Battery Energy Storage System Research Articles

Locational marginal price based scheduling strategy for effective utilization of battery energy storage in PV integrated distribution system

Integration of renewable energy resources (RERs) and complexities associated with varying load patterns urge the demand for energy storage systems (ESS) to ensure real power balancing of distribution system. Among various ESS, battery energy storage system (BESS) guarantees the reliable operation of microgrids in terms of economic and technical functionalities. The scheduling of microgrids becomes essential for the economic and optimal performance of both generating sources and loads. In this work, the load-shifting capability of BESS is utilized for the optimal scheduling of grid-connected microgrids. BESS has a significant role in the effective utilization of power from RERs during grid balancing. This paper discusses the scheduling of grid-connected microgrid with two scheduling strategies, which differ in the charging behaviour of BESS. Scheduling decisions are based on the value of locational marginal price (LMP) of distribution system. In scheduling strategy-I, BESS charges at off-peak hours, either from the PV system or from the grid. Meanwhile, in scheduling strategy- II, BESS charges from the PV system at peak or off-peak hours. These two scheduling methods are also compared with the scheduling of the distribution system without integrating the microgrid. The cost of power generation, power from the main grid, and active power losses of the distribution system are calculated by performing optimal power flow (OPF). The effectiveness of the proposed scheduling strategies are validated on the IEEE-69 bus system for 24 h time duration by using MATPOWER simulation tool. The results obtained after OPF establishes the requirement of effective utilization of BESS in PV integrated distribution system.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

A master-slave adaptive linear neuron-based approach for cost-effective use of battery energy storage systems in wind farms

Nowadays, clean and sustainable energy development is of essential concern, which justifies use of renewable energy sources. Wind energy is an important resource to provide such a demand. Due to the high costs of wind power generation compared to other renewable sources, the wind turbines should be designed in such a way that they usually operate at the highest point of their power. In this case, because of the random and alternating wind speed, the output power of the wind turbine generators and thus the windfarms fluctuate. When the capacity of windfarms is increased, the power injected from the windfarm into the grid can have significant negative effects on the power grid stability. In order to prevent these effects, it is necessary to use BESSs in windfarms. To this end, a waveform with acceptable fluctuations is considered for the windfarm output power. This waveform is compared with the output power waveform and then the difference between the two waveforms is compensated by a BESS. Proper tracking with the minimum delay reduces the required BESS capacity and thus initial investment cost for the windfarms is significantly reduced. In this paper, using real windfarm data, the conventional tracking methods for the windfarm output power waveform are analyzed and compared, first. Then, a novel tracking scheme, namely the MSA, is proposed, which is based on a two-fold master-slave adaptive linear neuron. Acquired results show that compared with the conventional methods, using the MSA scheme can reduce the costs of a 99 MW windfarm by 4.8 million dollars.

Efficient energy management of domestic loads with electric vehicles by optimal scheduling of solar-powered battery energy storage system

The increasing adoption of electric vehicles (EVs) and variable energy usage patterns substantially strain the electrical grid; indeed, optimal energy management, monitoring, and utilization are required for the reliable operation of the grid. This paper introduces a novel model design of a solar-powered battery energy storage system (SPBESS) as a viable substitute for conventional demand-side management (DSM) and time of use (ToU) pricing schemes, intending to optimize energy management and utilization with IoT monitoring. In addition, the IoT-based prototype has been developed to monitor and control the proposed system. To validate the proposed SPBESS model, the study examines two distinct cases: one encompassing domestic loads and the other integrating EV loads alongside household demand. Using ToU pricing, it determines the optimal charging and discharging strategies for the SPBESS, evaluates their implications for the system configuration and grid ToU pricing, and quantifies the annual reductions in power purchases, electricity bills, energy costs, and carbon emissions. Moreover, the study comprehensively analyzes the optimal power exchanges between the photovoltaic system, battery energy storage system, and the grid, precisely considering the specific load requirements and grid ToU pricing. The conducted research findings manifest considerable decreases in energy costs, with the domestic load condition witnessing a reduction from $0.312 per kilowatt-hour to $0.245 per kilowatt-hour and the scenario involving EV and residential loads experiencing a decline from $0.27 per kilowatt-hour to $0.197 per kilowatt-hour. Furthermore, the annualized cost savings for cases 1 and 2 amount to $1,336 and $4,544, respectively, yielding substantial reductions in polluting gas emissions. Besides, the IoT-based constructed prototype model design for real-time power parameters monitoring and control shows the importance of IoT-based monitoring for remote load control, allowing users to maximize energy efficiency, resource utilization, and system visualization.

Investigations into the Charge Times of Lead–Acid Cells under Different Partial-State-of-Charge Regimes

Partial state of charge (PSOC) is an important use case for lead–acid batteries. Charging times in lead–acid cells and batteries can be variable, and when used in PSOC operation, the manufacturer’s recommended charge times for single-cycle use are not necessarily applicable. Knowing how long charging will take and what the variability in time required is allows for better planning of operations and algorithm creation for battery energy storage system (BESS) manufacturers. This paper details and demonstrates a procedure for identifying the charging time of cells when different charge throughputs occur prior to reaching full charge. The results showed that the charging time in PSOC operations was highly variable when a charge-factor-controlled full-charge procedure was used. Also noted were that higher voltages for the same state of charge were reached as the number of cycles following reaching full charge increased. None of the regimes tested in this paper caused any significant capacity degradation, which demonstrates that PSOC operations can be performed even on cells not specifically designed for them, provided the correct regime is chosen.

BESS Revolutionizes Renewable Stability in Indonesia

This study examines the integration of Battery Energy Storage Systems (BESS) with Solar Power Plants (PLTS) to enhance electrical grid stability in Indonesia, where 90% of electricity is from fossil fuels. The intermittent nature of PLTS often destabilizes the network, causing frequency hunting or blackouts. Using Etap 12.6 software, simulations showed that adding BESS to hybrid PLTS-PLTD systems significantly mitigates these issues. For instance, a 50% drop in PLTS power without BESS required a 55% increase in PLTD power, but with BESS, this increase was only 34%. Thus, BESS is crucial for stabilizing renewable energy integration and supporting Indonesia’s Net-Zero Emissions goal. Highlights: BESS Stabilizes Power: Reduces impact of PLTS power fluctuations on PLTD. Simulation Insight: 50% PLTS drop increases PLTD by 34% with BESS. Net-Zero Goal: BESS supports Indonesia’s renewable energy and emission targets. Keywords: Renewable Energy, Solar Power, Battery Storage, Electrical Grid Stability, Indonesia

Evaluation of Different Methodologies for Wave Energy Conversion Systems Integration into the Power Grid Using Power Hardware-in-Loop Emulation

The ocean energy resources hold the promise of a sustainable solution within global efforts to diversify energy sources and mitigate climate change. Wave energy conversion (WEC) systems, as emerging technologies, offer adaptability and the potential to harness predictable wave energy. However, integration of WEC systems into a power grid brings challenges for system operators due to their nature of operation. Addressing these demands is a multilayered process that involves highly efficient power electronic devices, control systems, and efficient energy storage solutions. This paper specifically focuses on the methodologies of the grid integration of a specific wave energy conversion system—a point absorber developed by the company Sigma Energy. Proposed methodologies are experimentally tested using power hardware-in-loop (PHIL) emulation of a fully monitored and controlled small-scale microgrid equipped with a battery energy storage system (BESS), different emulators of loads, and distributed generators (DG).

Control strategies of 15-level modified cascaded H-bridge MLI with solar PV and energy storage system

The rapid integration of renewable energy sources (RES) into power systems emphasizes the need for advanced inverter technology that effectively manages the variability and integration challenges of renewable energy. Multilevel inverters (MLIs), recognized for their ability to minimize harmonic distortion and improve electrical performance, have become increasingly pivotal in addressing these needs. This paper focuses on the challenges associated with integrating MLIs into RES, particularly the complexity of control and coordination required to ensure efficient operation. We present a novel 15-level cascaded H-bridge multilevel inverter optimized for renewable energy applications, incorporating both solar photovoltaic (PV) systems and battery energy storage systems (BESS). The innovative control strategy employed uses a reinforcement learning algorithm to manage the dynamic interaction between the RES inputs and MLI outputs, ensuring optimized performance under varying conditions. The results demonstrate a robust, adaptable MLI design that reduces component count and enhances operational efficiency, thus validating the effectiveness of the proposed MLI in managing the intricacies of renewable energy integration, offering a significant improvement over traditional systems in terms of efficiency and reliability. The study's outcomes have implications for advancing RES integration technologies, marking a step forward in sustainable energy solutions.

Enhancing sustainable and climate-resilient agriculture: Optimization of greenhouse energy consumption through microgrid systems utilizing advanced meta-heuristic algorithms

Greenhouses offer controlled microclimates that enable year-round cultivation, improving food security and agricultural productivity. However, greenhouses are energy-intensive, with heating accounting for a significant portion of the associated costs. This study explores optimal microgrid configurations, economic viability, and policy recommendations for sustainable greenhouse agriculture in Nigeria. An in-depth energy assessment of a reference greenhouse in a South Korean facility is conducted. Distinct climatic differences between South Korea and Nigeria are highlighted, emphasizing the need for tailored greenhouse designs and energy solutions. Shifting focus to Nigeria, this study investigates the feasibility of hybrid renewable energy systems with a focus on wind and solar power across six geopolitical zones in Nigeria. The analysis encompasses technical, economic, and policy aspects, providing a holistic perspective on renewable energy adoption. Notably, the study uses an advanced optimization model, Teaching and Learning–Based Optimization algorithm, to assess the net present cost and baseload supply reliability, offering valuable insights for investors and policymakers. The result indicates diverse energy requirements across Nigeria, with total monthly peak energy demands ranging from 5374.80 kWh in the Southeast to 17,115.76 kWh in the Northwest, and a notable variation in the Levelized Cost of Electricity (LCOE), with the lowest at $0.07327 in Kano. Specifically, in Ogun, the net present cost for the WT-PV-ESS system stood at $520,935.45, while the PV-ESS system cost was substantially lower at $500,444.41. This confirms the effectiveness of location-specific analysis and shows the suitability of photovoltaic–battery energy storage systems for Nigeria's diverse regions, with unique considerations for specific areas. Policy recommendations, including feed-in tariffs, renewable portfolio standards, net metering, research support, and market development, provide a holistic framework for the adoption of renewable energy and sustainable agriculture. Improving infrastructure, market access, and financing for smallholder farmers is integral for improving food security and standards of living in rural Nigeria. In conclusion, Nigeria can leverage renewable resources to revolutionize its energy and agriculture sectors, setting an example for a sustainable and resilient future.

Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management

As electric vehicles (EVs) continue to rise in popularity, there has been an intensified focus on the distribution of power within hybrid renewable energy systems. In this context, the significance of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) models for mitigating grid pressures has been increasingly recognized. These models help mitigate grid pressures by using EVs as reliable loads. This research delves into the technical and economic aspects of a hybrid microgrid integrated with various components such as photovoltaic panels (PVs), wind turbines (WTs), battery energy storage systems (BESSs), and EV grid connections, situated at a specific latitude of 40°39.2′N and longitude of 29°13.2′E. The methodology employed is grounded in advanced stochastic metaheuristic approaches. The infrastructure accommodates two distinct loads: a primary load and another dedicated to EV charging. The cornerstone of the energy framework is renewables, particularly PV panels and wind turbines. A dynamic rule-based energy management scheme is implemented to ensure consistent fulfillment of energy demands with an emphasis on cost efficiency. In defining the dimensions of the microgrid, various algorithms including the Coati Optimization Algorithm (COA), Driving Training-Based Optimization (DTBO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Turbulent Flow of Water-based Optimization (TFWO), White Shark Optimizer (WSO), Zebra Optimization Algorithm (ZOA), and Flying Foxes Optimization (FFO) were used. This configuration was evaluated in terms of the annual system cost, present-day value, loss of power supply probability (LPSP), and leveled cost of energy (LCOE). Thanks to the strategic deployment of the TFWO algorithm, optimal results were achieved for the system, including a PV capacity of 411.0560 kW, a WT capacity of 327.0229 kW, and a BESS capacity of 561.1750 kW. With this configuration, the annual cost of the system was determined to be $4.0499 × 105, the present value was set at $4.6452 × 106, the nominal LPSP was calculated at 0.0487 %, and the LCOE was established at $0.1597/kWh. Notably, a significant portion of the energy demand, approximately 69.87 %, was met through renewables, with the remaining 30.13 % covered by traditional sources. Comprehensive sensitivity analysis of key parameters enabled forecasting of future trends. All research activities underwent thorough review, confirming the TFWO algorithm's superior efficiency and faster convergence compared to other methods. The optimization algorithms were implemented in MATLAB 2022b, and statistical evaluations performed using the R programming language.

Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns

Optimal microgrid design is pivotal in planning active distribution networks (ADNs) with intermittent renewable energy sources (RESs) and battery energy storage systems (BESSs). This paper introduces an innovative approach to clustering existing ADN systems, incorporating RESs and BESSs into a set of microgrids (MGs) termed a multi-microgrid (MMG). The approach accommodates diverse load patterns, considering the dynamic nature of residential, commercial, and industrial loads. The proposed methodology employs an innovative k-means algorithm that utilizes end node characteristics based on the graph partitioning method and the Silhouette technique, transforming the existing system into an MMG framework. Furthermore, an iterative pareto-fuzzy (IPF) method is suggested to determine optimal RES and BESS sizes, considering reliability, total cost, and the unutilized surplus power function. The framework integrates total line losses into the generation adequacy assessment of energy management strategy (EMS) for each MG within the MMG system. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE 33-bus distribution system. The implementation of the independent operation index (IOI) and round-trip efficiency (RTE) metrics demonstrates the effectiveness of the proposed approach in constructing an MMG, with achieved values exceeding 90 percent for IOI and 77 percent for RTE.

Constructing low-cost stable zinc-ion batteries with sodium-rich monoclinic manganese hexacyanoferrate cathode

Manganese-based Prussian blue analogues are ideal candidate cathode materials for constructing high-voltage zinc-ion batteries. However, challenges have persisted in realizing envisioned highly stable electrochemical cycling systems due to manganese dissolution under aqueous conditions and self-corrosion of zinc metal anodes. Considering practical application demands and cost-effectiveness, non-aqueous organic zinc-ion batteries have shown significant development potential by avoiding interference from active hydrogen and offering a stable wide voltage window. Herein, a sodium-rich monoclinic structured manganese hexacyanoferrate (m-MnHCF) was synthesized via a simple coprecipitation method, achieving a stable high-voltage zinc-ion battery energy storage system in a diluted 0.2 M Zn(CF3SO3)2 electrolyte in acetonitrile, a non-protonic polar solvent. The battery exhibited a discharge specific capacity of 77 mAh g−1 at the current density of 1 A g−1 after 620 cycles. Furthermore, by circumventing interference from active hydrogen, a cost-advantaged zinc powder anode (Zn-P) was successfully introduced into the energy storage system. Assembled Zn-P||m-MnHCF cells exhibited stable cycling for 100 cycles at a current density of 0.5 A g−1 with close to 100 % coulombic efficiency, showcasing limitless possibilities for the future, as demonstrated by illuminated LED arrays.

A Case Study on Electric Vehicles as Nationwide Battery Storage to Meet Slovenia’s Final Energy Consumption with Solar Energy

Despite the global importance of solar energy, its variability requires energy storage to balance production during peak and off-peak periods. Moreover, the transport sector is undergoing a global transition from internal combustion engines to electric vehicles. Since vehicles are idle 95% of the time, electric vehicle batteries, when connected to a grid, can effectively regulate intermittent photovoltaics using vehicle-to-grid technology. This conceptual study investigates the feasibility of a nationwide energy infrastructure that relies solely on solar energy, replacing other electricity sources, such as solid fuels, petroleum products, and natural gas, and utilizes electric vehicles as the sole battery energy storage system. This study aims to demonstrate the significant potential and benefits of such collaboration. The theoretical study combines historical data, assumptions, and conditions to build a simulation model that is modelled similarly as in previous conceptual studies of nationwide energy systems based solely on photovoltaics and electric vehicles, referenced in this article. In Slovenia, the total surface size suitable for the installation of photovoltaic systems is estimated to be 280 km2. The calculations show that a surface size of 217 km2 for photovoltaic systems can produce enough energy to cover Slovenia’s entire energy demand, Slovenia’s final energy consumption. However, simulations comparing photovoltaic production, total energy consumption (electricity, solid fuels, etc.), and the capacity of electric vehicle batteries show that a surface size of more than 500 km2 with photovoltaic systems and a 200% share of electric vehicles in the Slovenian vehicle fleet in 2022 will provide satisfactory results. Therefore, for a country like Slovenia, in addition to a solar power plant with a surface size of 280 km2, additional renewable energy sources are needed to cover the total energy demand, as well as additional battery energy storage systems in addition to electric vehicles.

ENERGY MANAGEMENT SYSTEM OF MICROGRID WITH RENEWABLE ENERGY SOURCES

The most recent development in the electrical system is the microgrid. Many scattered, networked energy storage, loads, and generation units make up a typical microgrid system. An energy system made up entirely of renewable resources is called a microgrid (MG), an energy storage device, and loads that can operate in grid-connected or islanded mode. The demand for the load and the power flow within MG should be managed by scheduling renewable resources. This paper presents a hybrid microgrid energy management system (MEMS) that combines grid-connected solar (PV), wind power from microturbines, and battery energy storage systems (BESS). Due to their increased energy efficiency, microgrid units are becoming more and more well-liked every day. Energy management for microgrids is required since renewable energy sources provide an imbalance in the Power System due to their stochastic behavior. Controlling the DC/DC bidirectional converter (DBC), which links the Ni-H battery to the DC bus of the grid-connected microgrid, is the primary goal of this study. The start-up of a wind/photovoltaic power generation system to the load is covered in this study. MATLAB/SIMULINK was used to model a microgrid system that is connected to the grid. Key Words: Microgrid, Photovoltaic (PV)

A hierarchical optimization technique for placement of battery energy storage system to improve grid transient stability

AbstractA battery energy storage system (BESS), due to its very fast dynamic response, plays an essential role in improving the transient frequency stability of a grid. The performance of the BESS varies with the system's installation site. Hence, the optimal location of the BESS is of utmost importance for improving transient frequency stability. Therefore, this paper presents a hierarchical approach for optimizing the BESS placement to improve a grid's transient frequency stability. In most research, frequency nadir and rate of change of frequency (ROCOF) have been considered for studying frequency stability. This paper considers two more parameters, along with frequency nadir and ROCOF, to study the transient frequency stability, settling time, and decay ratio. A novel frequency stability index (FSI) using the four transient frequency parameters has been developed. After a significant disturbance in a benchmarked test system, the FSI was used to identify the optimal location of the BESS for stabilizing the frequency. It has been observed that, after a sudden generator outage, the ROCOF and the frequency nadir improve the best when the BESS is located at the bus closest to the generator experiencing the outage. However, considering the other two parameters as well, the value of the FSI is the minimum; that is, the optimum solution is when the BESS is located at the bus that is the second closest to the generator experiencing the outage. Results of similar studies validate the proposed FSI in indicating the optimal location of the BESS in improving the transient frequency behavior of the system.

A novel battery management scheme for critical loads

AbstractThis article proposes a novel battery management system (BMS) to ensure uninterruptible power delivery to a 48 V DC bus used for electric vehicle charging stations, data centers, telecommunication systems, and critical care units such as hospitals. The proposed BMS facilitates constant current and constant voltage charging to maintain optimal battery performance during normal operation. This BMS is designed for effective control, monitoring and protection of two lead‐acid battery units to form battery energy storage system (BESS). Furthermore, it is capable of isolating batteries in abnormal conditions and operates them independently to provide reliable supply at output terminals with full capacity. The system utilizes a 30 V DC source derived from AC mains or solar photovoltaic system. This supply is used to charge the BESS and also supply to the load. In the event of failure of 30 V supply, it seamlessly transits to BESS mode to supply power to boost converter to maintain constant 48 V DC output at load terminal. The proposed system architecture not only enhances power reliability but also improves overall system efficiency, making it well‐suited for critical applications require continuous and stable power supply. Simulation studies using Matlab/Simulink and analytical results using TINA (Tool kit for Interactive Network Analysis) are presented to show that 48 V DC supply is maintained at output terminals during failure of input 30 V DC source or failure of one battery unit.

Optimizing virtual power plant allocation for enhanced resilience in smart microgrids under severe fault conditions using the hunting prey optimization algorithm

The rapid expansion of renewable energy sources (RESs), such as photovoltaic (PV), wind turbine (WT), micro turbine (MT), and fuel cell (FC), has presented both opportunities and challenges to power systems, particularly in terms of environmental sustainability and economic feasibility. While RESs offer benefits like reduced environmental pollution, decreased power losses, and enhanced power quality, their intermittent nature and uncertainties pose challenges, resulting in variable generation and instability in distribution systems. To address these challenges, the concept of aggregating distributed energy resources (DERs), battery energy storage system (BESS), electric vehicles (EVs), and controllable loads into a virtual power plant (VPP) managed by an energy management system (EMS) has emerged. This study aims to determine the optimal location and size of VPPs within radial distribution system (RDS) while considering network resilience to severe weather events. The problem is formulated as an optimization task with dual objectives: minimizing the operating cost of VPPs and reducing energy not supplied (ENS) during natural disasters such as floods and earthquakes. To address this optimization problem, a novel meta-heuristic optimization algorithm called the hunting prey optimization algorithm (HPOA) is applied. HPOA serves various functions within the RDS, specifically targeting the optimization of VPP location, VPP resource management, and the objective functions. A case study is conducted, incorporating RESs, BESS, and EVs, and compared against existing algorithms such as BESA and SMA using a standard IEEE 85-bus RDS. Simulation results conducted in MATLAB demonstrate that the proposed HPOA algorithm effectively determines the optimal size and location of VPPs, leading to improved economic, operational, and resilience indices in the network.

Model Predictive Control for Intersubmodule State-of-Charge Balancing in Cascaded H-Bridge Converter-Based Battery Energy Storage Systems

Model Predictive Control for Intersubmodule State-of-Charge Balancing in Cascaded H-Bridge Converter-Based Battery Energy Storage Systems

Design of Multi-Settlement Electricity Markets Considering Demand Response and Battery Energy Storage Systems Participation

Design of Multi-Settlement Electricity Markets Considering Demand Response and Battery Energy Storage Systems Participation

Multi-level anomaly detection of lithium battery energy storage system

Abstract The energy storage technology route represented by lithium battery energy storage strongly supports China’s energy structure transformation. The widespread use of lithium batteries also poses a significant safety risk that is often overlooked. Energy storage system security is facing severe challenges. It is very beneficial for the safety of energy storage systems to predict the potential faults of lithium batteries before they are used and find anomaly batteries in time. This paper proposes a three-stage anomaly detection method based on statistics and density concepts to provide real-time potential fault prediction of lithium battery energy storage systems. Considering both efficiency and accuracy, this method can detect anomaly samples from shallow to deep. Finally, the battery condition of a large energy storage plant was studied, and 11 anomaly batteries were detected. The proposed method is significant for safely operating large energy storage power stations.