Battery Storage System Research Articles

Data driven MCDM models for reliability-economic-environmental analysis of energy storage based autonomous micro-grid

The decreasing cost trends and favorable government policies and incentives have led to adoption of renewable energy sources (RES) worldwide. However, these sources have meteorological limitations which leads to intermittency in generation thereby raising reliability issues. Therefore, many autonomous micro-grids relying on RES sources incorporate backup technologies. In this paper, nine configurations of three prominent backup technologies — battery storage system (BSS), hydrogen storage system (HSS), and diesel generator (DG) with RES based on ten economic-reliability-environmental criteria have been analyzed to select optimal backup configuration. The used data driven model presented in this work has been carried out using BFPSO-MCS-MCDM approaches. The ranking results of three MCDM approaches (TOPSIS, EDAS, and PROMETHEE II) are aggregated to obtain the final ranking result. Furthermore, in each approach, nine sets of ranking results (using nine different criteria weights) are aggregated. To determine the nine criteria weights, three objective-based weighting methods with three subjective methods have been combined using Bayes' theory. The results of the configuration ranking show that the PV, wind turbine, and BSS configuration followed by the PV, wind turbine, and HSS configuration provides the optimal solution based on the life cycle analysis considering reliability-economic-environmental criteria. The configuration of PV, wind turbine, and BSS offers lowest around 78 % lower LOLE and configuration of PV, wind turbine, and HSS offers around 65 % lower LCOE as compared to configuration of PV and DG which shows highest LCOE, along with moderate value of other performance criteria.

Analysis of a lead-acid battery storage system connected to the DC bus of a four quadrants converter to a microgrid

The main problem found in the implementation of small microgrids where consumption is based on a certain number of loads (8,326,369 KWh total in the Canary Islands in 2017) [1] is the great variability of obtaining energy (361,1 MWh total in 2017) from renewable sources, the most important in the Canary Islands being wind and photovoltaic [2]. For these microgrids to be feasible and their power flows to be as constant as possible they must contain storage means, so that this energy can be used to stabilize the microgrid by compensating the irregularities of renewable energy sources [3], using them to perform regulation of voltage and frequency. In this sense, the batteries degrade very quickly when having to perform this type of highly dynamic efforts. The purpose of this paper is to make a model of lead-acid battery and investigate the possibilities of application that the use of these batteries could have in the field of renewable energy. Specifically in the simulation of power electronics and control of back-to-back converters that allows to analyze, in different ranges of applications in a microgrid system of Pozo Izquierdo, where the following measurements have been made.

Implementation of battery storage system in a solar PV-based EV charging station

Commercial production of solar-powered battery charging stations for electric vehicles has recently started largely as a result of the growing popularity of electric vehicle (EV) technology as well as the falling cost of photovoltaic (PV) systems. The rise in popularity of EVs may be attributed to both the progression of technology and rising awareness of environmental issues. In this work, a 400V DC bus voltage-based EV charging station is designed which is powered by both a PV system and a utility grid. Also, battery energy storage units are used to overcome the unpredictable behavior of the environment. An Energy management algorithm (EMA) has been presented to balance the power flow among PV, EVs, BESS, grid, and residential load. A sharing coefficient-based power sharing is done between the grid and battery to reduce the stress from the battery units. Then, a multi-step constant current charging method is used to charge the EVs in the charging station to maintain the proper state of charge (SOC) of EV batteries. During this study, different operating conditions are considered according to available PV power and load to validate the power management scheme in the EV charging station. The model is run using MATLAB simulation and opal-RT simulation. The obtained results have been demonstrated with an explanation and system performance verification.

Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid

This article proposes an approach to optimize energy management (EM) in a microgrid (MG) with a battery storage system (BSS) and distributed generation. The primary objective is to address the challenge of cost-effective and environmental-friendly operation of the microgrid, with a specific focus on minimizing operational cost and emission through optimal BSS utilization in the daily schedule. Balancing economic and environmental factors through optimization poses a complex problem, which is addressed here by applying the slime mould algorithm (SMA) along with the weighted sum and fuzzy decision-making method. Three scenarios are considered to demonstrate the MG's EM. In Scenario A, SMA outperforms differential evolution (DE), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA) by reducing operational cost by 17.59 %, 8.19 %, and 9.84 %, respectively, and achieves better emission reduction with reductions of 4.93 %, 15.74 %, and 9.91 % compared to DE, SCA, and GOA, respectively. SMA also dominates DE, SCA, and GOA in other scenarios, producing superior Pareto-optimal solutions. Additionally, comparing the Pareto-optimal results achieved with SMA, Scenario C outperforms Scenarios A and B by achieving reductions of 25.47 % and 35.20 % in operational cost, respectively, along with a corresponding 6.4 % and 36.26 % decrease in emission, highlighting the superior performance of Scenario C.

Enhancing Virtual Inertia Control in Microgrids: A Novel Frequency Response Model Based on Storage Systems

The integration of renewable resources in isolated systems can produce instability in the electrical grid due to its intermintency. In today’s microgrids, which lack synchronous generation, physical inertia is substituted for inertia emulation. To date, the most effective approach remains the frequency derivative control technique. Nevertheless, within this method, the ability to provide virtual drooping is often disregarded in its design, potentially leading to inadequate development in systems featuring high renewable penetration and low damping. To address this issue, this paper introduces an innovative design and analysis of virtual inertia control to simultaneously mimic droop and inertia characteristics in microgrids. The dynamic frequency response without and with renewable energy sources penetration is comparatively analyzed by simulation. The proposed virtual inertia control employs a derivative technique to measure the rate of change of frequency slope during inertia emulation. Sensitivity mapping is conducted to scrutinize its impact on dynamic frequency response. Finally, the physical battery storage system of the University of Cuenca microgrid is used as a case study under operating conditions.

Optimizing microgrid performance: Strategic integration of electric vehicle charging with renewable energy and storage systems for total operation cost and emissions minimization.

At present, renewable energy sources (RESs) and electric vehicles (EVs) are presented as viable solutions to reduce operation costs and lessen the negative environmental effects of microgrids (μGs). Thus, the rising demand for EV charging and storage systems coupled with the growing penetration of various RESs has generated new obstacles to the efficient operation and administration of these μGs. In this regard, this paper introduces a multi-objective optimization model for minimizing the total operation cost of the μG and its emissions, considering the effect of battery storage system (BSS) and EV charging station load. A day-ahead scheduling model is proposed for optimal energy management (EM) of the μG investigated, which comprises photovoltaics (PVs), fuel cells (FCs), wind turbines (WTs), BSSs, and EV charging stations, with shed light on the viability and benefits of connecting BSS with EV charging stations in the μG. Analyzing three case studies depending on the objective function-Case 1: execute EM to minimize total operation cost and maximize the profits of BSS, Case 2: execute EM to minimize total emission from the μG, and Case 3: execute EM to minimize total operation cost, maximize the profits of BSS, and minimize total emissions from the μG. The main aim of the presented optimization strategy is to achieve the best possible balance between reducing expenses and lessening the environmental impact of greenhouse gas emissions. The krill herd algorithm (KHA) is used to find the optimal solutions while considering various nonlinear constraints. To demonstrate the validity and effectiveness of the proposed solution, the study utilizes the KHA and compares the obtained results with those achieved by other optimization methods. It was demonstrated that such integration significantly enhances the μG's operational efficiency, reduces operating costs, and minimizes environmental impact. The findings underscore the viability of combining EV charging infrastructure with renewable energy to meet the increasing energy demand sustainably. The novelty of this work lies in its multi-objective optimization approach, the integration of EV charging and BSS in μGs, the comparison with other optimization methods, and the emphasis on sustainability and addressing energy demand through the utilization of renewable energy and EVs.

Grain size and heavy metal assessment in barchan dunes surrounding the Talatan PV power generation area, Qinghai Province

Heavy metal contamination in sediments near photovoltaic (PV) power generation areas poses potential environmental risks, requiring detailed characterization and source apportionment to facilitate sustainable management. This research explores the characteristics of sediment grains and the concentrations of heavy metals present in surface sediments from barchan dunes proximal to the Talatan Photovoltaic (PV) power generation area, located within Gonghe County, Qinghai Province. The sediments displayed an average grain size within the medium sand range, with marginal differences discernible between the windward and leeward slopes. With the exception of Mn, Cu, Zn, V, Pb, and Ba, concentrations of the remaining metals were found to surpass the baseline levels established by Chinese aeolian soil data, alluding to potential anthropogenic influences. Through employing coefficients of variation in multivariate statistical analysis, it was identified that the concentrations of Cr and Co were significantly elevated, suggesting potential anthropogenic contamination, which may be associated with photovoltaic industrial activities. Specifically, the elevated concentrations of Cr and Co suggested anthropogenic contamination, potentially associated with photovoltaic industrial activities. Utilizing a combination of Correlation Coefficient Analysis, Principal Component Analysis, and Cluster Analysis, three potential sources of heavy metals were identified: (1) industrial origin for elements such as Cu, Cr, Ni, Zn, and As; (2) elements with limited direct application in the photovoltaic industry but associated with materials and energy storage, namely Ba, V, and Mn; (3) anthropogenic inputs related to construction materials and battery storage systems in the photovoltaic park, specifically Co and Pb. The findings offer a significant understanding of the heavy metal characteristics and sources in proximity to the Talatan PV power generation area, emphasizing the impact of human activities on environmental quality. These insights underscore the necessity for enhanced monitoring and management of industrial activities to mitigate potential environmental impacts. Further research is recommended on a broader spatial scale to yield a more comprehensive understanding of this subject.

Improving the Performance of Hybrid System-Based Renewable Energy by Artificial Intelligence

Abstract Artificial intelligence (AI) has emerged as a critical indicator of technological progress in recent years. The present study uses AI to enhance the efficiency of a hybrid system that operates on renewable energy sources. The hybrid system we propose consists of a wind energy conversion system (WECS), a photovoltaic system (PVS), a battery storage system (BSS) and electronic power converters. AI manages these converters cleverly. We use the maximum power point tracking (MPPT)-based fuzzy logic controller (FLC) to regulate the boost converter in the PVS and the WECS. We propose an adaptive neuro fuzzy inference system (ANFIS)-based controller to control the bidirectional converter of the storage system. The design of this module intends to maintain voltage stability on the direct current (DC) bus and improve energy quality. We study and simulate this system using MATLAB/SIMULINK. The results of this research show that the FLC-MPPT technique outperforms the Perturb and Observe (P&O) algorithm in terms of efficiency in power production. The console we propose also shows good results in maintaining the voltage stability in the DC bus in comparison with the proportional integral (PI) controller. This paper has the potential to contribute to the development of environmentally friendly resource performance.

Enhanced Randomized Harris Hawk Optimization of PI controller for power flow control in the microgrid with the PV-wind-battery system

Microgrids, characterized by their ability to work individually or in combination with the main power system, play a pivotal role in addressing the growing demand for reliable and sustainable energy solutions. This work concentrates on the integration of sustainable energy sources, specifically photovoltaic (PV), and wind generation and a battery storage system within a microgrid framework. Additionally, a power flow control strategy is implemented to enhance the dynamic behaviour and stability of the microgrid. The proportional-integral (PI) controller is a fundamental component in regulating the microgrid’s power flow, ensuring optimal performance under varying operating conditions. However, tuning the PI controller parameters is a difficult task because of the dynamic and nonlinear nature of renewable energy sources. In this work, the application of the Enhanced Randomized Harris Hawk Optimization (ERHHO) to fine-tune the PI controller is proposed, using the algorithm’s ability to mimic the hunting behaviour of hawks in finding optimal solutions. The PV-Wind-Battery microgrid system is modelled, and the proposed algorithm is employed to optimize the PI controller parameters for efficient energy management. The ERHHO algorithm’s exploration-exploitation balance is harnessed to navigate the complex solution space and converge to optimal PI controller settings, thereby enhancing the microgrid’s stability and performance. The study evaluates the effectiveness of the proposed ERHHO-based PI controller tuning through comprehensive simulations. Performance metrics such as transient response, overshoot, settling time, and steady-state error are analysed to validate the robustness and efficiency of the proposed method. Compared to its nearest optimization algorithm, with the proposed algorithm rise time is reduced by 50%, overshoot is reduced by 75%, settling time is reduced by 66%, and finally, a percentage of reduction of steady-state error is 45%. The outcomes of this research contribute to the advancement of microgrid control strategies, offering a novel approach to PI controller tuning in the context of diverse renewable energy sources. The integration of the Harris Hawk Optimization algorithm provides a promising avenue for enhancing the operational efficiency and reliability of microgrids, paving the way for sustainable and resilient energy systems in the aspect of growing energy landscapes.

Designing control systems and multimode power management of WECS including Energy Storage

This research focuses on the design of a nonlinear controller and establishes an efficient management of aerogenerators. The system comprises a wind turbine driving a permanent magnet generator, which transfers power to the grid via a Vienna converter and a DC network associated with a battery storage system. To handle the unpredictable nature of wind power and consumption variations, one introduces a voltage controller and a unique energy management strategy. This approach operates in two modes: i) Maximum energy extraction uses intelligent adaptive optimization based solely on measurements of electrical signals; and ii) adaptive production, based on network demand. Dual employed modes meet the power factor criteria, which improves the aerogenerator’s efficiency. Additionally, the storage unit can store the surplus production and feed the grid when demand increases. Simulations demonstrate the achievement of all control objectives, emphasizing the usefulness of the proposed control and management system.

Feasibility Study of Using Wave-PV-Wind Hybrid System for AL Hallaniyat Island

Al Hallaniyat Island is located in the southern part of Oman. The island has a low number of population and is currently fed by a number of diesel generators with a capacity of 1568 kW. Decreasing the emission of greenhouse gases is one of the main goals for the country’s vision of 2040 as well as Oman’s commitment to net-zero emissions by 2050, in line with the Paris Agreement of limiting global warming to 1.5°C. The purpose of this paper is to study the feasibility of replacing diesel generators with 100% renewable energy sources. The proposed energy source for the Island will be a hybrid system consisting of the wave energy converter, PV, wind and battery storage system. Real load data was used in the model with measured wind speed and solar radiation. The Hybrid Optimization Model for Electric Renewables software is used in this study. Several systems were proposed but the most economical one is PV-Wind-Wave-Battery system that resulted in levelized cost of energy of US$/kWh 0.1586 and Net Present Cost (NPC) of US$ 5,010,594. Having PV-Wave-Battery will result in levelized cost of energy of US$/kWh 0.174 whereas having Wind-Wave-Battery will increase the energy cost to US$/kWh 0.42.

Distributed Unified Controller Design for Parallel Battery Storage System in DC Shipboard Microgrid

In this paper, a novel distributed unified controller is designed to solve the problems of unbalanced State of Charge (SoC), unreasonable load current sharing, and unstable DC bus voltage for parallel battery storage systems (BSSs) in DC shipboard microgrid (DC-SMG). Different from the droop-based secondary controller, the designed distributed unified controller is a droop-free primary controller, which can incorporate SoC balancing, current sharing, and voltage regulation functions in the primary control layer. On this basis, each BSS uses an improved dynamic diffusion algorithm (DDA) to iteratively estimate the average information, and completes information exchange by a neighbor-to-neighbor communication network. Furthermore, the small-signal stability, large-signal stability, and global steady-state performance of the system is investigated using eigenvalue analysis, mixed potential theory (MPT), and matrix theory, respectively. Finally, the Matlab/Simulink simulation model and the StarSim HIL experimental platform for DC-SMG are built. The results show that the designed distributed unified controller can simultaneously achieve dynamic SoC balancing, proportional load current sharing, and smooth average bus voltage regulation, and has a faster SoC convergence speed and more stable control effect than the state-of-the-art methods.

Maximizing micro-grid energy output with modified chaos grasshopper algorithms

This study presents a Modified version of Chaos Grasshopper Algorithm (MCGA) as a solution to the Techno-Economic Energy Management Strategy (TEMS) problem in microgrids. Our main contribution is the optimization of parameters to minimize the overall daily electricity price in an integrated clean energy micro-grid, incorporating fuel cell, battery storage, and photovoltaic systems. Through comparative simulations with established methods (HOMER, GAMS, GWO, and MILPA), we demonstrate the superiority of our proposed strategy. The results reveal that MCGA surpasses these methods, yielding significantly improved optimal solutions for the overall daily electricity price. Notably, the MCGA approach exhibits high precision, flexibility, and adaptability to power prices and environmental constraints, leading to accurate and flexible solutions. Thus, our proposed approach offers a promising and effective solution for the TEMS problem in microgrids, with the potential to greatly enhance microgrid performance.

Fuzzy logic control for PV-powered Lithium-Ion battery management

Nowadays, battery storage systems play a crucial role in both fixed and mobile applications. Lithium-ion batteries, in particular, emerge as a promising solution owing to their impressive power and energy density. Battery lifespan depends on charging and draining cycles. Overcharging can increase internal stress, heat, and battery part damage, limiting its lifespan. Discharging a battery too quickly or excessively might damage it and shorten its lifespan. Therefore, an intelligent management control system is an essential solution. This paper presents a fuzzy logic control for a PV-powered battery management system to control the charging and discharging processes of the battery, to prevent overcharging and guarantee an extended battery life span. This controller demonstrates a fast response time and a high level of accuracy. Furthermore, to improve the system performance and achieve cost-effectiveness, the power extracted from the PV system is maximized through an Incremental Conductance (INC) based Maximum Power Point Tracking (MPPT) approach. This approach presents superior tracking precision and demonstrates greater tracking accuracy when compared to alternative methods such as Perturb and Observe (P&O). The overall system and the control strategies are tested and validated in MATLAB/Simulink environment, and the simulation results affirm the effectiveness and robustness of the system.

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Technology Optimisation of Hybrid PV Systems for Covering Energy Needs of Emergency Shelters for Ukrainian War Refugees

Hybrid photovoltaic systems have become a common solution for decreasing energy consumption for specific objects and customers at present time. The efficiency of the entire system depends strongly also on the technology of the used battery inverter. In general, DC-coupled inverters are recognized to be more energy efficient. Surprisingly, in some specific cases, AC-coupled systems can offer better results. Ukraine escalates the problem with internal migration, that can be solved just through constructing new communities of emergency shelters. Integrating these units into Ukrainian overloaded and damaged distribution grids must be projected very precisely to limit not just power consumption but also power injection. Particular savings can be reached with the proper application of AC or DCcoupled systems. This article describes that phenomenon presented on specific real cases defined through own consumption profile, battery storage system management, climate conditions, and PV system design. The presented results show that the proper choice of battery inverter technology can save interesting amount of energy produced from the installed PV system. The AC coupled system can offer not just higher flexibility and modularity, but also higher energy efficiency of the hybrid system, lower grid feed-in.

Value Stacking of a Customer-Sited BESS for Distribution Grid Support: A Utility Case

This paper proposes a value stacking strategy for a utility-owned, customer-sited battery energy storage system for distribution grid support. The proposed strategy includes three steps: application identification, performance evaluation, and battery system planning. Outage mitigation, non-wires-alternative solution, and voltage support are identified as the primary, secondary, and tertiary applications for the proposed storage system. Reliability performance and economic benefit are calculated and compared for each application in five locations in the Commonwealth of Virginia. Planning results including placement, sizing and scheduling of the battery system are presented with real utility experience and constraints. This work contributes to providing a general and ready-to-use planning methodology for distribution system planners to deploy battery storage systems within service territories, strengthen system reliability performance, and achieve sustainability goals.

Integration of wind farm, energy storage and demand response for optimum management of generation and carbon emission

AbstractThe optimal operation of power systems is one of the most critical issues for operators. Optimizing the operation of power systems while accommodating the increasing penetration of RES, owing to the intermittent and variable nature of these resources, stands as one of the most critical challenges for power systems. Numerous primary challenges, including fluctuations in load and nonlinear constraints, further complicate the issue, given its substantial dimensions. Therefore, this paper introduces an approach for improving the management of optimal generation and the associated carbon emissions costs of traditional power plants, which is achieved through integrating wind farms and incorporating battery energy storage. This enhancement is achieved by integrating wind farms and utilizing battery storage systems while considering the costs associated with traditional units using fossil fuels and the expenses related to carbon emissions. The simulation incorporates seven load customer types and five traditional generation sources integrated with wind farms and battery storage devices. The simulation results demonstrate that the new problem formulation has significantly improved the ability and performance of optimizing the scheduling of power plant generations.

Physics-Aware Degradation Model of Lithium-ion Battery Energy Storage for Techno-Economic Studies in Power Systems

Power system operation and planning decisions for lithium-ion battery energy storage systems are mainly derived using their simplified linear models. While these models are computationally simple, they have limitations in how they estimate battery degradation, either using the energy throughput or the Rainflow method. This paper proposes a hybrid approach for lithium-ion battery system modeling suitable for use in power system studies that enhances representation of battery degradation at a reasonable computational cost. The proposed hybrid model combines a physics-based model for improved degradation estimates with a simple and linear energy reservoir model commonly used to represent a battery storage system. The advantage of constructing the battery model with this vision is that it allows its smooth integration into the mixed-integer optimization frameworks. The proposed model is evaluated using both power- and energy-based use cases from the electrical grid. The simulations demonstrate that degradation could be reduced by 45% compared to other modeling strategies while generating the same level of operation profits. The net result of using the proposed hybrid model could be an extended lifespan through better informed planning and operating decisions for energy storage assets.

Enhancement of Frequency Regulation by TFOID Controller in Hybrid Renewable Energy with Battery Storage System Based Multi Area Microgrids

Enhancement of Frequency Regulation by TFOID Controller in Hybrid Renewable Energy with Battery Storage System Based Multi Area Microgrids