Battery Storage System Research Articles

A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets

Publicly available electric vehicle charging hubs are expected to grow, to meet the increasing charging demand of EVs. A dominant class of these will be fast-charging hubs where the EVs will arrive for charging at all hours of the day, get the requested charge, and leave promptly. The profitability of these fast-charging hubs will be highly dependent on the variation of the day-ahead prices of electricity, volatility of the real-time power market, and the randomness of EV charging demand. The hubs can hedge against these uncertainties by committing power purchases in the day-ahead electricity market and by adopting dynamic real-time power management strategies. We develop a novel two-step methodology. The first step entails a mixed integer linear program (MILP) that assists the hubs in their day-ahead power commitment. The second step employs a Markov decision process (MDP) model that derives the real-time power management control actions. The MILP is solved using a commercial solver and the MDP is solved using a deep reinforcement learning algorithm. We demonstrate the effectiveness of our methodology for a fast-charging hub, housing 150 charging stations and a battery storage system, that operates in the Pennsylvania-New Jersey- Maryland interconnection (PJM) power grid.

An integrated financial and environmental evaluation framework to optimize residential photovoltaic solar systems in Australia from recession uncertainties

This study assesses the financial viability and environmental evaluation of Photovoltaic (PV) panels from the perspective of the recent economic recession due to the Russia-Ukraine war. The financial viability of PV installation is calculated based on the estimated price, solar rebates, feed-in tariff, energy supply cost, and other evaluation parameters available at the assessment time. This calculation implicitly assumes variable discount rates (4%, 7%, and 12%) to show how the future will unfold and its correlations with design parameters. Details of economic appraisal integrating current inflation, rebates, and incentives of solar systems have been analyzed for the first time in this study. Financial indicators reveal the advantages of installing a grid-connected solar system (SS) over a solar battery storage system (SSWB). Compared to other installation systems, the lowest payback (PB) and highest internal rate of return (IRR) are observed for a 7 kW grid-connected solar system. Relative uncertainties of solar installation systems represent the necessity of government subsidies (r = -0.602) for solar storage batteries. LCA signifies the energy-intensive process of manufacturing metallurgical-grade (MG) silicon is the primary cause of significant greenhouse gas (GHG) emissions and cumulative energy demand (CED) for PV panels. A potential amount of metal and fossil fuels is depleted for interconnective components of solar installation systems. Amorphous solar panels exhibit lower impacts than polycrystalline, but further upgradation in service life is required to become cost-effective and cope with current inflation.

Stochastic Multi-Objective Scheduling of a Hybrid System in a Distribution Network Using a Mathematical Optimization Algorithm Considering Generation and Demand Uncertainties

In this paper, stochastic scheduling of a hybrid system (HS) composed of a photovoltaic (PV) array and wind turbines incorporated with a battery storage (HPV/WT/Batt) system in the distribution network was proposed to minimize energy losses, the voltage profile, and the HS cost, and to improve reliability in shape of the energy-not-supplied (ENS) index, considering energy-source generation and network demand uncertainties through the unscented transformation (UT). An improved escaping-bird search algorithm (IEBSA), based on the escape operator from the local optimal, was employed to identify the optimal location of the HS in the network in addition to the optimal quantity of PV panels, wind turbines, and batteries. The deterministic results for three configurations of HPV/WT/Batt, PV/Batt, and WT/Batt were presented, and the results indicate that the HPV/WT/Batt system is the optimal configuration with lower energy losses, voltage deviation, energy not supplied, and a lower HS energy cost than the other configurations. Deterministic scheduling according to the optimal configuration reduced energy losses, ENS, and voltage fluctuation by 33.09%, 53.56%, and 63.02%, respectively, compared to the base network. In addition, the results demonstrated that the integration of battery storage into the HPV/WT enhanced the various objectives. In addition, the superiority of IEBSA over several well-known algorithms was proved in terms of obtaining a faster convergence, better objective value, and lower HS costs. In addition, the stochastic scheduling results based on the UT revealed that the uncertainties increase the power losses, voltage deviations, ENS, and HPV/WT/Batt cost by 2.23%, 5.03%, 2.20%, and 1.91%, respectively, when compared to the deterministic scheduling.

Towards Nearly Zero-Energy Buildings: Smart Energy Management of Vehicle-to-Building (V2B) Strategy and Renewable Energy Sources

As countries worldwide strive for net-zero emissions, electric vehicles (EVs) have gained significant momentum. EVs, particularly through Vehicle-to-Everything (V2X) technology, including Vehicle-to-Building (V2B), are recognized as key enablers for achieving nearly-zero energy buildings (NZEB). This research presents an advanced Smart Energy Management System (SEMS) with a novel V2B strategy, offering precise one-hour resolution to reduce grid reliance and optimize peak load management. The SEMS coordinates building energy and EV charging while harnessing the potential of renewable energy resources and battery storage systems. By carefully scheduling EV charging and discharging activities, it maximizes the efficient use of available energy resources. Importantly, the algorithm integrates real-world EV parking patterns, accounting for factors such as fleet composition, parking durations, and charging priorities. Economic evaluation encompasses not only direct EV battery degradation costs but also external expenses associated with environmental impacts. Simulation outcomes underscore a remarkable reduction in building grid power demand (65%) and a substantial decrease in carbon emissions (64%). The PV+V2B Scenario emerges as the preferred choice, striking an optimal balance between initial investment costs, energy savings, and carbon footprint reductions. This study holds great academic and practical significance, contributing to the advancement of smart, sustainable, and economically viable net-zero cities.

Intelligent solar photovoltaic power forecasting

This paper presents a day-ahead forecasting method for photovoltaic (PV) power plants in commercial sectors. The method is based on numerical weather prediction (NWP) models from open weather maps and power plant specifications. The output of the model is the predicted power output from the PV power plant, which is incorporated into an optimal control strategy of the PV plant using battery storage. The use of optimal algorithms assists in the PV power plant curtailment in cases of over-generation and reduces the dependence on conventional power sources such as generators in cases of under-generation by the PV plant. It was found that most forecasting methods do not incorporate PV power and storage systems for proper optimization and demand management. This can be seen as a gap for further research of forecasting models integrated with battery storage systems to improve PV power system performance. Results obtained show a good performance of the developed model. A root means square error (RMSE) of 425.79 W and 595.10 W and a mean absolute error (MAE) of 246.26 W and 238 W were achieved for a summer and winter day, respectively. Furthermore, an excellent positive correlation exists between the predicted output power and the observed results, with R2 values over 90%.

Life cycle assessment of electrochemical and mechanical energy storage systems

The effect of the co-location of electrochemical and kinetic energy storage on the cradle-to-gate impacts of the storage system was studied using LCA methodology. The storage system was intended for use in the frequency containment reserve (FCR) application, considering a number of daily charge–discharge cycles in the range of 50–1000. The results show that a significant environmental benefit (up to a 96% decrease in cradle-to-gate global warming potential, from 1.65 ± 0.12 to 0.059 ± 0.004 kg CO2-eq./kWh) can be obtained by the co-location of battery and flywheel storage systems, owing to the ability of the flywheel component to preserve battery lifetime by delivering the frequent charge events required in the FCR application. A moderate saving of 24% in global warming potential (from 1.65 ± 0.12 to 1.26 ± 0.11 kg CO2-eq./kWh) could already be achieved by switching from a battery to flywheel storage system with repeated charge–discharge cycling (200 or more charge events per day). This study highlights the need to consider the intensity of charge–discharge cycling when choosing an environmentally preferable storage technology as well as introducing a methodology for incorporating the number of daily cycles in the analysis.

Operating strategy optimization considering battery aging for a sector coupling system providing frequency containment reserve

Battery storage systems are increasingly used in the frequency containment reserve (FCR) application. Coupling the battery with a power-to-heat module can further increase the system’s profitability. Different studies optimized the hybrid system’s operation by considering country-specific regulations. However, lithium-ion battery aging has not yet been included. This work analyses the impact of system dimension and operation on net present value, considering battery aging. The operation strategy consists of adjusting the target SoE and using the deadband degree of freedom, which allows to suspend provision of FCR for minor frequency deviations. Battery lifetime is simulated using a semi-empirical aging model describing graphite anode aging mechanisms. The highest NPV is obtained with a battery dimension of 0.5 Wh/W, 90% target SoE, and normal deadband utilization. For the considered NMC battery cells, deadband utilization contributes to battery lifetime extension when operating at high SoE. Without this cell chemistry-specific lifetime gain, a lower target SoE of 60% and a larger system size of 0.6 is economically more advantageous. In a sensitivity analysis, battery aging was detected as the most crucial parameter influencing system profitability at high FCR prices. The results highlight the importance of including reliable battery aging simulations in future FCR operation optimizations.

Optimal power control in Renewable Energy sources using Intelligence algorithm

An intelligent, Adaptive Neuro-Fuzzy Inference System (ANFIS) based Xilinx Power Management system is proposed for the Hybrid renewable energy sources (HRES). The Xilinx Power Management generator block set (ANFIS-XL-PMS) controller is implemented for the purpose to control and avoid the non-linear nature opted in the HRES power system. Solar (PV), Wind Energy (WE), and Fuel cell (FC) are the primary power sources of the system, and a battery storage system (BES) is used as a backup. A comprehensive power management strategy is intended for the proposed system to control the flow of power between the various energy sources and the system's storage device. The simulation model for the HRES is elegantly designed using the MATLAB/Simulink platform and the Switching patterns are generated using the XLPMS controller. The system performance and output responses of the SMC-XL-ANFIS controller have been verified by carrying out the simulation studies and compared with the existing controller like Sliding Mode Controller (SMC-XL-PMS), and Artificial Neutral Network (ANN-XL-PMS). Simulation results show good performance in terms of voltage and current transient, settling time, load power efficiency, and power quality Also a Prototype model with a PIC microcontroller is designed and the output responses are analyzed.

Fundamentals and perspectives of electrolyte additives for non-aqueous Na-ion batteries

Despite extensive research efforts to develop non-aqueous sodium-ion batteries (SIBs) as alternatives to lithium-based energy storage battery systems, their performance is still hindered by electrode-electrolyte side reactions. As a feasible strategy, the engineering of electrolyte additives has been regarded as one of the effective ways to address these critical problems. In this review, we provide a comprehensive overview of recent progress in electrolyte additives for non-aqueous SIBs. We classify the additives based on their effects on specific electrode materials and discuss the functions and mechanisms of each additive category. Finally, we propose future directions for electrolyte additive research, including studies on additives for improving cell performance under extreme conditions, optimizing electrolyte additive combinations, understanding the effect of additives on cathode-anode interactions, and understanding the characteristics of electrolyte additives.

Artificial Neural Network with 3-Port Dc-Dc Converter Based Energy Management Scheme in Sustainable Energy Sources

In micro grids, energy management is referred to as an information and control system that offers the essential functionality to ensure that the energy supply from the generation and distribution systems occurs at the lowest possible operational cost. Energy management systems (EMS) support distributed energy resource utilization in micro grids, especially when variable generation and pricing are present. In this paper, an Artificial Neural Network (ANN)-based energy management approach for a hybrid wind, solar and Battery Storage System (BSS) is presented. To sustain the DC voltage, a 3 Port DC-DC Converter is also proposed. While renewable energy systems have numerous advantages, one of the challenges they face is the intermittency of power generation, leading to fluctuations in the power supply to the grid. Therefore, EMS aims to reduce these variations. Another goal is to maintain the battery state of charge (SOC) within the allowed ranges to extend the battery life. The implementation is carried out in Simulink/Matlab platform. To demonstrate the efficacy of the suggested approach, we compare the Total Harmonic Distortion (THD) of the proposed controller (1.52%) with that of conventional controllers, including the ZSI-based PID controller (3.05%), PI controller (4.02%), and FO-PI (3.32%) controller.

Modeling and simulation of a small-scale hybrid energy system using genetic algorithm for Yolan-Bayara Village, Bauchi State

To mitigate the downsides of conventional energy generation systems, such as contaminants in the air and the release of greenhouse gases that contribute to environmental degradation, hybrid energy systems (HES) are absolutely essential for revitalizing areas that are rural or remote to obtain favorable technical and economic benefits. It is very difficult to establish a hybrid energy system's ideal size while still trying to meet the reliability requirements and consumer demands for electricity for powering their homes, schools, hospital, and businesses. Using a Genetic Algorithm (GA) optimization approach, this abstract describes a study on the modeling and simulation of a small-scale hybrid system that integrates renewable energy sources with energy storage for Yolan-Bayara village Bauchi state. Photovoltaic (PV) panels, wind turbines, and a battery storage system make up the system. The battery storage system enables energy storage and retrieval when necessary, while the PV panels and wind turbines generate sporadic electricity. A mathematical model is created that includes several system parameters, such as environmental factors, and energy demand profiles in order to arrive at the best sizing approach. However, conventional optimization approaches may have trouble locating the overall optimal solution due to the complexity of the system and the uncertainty surrounding renewable energy sources. A Genetic Algorithm is used to solve this problem. To ensure effective energy use and low operating costs, the GA successfully adjusts the control settings to changing environmental circumstances and energy demand profiles. The outcomes show how GA-based optimization methods can improve the functionality and financial viability of small-scale hybrid systems. Such technologies could be extremely important for promoting green energy options and reducing climate change.

Hybridization of energy systems for air conditioning application in an educational building

This study reports the hybridization of energy systems for an air conditioning (AC) application in an educational building, using the Faculty of Engineering Lecture Theaters at Rivers State University, Port Harcourt, Nigeria as case study. It includes conceptual design of a hybrid energy system of thermoelectric and solar energy, analysis of cooling load to select suitable air conditioning system for the building using Carrier’s Hourly Analysis Program (HAP) software, analysis of the required energy to drive the air conditioning system, and techno-economic analysis of the system. The analysis carried out shows that a total of 4 packaged rooftop air conditioners each of 10.6kW (30,000 Btu/h) capacity would be required to handle the building’s cooling load of 38.28kW, and would require 344.5kWh of energy per day, out of which 249kWh/day would be generated from the thermoelectric generator (TEG) modules, and the remaining 95.5kWh/day would be generated from the photovoltaic (PV) modules. The 249kWh/day of energy required from the thermoelectric energy system would require a total of 1600 TEG modules of 26W each, and the 95.5kWh/day of energy required from the solar system would require a total of 65 PV modules of 330W each. The energy generated would be stored in a 679kWh capacity battery storage system which contains 72 batteries of 48V and 200Ah capacity each, which are wired into 3 parallel strings, which would supply the AC system. The techno-economic analysis carried out shows that the system designed can be implemented at a cost of ₦98,558,000, with a payback period of 9years, and would mitigate 72,050.5kg of CO2 emissions annually.

The function of the time constant in RC series circuit signal filtering

Battery storage systems are essential components in the realm of renewable energy systems and electric vehicles, providing critical support in managing power supply and demand. However, a prevalent issue within these systems is charge imbalance among individual battery cells, which can lead to suboptimal power efficiency, reduced reliability, and potential safety hazards. Addressing this challenge, researchers have focused on developing battery equalization techniques to ensure uniform charge distribution. Among the various strategies, switched-capacitor-based battery equalizers have emerged as a promising solution due to their cost-effectiveness, compact design, and controllability. This paper delves into the analysis of several switched-capacitor-based battery equalizers, including conventional, two-level, modular, chain structure types I and II, series-parallel, and single switched-capacitor equalizers. The study begins with the formulation of mathematical models that simulate the charge and discharge cycles of these equalizers, providing insights into their operational mechanisms. The goal is to enhance the understanding of switched-capacitor equalizers, paving the way for advancements in battery management systems that optimize performance and extend battery life.

Energy Balance in a Standalone PV Battery Hybrid Generation System on Solar-Powered Aircraft Using the Model Predictive Control Method

This paper proposes a battery state of charge (SOC)-based energy management strategy using hierarchical distributed model predictive control (HDMPC) for a standalone microgrid on solar-powered long-endurance aircraft. The microgrid was innovatively designed as a two-layer structure in which the first layer consists of a photovoltaic generation and battery storage system named the PV battery module (PBM). The second layer, named the microgrid subsystem (MGSS), consists of several PBMs, each of which supplies power to a specific DC load on the aircraft. The control system is divided into two levels: the grid-level model predictive control (MPC) and the converter-level MPC. The grid-level MPC adopts a distributed model predictive control strategy to obtain the reference power of each module. The converter-level MPC calculates the control variables of converters using a supervisory model predictive control (SMPC) strategy. The new microgrid structure and the proposed control strategy have improved the reliability of the energy system and increased its energy utilization rate.

The importance of battery storage systems in reducing grid issues in sector-coupled and renewable low-voltage grids

Transforming the energy system towards renewable energies and the electrification of the transport and heating sectors is necessary. A substantial part of this transformation occurs in the low-voltage grids. The increasing number of photovoltaic systems, electric vehicles and heat pumps poses new challenges for low-voltage grids. The load on lines and transformers is increasing due to higher electricity demand. Battery storage systems and the flexible operation of consumers can increase photovoltaic self-consumption and relieve low-voltage grids by using a grid-serving mode of operation and thus supplement grid expansion.We conducted time series-based load flow calculations using five representative low-voltage grids for four weeks of the year. We determined the loads and the need for curtailment caused by a high expansion of roof-mounted photovoltaic systems and new sector-coupled consumers and how these loads and curtailment can be reduced with the help of battery storage systems and flexible consumers.Our analyses show that consumption-related overloads can be completely avoided, and the share of locally consumed photovoltaic energy can be doubled. Digitalising the low-voltage grids is required to detect overloads and allow battery storage systems and consumers to react specifically to these grid overloads. Rural low-voltage grids are particularly affected by overloads, as they have greater rooftop photovoltaic potential and tend to be weaker. Battery storage systems and smart consumers are thus becoming essential cornerstones for the transformation of the energy system.

Multi-agent based energy community cost optimization considering high electric vehicles penetration

This paper proposes a multi-agent based solution to minimize the energy cost of an energy community, in day-ahead, that includes high penetration of electric vehicles. The proposed approach employs a structure of agents, including a central coordinator and energy management agents. To minimize the energy cost and to optimize the energy balance of the energy community, the proposed approach considers the energy demand and supply (i.e., photovoltaic generation), the battery storage systems’ charge and discharge actions, and the charging and discharging schedules of electric vehicles, including the possibility of vehicles to charge and discharge energy in public charging stations. The optimization problem is formulated as a mixed-integer linear programming problem, which is solved by an open-source solver and compared with the use of a commercial solver. The simulation results show that the proposed day-ahead approach can significantly reduce the cost of the energy community while ensuring a reliable and stable operation. Comparing the proposed solution with a centralized implementation, it is possible to significantly reduce the optimization time from hours to few seconds. Overall, the proposed multi-agent based solution provides a promising solution for the optimization of energy communities with high penetration of electric vehicles.

Data-driven non-parametric chance-constrained model predictive control for microgrids energy management using small data batches

This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven non-parametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small grid-connected microgrid with PV generation, a battery storage system, and a non-controllable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels.

Multi-Objective Operational Cost Management with Minimum Net Emission of a Smart Microgrid

This research article considers a microgrid (MG) with various distributed generation sources (DGs) such as microturbine (MT), fuel cell (FC), wind turbine (WT), and solar cell (PV) along with the battery storage system. This article aims to simultaneously minimize operational costs and emissions for a daily schedule, while considering uncertainties such as load demand, market prices, and electricity generated by different renewable energy sources (RESs). This article proposes the slime mould algorithm (SMA) to solve this complex optimization problem. Initially, SMA is tested for three scenarios considering the minimization of operational cost and emission, respectively, as two single objectives. In Scenario A, a reduction of 1.95% in operational cost and 4.57% in emissions is observed compared to the Grasshopper Optimization Algorithm (GOA). In scenarios B and C, SMA reduces operational cost by 0.78% and 2.65%, respectively, while also reducing emissions by 6.27% and 0.28%, respectively, when compared to GOA. The results obtained by other algorithms are worse than those obtained by GOA. This study also showed that SMA effectively considers multi-objective functions by using the weighted sum method and the fuzzy decision-maker to approach the Pareto-optimal solution, achieving a good tradeoff between operational cost and emissions in each scenario.

Optimal sources rating of electric vehicle based on generic battery storage system model

Batteries are the foundation stone of the hybrid-electric vehicle, where the powertrain is made of a battery and an energy source. An accurate battery model is a necessary tool for a successful sizing procedure. This paper presents a new generic battery model for the sizing process; it utilizes different methods of battery mocking up into one model. Firstly, a database is created based on battery tests performed under various conditions. Then, the model receives the real-time power demand and signals of the environment status; then, the algorithm characterizes the parameter changes. The algorithm interpolates and extrapolates the data to find the predicted operating point. The findings are fed into a battery-equivalent circuit where some basic parameters are revealed. Then, analytical equations are employed to supply all battery parameters. The outcomes show that the proposed generic model can better predict the battery parameters through all operating regions under varying conditions. The suggested algorithm could be designed for all types of batteries by reentering the data for any specific battery. A case study was made on the lithium ferro-phosphate (LiFePo4) battery. The experimental results demonstrate that the proposed model is more accurate than the others; thus, the sizing results are more optimal.

Hybrid power management and control of fuel cells‐battery energy storage system in hybrid electric vehicle under three different modes

AbstractIn most situations, fuel cells (FCs) are insufficient to supply power demands in hybrid electric vehicles (HEVs), thus battery storage systems (BSSs) are used to make the system more efficient like as rapid starting, high power density, and enhanced dynamic set response. How the power sources are regulated and distributed determines the accomplishment efficiency of the driving forces in such a configuration. The most critical aspect should be how to guarantee torque stability of the traction motor which is in our case a permanent magnet synchronous motor (PMSM) while attaining quick reaction. This study discusses a hybrid battery‐FCs energy storage and management system for a hybrid electric vehicle (HEV), as well as an integrated PMSM's passivity‐based control (PBC) technique to enable power integration and increase the hybrid electric vehicle (HEV)'s operating speed. The present paper is separated into two sections. First, a power management control (PMC) technology is used to manage the FCs‐battery system to guarantee that the HEV gets continuous power from the hybrid energy resources, where a fuzzy logic controller is used as an artificial intelligence system for the FCs converter to maximize the power produced. The second is dedicated to the new proposed PBC control of the EV's PMSM to rectify non‐linearities, external disturbances, and parametric fluctuations. The standard proportional‐integral (PI) control and sliding mode control (SMC) are built for comparison with the proposed PBC dedicated to the PMSM in order to properly assess the efficacy of the proposed control strategy. The simulation results obtained using MATALB/Simulink show that the proposed PBC technique and hybrid battery‐ proton‐exchange membrane PEMFCs energy management enables high power integration and increase the EV's operational speed.