Search Results (651)

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article

In the background of electricity market reform, combined heat and power (CHP) units must balance electricity market revenues with reliable heat supply. However, the flexibility of CHP units to confront various features of renewable outputs remains to be explored more thoroughly. In this study, day-ahead electricity price curves are classified into four typical categories adopting k-means clustering, featured by diverse temporal trends associated with the output of renewables. An integrated model—capturing the CHP, the battery energy storage system (BESS), and heating network dynamics—supports day-ahead operational optimization. The results suggest that distinct operational strategies are to be implemented under different price profiles. Moreover, incorporating a BESS and exploiting thermal inertia of the network expands arbitrage opportunities and profit from the electricity market. Lastly, an alternation in the operational goal of CHP units is proposed, namely, from thermal-economy-guided to comprehensive-economy-oriented. Comparative results underscore the benefits of the revised strategies. Full article

The increasing penetration of variable renewable energy sources has intensified the need for ancillary services to maintain grid stability, and demand-side flexibility, particularly through distributed energy systems (DESs), is expected to play an important role. This study proposes a two-stage optimization framework for DESs under CO₂ constraints that enables gas engines and battery energy storage systems (BESS) to provide regulating power equivalent to Load Frequency Control (LFC). The framework consists of an Equipment Sizing Optimization Model (ESM) and an Equipment Operation Optimization Model (EOM), both formulated as mixed-integer linear programming (MILP) models. The ESM determines equipment capacities using simplified operational representations, where partial-load efficiencies are approximated through linear programming (LP)-based constraints. The EOM incorporates detailed operational characteristics, including start-up/shutdown states and partial-load efficiencies, to perform daily scheduling. Information obtained from the ESM, such as the CO₂ emissions, the equipment capacities, and the BESS state of charge, is passed to the EOM to maintain consistency. A case study shows that providing regulating power reduces total system cost and that CO₂ reduction constraints alter the equipment mix. These findings demonstrate that the proposed framework offers a practical and computationally efficient approach for designing and operating DESs under CO₂ constraints. Full article

This paper proposes and validates a method for assessing the resilience of cyber–physical microgrids integrating Photovoltaic (PV) generation and Battery Energy Storage Systems (BESS). The approach combines two operational performance indicators—Voltage Deviation Index (VDI) and Energy Not Supplied (ENS)—with a composite resilience index that captures recovery dynamics following physical and cyber disturbances. The method is implemented in MATLAB Simulink R2022b on the IEEE 33-bus feeder, with PV at bus 6 and a BESS at bus 18. Two stress scenarios are analyzed: (i) loss of the main supply at bus 2 and (ii) a cyber-induced communication failure that triggers local (fallback) operation. Compared with the base case, the proposed strategy reduces VDI by approximately 27% and ENS by 12%, demonstrating significantly improved resilience without noticeable performance penalties. Full article

Graph representation learning (GRL) has emerged as a unifying paradigm for modeling the relational and heterogeneous nature of battery energy storage systems (BESS), yet a systematic synthesis focused on data-scarce (few-shot) battery scenarios is still lacking. Graph representation learning offers a natural way to describe the structure and interaction of battery cells, modules and packs. At the same time, battery applications often suffer from very limited labeled data, especially for new chemistries, extreme operating conditions and second-life use. This review analyzes how graph representation learning can be combined with few-shot learning to support key battery management tasks under such data-scarce conditions. We first introduce the basic ideas of graph representation learning, including models based on neighborhood aggregation, contrastive learning, autoencoders and transfer learning, and discuss typical data, model and algorithm challenges in few-shot scenarios. We then connect these methods to battery state estimation problems, covering state of charge, state of health, remaining useful life and capacity. Particular attention is given to approaches that use graph neural models, meta-learning, semi-supervised and self-supervised learning, Bayesian deep networks, and federated learning to extract transferable features from early-cycle data, partial charge–discharge curves and large unlabeled field datasets. Reported studies show that, with only a small fraction of labeled samples or a few initial cycles, these methods can achieve state and life prediction errors that are comparable to or better than conventional models trained on full datasets, while also improving robustness and, in some cases, providing uncertainty estimates. Based on this evidence, we summarize the main technical routes for few-shot battery scenarios and identify open problems in data preparation, cross-domain generalization, uncertainty quantification and deployment on real battery management systems. The review concludes with a research outlook, highlighting the need for pack-level graph models, physics-guided and probabilistic learning, and unified benchmarks to advance reliable graph-based few-shot methods for next-generation intelligent battery management. Full article

With the continuously rising penetration rate of variable renewable energy (VRE), issues related to power balance and frequency stability in power systems have become increasingly prominent. Battery energy storage systems (BESS) with grid-forming capabilities are regarded as an effective solution for providing rapid frequency support. However, the stochastic fluctuations of VRE output also lead to time-varying system inertia, which undoubtedly increases the complexity of energy storage planning. To address these problems, this study constructs a bi-level robust planning model for grid-forming energy storage considering frequency security constraints. First, a frequency response model for grid-forming BESS is established. By accurately describing the delay characteristics of different resources in frequency response, dynamic frequency security constraints (FSC) that can be embedded into the planning model are constructed. Subsequently, the study proposes an evaluation method for the spatial distribution of power system inertia, providing a basis for the optimal siting of BESS in the grid. On this basis, a bi-level robust planning model, considering VRE uncertainty, is constructed, which embeds an operational simulation model and incorporates FSC. To achieve an effective solution of the model, FSC is transformed into a second-order cone form, and a nested column-and-constraint generation (C&CG) algorithm is employed for solving. Simulation results on the modified NPCC-140 bus system verify the effectiveness of the proposed model. While reducing the total cost by 15.9%, this method effectively ensures the dynamic frequency security of the power system, improves the spatial distribution of inertia and significantly enhances the system’s ability to accommodate VRE. Full article

Lithium-ion batteries (LIBs) are vital components in electric vehicles (EVs) and battery energy storage systems (BESS). Accurate estimation of the state of charge (SOC) and state of health (SOH) depends heavily on precise battery modeling. This paper examines six commonly used equivalent circuit models (ECMs) by deriving their impedance transfer functions and comparing them with measured electrochemical impedance spectroscopy (EIS) data. The particle swarm optimization (PSO) algorithm is first utilized to identify the ECM with the best EIS fit. Then, thirteen bio-inspired optimization algorithms (BIOAs) are employed for parameter identification and comparison. Results show that the fractional-order R(RQ)(RQ) model with a mean absolute percentage error (MAPE) of 10.797% achieves the lowest total model fitting error and possesses the highest matching accuracy. In model parameter identification using BIOAs, the marine predators algorithm (MPA) reaches the lowest estimated MAPE of 10.694%, surpassing other algorithms in this study. The Friedman ranking test further confirms MPA as the most effective method. When combined with an Internet-of-Things-based online battery monitoring system, the proposed approach provides a low-cost, high-precision platform for rapid modeling and parameter identification, supporting advanced SOC and SOH estimation technologies. Full article

State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article

The DC collection system for wind farms serves as a critical component for power transmission, making its reliable operation paramount. Currently, research on the operational reliability of DC collection systems in wind farms remains largely confined to evaluation, with limited studies focused on enhancing reliability. This article introduces a Battery Energy Storage System (BESS) into the DC collection system from the perspective of improving reliability through the BESS. However, the introduction of a BESS significantly increases economic costs, with storage lifespan being a key factor affecting economic viability. To balance system reliability and BESS economics, this paper proposes a multi-objective configuration optimization method that accounts for the BESS lifespan, and employs the CRITIC-MARCOS integrated decision-making method to determine the optimal BESS configuration. Finally, the proposed method was validated in a 100 MW DC collection system for wind farms, offering valuable insights for BESS configuration schemes in such systems. Full article

Lithium-ion batteries (LIBs) dominate energy storage for electric vehicles (EVs) due to their high energy density, long cycle life, and low self-discharge. However, high costs, complex manufacturing, and the requirement for advanced battery management systems (BMSs) constrain their broader deployment. Therefore, extending the utility of LIBs through reuse is essential for economic and environmental sustainability. Retired EV batteries with 70–80% state-of-health (SOH) can be repurposed in battery energy storage systems (BESSs) to support power grids. Effective reuse depends on accurate and rapid assessment of SOH and state-of-safety (SOS), which relies on precise state-of-charge (SOC) detection, particularly for aged LIBs with elevated thermal and electrochemical risks. This review systematically surveys SOC, SOH, and SOS detection methods for second-life LIBs, covering model-based, data-driven, and hybrid approaches, and highlights strategies for a fast and reliable evaluation. It further examines power electronics topologies and control strategies for integrating second-life LIBs into power grids, focusing on safety, efficiency, and operational performance. Finally, it analyzes key factors within the closed-loop supply chain, particularly reverse logistics, and provides guidance on enhancing adoption and supporting the establishment of circular battery ecosystems. This review serves as a comprehensive resource for researchers, industry stakeholders, and policymakers aiming to optimize second-life utilization of traction LIBs. Full article

Hybrid microgrids struggle to manage electricity due to renewable source, storage, and load demand variability. This paper proposes a centralized controller employing hybrid deep learning and evolutionary optimization to overcome these issues. Solar panels, BESS, EVs, dynamic loads, steady loads, and a switching main grid make up the hybrid microgrid. To capture spatial and temporal patterns, a centralized controller uses a deep learning model with a CNN–LSTM architecture. The imperialist competitive algorithm (ICA) optimizes neural network hyperparameters for more accurate controller outputs. The controller controls grid switching, voltage source converter power, and EV reference current. R² values of 0.9602, 0.9512, and 0.9618 show reliable controller output predictions. A typical test case, low sunshine, and no EV or BESS initial charging are validation situations. Its constant power flow, uncertainty management, and adaptability make this controller better than others. Even with intermittent energy and limited storage capacity, the ICA-optimized hybrid deep learning controller stabilized smart-grids. Full article

This study examines the economic performance of residential photovoltaic systems combined with battery storage (PV-BESS) under Poland’s net-billing regime for a single-family household without subsidy support in 10-year operational horizon. These insights extend existing European evidence by demonstrating how net-billing fundamentally alters investment incentives. The analysis incorporates real production data from selected locations and realistic household consumption profiles. Results demonstrate that optimal system configuration (6 kWp PV with 15 kWh storage) achieves 64.3% reduction in grid electricity consumption and positive economic performance with NPV of EUR 599, IRR of 5.32%, B/C ratio of 1.124 and discounted payback period of 9.0 years. The optimized system can cover electricity demand in the summer half-year by over 90% and reduce local network stress by shifting surplus solar generation away from midday peaks. Residential PV-BESS systems can achieve economic efficiency in Polish conditions when properly optimized, though marginal profitability requires careful risk assessment regarding component costs, durability and electricity market conditions. For Polish energy policy, the findings indicate that net-billing creates strong incentives for regulatory instruments that promote higher self-consumption, which would enhance the economic role of residential storage. Full article

This article presents research on developing a synthetic measure to assess the readiness of individual EU countries to store energy from renewable energy sources. The authors developed individual measures that describe both the technical aspects of energy storage and the systemic and strategic aspects related to energy security and energy transition. These measures enabled the development of a synthetic measure, the Energy Storage Readiness Index (ESRI-BESS), and scenarios for the development of energy storage facilities in the European Union. TOPSIS and Monte Carlo methods were used. In the research presented, the authors focused their analyses on how the system interacts with storage facilities, rather than on what is installed. A quantitative set of indicators was constructed, embedded in the 4A energy security model. The resulting indicator measures not only whether storage facilities exist but also whether the system is prepared to ensure the country’s energy security. The results obtained indicate the need to build a flexible regulatory framework adapted to the growing role of storage facilities, that is, to facilitate and accelerate the process of connecting storage facilities to the grid. In the context of 4A, it is important to note that energy storage facilities can strengthen all four pillars of energy security when infrastructure development is paralleled by reforms and grid integration. The ability to store and flexibly manage energy is becoming a new dimension of energy transformation. Full article

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts. Full article

Residential photovoltaic and battery energy storage systems (PV/BESS systems) are gaining attention as a measure against natural disasters and rising electricity prices. This paper aims to propose an operational strategy that balances electricity cost reductions, battery lifespans, and outage mitigation for the residential PV/BESS system. The optimization model considering battery degradation determines normal operations with balancing cost reductions and degradation. Additionally, a rule-based approach simulates system performance during various outages and evaluates supply continuity using a resilience metric: the percent continuous supply hour. Outage mitigation benefits are quantified by considering the distribution of residential values of lost load (VoLLs). Results show that the operation considering degradation maintains a high state of charge (SoC) at all times. For 25.7% of households with large demand, electricity cost reductions exceed equipment costs. Outage simulations demonstrate that the mean energy supplied during a 48-h outage ranges from 14 kWh to $26.7$ kWh. Furthermore, the proposed operation increases the resilience metric from 20% to 30% under severe and unpredictable outages. Finally, incorporating outage mitigation benefits increases the proportion of households adopting PV/BESS systems by 21.5% points. Full article