Battery Energy Storage Research Articles

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.

Application of a simple organic acid as a green alternative for the recovery of cathode metals from lithium-ion battery cathode materials

Industrialization, technology, and population growth have on one hand resulted in the rapid rise for energy demand and on the other hand led to the pursuit for alternative carbon neutral energy sources to satisfy demand, address climate change and promote sustainable development. The transition to renewable energy sources has resulted in the rapid rise in energy storage options with battery technologies at the forefront. Lithium-ion batteries (LIBs) have emerged as a leading battery energy storage option. The rise in LIB technology demand has resulted in a proportional increase in the demand for the various materials used in their manufacture. Primary sources of several of the LIB component materials largely consist of mining activities, however, recycling has emerged as a promising secondary material source. In this work, we evaluate the application of a green, organic acid treatment approach utilizing propionic acid in the recovery of key metals from LIB cathode materials. The study delved into exploring the application of both commercially sourced virgin LIB cathode powder (VCP) and recovered spent LIB cathode powder (SCP) and investigating the system leaching characteristics. The highest metal recoveries were obtained on leaching the SCP, with metal recoveries determined as 92.9%, 87.4%, 92.7% and 94.0% for Co, Li, Mn and Ni respectively. The difference in the recoveries on leaching metals from the VCP and SCP was under 5% for each metal. Further, the leaching model was determined as chemical reaction-controlled on using the SCP, and the activation energies were evaluated as 60.37 kJ/mol, 53.38 kJ/mol, 63.98 kJ/mol and 60.20 kJ/mol for Co, Li, Mn and Ni respectively, agreeing with the deduced chemical reaction-controlled leaching mechanism. By application of a simple organic acid, propionic acid, for organic acid leaching operations we contribute to the diversification of the lixiviant options for LIB waste treatment.

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

Energy storage battery state of health estimation based on singular value decomposition for noise reduction and improved LSTM neural network

Accurate estimation of the State of Health (SOH) of batteries is important for intelligent battery management in energy storage systems. To solve the problems of poor quality of data features as well as the difficulty of model parameter adjustment, this study proposes a method for estimating the SOH of lithium batteries based on denoising battery health features and an improved Long Short-Term Memory (LSTM) neural network. First, in this study, three health features related to SOH decrease were selected from the battery charge/discharge data, and the singular value decomposition technique was applied to the noise reduction of the features to improve their correlation with the SOH. Then, the whale optimization algorithm is improved using cubic chaotic mapping to enhance its global optimization-seeking capability. Then, the Improved Whale Optimization Algorithm (IWOA) is used to optimize the model parameters of LSTM, and the IWOA-LSTM model is applied to the battery SOH estimation. Finally, the model proposed in this research is validated against the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. The experimental results show that the prediction error of battery SOH by the method proposed in this study is less than 0.96%, and the prediction error is reduced by 49.42% compared to its baseline model. The method presented in the article achieves accurate estimation of the SOH, providing a reference for practical engineering applications.

The research of electrochemical performance of Mg-doped NaNi1/3Mn1/3Fe1/3O2 positive electrode materials

Abstract The demand for Li-ion batteries is increasing year by year, which poses a great challenge to the scarce Li and Co resources. Na-ion batteries are considered to be one of the potential energy storage batteries because of their low raw material cost, wide source of sodium resources, and prices that are not susceptible to market fluctuations. The layered oxide positive electrode (such as NaNi1/3Mn1/3Fe1/3O2, NMF) is a potential replacement due to its high-capacity characteristics and low-cost advantages. However, the rate performance of NMF is compromised by the slow migration kinetics of Na+ ions. Here, we used the Mg doping strategy to realize the enhancement of the rates and cycling properties of NMF. Our research shows that the 2% Mg-doped cathode material has superior rate performance and cycling performance. The discharge-specific capacities are 120.5 mAh/g at 0.1C and 88.9 mAh/g for 100 cycles at 0.5C with a high retention rate (85.2%).

Distributed Cooperative Dispatch Method of Distribution Network with District Heat Network and Battery Energy Storage System Considering Flexible Regulation Capability

Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method.

A novel development of advanced control approach for battery-fed electric vehicle systems

In today’s context, there is a clear preference for DC microgrids over AC microgrids due to their better compatibility with generating sources, loads, and battery energy storage systems (BESS). However, the intermittent nature of renewable resources disrupts the balance between power generation and load demand. It raises concerns regarding power management and quality in the power system. Control strategies are essential to address these challenges. This article focuses on developing a novel control strategy to ensure stability in microgrid systems. The proposed control structure utilizes a second-order multi-agent system (MAS) to enhance the power-sharing and coordination in the microgrid network. For effective control of battery energy storage units, a Voltage–Power (V-P) reference-based droop control and leader–follower consensus method is employed. The control approach consists of primary and secondary control layers. The primary layer uses a V-P reference-based droop control strategy to allocate load components to storage units. The secondary control layer aims to restore DC bus voltage using a MAS-based consensus protocol. The MAS approach offers greater flexibility and requires less computational power than other strategies such as Model Predictive Control (MPC). The enhanced control structure incorporates a current ratio modification loop to adjust the current ratio between the converters, thereby modifying gain and improving the voltage profile. This novel control optimizes the reliability and stability of the proposed DC microgrid system. The effectiveness of the enhanced consensus-based secondary control strategy is demonstrated using the MATLAB/Simulink platform.

IoT-Enabled energy conservation in residential Buildings: Machine learning models for analyzing annual solar power consumption

This study investigates enhancing the self-consumption of solar panels by integrating thermal and electrical energy storage using lithium-ion batteries in a duplex building over a year. Machine learning algorithms are applied to understand energy usage patterns. An IoT-connected model is introduced to reduce electricity expenses by boosting consumption with solar energy and extending solar panel lifespan through optimization. The model includes lithium-ion batteries for energy storage and a thermal cooling system to regulate panel temperatures. Data collected from sensors monitoring occupancy, temperature, humidity, energy, and thermostats are sent to a cloud-based platform via IoT. Results show peak monthly energy conservation measures of 41.6 kW and 28 kW during summer for the two residences. Winter witnesses a reduction of 13.9 % and 7 % in energy consumption, while autumn sees an 18 % decrease for one residence and stability for the other. Real-time IoT monitoring aids model training, with the Gaussian Probability model emerging as the most accurate. Optimization analysis underscores the thermal cooling system’s efficacy in improving solar panel performance and reducing electricity costs. Integration of machine learning and IoT curtails energy consumption to 22 kWh and 15.7 kWh for the respective residences, offering inhabitants free energy from renewable sources and ensuring swift recovery of solar panel installation costs.

Price-based demand response with renewable energy sources and peer-to-peer trading for residential microgrid with electric vehicle uncertainty

This article deals with the impact of demand response (DR) with peer-to-peer (P2P) energy trading for a residential smart home consisting of smart appliances, an electric vehicle, a battery energy storage system (BESS), and renewable energy-based distributed generation (DG) such as solar PV and wind. The proposed work deals with two stages, such as DR implementation and P2P energy trading. The first stage of implementation deals with the optimal scheduling of smart appliances in a residential home using a Binary Dragonfly Algorithm (BDA). Each appliance in the smart home is scheduled based on the slab tariff, Time of Day (ToD), and Real-Time Pricing (RTP) with DR to identify the most economical tariff structure for the residential home. Five different cases are examined to identify the best smart home for effectively utilizing DGs by reducing grid dependency and electricity costs.The RTP tariff is proved to be more beneficial than the slab tariff and ToD tariff because the electricity cost of the smart home is very minimal for all the cases with RTP, especially in case 5 cost is reduced by ₹12.0613 than slab tariff and ₹8.414 than ToD. Hence, the RTP tariff is chosen as the optimal tariff structure for the next stage of the proposed work.The second stage of implementation deals with the P2P trading between the smart homes with a proposed enhanced bidding strategy to reduce the grid dependency and electricity cost of the individual smart homes. The proposed enhanced bidding strategy involves a double auction mechanism to determine the trading decision and trading cost for the benefit of both consumers and prosumers. The electricity cost reduction achieved by consumer 1 and consumer 2 from the enhanced smart bidding strategy are ₹92.7615 and ₹15.1525, respectively. The results also proved that prosumer 1 and prosumer 2 obtained a profit of ₹78.2170 and ₹28.6970, respectively. The EV uncertainty analysis is also examined in smart homes to validate the effectiveness of the proposed bidding strategy. The simulation results proved that the proposed DR-based P2P trading with an enhanced bidding strategy reduces grid dependency and electricity costs, including EV uncertainty conditions.

Highly sensitive protection scheme considering the PV operation control models

The integration of distributed generation (DG) based on inverters into power systems has increased significantly, necessitating a thorough understanding of its impact on fault analysis and the performance of distribution networks' protection mechanisms. This study addresses this issue by examining how various inverter management modes influence protective relay systems within IEEE 9-bus redial and mesh networks, CIGRE and IEEE 33-bus networks featuring Photovoltaic (PV) farms and Battery Energy Storage Systems (BESS), by IEEE1547–2018 and German grid code standards. By analyzing grid-connected scenarios with five distinct PV control modes, the research introduces a novel protection methodology termed the Photovoltaic Overcurrent Relay (PVOCR). This method introduces a current-voltage characteristic to optimally coordinate Overcurrent Relays (OCRs), aiming to reduce their operational time and eliminate mis-coordination events. The proposed PVOCR is evaluated against standard inverse time, SOCR, and modern adaptive voltage, VOCR, relay schemes across various fault scenarios differing in type and location. Furthermore, the PVOCR scheme effectively operates across all PV inverter modes without experiencing miscoordination events, whereas the SOCR and VOCR schemes encountered such issues during the operation of Control 4. These results underscore the potential utility of the PVOCR methodology in enhancing the reliability and efficiency of protection systems in inverter-based DG networks.

Simultaneous measurement of temperature and pressure sensing technology based on double cavity matching in batteries

A compact sensor based on fiber Fabry-Pérot (FP) cavities dual-cavity matching technique cascaded with fiber Bragg grating (FBG) is proposed and demonstrated to measure pressure and temperature variations inside battery. The sensitivity amplification mechanism for two-parameter measurements is analyzed and a cased reference chamber scheme is designed. Low-cost, small-size, high-temperature and corrosion-resistant all-fiber-optic high sensitivity gas pressure sensors targeting the interior of energy storage batteries are fabricated and cascaded with FBGs to measure temperature parameters. The experimental results show that the sensor has a sensitivity of −26.8322 nm/MPa in a pressure range of 1.86 MPa, the gas pressure inside the battery and the temperature inside and outside the battery at different discharge rates of 1500mAH capacity monobloc batteries can be measured practically, directly and instantly. It has potential applications in detecting subtle pressure variations caused by gas precipitation inside the battery and in temperature measurement, and provides a new concept and a feasible technological path for the measurement of key parameters inside lithium-ion batteries.

Integrated Battery and Hydrogen Energy Storage for Enhanced Grid Power Savings and Green Hydrogen Utilization

Integrated Battery and Hydrogen Energy Storage for Enhanced Grid Power Savings and Green Hydrogen Utilization

Techno-economic analysis of a novel solar-based polygeneration system integrated with vanadium redox flow battery and thermal energy storage considering robust source-load response

In this study, a novel solar-based polygeneration system incorporated with a partially covered parabolic trough photovoltaic thermal (PCPVPVT) collector, vanadium redox flow battery (VRFB), thermal energy storage, and absorption chiller/heat pump is proposed. A robust energy management strategy is developed to alter the operating states of the system to respond to the inevitable intermittent source inputs and varying demands throughout the year. Furthermore, a comprehensive comparison of the techno-economic performance differences between the novel system and the Power to Hydrogen to Power (P2H2P) combined cooling, heating, and power (CCHP) system is carried out. In addition, the sensitivity analysis of the key technical and external economic parameters as well as a multi-objective optimization are investigated. The results illustrate that: the charging, discharging, and round-trip efficiencies of the VRFB unit reach 88.99%, 88.23%, and 78.52%, respectively, and the current of the VRFB has significant effects on the termination voltage, charge and discharge time, efficiencies, and stored and output energy of the VRFB. The PV coverage ratio of the PCPVPVT and the capacity of the VRFB unit considerably affect the system's thermodynamic and economic performance. The exergy efficiency of the proposed system is always greater than that of the P2H2P CCHP system because the efficiency of the VRFB with 78.52% is significantly higher than that of the P2H2P electricity storage with 41.73%, and the maximum difference reaches 4.25%. The levelized cost of energy of the present system is 0.0503 $/kWh, which is less than that of the P2H2P CCHP system by approximately 6.85%, indicating superior economic strengths.

Technoeconomic analysis of solar PV electrification to remote areas of Dera Ghazi Khan: A case study

With the increase in population at an immense rate, electricity demand is growing exponentially. Researchers and policymakers are seeking alternating means of power generation to meet the load demand. These resources should be cost-effective, environmentally friendly and least carbon emissions. To mitigate the load demand Renewable Energy Sources (RES) are integrated into electrical networks. In this work, an off-grid solar photovoltaic (PV) system is designed for rural areas of Dera Ghazi Khan (DG Khan), Pakistan. These areas often lack access to reliable grid power. Installing PV systems in these areas can help provide a reliable source of electricity, reduce reliance on fossil fuels, and improve living conditions. This case study is simulated using PVsyst 7.2. of Roonghan village such as hospitals, shops, and residential houses for techno-economic analysis of off-grid solar PV. The economic viability shows that installing off-grid solar PV in DG Khan is much cheaper. The electricity taken from the grid that's almost 80.01%is expensive. This case study helps other researchers and policymakers to mitigate the electricity requirement of remote areas located far away from the national grid. To install a transmission line, having cost $26.98 corer but using an off-grid to provide electricity to the same area is $2.16 corer. Additionally, it integrates Battery Energy Storage (BES) with Renewable Energy Sources (RES) to achieve a 5 % annual energy cost reduction and enhanced self-sufficiency, filling a gap in existing literature.

Seed-Assisted Reversible Dissolution/Deposition of MnO2 for Long-Cyclic and Green Aqueous Zinc-Ion Batteries.

Manganese oxide (MnO2) based aqueous zinc-ion batteries (AZIBs) are considered to be a promising battery for grid-scale energy storage. However, they usually suffer from the great challenge of capacity attenuation due to Mn dissolution and irreversible structural transformation. Herein, full use of the shortcomings is made to design high-performance cathode-free AZIBs. Manganese-based Prussian blue analog (Mn-PBA) is selected as a seed layer to provide a stable MnO2 electrodeposition surface. Thanks to the large specific surface area and manganophilic nature of Mn-PBA, the deposition/dissolution kinetics between Mn2+ and MnO2 are significantly enhanced. Systematic studies revealed the mechanism of MnO2 deposition-dissolution related to the reversible transformation of manganese oxide hydroxide and zinc hydroxide sulfate hydrate. Based on this, the developed cathode-free AZIBs exhibit outstanding rate performance (with a specific capacity of 273.7 mAh g-1 at 1 A g-1) and extraordinary cycle stability (maintaining a specific capacity of 52.3 mAh g-1 after 50000 cycles at 20 A g-1). Furthermore, the AZIBs with non-toxic, biocompatible materials can be directly discarded after use, without causing pollution to the environment, which is expected to help achieve the sustainable development goals.

Stochastic Convex Cone Programming for Joint Optimal BESS Operation and Q-Placement in Net-Zero Microgrids

Microgrids have emerged as a pivotal solution in the quest for efficient, resilient, and sustainable energy systems. Comprising diverse distributed energy resources, microgrids present a compelling opportunity to revolutionize how we generate, store, and distribute electricity, while simultaneously reducing carbon footprints. This paper proposes an optimal battery energy storage system (BESS) management scheme, along with capacitor placement for reactive power (Q)-compensation, and scheduling for the purpose of a renewable-based microgrid’s loss minimization. The proposed model evaluates the impact of BESS management on energy efficiency and analyzes how optimal scheduling of BESS influences system losses. Furthermore, it investigates the coordinated planning and operation of active assets within the microgrid, such as controllable capacitor banks, in enhancing overall efficiency. The model is formulated as a mixed-integer second-order cone programming (MISOCP) problem which is solved for both deterministic and stochastic generation and consumption data. The proposed model is tested on a 21-bus microgrid comprising photovoltaic and hydropower energy resources, and the efficacy of the model is approved by several case studies. The simulation results show that the proposed method can reduce microgrid energy losses by approximately 12 percent using the deterministic approach and around 14 percent with the stochastic approach.