Battery Energy Storage System Research Articles

Integration and control of grid‐scale battery energy storage systems: challenges and opportunities

Integration and control of grid‐scale battery energy storage systems: challenges and opportunities

An enhanced sensitivity‐based combined control method of battery energy storage systems for voltage regulation in PV‐rich residential distribution networks

AbstractCommercial off‐the‐shelf (OTS) photovoltaic systems coupled with battery energy storage units (PV‐BES) are typically designed to increase household self‐consumption, neglecting their potential for voltage regulation in low voltage distribution networks (LVDNs). This work proposes an enhanced sensitivity‐based combined (ESC) control method for voltage regulation, using BES control as level 1 and reactive power compensation as level 2. A centralized controller manages charging/discharging intervals, while local inverters handle real‐time power rates and reactive power, ensuring effective LVDN voltage regulation. The BES set points are obtained concerning the measured local bus voltage and according to enhanced sensitivity coefficients. The enhancement algorithm ensures that the full capacity of BES is utilized and that there is adequate capacity during charging and discharging time intervals. The proposed method, tested on 8‐bus and 116‐bus LV test feeders, outperforms OTS and an adaptive decentralized (AD) control method by completely preventing overvoltage issues, minimizing various changes in the direction of BES power, and reducing voltage deviation without significantly affecting consumers' grid dependency.

Multi-Scale Risk-Informed Comprehensive Assessment Methodology for Lithium-Ion Battery Energy Storage System

Lithium-ion batteries (LIB) are prone to thermal runaway, which can potentially result in serious incidents. These challenges are more prominent in large-scale lithium-ion battery energy storage system (Li-BESS) infrastructures. The conventional risk assessment method has a limited perspective, resulting in inadequately comprehensive evaluation outcomes, which impedes the provision of dependable technical support for the scientific appraisal of intricate large-scale Li-BESS systems. This study presents a novel Li-BESS-oriented multi-scale risk-informed comprehensive assessment framework, realizing the seamless transmission of assessment information across various scales. The findings from a previous smaller-scale analysis serve as inputs for a larger scale. The evaluation process of this method is more scientifically rigorous and yields more comprehensive results compared to assessment technologies just relying on a single perspective. By utilizing the proposed comprehensive assessment methodology, this study utilized the emergency power supply of nuclear power plants (NPPs) as an application scenario, demonstrating the complete implementation process of the framework and conducting a comprehensive assessment of Li-BESS feasibility as an emergency power source for NPPs. Our findings propose a novel paradigm for the comprehensive assessment of Li-BESS, which is expected to serve as a scientific foundation for decision-making and technical guidance in practical applications.

Novel state of charge estimation method of containerized Lithium–Ion battery energy storage system based on deep learning

State of charge (SOC) is a critical indicator for lithium–ion battery energy storage system. However, model-driven SOC estimation is challenging due to the coupling of internal charging and discharging processes, ion diffusion, and chemical reactions in the electrode materials. These factors result in frequent changes in their internal characteristics. Therefore, we devise a novel deep learning SOC estimation method based on the attention mechanism (A) and convolutional neural networks-long short term memory (CNN–LSTM) network model. The CNN layers are employed to extract the critical features of the battery dataset. The LSTM layers are utilized to capture the long-term dependencies in the time series data. The attention mechanism can assign attention weights to different features within the output of the convolution layer. The A–CNN–LSTM method not only amplifies the positive influence of critical knowledge but also provides a global reference. The comparative experiments of three datasets and ablation study are carried out. The root mean square error, mean absolute error, and mean absolute percentage error obtained from the A–CNN–LSTM model decreased by 32.75 %, 45.88 %, and 53.36 %, respectively, compared with the best results obtained from the existing CNN, LSTM, gate recurrent unit (GRU), CNN–LSTM, and CNN–GRU models. The proposed model demonstrates greater stability and consistency than other models, underscoring its efficacy in SOC estimation.

Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is employed.Two scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for small-scale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary algorithms.This study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

Transition from Electric Vehicles to Energy Storage: Review on Targeted Lithium-Ion Battery Diagnostics

This paper examines the transition of lithium-ion batteries from electric vehicles (EVs) to energy storage systems (ESSs), with a focus on diagnosing their state of health (SOH) to ensure efficient and safe repurposing. It compares direct methods, model-based diagnostics, and data-driven techniques, evaluating their strengths and limitations for both EV and ESS applications. This study underscores the necessity of accurate SOH diagnostics to maximize battery reuse, promoting sustainability and circular economy objectives. By providing a comprehensive overview of the battery lifecycle—from manufacturing to recycling—this research offers strategies for effective lifecycle management and cost-effective, environmentally sustainable secondary battery applications. Key findings highlight the potential of second-life EV batteries in ESSs. The integration of the considered diagnostic methods was shown to extend battery lifespan by up to 30%, reduce waste, and optimize resource efficiency, which is crucial for achieving circular economy objectives. This paper’s insights are crucial for advancing sustainable energy systems and informing future research on improving diagnostic methods for evolving battery technologies.

Design and implementation of a control system for multifunctional applications of a Battery Energy Storage System (BESS) in a power system network

This work proposes a design and implementation of a control system for the multifunctional applications of a Battery Energy Storage System in an electric network. Simulation results revealed that through the suggested control approach, a frequency support of 50.24 Hz for the 53-bus system during a load decrease contingency of 350MW was achieved. Without the control system, the frequency was 50 .38Hz. Such a high frequency if not addressed, may result in a loss of synchronization among interconnected synchronous machines which could result in a decrease in voltage stability of the studied network. Besides, a reduction of about 2.05 MW in the active power losses was accomplished and a reactive power support of 3.63Mvar was realised. Thus, through the proposed strategy, Battery energy storage system has been enabled for frequency regulation, power loss minimization and voltage deviation mitigation resulting in an overall enhancement of the power quality of the electric power delivered in the studied networks.

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

Optimal sizing of battery energy storage system (BESS) for multiple applications using regression analysis and deep sleep optimizer algorithm

The multifunctional applications of battery energy storage system in a power system network will reduce the significant slack time of use as evident in many single-based applications. In order to deploy BESS for multiple applications, it is of utmost importance that the optimal size for the desired multiple functions, firstly be determined. This work proposes a novel methodology for the optimal sizing of battery energy storage system for frequency support, power loss minimization and voltage deviation mitigations. The suggested sizing methodology takes into account the level of penetration of the renewable energy sources in the power network. Regression analysis is used for mathematical formulations while Deep Sleep Heuristic algorithms in MATLAB environment is used for the optimization process for BESS optimal size. The robustness of the proposed method was tested by using IEEE modified 39-bus system. Simulation results show that with the BESS optimal size integrated into the network, voltage deviations were mitigated by about 20 % and power losses were reduced from 65.3 MW to 59.68 MW. Also, the system frequency nadir during the outage of the largest single generating, was sustained at 59.60 Hz whereas, without BESS it was 59.15 Hz. This is critical because such a frequency decline may activate the underfrequency load shedding relays.

Optimizing Energy Management and Sizing of Photovoltaic Batteries for a Household in Granada, Spain: A Novel Approach Considering Time Resolution

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

Optimal scheduling of battery energy storage system operations under load uncertainty

This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an optimal battery usage schedule that minimizes the worst-case energy costs considering peak load costs. The model leverages deep-learning-based probabilistic forecasting in the construction of the ambiguity set. Specifically, we develop a Deep Autoregressive Recurrent Networks model to generate a probabilistic forecast of energy loads over a time horizon. The output of the forecasting model is then used to construct a marginal-moment ambiguity set for the distributionally robust optimization model. To solve the proposed model, we establish a closed-form characterization of the optimal second-stage objective function value. Leveraging this closed-form expression and using second-order conic duality, we derive an exact single-level mixed integer second-order conic reformulation of the problem. Extensive computational experiments, conducted on a real dataset, demonstrate the value of our proposed model and the effectiveness of the resulting battery schedule. The results show that the proposed model outperforms several benchmarks, including two-stage stochastic programming. Furthermore, the accuracy of the load forecast significantly impacts the effectiveness of the optimal battery schedule in eliminating peak loads by achieving up to 18% reduction in the maximum energy load.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Practical solutions for microgrid energy management: Integrating solar forecasting and correction algorithms

Nowadays, the shift towards renewable energies is happening at an exponential pace. Microgrids are an integral part in integrating renewable energies into the global energy mix. Due to the volatility of renewables, such as solar or wind, proper energy management is needed to avoid generation or load curtailment. This issue is addressed throughout the literature using a wide variety of strategies, ranging from classical linear to nature inspired metaheuristic optimization algorithms. This paper goes beyond the simulation phase of different optimization algorithms and addresses the optimal energy management problem by proposing a novel framework to integrate optimization strategies into the daily operation of microgrids. The proposed framework showcases 3 practical methods for controlling the distributed generators, as well as a communication scheme which facilitates the data transfer between the machine running the optimization strategy and the energy management system of the microgrid. The framework is demonstrated on a small-scale islanded microgrid setup, located at the Technical University of Cluj-Napoca. The microgrid setup consists of a photovoltaic array, a battery energy storage system and two dispatchable generators. A two-stage optimization strategy is used, consisting of a day-ahead stage, which uses the solar forecast provided by the Global Forecast System, prior to each day, to issue a working plan for the dispatchable generators, and an intra-day stage, which uses hourly solar forecasts, issued during the day, to adjust the operation of the microgrid and minimize the error between the day-ahead solar forecast and the actual weather conditions. Mixed integer linear programming is used to solve the optimization problem and validate the correct operation of the proposed framework over two case studies. The results show that the microgrid works as intended, in both scenarios, as well as the benefits of using an intra-day correction stage.

Takagi-Sugeno fuzzy gain controller for Vehicle-to-Grid (V2G) load frequency control

Load Frequency Control (LFC) has gained more importance with the introduction of deregulated Renewable Energy Sources (RES) connectivity with the grid. Electrical Vehicles (EVs) can feed electricity back into the grid in Vehicle-to-Grid (V2G) mode to maintain stability. However, the increasing number of EVs penetrating the grid causes frequency instability in the power system. If required, EVs may utilize bi-directional chargers to transfer power back to the grid in the V2G mode while they are charging or in a grid-connected state, restoring the frequency instability of the grid. The frequency restoration response time is important to reset the grid frequency fluctuations in the shortest time possible to avoid shutting down the power system. This paper presents a Takagi-Sugeno (T-S) fuzzy linear output controller for LFC in two-area systems with tie-line control. This work models EV batteries as a single lump of large-capacity battery energy storage systems. The EV's battery system provides ancillary power to the two-area power system to reset it to a steady state after a load disturbance. The T-S fuzzy controller's linear output dependency on its inputs enables it to respond efficiently to load variations in the nonlinear two-area power systems. The proposed controller parameters are evaluated from stability analyses and its robustness is tested with sensitivity analysis. It is compared with other fuzzy controllers, and it demonstrates a fast-settling time and reduced frequency deviation response.

Experimental investigation of adaptive multi-generalized integrator-based controller for electronically interfaced hybrid microgrid system

This research work explores the conceptualization and evaluation of a hybrid microgrid that taps into the potential of solar photovoltaic systems, wind energy conversion systems, and battery energy storage systems. In practice, nonlinear loads pose power quality issues that significantly impact the performance of hybrid microgrids. To tackle these challenges, an adaptive multi-generalized integrator (MGI) filter is proposed. This filter extracts the fundamental signal from the distorted utility grid voltages. It features a pre-filter with a DC-off set rejection loop, effectively minimizing the DC-offsets from the distorted grid voltages. This facilitates the distribution of active power generated by the hybrid microgrid's sources while simultaneously addressing various power quality problems. In addition, a dynamic power management control approach enhances grid resilience by operating the hybrid microgrid in grid-connected and islanding modes. It provides uninterrupted and high-quality power supply to consumers during grid outages. The performance of this filter is comprehensively assessed through numerical simulations in MATLAB®/Simulink® environment. A real-time laboratory prototype is developed with WAVECT® WUC300 FPGA controller, demonstrating the practical application of the proposed filter. It ensures the minimum DC-offsets of 0.02V, fast convergence, and minimum oscillations. The grid synchronization and power quality index of the grid currents simultaneously achieve a THD of 2.82 %, complying with the IEEE-1547 and IEEE-519 standards. Importantly, the proposed adaptive-MGI filter outperforms the conventional SOGI and LMF filters in terms of nonlinear load tracking capabilities, with a response speed of less than 200μs, thereby highlighting its practical relevance and importance.

Multi-objective optimization of HESS control for optimal frequency regulation in a power system with RE penetration

Concerns over fossil fuel emissions and their hazardous effects have led to a shift away from conventional power plants and focus more on the expansion of renewable energy sources. This shift has resulted in reduced inertia and resulted in poor frequency control within electrical power systems. Hybrid Energy Storage Systems (HESS) have been proposed as an effective solution to enhance frequency stability and address the reduced inertia issue. This research evaluates three distinct control models for HESS, Incorporating Supercapacitor Energy Storage (SCES) and Battery Energy Storage Systems (BESS). To optimize the control parameters with the best objectives, all possible sets of objectives with four different optimization algorithms are studied. The three control models considered for the HESS incorporate Virtual Synchronous Generator (VSG) or Virtual Inertia (VI) control with independent or simultaneous optimization of control parameters. The performances of the three control models are evaluated based on three test scenarios incorporating uncertainties, reduced inertia, and uniform and random load disturbances. The findings indicate that the Independently Optimized Virtual Synchronous Generator HESS (IO VSG-HESS) achieves the best settling time post-contingency but offers the least improvement in frequency nadir with an average of 0.31 %. Conversely, the Simultaneously Optimized Virtual Inertia And Virtual Synchronous Generator Controlled HESS (SO VI-VSG-HESS) excel in mitigating small frequency fluctuations with an average improvement in frequency standard deviation of 87.65 %. The Simultaneously Optimized Virtual Synchronous Generator Controlled HESS (SO VSG-HESS) provides the best frequency nadir, with an average improvement of 0.63 %, but with a slight increase in settling time.

Distributed battery energy storage systems for deferring distribution network reinforcements under sustained load growth scenarios

Energy storage systems can be leveraged in electricity distribution network planning as mitigation alternatives to traditional grid reinforcements if they are strategically installed and operated to reduce congestion and voltage limit violations. This paper examines the technical and economic viability of distributed battery energy storage systems owned by the system operator as an alternative to distribution network reinforcements. The case study analyzes the installation of battery energy storage systems in a real 500-bus Spanish medium voltage grid under sustained load growth scenarios. The results show that, in general, dedicated battery energy storage systems are only a cost-efficient alternative in distribution system planning under very specific conditions, such as when low load growth rates are expected. Nevertheless, they are only required for peak shaving a few days per year. For the analyzed case study, the recoverable portion of their total cost through deferral of distribution system upgrades is higher than the fraction of cycles required for peak shaving under all sustained load growth scenarios. Therefore, it is also explored if mobile battery energy storage systems, capital grants, and revenue stacking can enable battery energy storage systems to become an efficient distribution system planning alternative.

Home energy management system for residential fuel cell-based combined heat and power system

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings

The implementation of BESS (battery energy storage systems) and the efficient optimization of their scheduling are crucial research challenges in effectively managing the intermittency and volatility of solar-PV (photovoltaic) systems. Nevertheless, an examination of the existing body of knowledge uncovers notable deficiencies in the ideal arrangement of energy systems' timetables. Most models primarily concentrate on a single aim, whereas only a few tackle the intricacies of multi-objective scenarios. This study examines homes connected to the power grid equipped with a BESS and a solar PV system. It leverages four distinct reinforcement learning (RL) algorithms, selected for their unique training methodologies, to develop effective scheduling models. The findings demonstrate that the RL model using Trust Region Policy Optimization (TRPO) effectively manages the BESS and PV system despite real-world uncertainties. This case study confirms the suitability and effectiveness of this approach. The TRPO-based RL framework surpasses previous models in decision-making by choosing the most optimal BESS scheduling strategies. The TRPO model exhibited the highest mean self-sufficiency rates compared to the A3C (Asynchronous Advantage Actor-Critic), DDPG (Deep Deterministic Policy Gradient), and TAC (Twin Actor Cretic) models, surpassing them by ∼ 3 %, 0.72 %, and 3.5 %, correspondingly. This results in enhanced autonomy and economic benefits by adapting to dynamic real-world conditions. Consequently, our approach was strategically designed to deliver an optimized outcome. This framework is primarily intended for seamless integration into an automated energy plant environment, facilitating regular electricity trading among multiple buildings. Backed by initiatives like the Renewable Energy Certificate weight, this technology is expected to play a crucial role in maintaining a balance between power generation and consumption. The MILP (Mixed Integer Linear Programming) architecture achieved a self-sufficiency rate of 29.12 %, surpassing the rates of A3C, TRPO, DDPG, and TAC by 2.48 %, 0.64 %, 2 %, and 3.04 %, correspondingly.

Leveraging supercapacitors to mitigate limitations and enhance the performance of battery energy storage systems: A simulation and experimental validation

The importance of supercapacitors has grown significantly in recent times due to several key features. These include their superior power density, faster charging and discharging capabilities, eco-friendly nature, and extended lifespans. Battery Energy Storage Systems (BESS), on the other hand, have become a well-established and essential technology in the field of energy storage. However, BESS still faces challenges such as lower power density, limited cycle stability, and potential chemical hazards. Additionally, batteries perform poorly during transient events (sudden changes in power) due to their larger time constants, which can also shorten their lifespan. By strategically combining supercapacitors with BESS, their combined strengths can optimize battery performance. This study explores the role of supercapacitors in enhancing battery performance through both simulation and experimentation. The results obtained experimentally matched with simulated results.