Search Results (271)

This paper presents a practical and communication-independent energy management system (EMS) for a DC microgrid supply within the firezone of a cruise ship. The proposed approach prioritizes operational reliability and fault tolerance under emergency conditions, where communication availability and control complexity should be minimized. The proposed DC microgrid integrates photovoltaic systems (PVs), fuel cell systems (FCs), and lithium-iron-phosphate (LFP) battery energy storage systems (BESSs), coordinated through a rule-based EMS combined with droop-controlled converters. The electrical topology considered in this study is a collaborative development of the project consortium of the publicly funded project Sustainable DC Systems (SuSy), featuring a novel configuration with two independent horizontal busbars for the Cabin Area Distribution (CAD) and Technical Area Distribution (TAD). The EMS can manage two operational scenarios: (i) regular operation, with two decentralized droop controls where power generation is distributed among all generators based on their respective capacities, and a power curtailment strategy is applied to prevent overcharging of BESSs; and (ii) irregular operation, where a fault on one of the vertical busbars triggers the use of reserved battery storage capacity on both sides of the ship and activates load-shedding to ensure continued operation of critical loads and sustain grid functionality. The effectiveness of the proposed architecture is validated through detailed MATLAB/Simulink simulations. Under regular conditions, the EMS achieves stable voltage regulation, balanced power sharing, and efficient energy curtailment. During fault conditions, the battery storage on both sides successfully supports the critical loads. The fuel cells are operated in power-controlled mode effectively up to their full rated 6$\mathrm{k}$$\mathrm{W}$ capacity while the DC bus voltage stabilization is ensured by the battery energy storage systems. These results validate the proposed EMS as a robust and low-complexity solution for maritime DC microgrids, offering stable voltage regulation, effective load prioritization, and resilient operation of critical loads. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

Lithium-ion battery energy storage systems (BESSs) face significant safety challenges arising from internal short-circuit (ISC) faults, which can ultimately trigger thermal runaway. To address this, this paper proposes an ISC fault diagnosis method based on multivariate information entropy (MIE). The proposed approach fuses voltage and temperature time series from battery cells to extract fault features via MIE. Furthermore, a hierarchical diagnosis framework incorporating statistical confidence intervals is developed to enable robust ISC fault diagnosis. Experiments were conducted on 180 Ah lithium iron phosphate batteries, utilizing external resistors to simulate ISC faults of varying severity. The method was further validated using real-world fault data from an electric vehicle accident. Results demonstrate that the proposed method effectively distinguishes between normal and faulty cells, with MIE values exhibiting a monotonic increase as fault severity intensifies. In the real-world dataset, the method identifies the faulty cell 240 s before a discernible voltage drop, demonstrating its capability for early ISC detection. Full article

The increasing penetration of converter-interfaced renewable energy sources has led to reduced system inertia and increased frequency stability challenges in modern power systems. Battery energy storage systems (BESSs) provide fast active power support. However, their effectiveness depends on the installation location, power rating, and network characteristics. This paper proposes a power-flow-informed, sensitivity-based method for the optimal placement and sizing of distributed BESSs to improve the frequency nadir and rate of change of frequency (RoCoF). The method integrates marginal frequency sensitivity obtained from time-domain simulations with network coupling information derived from power-flow analysis within a constrained optimization framework solved using particle swarm optimization. The network coupling weight, derived from voltage sensitivity, represents the steady-state electrical connectivity and active power redistribution capability, rather than transient frequency dynamics. It is used in combination with frequency sensitivity to improve the discrimination of candidate buses. The method is evaluated on the IEEE 39-bus system under multiple generator outage contingencies. For the most severe contingency (G01), the baseline system exhibits a frequency nadir of 55.9230 Hz and an RoCoF of 0.2404 Hz/s. With the proposed method, the frequency nadir improves to 58.6561 Hz, corresponding to an increase of 2.7330 Hz (4.88%), while the RoCoF is reduced to 0.1224 Hz/s (49.17% reduction). The optimal solution distributes a total BESS capacity of 298 MW across multiple buses, with the largest allocation of 46 MW at Bus 36. Across additional contingencies, the proposed method consistently achieves higher frequency nadirs and lower RoCoFs compared with both the baseline system and benchmark placement methods. The results demonstrate that combining dynamic frequency sensitivity with power-flow-based network coupling provides a physically consistent and computationally efficient strategy for distributed BESS allocation in low-inertia power systems. Full article

Grid-scale Battery Energy Storage Systems (BESSs) are becoming essential components of modern power grids undergoing rapid decarbonisation. This review examines how nine jurisdictions—Great Britain, Germany, Spain, Italy, France, California (USA), Australia, Singapore, and China—are enabling the growth of BESSs, focusing on market access and revenue streams, investment risks and mitigation strategies, support mechanisms, and regulatory conditions. A central finding is that batteries typically become investable only when they can stack revenue from multiple sources, including energy arbitrage, ancillary services, and capacity markets. Regulation proves as important as technology: frameworks that fail to recognise storage as a distinct asset class expose projects to double charging, unclear licensing, and limited market access. Grid connection delays, declining revenues in saturating ancillary service markets, and safety compliance represent significant practical barriers. International experience indicates that BESSs scale fastest when decarbonisation policy is credible and market rules enable diversified, financeable revenue streams. Full article

This study presents a techno-economic analysis of deploying distributed energy resources (DERs), specifically photovoltaic (PV), battery energy storage systems (BESSs) and electric vehicles (EVs), in apartment buildings configured as Virtual Power Plants (VPPs). Utilizing cooperative game theory, the research models strategic collaboration between apartment residents (demand side) and utility operators (plant side) to maximize energy efficiency and economic returns. The VPP structure is analyzed over a 15-year life cycle, incorporating net present value (NPV), payback period (PBP), and government subsidy impacts. A cooperative game framework is applied using the Shapley value to ensure fair profit allocation based on each party’s contribution. Results indicate improved self-sufficiency, peak load reduction, and mutual financial benefits. Scenario analyses show that government subsidies to the plant side significantly increase the likelihood of successful cooperation, while declining DER costs enhance the VPP’s economic viability. The findings demonstrate that apartments configured as VPPs achieve strong economic viability (39% ROI, 10.5-year payback) and operational performance (70% self-sufficiency, 40% peak reduction) when grid arbitrage is enabled and moderate government subsidies (35% PV, 45% BESS) are provided. This research provides a replicable model for urban energy planning and policy development, promoting sustainable energy transitions through shared DER infrastructure and cooperative stakeholder engagement. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

Isothermal dispatch models for battery energy storage systems (BESSs) systematically underestimate degradation costs because dispatch-induced Joule heating elevates cell temperature and accelerates ageing through Arrhenius-type kinetics. This paper proposes three integrated contributions. First, a thermal–electrochemical coupling loop embeds a first-order lumped thermal model within the dispatch simulation: cell temperature is updated from I²R heat generation and Newton cooling at each time step, and the resulting temperature trajectory feeds into the Arrhenius stress factors of a semi-empirical degradation model combining $\sqrt{\Delta t}$-based calendar ageing with Rainflow-based cycle ageing, enabling the optimiser to discover thermally self-regulating strategies. This coupling is critical because, as the results demonstrate, ignoring it leads to systematic underestimation of degradation costs by up to 13%. Second, the resulting four-objective problem (negative profit, thermally coupled degradation cost, SOC deviation, and CVaR imbalance penalty) is solved by a hypervolume-contribution-enhanced NSGA-III (HVC-NSGA-III), which augments reference-point selection with an archive pruned by removing the solution of the smallest individual hypervolume contribution, concentrating Pareto resolution in the knee region. Third, an SOH-adaptive knee-point selection assigns the degradation weight as a monotone function of ageing degree $(1-\mathrm{SOH})/(1-{\mathrm{SOH}}_{\mathrm{EOL}})$, automatically tightening dispatch conservatism as remaining useful life diminishes. Simulations on ENTSO-E data over 96 h show the following: (i) thermal coupling shifts the Pareto front by 8–15% in the degradation dimension with temperature excursions up to 7 K; (ii) HVC-NSGA-III improves hypervolume by 8.7% over standard NSGA-III; (iii) SOH-adaptive selection reduces capacity loss by 27.4% at only 9.1% revenue cost; and (iv) ablation confirms Rainflow (24.8%) and thermal coupling (13.1%) as the two largest contributors. Full article

The building sector is responsible for 40% of CO₂ emissions in Portugal, making the integration of renewable energy systems increasingly relevant. Photovoltaic (PV) technologies have become more accessible due to declining levelized costs of energy, and when coupled with battery energy storage systems (BESSs), they can enhance grid independence, reduce household energy expenses, and mitigate peak load stress. However, high upfront costs still limit adoption, particularly among vulnerable communities. This study evaluates the technical, economic, and environmental performance of PV systems, with and without BESSs, compared with an existing solar thermal configuration in a social-housing neighbourhood in Porto, Portugal. Numerical simulations were conducted for three scenarios, optimising system sizing and ensuring hourly energy flow balance between generation, storage, and grid supply. Results indicate that all configurations are technically feasible within Porto’s climate conditions, though with distinct investment needs, payback periods, and CO₂ reduction outcomes. The findings offer practical guidance for designing renewable energy solutions tailored to social housing, supporting both decarbonization goals and long-term mitigation of energy poverty. Full article

This paper develops and applies a techno-economic optimization framework for sizing photovoltaic (PV) and battery energy storage systems (BESSs) in grid-connected energy communities. An in-house developed modeling platform featuring custom MATLAB (R2025a) code implements a mixed-integer linear programming (MILP) model that minimizes differential net present value (NPV) over a 25-year lifetime, integrating capital expenditures, operating cash flows, and carbon taxation. The formulation captures temperature-dependent PV efficiency, battery round-trip efficiency, and time-varying electricity prices, and is validated on a real campus energy community with hourly demand, irradiance, and tariff data. Two design scenarios are examined: the optimal unconstrained case and a budget-constrained configuration (CAPEX ≤ 2.0 M€). Results show the unconstrained system installs 3.19 MW_p PV and 12.3 MWh storage, achieving 78.9% self-sufficiency and a 78.9% emissions reduction. The constrained case installs 0.99 MW_p and 1.68 MWh, achieves 32.0% self-sufficiency, and delivers a 4.46 M€ NPV with payback in 3.9 years. Under current costs and tariffs, PV-dominated configurations provide the highest value, with limited battery benefit except under generous budgets or higher carbon prices. A dedicated CAPEX sensitivity analysis explores PV and battery cost variability and its impact on optimal sizing and economic outcomes. The core methodological contribution is a master-planning formulation that solves design decision variables and optimal dispatch concurrently within a single MILP. The flexible platform enables future reassessment as technology, tariff, and policy landscapes evolve. Full article

Conventional control strategies for battery energy storage systems (BESSs) fail to achieve symmetrical and coordinated control between the DC/DC and DC/AC conversion stages, resulting in unsatisfactory DC capacitor voltage fluctuation suppression and threatening the safe and stable operation of the system. To address this issue, this study proposes an improved model predictive control (MPC)-based control method that explicitly considers DC capacitor voltage fluctuation suppression. First, a dynamic mathematical model of the BESS is established by jointly considering its DC/DC and DC/AC energy conversion stages. The model is then discretized using the first-order forward Euler method to facilitate controller implementation. Second, the cost function of the proposed MPC-based control method is designed to simultaneously incorporate DC capacitor voltage fluctuation suppression and output current tracking errors on both the DC and AC sides. Finally, the switching states of the DC and AC converters are selected as the control set, and the optimal switching signals for the BESS are determined by optimizing the aforementioned cost function. Verification results demonstrate that, compared with traditional control strategies, the proposed strategy achieves more symmetrical stable and dynamic performance and reduces DC side capacitor voltage fluctuation by approximately 80%, thereby effectively ensuring the safe and stable operation of the system. Full article

Bidirectional Interleaved Converters (BICs) are widely used in Battery Energy Storage Systems (BESSs) due to their modular structure, high efficiency, and reduced current ripple. However, under partial-load operation, conventional control strategies with fixed or purely current-based phase shedding repeatedly activate the same converter branches, resulting in increased switching losses, thermal imbalance, and uneven aging of power semiconductors. This paper proposes a temperature-adaptive control strategy for BICs aimed at improving light-load efficiency while actively balancing thermal stress between converter branches. The approach combines a current-adaptive phase-shedding algorithm with a temperature-based branch rotation mechanism, where real-time transistor junction temperature is used as the primary decision variable for branch activation and deactivation. An electro-thermal real-time simulation model of a two-branch BIC is developed using the Controller Hardware-in-the-Loop (CHIL) methodology in the Typhoon HIL environment. The proposed control strategy is validated through real-time CHIL experiments in both boost and buck operating modes under representative battery load profiles. The results demonstrate a reduction in average and peak transistor junction temperatures, improved thermal distribution between converter branches, and more uniform branch utilization, while preserving stable current regulation and power flow. The presented method represents a practical extension of conventional phase-shedding techniques and provides an implementation solution for improving efficiency and reliability of BICs in BESS applications. Full article

The resilience of Intelligent Transportation Systems (ITSs) heavily relies on distributed Battery Energy Storage Systems (BESSs) deployed in harsh, unattended highway environments. Traditional maintenance strategies often fail to account for the dynamic feedback between battery aging, environmental stress, and maintenance response latency. This study proposes a system dynamics (SD) framework to evaluate and optimize the resilience of these critical power infrastructures. By modeling the nonlinear interactions among sensor data, controller logic, and remote discharge terminals, we simulate the system’s dynamic behavior over a 36-month lifecycle. The results reveal a critical “scalability threshold”: when battery pack quantity exceeds 40 units, the system’s self-healing time increases disproportionately, degrading resilience. Furthermore, the study identifies 384 V as the optimal “Resilience Topology Voltage”, offering the fastest recovery speed by balancing thermal stability with consistency management efficiency. These findings provide theoretical guidelines for configuring BESS capacity and optimizing remote maintenance protocols to ensure uninterrupted highway operations. Full article