utilizing Mixed Integer Programming (MIP), enabling dynamic allocation of energy resources to meet demand while minimizing reliance on cost-intensive grid energy. An ensemble learning technique, specifically a stacking algorithm, is employed to construct a robust forecasting pipeline for PV and wind energy generation. The forecasting model achieves remarkable accuracy with a Root Mean Squared Error (RMSE) of less than 0.1 for short-term (15 min and one day ahead) and long-term (one week and one month ahead) predictions. By combining optimization and forecasting methodologies, this research contributes to advancing grid management systems capable of harnessing renewable energy sources efficiently, thus facilitating cost savings and fostering sustainability in the energy sector.