This paper presents a comprehensive techno-economic assessment of a community battery energy storage system (BESS) participating concurrently in energy arbitrage and frequency control ancillary services (FCAS) markets, while also providing customer savings through coordinated demand management. The proposed framework employs a mixed-integer linear programming (MILP) model to co-optimize the charging, discharging, and reserve scheduling of the battery under dynamic market conditions. The model explicitly incorporates key operational and economic factors such as round-trip efficiency, degradation cost, market-participation constraints, and revenue from multiple value streams. By formulating the optimization problem within this MILP structure, both the operational feasibility and the economic profitability of the system are evaluated over annual market cycles. Simulation results demonstrate that integrating FCAS participation with conventional energy arbitrage substantially enhances total revenue potential and improves asset utilization, compared with single-service operation. Furthermore, the coordinated management of community demand contributes to additional cost savings and supports local grid reliability. The findings highlight the critical role of co-optimized control and multi-market participation strategies in improving the financial viability and grid-support capabilities of community-scale BESS deployments.

The conventional full power converter (FPC) for battery energy storage applications is limited by bulky components and suboptimal efficiency. In response, a series-connected step-up/down partial power converter (SUDPPC) with high power density is proposed in this paper. It consists of an LLC resonant converter operating at a fixed switching frequency cascaded with a full-bridge converter capable of providing bipolar output. By connecting the SUDPPC in series with the load, the voltage stress on the series side and the current stress on the parallel side are markedly reduced. The four-quadrant function provides support for further optimization of the rated power level. Universal series interconnection schemes are elaborated, and design guidelines are formulated based on power distribution characteristics. Furthermore, the topology is evaluated in terms of nonactive power and component stress factor (CSF), and benchmarked against a four-switch buck/boost FPC and a phase-shifted full-bridge step-up partial power converter (SUPPC). Finally, a 1.1kW prototype is developed to experimentally validate the theoretical analysis, demonstrating that only 14.3% of the total active power is processed under full-load conditions, with a peak efficiency of 98.15%.

ABSTRACT To address the safety risks caused by thermal runaway in lithium iron phosphate (LiFePO₄) energy storage batteries, and to improve the timeliness and accuracy of fault monitoring while ensuring the safe operation of energy storage systems, this study focuses on a 314 Ah liquid‐cooled battery module. It conducts a coupled gas‐mechanics‐acoustics multi‐physics simulation and experimental validation of the thermal runaway process. Mechanical response, acoustic propagation, and gas diffusion models were built using ABAQUS, COMSOL, and Ansys Fluent, respectively. Combined with experimentally measured stress peaks (1308 kg), acoustic signal characteristics, and hydrogen diffusion data, model inputs and validation were completed. The NSGA‐III multi‐objective optimization algorithm was applied to determine the optimal sensor configuration. The results show that: the thermal runaway stress is transmitted to the seventh cell in the module within 0.001 s, so the stress sensor should be placed on the large surface of this cell; the transient acoustic signal peak pressure during safety valve activation exceeds 17 Pa (sound pressure level > 118.6 dB), achieving millisecond‐level full coverage, and a single broadband acoustic sensor placed at the top center of the module is sufficient for monitoring; thermal runaway gas response is optimal in the top center area (Position 5), with the edge fault scenario detected first at 1.1 s and reaching the warning threshold (0.01 kmol/m³) at 1.6 s, while in the center fault scenario, the warning threshold is reached at 0.1 s. Gas sensors should therefore be preferentially arranged at the top center of the module. The sensor optimization layout strategy proposed in this paper provides theoretical and technical support for the safe operation of energy storage batteries and helps improve the effectiveness of fault monitoring in energy storage systems.

Sodium-ion batteries (SIBs) have attracted increasing attention as a cost-effective and sustainable alternative to lithium-ion batteries (LIBs) for large-scale energy storage owing to the abundance of sodium and its electrochemical similarity to lithium. However, the development of suitable anode materials remains a key challenge. Alloy-type metals are considered promising anode candidates because they offer high theoretical capacities and multiple electron transfer reactions. In this study, we investigate a Bi-Sn alloy foil anode prepared by rolling to a thickness of 36 μm and punching into 4 mm-diameter discs. The foil is employed directly as the anode without the addition of conducting agents or binders, enabling the intrinsic electrochemical behavior of the active material to be evaluated. Electrochemical tests were performed in Swagelok-type cells using sodium metal as the counter electrode and two different electrolytes: 1 M NaPF6 in 1,2-dimethoxyethane (DME) and 1 M NaPF6 in ethylene carbonate/diethyl carbonate (EC/DEC). The Bi-Sn foil demonstrates excellent cycling performance in DME, retaining a capacity of 530 mAh g-1 (14.84 mAh cm-2) after 100 cycles at 0.1 Cequivalent to 91.5% of its theoretical capacity. In contrast, rapid capacity fading is observed in EC/DEC, underscoring the critical role of electrolyte chemistry in alloy-type anodes. Morphological analyses reveal that during cycling in DME, the Bi-Sn foil undergoes significant mechanical deformation, including cracking and pulverization into nanoscale domains. However, the fragmented particles spontaneously reconstruct into a porous structurea phenomenon referred to as self-healing. This porous structure maintains electrical connectivity to the current collector, enabling capacity retention. These findings demonstrate that pulverization is not inherently detrimental to alloy-type anodes; rather, it can be mitigated by using an ether-type electrolyte to facilitate self-healing. This strategy offers a new pathway for the development of alloy-type anodes composed of low-melting-temperature metals, such as Bi, Sn, and Pb.

Physical rolling to construct sodium‑tin alloy interface to stabilize sodium metal anodes.

This work aims to multi-criteria assessment of a system with compound power sources and inter-connected. The basic methodology named Method of Computing and Valuation of Full Potentials (CVFP) is applied in a pilot project, in which CVFP has four dimensions of the development (Technic-economical, Environmental, Social, and Political) as the milestones for the multi-criteria assessment. In this case, a medium voltage consumer appears connected to the electricity distributor with a diesel generator plus a system of energy storage using batteries (battery energy storage systems). The study concludes that the option of using energy storage in smart grids offers more positive results than the diesel option in terms of social and environmental attributes (such as job creation and carbon dioxide [CO 2 ] emission mitigation). However, in technical and economic terms, storage still lacks incentives for its use, being a positive solution when focusing on future gains; diesel, on the other hand, is cheaper and requires maintenance based on already widespread technical knowledge. Politically, both sources lack regulatory aspects for their use; however, diesel, already widespread in the country, acts as a consumption-reducing agent during peak demand periods. On the other hand, the use of storage is not yet widely known and depends on equipment and international expertise. Therefore, storage combined with smart grids shows significant gains in mitigating emissions, which, in a process of seeking to replace non-clean sources and their social and environmental impacts, can be decisive in its selection.

Rapid deployment of rooftop photovoltaics (PV), electric heating, and electric vehicles (EVs) is stressing low-voltage feeders in cold climates, where winter peaks push aging transformers to their limits. This paper quantifies how much stationary and mobile storage is required to keep feeder power nearly flat over a full year in such conditions. A mixed-integer linear programming (MILP) model co-optimizes stationary battery energy storage systems (BESSs) and EV flexibility, including lithium-ion degradation, under a flatness constraint on transformer loading, i.e., the magnitude of feeder power exchange (import or export) around a seasonal target. The framework is applied to a 48-dwelling neighborhood in Ardahan, northeastern Turkey (mean January ≈ −8 °C) with rooftop PV and an emerging EV fleet. Three configurations are compared: unmanaged EV charging, optimized smart charging, and bidirectional vehicle-to-grid (V2G). Relative to the unmanaged case, smart charging reduces optimal stationary BESS capacity from 4.10 to 2.95 MWh, while V2G further cuts it to 1.23 MWh (≈70% reduction) and increases flat-compliant hours within ±0.5 kW of the target transformer loading level from 92.4% to 96.1%. The levelized cost of demand equalization falls from 0.52 to 0.22 EUR/kWh, indicating that combining modest stationary BESSs with V2G can make feeder-level demand flattening technically and economically viable in cold-climate residential districts.

Deep Reinforcement Learning (DRL) shows good performance for optimizing battery energy storage systems (BESS) coordinated operations with photovoltaic plants (PV), yet most studies rely on simulations. Bridging the gap to practical application requires validation using real-world operational data. This paper provides such empirical evidence by developing and rigorously evaluating an integrated forecast-and-control framework on three distinct utility-scale PV-BESS assets in Chile. The framework couples a Sequence-to-Sequence (Seq2Seq) LSTM for point forecaster embedded in a probabilistic scenario-generation pipeline of PV generation with nodal prices with DRL agents (Proximal Policy Optimization - PPO and Soft Actor-Critic - SAC) trained on 1,000 generated scenarios per site. Using two years (2022–2023) of operational plant data, meteorology, and market prices, we benchmark DRL policies against theoretical limits (Oracle), a deterministic predict-then-optimize baseline, a scenario-based model predictive control (MPC), and a random Dummy policy over 14-day horizons using a 900/100 train–test split. The Seq2Seq forecaster improves accuracy (e.g., 34.5% reduction in RMSE for prices vs. SARIMAX). We find that the DRL agents consistently outperform the predict–then–optimize baseline, achieving mean 14-day profits near USD 55k, and exhibiting robust, adaptive contracyclical behavior without excessive cycling. Our study provides a reproducible blueprint and empirical validation for data-driven BESS control, demonstrating its practical viability and economic benefits in real-world operating conditions.

Cobalt has emerged as a critical mineral in the global transition toward low-carbon energy systems, particularly due to its role in lithium-ion batteries for electric vehicles and renewable energy storage. While this transition is frequently framed as environmentally sustainable, far less attention has been paid to the social and structural vulnerabilities embedded within cobalt supply chains, especially in artisanal mining contexts. Existing studies tend to address labor abuses, environmental degradation, or governance failures in isolation, leaving a gap in understanding how these factors interact systemically within the energy–climate–mining nexus. This study addresses this gap by applying the Pressure and Release (PAR) Model to examine how global demand for “clean energy” cobalt intersects with historical extraction patterns, weak institutions, and contemporary political–economic pressures to produce vulnerability in artisanal cobalt mining communities in the Democratic Republic of Congo (DRC). Using qualitative analysis of secondary data from geological reports, policy documents, public health studies, and supply-chain investigations, the study analyzes root causes (such as corruption and extractive economic legacies), dynamic pressures (including institutional weakness and foreign dependency), and unsafe conditions (child labor, toxic exposure, and community health risks). The findings demonstrate that the energy transition, while climate-motivated, reproduces structural vulnerabilities in mining regions by intensifying demand without addressing underlying social and governance failures. By illuminating how climate-driven mineral demand translates into localized risk and exploitation, the PAR Model provides a critical framework for understanding why “clean” energy systems can generate socially unsustainable outcomes. The study contributes to energy and environmental justice scholarship by highlighting the need for supply-chain accountability, equitable governance, and structural reform as integral components of a just energy transition.

Existing design methodologies for off-grid wind–solar–hydrogen integrated energy systems (WSH-IES) are typically case-specific and lack portability. This study aims to establish a unified design framework to enhance cross-scenario applicability while retaining case-specific adaptability. The proposed framework employs the superstructure concept, dividing the off-grid WSH-IES into three subsystems: energy production, conversion, and storage subsystems. The framework integrates equipment selection and capacity sizing into a unified optimization process described by a mixed-integer programming model. Additionally, the modular constraint template ensures generalizability across scenarios by linking the local resource protocol to the techno-economic parameters of the equipment, allowing the model to be adapted to various situations. The model was applied to two case studies. Economic analysis indicates that the pure electricity architecture is dominated by energy storage (battery costs account for 96.8%), while the hybrid architecture redistributes expenditures between batteries (67.8%) and electrolyzers (28.4%). It utilizes hydrogen as a complementary medium for long-duration energy storage, achieving cost risk diversification and enhanced resilience. Under current techno-economic conditions, real-time bidirectional electricity–hydrogen conversion offers no economic benefits. This framework quantifies cost drivers and design trade-offs for off-grid WSH-IES, providing an open modeling platform for academic research and planning applications.

Integration of distributed generation (DG) units could reduce the duration of power outage for a certain number of consumers in distribution networks after a network failure. Prerequisites are that i) the DG unit has the technical characteristics that enable its islanded operation, the degree of automation of the distribution network allows it and ii) it is in accordance with the current technical regulations of the country. The extent to which integration of distributed sources can improve reliability depends on the DG size, type, and the point of common coupling. Besides, an energy storage system could be installed along with DG unit, so that energy supply availability during fault periods can be secured. This paper proposes an algorithm based on mixed-integer linear programming (MILP) approach for optimal placement of photovoltaic power plant (PVPP) and battery energy storage system (BESS). The optimal location is determined so that expected non supplied energy is minimized. The uncertainties in the estimation of production of PVPP, load and BESS state of charge (SoC) were taken into account by Monte Carlo simulations (MCS). Key words. Distribution system, PV plant, battery energy storage, MILP, Monte Carlo simulation.

Salt, solvent & shield strategies: Electrolyte engineering for efficient energy storage in aqueous zinc-ion batteries

Enhancing energy storage of nickel-zinc battery through comprehensive d-p orbital hybridization regulation

Advances in solid-state lithium–sulfur batteries for next-generation energy storage

Phase-change thermal batteries for renewable energy storage and waste heat recovery demand high energy density and fast charging1-5, which are mutually exclusive because phase-change materials (PCMs) with high melting enthalpy are usually poor heat conductors6-8. The charging rate can be improved by making composite phase-change materials (CPCMs) with increased thermal conductivity9 and/or by exerting an external force to realize close-contact melting (CCM)10-12. However, these methods inevitably result in energy density losses and/or extra energy consumption. Here we report a strategy to boost the charging rates without sacrificing energy density, based on a rational design of a composite coating that enables slip-enhanced close-contact melting (sCCM) inside sealed thermal batteries. Using organic PCMs, we demonstrate a record-high power density of 1,100 ± 2% kW m-3 in a prototype. Our coating design integrates a pulse-heated (PH) layer that premelts the PCM to initiate CCM, together with a liquid-like slip surface that ensures unimpeded sinking of the remaining solid and sustains the sCCM mode throughout charging. We develop a model to explain how the slip surface enhances the charging rate. With high cycling life, adaptability and scalability, this strategy is generalizable to diverse PCMs, enabling high-performance thermal energy storage over a wide range of temperatures.

Recycling of spent lithium iron phosphate batteries-a review of processes, economics, and carbon footprint.

With the ever-increasing demand for high-energy-density lithium-ion batteries (LIBs) in multiscale energy storage, safety concerns have emerged as critical obstacles hindering their widespread application. The excess heat generated during electrochemical...

Chemically stable two-dimensional MXene quantum dots (MQDs) have gained significant attention owing to their exceptional optical properties, tunable surface chemistry, and promising biocompatibility. Leveraging these properties, MQDs have found broad applicability across diverse domains, including optoelectronics (LEDs, lasers, detectors, and solar cells), energy storage (batteries and supercapacitors) and energy conversion (CO2 reduction and hydrogen evolution), sensing, and biomedicine. This review provides a comprehensive overview of recent advancements in eco-friendly synthesis and surface modification strategies aimed at enhancing the radiative recombination efficiency of fluorescent MQDs. Furthermore, we critically assess the wide-ranging practical applications of MQDs and evaluate the progress achieved through both experimental and computational approaches. Special emphasis is placed on the most promising avenues for improving their optical performance and integration into high-efficiency devices. Finally, we outline key challenges and offer insights into future research directions. This review bridges fundamental understanding with technological development, reinforcing the transformative potential of MQDs in next-generation applications.

Analysis of the timing sequence of heat and gas generation during thermal runaway of lithium iron phosphate batteries for energy storage

Digital twin for battery energy storage systems