Battery Energy Storage Research Articles

A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

To incorporate Local Energy Management System (LEMS) based Tertiary Controller (TC) operation with multiple Photovoltaic based Distributed Generations (PV-DGs) level Primary Controller/ PC: Independent DG Controllers (IDGC) more adequately, a new representation learning (Symmetrical Input-Output weight Encoded Data Compression/ SIODC, with Exponential Expansion based Random Vector Functional Link Network towards Softmax layer’s Control Reference Estimation/ ExRVFLN-SoCRE) based MPPT controller is proposed in this paper in terms of Secondary Controllers (SCs). A new Hybrid LEMS (HyLEMS) is presented towards the proposed Deep Neural Network based SCs (i.e. DNN-SCs) in a distributed (SCdist), as well as centralized (SCcent) manner, to ease the computational, and communication requirements. To investigate that multiple PV-DGs based duty cycle (kth instant Control References/CRs estimation) controllers are considered here with auxiliary Battery Energy Storage Systems (BESS), and AC utility, integrated to Common DC feeder in terms of DC-DC converter dynamics. To avoid Control Reference Estimation Error (CREE) due to initial randomization, optimization subroutines are incorporated for SIODC by generalized semi-supervised learning, and for ExRVFLN-SoCRE by Lagrange multiplier weighted, rms based cost function. The proposed control performance is verified in MATLAB Simulink® based average modeling, and validated through dSPACE DS1104 based RTI with multi-PV (emulators) test-bench as well.

Battery energy storage with renewable energy sources integration in unbalanced distribution network considering time of use pricing

The increasing demand for sustainable energy solutions and the escalating energy demand have facilitated the emergence of renewable energy sources (RES), such as photovoltaic (PV) sources. Combining RES with battery energy storage system (BESS) technology reduces peak hour demand and allows for economical charging and discharging for time-based energy pricing. Integrating PV and BESS in an unbalanced system with multiple RES sources is a challenging task. It requires a robust algorithm to minimise power loss in the distribution system and decrease the voltage unbalance factor (VUF). This paper presents a new multi-objective Pelican optimisation algorithm (MOPOA) for the optimal allocation of PV and BESS. The MOPOA helps find the best placement of PV and BESS in an IEEE-33 bus unbalanced radial distribution system (URDS). The proposed algorithm combines multiple benefits from a lower net present cost (NPC) and a higher voltage profile enhancement index (VPEI). The case studies and simulation results show that the proposed method places PV and BESS in IEEE 33-bus URDS optimally, satisfying all of the system’s requirements. The results indicate an improvement in the minimum VUF factor of 4.4% in a winter day and 4.3% in a summer day; a reduction in active power loss of 16% in a winter day and 7.1% in a summer day; and a reduction in reactive power loss of 7.5% in a winter day and 7.2% in a summer day. Thus, the recommended strategy effortlessly accelerates to a suboptimal solution.

Efficient allocation of energy storage and renewable energy system for performance and reliability improvement of distribution system

This paper aims to investigate the impact of optimal planning of renewable energy systems and independent battery energy storage systems (BESS) on the performance and reliability of distribution systems (DS). A unique energy management scheme (EMS) controls the charging and discharging schedules of the BESS. The transit search optimization (TSO) algorithm determines the optimal allocation of wind turbine (WT) and solar photovoltaic (SPV) generator units, along with BESS units. The optimization focuses on improving the DS reliability in terms of energy supply, increasing the feeder security margin, and reducing apparent power loss. Multiple case studies are designed to study the efficacy of the problem outcomes and are validated using Bus 4 of the Roy Bollington Test System (RBTS). To obtain results that are close to actual operating conditions, the modelling process takes into account the time-varying load profile and the dynamic power outputs from the SPV and WT units. The findings show that the suggested method works well by enhancing the feeder security margin by a maximum of 11.25%, reducing expected energy not supplied (EENS) by 4.3%, and improving the power loss profile by 11.5%. In addition, the feeder upgrade deferral (FUD) years also show a maximum improvement of 20.8%.

Voyage scheduling and energy management co-optimization in hydrogen-powered ships

The maritime sector is striving to lessen its dependence on fossil fuels, motivated by both economic factors related to fuel efficiency and the policies established by the International Maritime Organization (IMO). Consequently, there has been a growing focus on developing and deploying all-electric ships (AES) as a more sustainable alternative to conventional fossil fuel-powered vessels. AES presents several advantages over conventional ships, including zero emissions, enhanced energy efficiency, and greater sustainability. However, the implementation of AES also brings new challenges, such as the necessity to manage a hybrid microgrid and optimize voyage scheduling. Hybrid fuel cell (FC)-battery energy storage (BES) technology can significantly contribute to addressing these requirements. To tackle these challenges, this article introduces a comprehensive framework for the optimal planning of hybrid microgrids and voyage scheduling in AES. The framework incorporates multi-objective optimization techniques based on genetic algorithms to achieve a balance between cost and voyage time. The proposed framework is applied to a case study of a ship operating on a fixed route between two ports. The simulation results indicated that the total cost per voyage, associated with the objective function of cost minimization and voyage time reduction, has decreased by 12% and increased by 40%, respectively, when compared to the regular ship speed profile. Additionally, the travel time has been reduced by half an hour and 2 h, respectively, in comparison to the regular ship speed profile. To verify the results, the optimization problem for cost and voyage scheduling is also solved by Mayfly Algorithm (MA) separately. Thus, it can be illustrated the effectiveness of the framework in enhancing energy efficiency and fuel economy while preserving the ship's performance.

Integrated Home Energy Management with Hybrid Backup Storage and Vehicle-to-Home Systems for Enhanced Resilience, Efficiency, and Energy Independence in Green Buildings

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management.

Optimizing solar energy utilization: insights on energy storage battery capacities and residential self-sufficiency

Optimizing solar energy utilization: insights on energy storage battery capacities and residential self-sufficiency

Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based systems, necessitating innovative solutions. The research proposes a deep learning-based optimal energy management system designed specifically for home micro-grids that incorporate PV systems with battery energy storage, Enhanced Long Short-Term Memory (LSTM)-Based Optimal Home Micro-Grid Energy Management (OHM-GEM). Integrating an improved type of LSTM neural network called LSTM into the energy management system improves the reliability of PV power output predictions. The dependability of PV power production forecasts is increased by including a refined version of the LSTM neural network in the energy management system. The efficiency of the OHM-GEM system in maximizing PV system integration into buildings is shown by the authors using simulated data. With considerable gains in energy efficiency, cost savings, and decreased reliance on non-renewable energy sources, the results highlight the possibility of this approach to forward sustainable energy practices.

Co-Regulating Solvation Structure and Hydrogen Bond Network via Bio-Inspired Additive for Highly Reversible ZincAnode.

The feasibility of aqueous zinc-ion batteries for large-scale energy storage is hindered by the inherent challenges of Zn anode. Drawing inspiration from cellular mechanisms governing metal ion and nutrient transport, erythritol is introduced, a zincophilic additive, into the ZnSO4 electrolyte. This innovation stabilizes the Zn anode via chelation interactions between polysaccharides and Zn2+. Experimental tests in conjunction with theoretical calculation results verified that the erythritol additive can simultaneously regulate the solvation structure of hydrated Zn2+ and reconstruct the hydrogen bond network within the solution environment. Additionally, erythritol molecules preferentially adsorb onto the Zn anode, forming a dynamic protective layer. These modifications significantly mitigate undesirable side reactions, thus enhancing the Zn2+ transport and deposition behavior. Consequently, there is a notable increase in cumulative capacity, reaching 6000mA h cm⁻2 at a current density of 5mA cm-2. Specifically, a high average coulombic efficiency of 99.72% and long cycling stability of >500 cycles are obtained at 2mA cm-2 and 1mA h cm-2. Furthermore, full batteries comprised of MnO2 cathode and Zn anode in an erythritol-containing electrolyte deliver superior capacity retention. This work provides a strategy to promote the performance of Zn anodes toward practical applications.

Preface

With the rapid progress of science and technology, the research and application in the fields of energy technology and materials science are increasingly in-depth, and experts and scholars around the world are committed to promoting the innovation and development in these areas. It is against this background that we welcomed the highly anticipated 2024 8th International Conference on Energy Technology and Materials Science (ICETMS 2024). The conference was successfully held in Chongqing, China from May 31st to June 2nd, 2024, attracting the participation of almost 100 scholars, researchers and industry experts from all over the world. The convening of this conference has benefited from the active participation and enthusiastic support of experts and scholars around the world. We received a large number of high-quality paper submissions, covering New and Renewable Energy Technologies, Traditional Energy Transformation and Cleaner Production, Energy Storage Technologies and Battery Materials, Energy Security and Sustainable Development, Energy System Simulation and Optimization, and many other aspects. After a rigorous review process, excellent papers from different countries and regions were finally accepted, which fully demonstrated the latest research results and innovative ideas in these domains. In the course of the conference, we invited well-known experts and scholars at home and abroad to give keynote speeches and reports, and their wonderful speeches provided valuable academic inspiration and reflection for the participants. Among these, with the research Industrial Fabrication of Electrocatalysts for Green Hydrogen Production, Prof Hui Pan (University of Macau, China) introduced the design, synthesis and possible industrial production of novel electrocatalysts for oxygen and hydrogen evolution reactions (HER and OER). And his research demonstrated that multi-metal compound systems were catalytically active for OER and HER in electrochemical energy devices, which may find practical application in hydrogen-energy technologies. At the same time, we also organized a number of group discussions, providing participants with more in-depth communication and interaction opportunities. The successful holding of this conference not only promoted the academic exchanges and cooperation in the fields of energy technology and materials science, but also injected new vitality and impetus into the development of these domains. Finally, we would like to express our heartfelt thanks and high respect to all the experts and scholars who participated in this conference. At the same time, we would also like to thank the organizing committee for its hard work and selfless dedication, which has provided a strong guarantee for the success of the conference. We look forward to continuing to work with experts and scholars in the coming days to promote the development and progress of energy technology and materials science! List of Committee Member is available in this Pdf.

Enhanced Dynamic Droop Control for Microgrid Frequency and Voltage Stabilization Using Hybrid Energy Storage Systems: A SECANT Method Approach

Due to their variable and intermittent nature, the integration of renewable energy sources poses control challenges related to voltage and frequency stability in isolated microgrids. This paper proposes an enhanced dynamic droop control strategy optimized in active time along with a Hybrid Energy Storage System (HESS) comprising Battery Energy Storage System (BESS), supercapacitors (SUPCA), and Superconducting Magnetic Energy Storage (SMES) to improve microgrid stability. The Dynamic Droop Gains (DDG) are continuously tuned using the rapid-converging SECANT numerical method to enhance transient response and steady-state performance, this was achieved using MATLAB/Simulink. The HESS combines the complementary characteristics of BESS, SUPCA and SMES to balance steady power supply and temporary overload capacity. Detailed simulation studies on a microgrid test system verify that the proposed control strategy significantly enhances voltage/frequency regulation, power sharing accuracy, BESS lifespan and overall stability compared to conventional droop techniques. The SUPCA further improves the transient performance and power quality by mitigating fluctuations. The research demonstrates an innovative way to harness advanced control algorithms and emerging storage technologies for next-generation resilient and sustainable microgrids.

Life prediction method of battery energy storage system in frequency modulation application

Abstract To tackle the challenge of lifespan reduction in lithium batteries during frequency modulation, this study introduces a novel Remaining Useful Life (RUL) prediction methodology. The proposed approach integrates Variational Mode Decomposition (VMD) with Gated Recurrent Unit (GRU) networks, thereby efficiently synthesizing data from both operational parameters and capacity metrics. Firstly, VMD is utilized to decompose the initial capacity data of lithium batteries into separate components characterized by high and low frequencies. Subsequently, for the high-frequency elements, GRU is employed for rolling iterative prediction, while for the low-frequency elements, features are extracted from the operational data and inputted into GRU for prediction. Finally, the component prediction results are restored to obtain capacity prediction values. Validation conducted using the NASA dataset shows that the root mean square error of capacity prediction is minimized to 0.0122, with a corresponding minimum average absolute error of 0.0096, and RUL prediction errors are generally within 2 cycles.

Efficiency of Battery Systems from the Point of View of Economic Return

Abstract Battery energy storage systems (BESS) are becoming increasingly important and their number of applications in energy systems is constantly increasing. Although batteries cannot solve the problem of electricity storage in the long term, BESS systems have proven to be suitable for short-term provision of flexibility within the day. Their real usability is shaped by the provision of balancing services to ensure a stable supply of electricity in distribution networks. BESS systems were able to respond to the dynamic development of the electricity market in recent years and achieve excellent results from the point of view of economic return. The aim of the contribution is to present the results achieved in selected cases of BESS use and to determine their economic return. The size of the battery (installed power and capacity) is designed to be able to work in individual modes and operating cases. A critical element for the design of the battery size is the technical conditions for the provision of balancing services. On the basis of market research, we determine the costs of acquiring and operating the battery, including the need for a software superstructure for controlling the battery itself and ensuring the necessary business functions. According to the defined criteria, we evaluate the technical operation of the battery located at the point of consumption (behind the meter) using the battery model in several operating cases. Subsequently, we analyse its sales based on the development of market prices and the conditions in which the battery is placed.

Bidirectional Three-Port Converter for Modular Multilevel Converter-Based Retired Battery Energy Storage Systems

Bidirectional Three-Port Converter for Modular Multilevel Converter-Based Retired Battery Energy Storage Systems

Climate-resilience Readiness of Emerging Clean Energy Technologies for India’s Energy Transition: A Deep Dive into Select Technologies

Abstract Sustainability and decarbonization need to be a central component of economic development in the backdrop of climate change, mandates and stakeholder pressures. Climate-resilient infrastructure can significantly contribute to this sustainable and clean development. As energy lies at the heart of decarbonization, this study focuses on one of the critical infrastructures, power sector, and examines the climate-resilience readiness of select clean technologies in light of India’s decarbonization challenge. Battery Energy Storage System (BESS); offshore wind and green hydrogen are studied based on secondary data, incorporating an exploratory and descriptive research design, while using the logic of deduction. Research sets out the barriers faced by India’s power sector in climate-resiliency transition, identifies indicators for measuring climate-resilience and assesses select technologies for their climate-resilience readiness for India’s energy transition. Barriers fall under 5 key categories – technical, monetary, governance, data and social. Indicators for resilience measurement encompass themes of reliability, robustness and resourcefulness. Resilience assessment reveals that BESS, offshore wind and green hydrogen are climate-resilient technologies, albeit, not without gaps which demand policy interventions. Although climate-resilience readiness of clean energy technologies will be of significance, 4 levers of energy transition will hold equal value – policy and regulations, technology, sustainable financing and ESG considerations.

Towards standalone commercial buildings in the Mediterranean climate using a hybrid metal hydride and battery energy storage system

Given the critical role of hybrid energy storage systems in the building sector for enhancing renewable energy reliability and integration, this study examines the techno-economic feasibility of adopting a dual-level energy storage system for a PV-driven commercial building in the Mediterranean climate. The proposed system encompasses both hydrogen metal hydride and battery storage units. Aimed at off-grid electrification, the optimal component sizing of the hybrid system is identified by establishing a statistical optimization framework using the response surface methodology. Dynamic simulations of the hybrid system are performed through a TRNSYS model coupled with a numerical code that simulates the metal hydride system. Regarding optimal net-zero solutions, it is shown that the share of the direct PV electricity supply to end-use fluctuates within a limited range for all scenarios, namely between 57.7 and 60.5 %, while the share of each storage system varies distinctively in each solution. The results indicate a striking difference between scenarios in terms of economic aspects; differences of $ 19,791 and $ 32,621 are observed between the minimum and maximum values of the initial investment and life cycle cost, respectively. The levelized cost of electricity varies between 0.354 and 0.403 $/kWh, in which the case having the lowest payback period (10.8 years) demonstrates the lowest levelized cost of electricity too. Furthermore, an inverse relationship between the levelized cost of hydrogen and production rate is observed; the lowest levelized cost and the highest annual production are achieved in the scenario with the lowest BESS capacity and the highest electrolyzer power and hydrogen storage volume, which are equal to 5.77 $/kg and 304.7 kg H2/yr, respectively.

Optimal Scheduling of Renewable Sources Based Micro Grid With PV and Battery Storage Using Crayfish Optimization Algorithm

ABSTRACTEnvironmental concerns and energy security are pressing issues of the 21st century, with a heavy reliance on fossil fuels causing significant environmental pollution and resource depletion. To mitigate these problems, it is crucial to explore and implement alternative clean energy sources. This manuscript proposes a novel crayfish optimization algorithm (COA) for optimal scheduling in a hybrid power system that incorporates various renewable energy sources, like battery energy storage systems (BESS), fuel cells (FC), wind turbines (WT), micro turbines (MT) and photovoltaic (PV) panels. The importance of the work lies in its ability to optimize the entire operating costs of a grid‐connected microgrid while improving the accuracy and efficiency of energy management. The COA method addresses economic dispatch problems and manages energy within the grid‐connected microgrid, accounting for high levels of uncertainty. The proposed approach, tested using MATLAB Simulink, achieved a cost value of 252, outperforming existing methods such as GTO, PSO, SSA, and ALO. This illustrates the potential of the proposed technique to provide more cost‐effective and efficient energy management solutions in hybrid power systems.

Dynamic evolution of reservoir permeability and deformation in geothermal battery energy storage using abandoned mines

Retasking existing subsurface abandoned mines as infrastructure for solar energy storage could be a feasible approach in overcoming the low thermal gradient present in shallow formations. In this work, the potential for thermal storage in the high permeability goaf of abandoned mines through diurnal cyclic injection-then-extraction using coupled thermo-hydro-mechanical modeling was explored by coupling FLAC3D with TOUGH2. The temperature sensibility of reservoir during 30 days of cyclic injection-then-production was examined at various injection temperatures (ranging from 50°C to 250°C) and rates (ranging from 1 kg/s to 10 kg/s) and for representative reservoir physical and thermal properties, including variable thermal expansion coefficients. The simulation results reveal that: The principal mechanisms driving reservoir deformation result from the combined influence of thermal poroelastic and thermal effects. With the change of reservoir temperature, the reservoir is perturbed by pressure and thermal stresses causing permeability evolution. Permeability reduces ∼10% for a maximum injection temperature of 250°C – although effects are reduced the lower injection temperatures. The pore pressure fluctuations for an injection rate of 10 kg/s is ∼6.5 times that for a rate of 1 kg/s. The pressure perturbation of the reservoir during the injection process decreases with the injection rate, and the reservoir is relatively more stable. When the thermal stress becomes predominant, the reservoir volume expands. Uplift displacements 220 m above the hot injection well are trivial an of the order of ∼1.5 mm at a mean temperature of 163°C.

Optimal Sizing and Siting of Fresh and Second-Life Battery Energy Storage Systems Based on Linearized Optimal Power Flow for High Photovoltaic Penetration: A Comparative Study

Optimal Sizing and Siting of Fresh and Second-Life Battery Energy Storage Systems Based on Linearized Optimal Power Flow for High Photovoltaic Penetration: A Comparative Study

Distributed Event-Triggered Learning-Based Control for Battery Energy Storage Systems Under Persistent False Data Injection Attacks

Distributed Event-Triggered Learning-Based Control for Battery Energy Storage Systems Under Persistent False Data Injection Attacks

Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm

Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm