Battery Energy Storage Research Articles

Data-informed state of health estimation for maritime lithium-ion battery systems using an ensemble of simple linear models

Electric ships utilising batteries for onboard energy storage have increased in numbers in recent years. One safety aspect of such ships is that the batteries have sufficient capacity at all times during operation. It is therefore necessary to monitor the battery capacity and to display a measure of this to the navigator on the bridge. Classification societies require that the state of health from the BMS shall be verified by an independent method, and this is often obtained by regular physical tests. In this paper, a data-driven method based on sensor data is presented as an alternative approach to verifying the state of health. The method presented in this paper offers significant advantages in that it is a snapshot method, and that it does not require training data. It uses an ensemble of simple linear model based on Coulomb counting and it is tried out on both laboratory cycling data and operational data from several battery systems onboard ships in normal operation. Results are quite good on lab data, and promising on operational data, but proper validation of the approach is difficult without longer time-histories of operational data.

Optimization of photovoltaic and battery energy storage configuration utilizing the JAYA algorithm

To optimize the capacities and locations of newly installed photovoltaic (PV) and battery energy storage (BES) into power systems, a JAYA algorithm-based planning optimization methodology is investigated in this article. For this purpose, a series of mathematical models with constraint conditions are put forward to describe the dynamic properties of PVs and BES systems. Then, a general two-level planning model for maximizing the benefits of society is employed by introducing objective functions at the investment and operational levels from comprehensive influencing factors under different companies. To determine the optimal locations and capacities for configuring renewable energy sources, the proposed planning framework is solved using the JAYA algorithm. Finally, the effectiveness and reliability of the proposed configuration method are validated using an Institute of Electrical and Electronics Engineers (IEEE) 24-bus system with PVs and BES systems. Comparing the results of the various cases, it is obvious that the JAYA-based two-level planning optimization method can find the optimal configuration with minimum cost in shorter convergence times. Hence, the configuration strategy determined via the planning optimization method using the JAYA algorithm offers valuable guidance for the installation capacities and layouts of PVs and BES systems in power systems, which underscores their practical significance in energy management.

Integrated Energy Management in Small-Scale Smart Grids Considering the Emergency Load Conditions: A Combined Battery Energy Storage, Solar PV, and Power-to-Hydrogen System

This study introduces an advanced Mixed-Integer Linear Programming model tailored for comprehensive electrical and thermal energy management in small-scale smart grids, addressing emergency load shedding and overload situations. The model integrates combined heat and power sources, capable of simultaneous electricity and heat generation, alongside a mobile photovoltaic battery storage system, a wind resource, a thermal storage tank, and demand response programs (DRPs) for both electrical and thermal demands. Power-to-hydrogen systems are also incorporated to efficiently convert electrical energy into heat, enhancing network synergies. Utilizing the robust Gurobi solver, the model aims to minimize operating, fuel, and maintenance costs while mitigating environmental impact. Simulation results under various scenarios demonstrate the model’s superior performance. Compared to conventional evolutionary methods like particle swarm optimization, non-dominated sorting genetic algorithm III, and biogeography-based optimization, the proposed model exhibits remarkable improvements, outperforming them by 11.4%, 5.6%, and 11.6%, respectively. This study emphasizes the advantages of employing DRP and heat tank equations to balance electrical and thermal energy relationships, reduce heat losses, and enable the integration of larger photovoltaic systems to meet thermal constraints, thus broadening the problem’s feasible solution space.

SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm

ABSTRACTLithium batteries are increasingly favored for energy storage due to their high energy density, long cycle life, and robust charge and discharge rates. However, safety concerns necessitate the implementation of a battery management system (BMS) to monitor battery status, maintain energy balance, and provide failure warnings to ensure safe operation. This paper proposes an efficient BMS for high‐voltage, high‐current lithium battery energy storage. The approach leverages a multihead‐attention‐enhanced long short‐term memory (LSTM) neural network combined with an adaptive unscented Kalman filter to accurately calculate the battery's state of charge (SOC) and state of health (SOH). To improve accuracy, various factors such as temperature and internal resistance were considered. The algorithm was validated through hardware and simulation experiments, with experimental data compared to estimation results to demonstrate its precision. The findings show strong convergence and tracking capabilities, with SOC estimation presenting a maximum error of 1.5% and SOH estimation a maximum error of under 0.4%. We expect that this approach will allow for a more refined evaluation of SOC and SOH in lithium‐ion batteries, potentially improving Li‐ion battery system management.

Integrated optimization for sizing, placement, and energy management of hybrid energy storage systems in renewable power systems

Power systems reliant on renewable energy sources (RES) encounter supply-demand imbalances and stability challenges due to their inherent uncertainties. Hybrid energy storage systems (HESS) have emerged as a flexible and cost-effective solution to address these issues. This paper proposes an integrated optimization method for the capacity, location, and energy management of a HESS in RES-based power systems. The method optimizes battery energy storage system (BESS), electrolyzer (EL), fuel cell (FC), and hydrogen storage tank (HST) to minimize total costs, including power purchase, curtailment compensation, operation and maintenance (O&M), and investment costs, alongside voltage profile deviation index (VPDI) and active power loss index (APLI). A novel hybrid framework is proposed, utilizing mixed-integer linear programming (MILP) optimization at the master level and non-dominated sorting genetic algorithm II (NSGA-II) optimization at the slave level. This framework ensures that MILP provides robust solutions, while NSGA-II derives balanced optimal solutions from the optimal Pareto front. The proposed method was validated using real seasonal data from Korea and tested in IEEE 33 and IEEE 69 bus systems against various benchmark cases. Results indicate significant improvements, with VPDI enhanced by 16.00 % and 10.11 %, APLI by 15.64 % and 12.15 %, and total costs reduced by 58.67 % and 50.56 %, respectively. The sensitivity analysis demonstrated the robustness of the proposed method, showing zero sensitivity to iterations and positive correlations with increased levels of RES. Further verification against other optimization algorithms showed that the proposed method excels in cost efficiency, voltage stability, and line loss reduction, providing highly reliable results.

Distributed secondary frequency control and state of charge (SoC) balance for droop-controlled battery energy storage systems (BESSs) with a unified SoC relative variation rate

The state of charge (SoC) balance, power sharing, and frequency restoration are common control objectives of battery energy storage systems. However, the SoC balance scheme induced by the power allocation through existing droop controllers can cause the capacity parameters of battery cells to be unequal to the droop coefficient, which is the result of battery capacity degradation. Under this limitation, previous capacity-based droop controllers and the secondary controllers are no longer suitable to address the imprecise power sharing and frequency restoration caused by this problem. Therefore, a power allocation scheme based on the current SoC level ratio is designed to induce a new droop controller and ensure that the SoC simultaneously drops to 0. In order to restore frequency in a distributed manner and obtain the SoC level ratio, a distributed nominal frequency controller, SoC average estimator, and power-sharing controller are designed based on multi-agent systems in both asymptotic and finite-time manners. In the asymptotic scheme, the steady-state performance of the SoC estimator is adjustable, and power sharing and frequency restoration are zero errors. In the finite-time scheme, the influence of parameters on convergence time is well analyzed, and some conservative calculation methods are provided. Several time-domain simulation examples are designed on an improved IEEE-57 bus system to verify the distribution of asymptotic and finite-time schemes.

99.6% efficiency DC-DC coupling for green hydrogen production using PEM electrolyzer, photovoltaic generation and battery storage operating in an off-grid area

This paper presents a novel connection and control strategy for a hydrogen generation system using a proton exchange membrane electrolyzer powered by solar energy in an off-grid area without network backup. Given that, the proposed architecture is based on the indirect control of the Photovoltaic plant (achieved by removing a power-converter), the presented solution is more efficient and more reliable than the traditional scheme. Furthermore, with this innovation one can reach the same hydrogen production with smaller electrolyzers.The methodology includes detailed models of the photovoltaic panel and the electrolyzer, along with a control strategy that considers the degradation mechanisms of the electrolyzer to ensure reliable and prolonged operation. The results show that the proposed strategy keeps the operating power of the electrolyzer constant, even under variations in irradiance, thanks to energy storage in batteries. It is demonstrated that the proposed system offers efficiency above 99.6% during the analyzed period, with a 100% utilization rate of the electrolyzer, avoiding periods of inactivity and high current peaks. The study also includes simulations and experimental tests that confirm the feasibility and effectiveness of the presented solution, highlighting its advantages in terms of efficiency and investment costs compared to direct connections and other existing methods.

Improving both energetic and kinetic performances of osmotic battery for grid energy storage

Osmotic battery (OB), alternating the operation of reverse osmosis (RO) for charging and pressure-retarded osmosis (PRO) for discharging, is an emerging grid-scale energy storage system (ESS) that offers complementary advantages over other existing grid ESSs. OB utilizes osmotic pressure difference of two solutions as a media for energy storage. However, OB faces the issue of energetic-kinetic trade-off generally applicable in all the ESSs. This study aims to quantitatively analyze this trade-off in OB and to develop effective strategies to address this issue. Our analyses suggest that this trade-off can be addressed by (1) raising the initial high-salinity solution concentration (cHS,0), and (2) using more water permeable osmotic membranes with a suitable high-salinity solution. For example, increasing the cHS,0 from 1.2 M to 2.4 M increases energy density from 0.5 to >1.0 kWh·m−3 along with an enhanced power density and a stable high roundtrip efficiency (RTE) above 66 %, albeit requiring membranes with substantially improved mechanical strength. Furthermore, using high-permeance nanofiltration-type osmotic membranes with divalent high-salinity solution could improve the peak power density to above 50 W·m−2 while maintaining energetic performance. This study not only offers effective strategies to ease the energetic-kinetic trade-off but also recommends important directions for developing future osmotic membranes for OB.

Multi-Objective Model for Residential Energy Management in Context of Individual Self-Consumption

The European Union (EU) strongly urged its member states to take action to establish energy communities. Domestic consumers now have the opportunity to organize themselves to play an active and decisive role in the electricity supply chain and make a solid contribution to an ecological footprint. This work proposes residential energy management, considering renewable resources production (photovoltaic panels) and energy storage systems for individual self-consumption approach and boosting future participation in energy community interactions. A multi-objective optimization model using Biased Random Key Genetic Algorithm (BRKGA) was adapted and implemented to minimize electricity consumption costs and maximize self-consumption usage. An energy storage system with 10kW was considered to optimize electricity management. Two scenarios were proposed, one considering a residential prosumer with a photovoltaic (PV) power installed value of 3.56kWp and the other with 4.20kWp. The results showed a reduction in electricity consumption between 7%-8% and an increase in self-consumption supported by a battery energy storage system of nearly 50%. Data from an actual system were used to obtain realistic scenarios.

State of Health Estimation With Incrementally Integratable Data-Driven Methods in Battery Energy Storage Applications

State of Health Estimation With Incrementally Integratable Data-Driven Methods in Battery Energy Storage Applications

Degradation-infused energy portfolio allocation framework: Risk-averse fair storage participation

This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service electricity markets. The proposed framework attempts to address the challenge of including the rainflow algorithm for cycle counting by directly developing a closed-form of marginal degradation as a function of dispatch decisions. Further, this closed-form degradation profile is embedded into an energy portfolio allocation (EPA) problem designed for making the optimal dispatch decisions for all the batteries together, in a shared economy manner. We term the entity taking these decisions as ‘facilitator’ which works as a link between storage units and market operators. The proposed EPA formulation is quipped with a conditional-value-at-risk (CVaR)-based mechanism to bring risk-averseness against uncertainty in market prices. The proposed DI-EPA problem introduces fairness by dividing the profits into various units using the idea of marginal contribution. Simulation results regarding the accuracy of the closed-form of degradation, effectiveness of CVaR in handling uncertainty within the EPA problem, and fairness in the context of degradation awareness are discussed. Numerical results indicate that the DI-EPA framework improves the net profit of the storage units by considering the effect of degradation in optimal market participation.

A distributed double-layer control algorithm for medium voltage regulation and state of charge consensus of autonomous battery energy storage systems in distribution networks

The increasing integration of Distributed Generation (DG) based on Renewable Energy Sources (RES) in traditional distribution systems necessitates the adoption of smart grid strategies. These strategies rely heavily on energy storage actuation to manage the inherent variability of RES and ensure autonomous operation. This article presents a hierarchical digital control strategy for managing distribution power systems, utilizing Battery Energy Storage Systems (BESS) to regulate voltage amplitude and enhance overall behavior for efficient energy management. At the primary control level, operating at a minute-scale actuation rate, an integral voltage regulation loop determines the BESS power injection or consumption, refined by a fuzzy conditioning of signals that accounts for the state of charge and operational modes of each device. Laplacian and random switching secondary consensus control strategies, operating at a slower rate, ensure coordinated action among individual units. The convergence of these strategies is analytically validated using Lyapunov’s theory under idealized conditions. Extensive dynamic simulations over a 24-hour period demonstrate the proposed strategy’s effectiveness in improving power quality and coordinating voltage regulation, particularly in terms of enhancing the power factor and reducing the standard deviation of voltage and power variables over time in a coordinated manner.

Enhanced Kullback–Leibler Divergence Based Pilot Protection for Lines Connecting Battery Energy Storage Stations

In light of the challenges faced by existing protection methods when battery energy storage station (BESS) and various nonideal conditions emerge, enhanced Kullback–Leibler divergence-based time-domain pilot protection is put forward in this article. Given the discrepancy in current waveforms between internal and external faults, KLD is employed to quantify the degree of similarity between two current waveforms, enabling the accurate distinction between internal and external faults. To eliminate the asymmetry of KLD, improved KLD(IKLD) is introduced. Whereafter, a technique is proposed to convert the current sampling sequence into a probability distribution that can be utilized by IKLD. To counteract the negative influence of current transformer(CT) saturation on IKLD-based protection, the time-domain waveform features of summation current are adequately employed to develop an enhanced KLD(EKLD). Further, high and low thresholds are legitimately designed to ensure both dependability and security. The flowchart of EKLD-based pilot protection is described, and its effectiveness is extensively validated by variation in fault resistance, location, type and operating mode of BESS using PSCAD and a hardware-in-the-loop(HIL) testing platform. Additionally, proposed protection outclasses power frequency component-based current differential protection and some up-to-date presented time-domain protection methods.

Distributionally robust optimal configuration of battery energy storage system considering nodal RoCoF security constraints

The large-scale integration of renewable energy source (RES) exacerbates net load fluctuations, reduces system inertia, limits frequency response capabilities, and leads to uneven spatial distribution of inertia resources. To ensure the economic and safe operation of the system, we propose a distributionally robust optimal configuration scheme of battery energy storage system (BESS) considering nodal RoCoF security constraints to address these issues. First, we describe and model the dynamic frequency response characteristics after disturbances, derive the expressions for the system frequency nadir and maximum nodal RoCoF (MN-RoCoF). Second, we address net load uncertainty using the distributionally robust chance constrained (DRCC) approach and incorporate frequency security constraints into the BESS configuration optimization model, embedding the short-term system operation model into the long-term BESS planning. Then, to tackle the highly nonlinear MN-RoCoF constraint, we propose a divide and conquer-based support vector machine (D&C-SVM), to extract the linear relationship between the BESS virtual inertia constant and MN-RoCoF, reformulating the proposed scheme into a mixed-integer second-order cone programming (MISOCP). Finally, we conducted case studies on the IEEE 39-bus and 118-bus test systems. The results verify that the proposed configuration scheme can ensure that the RoCoF at all nodes remains below 1 Hz/s under the preset disturbances. The proposed D&C-SVM demonstrate 99 % accuracy in managing the nonlinear MN-RoCoF constraint. Moreover, the uncertainty in net load is well managed, with all chance constraints satisfied with a confidence level of over 95 %.

Data-driven scheduling of a grid-connected university campus battery energy storage system considering variable weather and energy pricing

BESS can provide many crucial grid services including voltage and frequency support, however, capital costs can hinder widespread installation. Thus this work develops a data-driven MILP approach for BESS dispatch to enhance consumer cost recovery through energy arbitrage and mitigate the barrier of high capital costs. The proposed algorithm uses location-relevant weather data, to forecast PV generation and load consumption, as well as market data to create a BESS dispatch schedule. The proposed BESS algorithm is validated on a Raspberry Pi 4b microcontroller using real-time HIL simulation for 72 h on a real-world-based university campus microgrid consisting of a 9MWh/3MW BESS and 1.4MW DC/1.1MW AC PV solar canopy. Economic analyses indicate the proposed algorithms ability to decrease system energy costs when using LMP pricing, translating in a 7.27% decrease in LCOE compared to a 3.12% to 3.20% decrease when using an RB scheduling method. Similarly, LCOE is reduced by 5.32% when using TOU energy pricing. Additionally, a sensitively analysis of PV and load forecasting error with error biases ranging from −10% to 10% indicate a resulting increase in LCOE of 0% to 0.674%. These key findings indicate increased economic viability when implementing the proposed dispatch approach.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

Experimental and numerical investigation of a composite thermal management system for energy storage battery based on air cooling

Experimental and numerical investigation of a composite thermal management system for energy storage battery based on air cooling

Cellulose/sodium alginate gel electrolyte membranes with synergistic hydrogen bonding and metal ion coordination networks for high performance aqueous zinc-ion batteries

Cellulose has outstanding potential for application in energy storage batteries due to its high temperature resistance, high electrolyte affinity, renewability, and suppression of the shuttle effect, but single cellulose membranes still suffer from problems such as inhomogeneous pore distribution and unstable three-dimensional network structure. In this study, a green and sustainable regenerative cellulose (RC)/sodium alginate (SA) gel electrolyte membrane is developed by sol-gel process, the double crosslinked network scaffold centered on Zn2+ was constructed by the synergistic hydrogen-bonding and metal ion- coordination network, the stable and uniform pore structure was also formed. The obtained RC-SA gel electrolyte membrane exhibits outstanding performance, featuring a dual crosslinked network with abundant pore structure and numerous polar groups that effectively enhance Zn2+ transport, significantly improving battery cycling performance. The corresponding RC-SA gel electrolyte membrane demonstrates high ionic conductivity (6.30 mS·cm−1) and Zn2+ transference number (0.66), leading to excellent reversible capacity (159 mA·h·g−1) and self-discharge suppression capability (maintaining 99.2 % of capacity after a 24 h standstill) in Zn//V2O5 full-cell. The coulombic efficiency and cycling stability of the Zn//Cu half-cell and Zn//Zn symmetric cell using RC-SA gel electrolyte membrane outperforms that of the glass fiber separator, highlighting its multifunctionality and potential applications.

Classification of Design Methodologies of Dual Active Bridge-Based Resonant Converters for Battery Energy Storage Systems

Optimized guidelines for the design of power converters are crucial to achieve the expected goals in terms of performance, efficiency, power density, etc. Therefore, they are the basis for industrial success or failure. Resonant converters based on Dual Active Bridges (DABs) are particularly sensitive to the design process due to their inherently nonlinear behaviour; thus, they are in the spotlight for research and development at present. Plenty of design methodologies can be found in the literature but each of them is specific to the perspective of the authors, the performed analysis, the assumptions made, and the design objectives. It is critical to understand the Key Performance Indicators (KPIs) and design methodologies of a resonant DAB converter. There is a significant lack of articles that concisely and clearly summarize this. Different design methodologies are analyzed and compared with respect to the most important KPIs, and the most relevant demos and experiences are pointed out so that designers can select the best choice for their assignment. These results will help designers understand the design methodologies and carefully choose one based on the application, analysis, and design objectives.

Thermodynamic performance analysis of retrofitting decommissioned coal-fired power plant by Carnot battery energy storage

Retrofitting decommissioned coal-fired power plants (CFPPs) to the Carnot battery (CB) with thermal energy storage (TES) could be an effective way to help the grid absorb more renewable energy. Towards the parameter matching problem in reconstructing the retired CFPP into CB, this paper proposes the feedwater temperature adjustment method and the steam parameter adjustment method to adjust the structure of the Rankine cycle. The results show that the feedwater temperature adjustment method and steam parameter adjustment method can improve the round-trip efficiency of power-to-heat (P2H) type schemes by 0.29 % to 0.95 % and 0.91 % to 0.97 %, respectively. As for CB constructed by heat pump (HP) form, the two methods can improve the round-trip efficiency by 0.09 % to 0.38 % and 0.95 % to 1.02 %, respectively. The sensitivity analysis shows that the feedwater temperature adjustment method is suitable for P2H type schemes and sensitive to variations in exhaust steam pressure. While the steam parameter adjustment method is sensitive to the heat absorption parameter on the heat source side of the matching process, a 1.36 % increase in efficiency can be achieved by regulating the thermodynamic parameters. Under combined heat and power mode, the energy utilization efficiency of CB can reach the maximum of 79.86 % (P2H) and 94.30 % (HP), respectively. At this point, two adjustment methods mentioned above can still be used to obtain efficiency gains close to the power generation model at different heat supply loads. The discounted CO2 reduction factors of different methods can reach about 0.38 t/MWh (P2H) and 0.46 t/MWh (HP) under different design conditions, respectively. This work would be expected to provide a detailed reference for the utilization of decommissioned CFPPs retrofitted to CB for renewable energy consumption.