Battery Based Energy Storage System Research Articles

Frequency control in an isolated wind‐diesel hybrid system with energy storage and an irrigation water supply system

AbstractWind‐Diesel Hybrid Systems (WDHSs) integrate wind turbines into diesel power systems, reducing costs and emissions in isolated grids. Due to the no‐load consumption of the Diesel Generators (DGs), fuel savings are only possible when the DGs are shut down. This requires a proper implementation of the frequency control to avoid perturbations because of the wind speed variations. During wind‐only (WO) operation, the Synchronous Machine (SM) generates the isolated grid voltage and the frequency controller varies the energy stored/supplied by an Energy Storage System and consumed by the controllable loads to balance the power. In this paper, a Battery‐based Energy Storage System (BESS) uses Li‐Ion batteries with a Dual Active Bridge (DAB) and a grid‐tie inverter connected to the isolated network. The controllable load is an Irrigation Water Supply System (IWSS), consisting of a pump supplying water to a reservoir tank. The pump is driven by a variable speed drive that uses a Permanent Magnet Synchronous Motor (PMSM). The coordinated control of BESS and IWSS gives full priority to the BESS for harnessing the wind potential whereas the IWSS consumes the excess of wind power. The full Wind Diesel Power System (WDPS) is modelled and simulated to validate the proposed system for different case scenarios.

Simulation and Measurement Analysis of an Integrated Flow Battery Energy-Storage System with Hybrid Wind/Wave Power Generation

This study aims to evaluate the power-system stability and the mitigation of fluctuations in a hybrid wind/wave power-generation system (HWWPGS) under different operating and disturbance conditions. This evaluation is performed by employing a vanadium redox flow battery-based energy storage system (VRFB-ESS) as proposed. The measurement results obtained from a laboratory-scale HWWPGS platform integrated with the VRFB-ESS, operating under specific conditions, are used to develop the laboratory-scale simulation model. The capacity rating of this laboratory-scale simulation model is then enlarged to develop an MW-scale power-system model of the HWWPGS. Both operating characteristics and power-system stability of the MW-scale HWWPGS power system model are evaluated through frequency-domain analysis (based on eigenvalue) and time-domain analysis (based on nonlinear-model simulations) under various operating conditions and disturbance conditions. The simulation results demonstrate that the fluctuations and stability of the studied HWWPGS under different operating and disturbance conditions can be effectively smoothed and stabilized by the proposed VRFB-ESS.

A Hierarchical Driving Control Strategy Applied to Parallel SiC MOSFETs

SiC (silicon carbide) MOSFETs have been extensively used in the power electronics industry due to their exceptional characteristics. First, it was found in this study that their driving loss is larger than their conduction loss in high-frequency applications. Based on this finding, this study proposes a hierarchical driving control strategy for improving the parallel-converter efficiency of SiC MOSFETs under light loads. Efficiency under light loads is of great importance for battery-based energy storage systems. To minimize the sum of the conduction loss and driving loss in parallel devices, this study proposes a current-monitoring hierarchical driving strategy based on an active-clamped flyback converter. By monitoring the output current of the converter, the strategy minimizes the sum of the driving and conduction losses by switching the driving state under different loads. The results of simulations indicate the effectiveness of the load-current-monitoring strategy. To verify the effectiveness of this method, a principle prototype of two SiC MOSFETs connected in parallel at 12 V/5 A was fabricated and tested, and the test results showed that there was a maximum improvement of 1.4% in the converter’s efficiency when the load current was in the range of 0.5–1.5 A.

Battery Energy Storage System Optimal Sizing in a Battery Electric Vehicle Fast Charging Infrastructure

The growing number of battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV) brings the need of more fast charging stations across cities and highway stops. This charging stations toned to be connected to the electrical grid via existent facilities, causing constraints such as power availability.
 This study brings an approach for the planning and operation of such energy hubs by coping with this challenge by deploying a Battery-based Energy Storage System (BESS). With the BESS integration, it is expected to minimize utilization and overall energy costs, preventing infrastructure upgrades, and enhancing the integration of renewable energy resources.
 This approach sizes a stationary energy storage system with lithium-ion technology batteries through a co-optimization of the planning and operation stages, integrated in an electrical installation that will implement fast charging stations. This sizing is a result of an optimization based on the interior point algorithm, where the objective is to minimize the costs of maintenance, operation, and installation of a BESS, while properly modelling the different resources such as the BESS, the charging station and EV charging and PV generation.

Optimal scheduling of BESS for congestion management considering reliability and OTS

The present research explores transmission congestion control in Battery based Energy Storage Systems (BESS) integrated grids using Optimal Transmission Switching (OTS). The oversimplification of underlying assumptions in prior OTS-based congestion management strategies leads to excessive line switching which is undesirable. Hence, a novel reliability constrained alternating current optimal power flow (AC-OPF) based OTS is presented to mitigate the line congestion in the BESS integrated grids. The suggested approach is solved to acquire the OTS solution for 24 h time frame with the primary goal of minimising generating costs subjected to ac power flow equality, BESS, reliability, and transmission congestion constraints. The proposed approach makes use of the integer variable to represent the transmission switching operations and is solved by using a mixed-integer non-linear programming (MINLP) solver. The inclusion of reliability constraints ensures the system reliability by limiting the value of load failure probability to a pre-specified limit. The feasibility of this approach is validated by testing it on a standard IEEE test system for various loading scenarios. The results exhibit that coordinated charging/discharging of BESS supports reducing the number of lines switchings required for congestion management compared to the existing approaches, substantially.

Battery-Based Energy Storage System from Solar Panels Utilization for Worship House Applications in Carang Wulung Village

Due to the topography and hilly terrain in the area, there is still a hamlet in Carang Wulungvillage that does not have a low-voltage electricity network from PLN, namely Gondang Hamlet. As a result of this limitation, there is a place of worship (musala) in Gondang Hamlet that has not been adequately taken care of and is considered unfit when viewed from the availability of electricity. The community in this predominantly Muslim hamlet experiences lighting problems during congregational prayers at night until dawn. The lack of a power source for speakers also prevents the use of speakers to mark the prayer times. Based on this background, a battery-based energy storage system from solar panels was developed for this community service activity. The stages of community service activities carried out include surveying the needs for the installation of solar panel and battery installation systems, assembling several system components, testing the laboratory-scale system, installing the system that has been assembled and tested, and conducting socialization and training for residents on the use, operation, and maintenance of the solar panel system. The residents are happy that this community service activity has been carried out because they can worship comfortably at any time without relying on PLN as a source of electricity

Coordinating Multiple Resources for Emergency Frequency Control in the Energy Receiving-End Power System With HVDCs

High voltage transmission lines, especially high-voltage direct-current (HVDC) lines, have been built around the world linking the populated load centers with major green energy resources at a far distance or offshore. Any major faults on these long-distance point-to-point connections may lead to large power unbalances and cause frequency security problems in the regional grid at the energy receiving end. In this work, an emergence frequency control strategy is proposed, utilizing available frequency response resources at the receiving end, including synchronous generators, local wind farms, battery-based energy storage systems, pumped hydro storages, etc. First, a system frequency response model consisting of multiple resources is formulated, including the secondary frequency drop caused by wind farms. Then, the order of the model is reduced to derive the analytical expressions of the frequency nadirs and the quasi-steady-state frequency. Finally, the emergency frequency control decision method is proposed with the consideration of the system frequency security constraints. The proposed strategy is studied and analyzed with simulations on a generalized model of Huadong regional grid in eastern China.

On the State-of-the-Art of Solar, Wind, and Other Green Energy Resources and Their Respective Storage Systems

In this article, we provide a brief overview of solar photovoltaic and thermal energy, wind turbines with vertical and horizontal axes, and other sustainable energy production systems as well as energy storage systems. In some remote areas away from easy access to electricity and fresh water, a self-contained and self-sustainable off-grid energy production and storage farm is very much needed. In this so-called sustainable energy farm, solar photovoltaic and solar thermal energies along with wind energy can be harvested and stored in a secured battery warehouse with mobile wireless surveillance systems with unpredictable coherent motions. In this battery-based energy storage system, special fire protection measures must also be employed with heat sensors and early detection and alarm systems. The alarm system will be connected to both local fire stations and automated fire extinguishing systems. A subsequent paper will present the details of this wireless fireproof alarm powered by sustainable energy resources. The main purpose of this paper is to document, provide references, and inform about the state-of-the-art achievements in the field of renewable energies, particularly, solar, wind, and other green energy resources.

Multi-Agent-Based Controller for Microgrids: An Overview and Case Study

A microgrid can be defined as a grid of interconnected distributed energy resources, loads and energy storage systems. In microgrid systems containing renewable energy resources, the coordinated operation of distributed generation units is important to ensure the stability of the microgrid. A microgrid needs a successful control scheme to achieve its design goals. Undesirable situations such as distorted voltage profile and frequency fluctuations are significantly reduced by installing the appropriate hardware such as energy storage systems, and control strategies. The multi-agent system is one of the approaches used to control microgrids. The application of multi-agent systems in electric power systems is becoming popular because of their inherent benefits such as autonomy, responsiveness, and social ability. This study provides an overview of the agent concept and multi-agent systems, as well as reviews of recent research studies on multi-agent systems’ application in microgrid control systems. In addition, a multi-agent-based controller and energy management system design is proposed for the DC microgrid in the study. The designed microgrid is composed of a photovoltaic system consisting of 30 series-connected PV modules, a wind turbine, a synchronous generator, a battery-based energy storage system, critical and non-critical DC loads, the grid and the control system. The microgrid is controlled by the designed multi-agent-based controller. The proposed multi-agent-based controller has a distributed generation agent, battery agent, load agent and grid agent. The roles of each agent and communication among the agents are designed properly and coordinated to achieve control goals, which basically are the DC bus voltage quality and system stability. The designed microgrid and proposed multi-agent-based controller are tested for two different scenarios, and the performance of the controller has been verified with MATLAB/Simulink simulations. The simulation results show that the proposed controller provides constant DC voltage for any operation condition. Additionally, the system stability is ensured with the proposed controller for variable renewable generation and variable load conditions.

Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model

Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the jellyfish-Ex-AI model was applied. A very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 were observed with the Stacked-LSTM model, demonstrating our proposed methodology’s utility.

Full-deployed energy management system tested in a microgrid cluster

Under a hierarchical structure perspective, the integration of tools like the internet of things, cloud computing, and machine learning into a microgrid real-time energy management system involves superior capabilities like an autonomous and scalable design, massive storage capabilities, real-time information analysis and processing, and security issues control, to mention a few. This paper evaluates most of them using a cloud-based real-time energy management system integrated into a real-life hardware-in-the-loop testbed. The proposed cloud-based system is tested by solving an economic power dispatch problem including the equalization of the multiple battery-based energy storage systems interacting within a multi-microgrid environment. The test assessment combined reviewing and running microgrid models, incorporating these models into a real-life power-hardware-in-the-loop unit, linking the testbed to a cloud server, and merging the energy management system with on-demand computing tools, primarily machine learning and the internet of things. As established by the experimental evidence, this paper cites the benefits of combining machine learning techniques and internet of things tools with a scalable and autonomous real-life cloud-based energy management system architecture to improve the framework's functionality, enhance the energy forecasting for generation and usage, and cut down the price paid to the service provider.

Investigating the influence of lithium-ion batteries degradation on the parameters of the electric power storage system

This article deals with the use of a battery-based energy storage system (ESS) to ensure the required power output of power plants (PP) based on renewable energy sources (RES) integrated into the electric power system (EPS). A model of a lithium-iron-phosphate battery-based ESS has been developed that takes into account the calendar and cyclic degradation of the batteries, and the limitations of the conversion subsystem. The nominal capacity and power of the ESS is proposed to be chosen based on two levels of tolerances: the preset range of RES-based power output and the relative period of deviation from the committed power. When choosing the ESS parameters, the features of its operation, as well as restrictions on the part of the EPS, were taken into account. The developed method was applied to the EPS model including solar power plants (SPP) and wind power plants (WPP). In the end of the article, the obtained results are analyzed and the effect of the ESS operation on its residual capacity and service life is shown.

Performance Analysis of a Relift Luo Converter-Derived Dual-Output DC to DC Converter for Microgrid Applications

A microgrid typically maintains multiple voltage bus bars with AC or DC or both. The various subsystems participating in the microgrid are connected to the appropriate voltage bus bars. Renewable energy sources like the wind energy or the solar photovoltaic energy may also be integrated to the microgrid. A battery-based energy storage system is also usually required. In this work, a dual-output DC to DC converter derived from the double lift or the relift Luo converter is proposed and validated. The proposed system uses a solar photovoltaic source and delivers a high-voltage DC output to deliver power to the high-voltage DC bus bar. A battery of medium voltage is connected across the second output of the dual-output converter. The proposed idea is validated using modeling and simulations in the MATLAB Simulink environment and an experimental prototype.

A Transferred Recurrent Neural Network for Battery Calendar Health Prognostics of Energy-Transportation Systems

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications.

DC Bus Voltage Stabilization and SOC Management Using Optimal Tuning of Controllers for Supercapacitor Based PV Hybrid Energy Storage System

The global initiative of decarbonization has led to the popularity of renewable energy sources, especially solar photovoltaic (PV) cells and energy storage systems. However, standalone battery-based energy storage systems are inefficient in terms of the shelf and cycle life, reliability, and overall performance, especially in instantaneous variations in solar irradiance and load. In order to overcome this, a combination of a supercapacitor and battery-based hybrid energy storage system (HESS) is considered as an emerging and viable solution. The present work proposes an optimally tuned tilt-integral (TI) controller to develop an efficient power management strategy (PMS) to enhance the overall system performance. The controller parameters are tuned by optimization of the time-domain design specifications using a gradient-free simplex search technique. The robustness of the proposed TI controller is demonstrated in comparison to PI and fractional-order PI (FOPI) controllers. Furthermore, extensive experimentation was carried out to analyze the effectiveness of the proposed approach for DC bus voltage stabilization and state-of-charge (SOC) management under varying operating conditions such as solar irradiance, load, temperature, and SOC consumption by battery.

Multi-layer state of health balancing control for a battery-based energy storage system to extend cycle life based on active equalization circuits

State of health (SoH) imbalance causes capacity waste and cycle life reduction of the battery-based energy storage systems (BESS), which demands SoH balancing control of the parallel-connected packs and the series-connected cells of the BESS. This study proposed a multi-layer SoH balancing control to extend the cycle life of the BESS. For the series-connected battery cells, active equalization circuits based on the fly-back converter are used to exchange energy between cells and control the depth of discharge (DoD) of cells to achieve the SoH balance. For the parallel-connected packs, SoH is equalized by distributing the output power based on the SoH balancing principle and the minimum power distribution constraints of packs caused by the bidirectional balancing current among the series-connected cells are considered. Because the proposed method achieves the SoH balancing by utilizing the active equalization circuits to transfer the energy between the cells with different SoH, it can achieve higher capacity and energy utilization efficiency and lower heat release compared with the passive equalization typology that dissipates the energy. The effectiveness of the proposed method is verified by a case study based on a 3-packs-15-cells BESS. Simulation results show that this method can prolong the cycle life of BESS by about 52.9% compared with the active SoC balancing method using the same fly-back converter and 18.3% with a passive multi-layer SoH balancing method, respectively.

Comparison of the performances of heuristic optimization algorithms PSO, ABC and GA for parameter estimation in the discharge processes of Li-NMC battery

The effects of the studies performed for the development of cells, which are the fundamental components of electrochemical battery units are felt in many different areas such as electric rail transportation systems, battery-based energy storage systems, battery units in electric vehicles, and energy storage units for individual use. For this goal, studies conducted by other searchers in the similar field have been investigated. In this paper, optimization techniques are used to guess the model parameters with major righteousness using the electrical equivalent circuit model of the battery. The discharge processes of the 18650 cylindrical type 2000 mAh Li-NCM battery cell with 1 A pulsed constant current at 25 ºC have been investigated. The real parameter values obtained have been transferred to the electrical equivalent circuit model. The open circuit voltage is determined as a functional term depending on the state of current supply level by using the curve fitting method in the Matlab. Studies have been carried out on particle swarm optimization algorithm, artificial bee colony algorithm, and genetic algorithm to estimate the battery output terminal voltage by using the open circuit voltage. Comparisons have been made and differences have been analyzed between the technics by using different statistical methods of true error values, the correct prediction ability, and response speed. As a result, the optimization method that makes the most accurate estimation has been determined.

A Balancing Current Ratio Based State-of-Health Estimation Solution for Lithium-Ion Battery Pack

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a feasible health assessment strategy for battery pack is important but challenging due to the joint requirements of the computational burden, modeling cost, estimation accuracy, and battery equalization. This article proposes a balancing current ratio (BCR) based solution to achieve reliable state-of-health (SoH) estimations of all series-connected cells within a pack while significantly reduce the overall reliance on cell-level battery models. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy to effectively estimate SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance model is used for modeling, the typical estimation error of our proposed solution can still be bounded by only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also significantly improved.

Techno-socio-economic-environmental estimation of hybrid renewable energy system using two-phase swarm-evolutionary algorithm

A multi-objective optimal sizing model for an off-grid hybrid renewable energy system (HRES) consisting of solar photo-voltaic (PV), wind turbine (WT), diesel generator (DG) and lead-acid battery-based energy storage (ES) system is developed including techno-socio-economic and environmental considerations. The problem is formulated using six objectives; levelized cost of energy (LCOE), renewable fraction (RF), loss of power supply probability (LPSP), human development index (HDI), job creation (JC) and emission curtailment (EMC). To solve this real-world problem with complex practical constraints, a hybrid two-stage PSO-DE optimization (HPSODE) algorithm is constructed by connecting the improved variants of PSO and DE in series. The exploration capability of swarm intelligence is synthesized with the exploitation potential of classical evolutionary concepts, to achieve better performance with least dependence on algorithmic tuning parameters. A fuzzy satisfaction module (FSM) is appended to identify the solution with the highest possible attainment level for all the considered objectives. The selected optimization goals have a significant bearing on the optimal design, some are contradictory while others are similar in nature; therefore, impact analysis is presented. For the minimum cost solution, attainment of LCOE is 100%, HDI attainment is 0.27%, JC is 4.04%, emission curtailment is 6.29%, LPSP attainment is 91.25% and attainment of RF is 92.33%. On the other hand, when all six objectives are optimized, LCOE is attained by 68.61%, HDI by 92.97%, JC by 84.04% and EMC goal is satisfied by 78.98% while attainment of both LPSP and RF is 100%. For the selected location, the best configuration of HRES is PV-WT-ES, with total capacity of 175.44 kW. The levelized cost is 0.0779 $/kWh, human development index (HDI) is 0.6454, 0.2711 jobs are created and annual emission curtailment is 290147.28 kg, whereas unmet power is zero. Well distributed Pareto-optimal solutions are obtained, which offer multiple options to the designer. The results of the proposed HPSODE-FSM approach are compared and corroborated with results from literature and also with other state-of-the-art algorithms.

Capacities prediction and correlation analysis for lithium-ion battery-based energy storage system

Lithium-ion battery-based energy storage system plays a pivotal role in many low-carbon applications such as transportation electrification and smart grid. The performance of battery significantly depends on its capacities under different operational current cases, which would be affected and determined by its component parameters interacting with one another. Due to the complex interdependency of electrical, chemical, and mechanical dynamics within a battery, it is a key but challenging issue to predict battery capacities under various current cases and analyze correlations of key parameters within a battery. This paper proposes an XGBoost-based interpretable machine learning framework, which fills the gap of predicting and analyzing how battery capacities under different current rates with respect to battery component parameters of interest. The parameter importance ranking is obtained by using the Gini index within the XGBoost model, while the correlations of all parameter pairs are quantified by using the predictive measure of association. The proposed framework is tested in two popular lithium-ion battery types with three various current levels. Illustrative results show that the proposed XGBoost-based framework is able to not only produce satisfactory capacity prediction performance but also provide reliable importance as well as correlation quantifications of involved battery component parameters. These could promote the prediction and analysis of battery capacities under different current rates, further benefitting the monitoring and optimization of battery management for wider low-carbon applications.