Search Results (1,991)

A microgrid is a localized distribution network composed of electricity users who have access to local renewable and other energy sources. While the utility grid plays a critical role in the nation’s economy, security, and the well-being of its residents, connecting microgrids to the wider network via utility substations can introduce significant cybersecurity risks. Unlike most existing studies that rely on simulation, this research designs and implements a physical microgrid testbed to examine cybersecurity vulnerabilities in microgrid systems. We examine the impact of various cyberattacks—including denial of service (DoS) and communication hijacking—on microgrid operations, with a particular focus on system stability and communication networks. The findings reveal critical weaknesses within the existing communication infrastructure, providing valuable insights for designing more resilient and secure microgrids. This work offers a practical framework for addressing cybersecurity challenges in real-world industrial utility networks. Full article

Global urbanization is shifting from incremental expansion to stock optimization, and old residential communities have become important spatial units for low-carbon transition. However, in existing built environments, traditional process-based inventory methods face practical constraints, including missing original drawings, complex site conditions, and severe vegetation obstruction. As a result, systematic accounting of buildings, landscapes, and natural carbon sinks remains difficult. This study integrates life cycle assessment (LCA), BIM reverse modeling, 3D point clouds, DesignBuilder simulation, inventory-based accounting, and i-Tree Eco to construct a life cycle carbon emission accounting framework for old residential communities. The framework links current-condition data reconstruction, quantity take-off, operational energy simulation, landscape inventory accounting, and vegetation carbon sequestration assessment. It is applied to Nanyuan Xincun in Hefei to quantify the community-scale carbon source–sink structure. The results show that Nanyuan Xincun presents a clear operation-led emission pattern, with the operation and maintenance phase accounting for 82.52% of total positive emissions. Within architectural engineering, operation and maintenance accounts for 82.91%, while material production accounts for 13.28%. Landscape engineering shows a more mixed structure, with operation and maintenance accounting for 52.95% and material production accounting for 36.49%. Vegetation carbon sequestration analysis shows that mature trees and shrubs are the main ecological carbon assets. Annual sequestration reaches 16.95 t-CO₂e/a, and trees and shrubs contribute 92.85% of total vegetation carbon storage. Under current vegetation conditions, annual sequestration is equivalent to 32.99% of annual landscape operation emissions, indicating considerable ecological compensation potential. Based on these findings, this study proposes four optimization pathways: operational energy reduction, low-carbon material substitution, construction and demolition waste recycling, and mature tree protection. These pathways provide data support for refined carbon management and low-carbon renewal in existing communities. Full article

The growing integration of solar, wind and battery energy storage (BES) of the microgrids (MGs) has increased the necessity of real-time energy management, especially in the multi-microgrid (multi-MG) setting, where the generation and the load change stochastically. This paper presents a Java Agent DEvelopment (JADE)-based Multi-Agent System (MAS) for real-time energy management of a low-voltage hybrid multi-MG system incorporating solar photovoltaic (PV), wind generation, and battery energy storage (BES). The proposed framework’s novelty lies in its physical campus-scale hardware deployment—validated across four operating scenarios (single MG off-grid, single MG on-grid, dual MG off-grid, and dual MG on-grid)—combined with autonomous inter-MG power sharing, which distinguishes it from existing simulation-only MAS-based microgrid studies. The suggested framework facilitates decentralized communication between interconnected MGs and the utility AC grid to facilitate the proper management of power flow, its exchange, and the reliability of the system. The intelligent agents are used to coordinate solar, wind, BES, and load changes in order to adjust to changing demand conditions. The system is physically implemented on a campus rooftop with two 1 kW solar PV arrays and two 1.5 kW wind turbine generators, each paired with a 24 V, 150 Ah battery bank, operating on a 24 V DC bus. Results across 24 h real operational profiles demonstrate effective power balance maintenance, renewable energy maximization, and constraint-compliant battery operation (SOC is bounded within 20–90%). A direct comparison with a conventional centralized JavaScript-based EMS confirms equivalent dispatch accuracy while demonstrating superior scalability, fault tolerance, and modularity of the proposed JADE MAS architecture. Full article

With the increasing penetration of renewable energy, power systems require fast and reliable frequency regulation resources. Vehicle-to-grid (V2G) aggregation can provide fast response capability. However, it relies heavily on communication networks and is vulnerable to communication degradation and false data injection attacks (FDIAs). To address this challenge, this paper proposes a detection-free resilient control method for V2G-based frequency regulation. Rather than relying on explicit attack detection or compensation, the proposed method achieves decision-level adaptation from closed-loop system feedback through dynamic selection and switching of aggregator subsets. In this way, unreliable or compromised aggregators are implicitly avoided, improving system robustness under uncertain communication and cyber conditions. To further enhance robustness, a diffusion-based adversarial reinforcement learning framework is developed. A conditional diffusion model is used to generate diverse capacity scenarios with spatial heterogeneity. Adversarial training formulates the interaction between the attacker and the defender as a zero-sum game. This enables the learning of robust selection–switching policies under worst-case disturbances. Simulation results on the IEEE 39-bus system show that the proposed method improves frequency regulation performance under communication degradation and FDIA. The RMS frequency deviation is reduced from 0.13426 Hz to 0.09174 Hz compared with the no-defense case. Full article

Because smart microgrids can flexibly integrate distributed energy resources and support grid-connected and islanded operation modes, they enhance power supply reliability and promote the efficient utilization of renewable energy. However, the open communication environment and physically exposed infrastructure introduce critical security challenges, including risks of physical hijacking and data leakage. Many existing authentication protocols either fail to address these threats or rely on heavyweight cryptographic operations such as bilinear pairings and modular exponentiation, resulting in high computational and communicational overhead. To address these issues, a lightweight authentication and key agreement (AKA) protocol for smart microgrids is proposed. The protocol symmetrically integrates Physical Unclonable Functions (PUFs) into the smart meter (SM) and smart microgrid control center (SMC) to protect stored secret information against capture attacks. Meanwhile, the SM and SMC register with the data center (DC) in a symmetric manner. During the AKA phase, the DC only assists in authenticating the identities of the SM and SMC online in a symmetric way, without participating in session key computation, thereby reducing the trust burden and computational load on the smart meters and control center. Formal security proof and informal security analysis demonstrate that the proposed protocol can resist known attacks such as physical hijacking and data leakage. Compared with existing smart microgrid authentication protocols, the proposed protocol has performance advantages and the lowest computational cost, confirming its suitability for resource-constrained microgrid environments. Full article

This study presents a comprehensive clustering analysis of residential prosumer profiles for optimizing control and peer-to-peer (P2P) energy trading in community renewable energy systems (CRES). Using data from 25 prosumer households equipped with rooftop solar photovoltaic (PV) systems and electric vehicle (EV) charging capabilities, this study implements and compares k-means and spectral clustering algorithms to identify optimal segmentation strategies for prosumer energy management. K-means clustering identifies seven practical prosumer categories with a silhouette coefficient of 0.17, while spectral clustering achieves superior mathematical separation with a silhouette coefficient of 0.275 in ten clusters, though producing six singleton outliers. The k-means solution demonstrates three primary prosumer categories: net producers, net consumers, and balanced profiles. Cluster size variation requires adaptive optimization, while singleton outliers need custom strategies. EV ownership impact consumption, so future proliferation demands dynamic clustering, and these findings will guide metaheuristic algorithms for energy trading and pricing. Full article

Rooftop photovoltaic (PV) deployment in historic European cities is often treated as an energy-maximization task, with limited attention to distributive equity and cultural heritage constraints. Linking rooftop PV deployment to sustainable development requires screening approaches that address not only renewable electricity generation, but also social inclusion, resource efficiency, and the protection of cultural heritage. This study presents an open-data, GIS-based screening framework to support Renewable Energy Community targeting in Bologna, Italy. Roof polygons from OpenStreetMap (51,950 roofs) were combined with a cultural-heritage Web Feature Service (892 assets) to classify roofs as protected and excluded (645 roofs), heritage-sensitive within a 25 m buffer (4352 roofs), or unconstrained. A conservative PV proxy estimated usable roof area and annual generation using fixed system parameters and a PVGIS-derived specific yield (1359.39 kWh kWp⁻¹ yr⁻¹). Social conditions were represented with the composite fragility index and 2024 population totals to compute normalized per capita PV potential. Three prioritization strategies were compared: S1 (technical opportunity), S2 (equal-weight energy–equity score), and S3 (S2 moderated by heritage concentration). After heritage conditioning, the citywide potential totals 7.38 million m² usable area (≈1.11 GWp; ≈1.51 TWh·yr⁻¹), and per capita potential ranges from 2.33 to 4.55 MWh·cap⁻¹·yr⁻¹. Equity weighting shifts priority toward more vulnerable areas, while heritage-dense areas remain lower-ranked. The workflow outputs transparent rules, priority maps, and reproducible layers for sustainability-oriented municipal decision-making by connecting urban decarbonization, energy justice, efficient use of existing rooftops, and heritage-compatible renewable energy planning. Full article

Home energy management systems (HEMS) aim to provide intelligent control of the thermal comfort inside smart buildings with the minimum energy cost, while satisfying the energy consumption requests and increasing the use of energy from renewable sources. The capabilities of these intelligent HEMS agents are restricted due to the personalized observability of the environment, resulting in limited knowledge gathering and potentially sub-optimal decisions. Furthermore, several buildings have recently been organized into small energy communities, with the ultimate goal of sharing intelligence between agents in federated learning schemes.In this context, we propose a personalized federated deep reinforcement learning method using Moreau envelopes (pFedMe) for joint energy cost and household comfort optimization in energy communities that consist of multiple smart homes. Specifically, a Twin-Delayed Deep Deterministic Policy Gradient (TD3) actor–critic model is introduced, dynamically observing the state of the smart home environment and suggesting control actions on the operation of the Energy Storage System and on the regulation of the indoor temperature. The TD3 actor–critic model leads to improved policy performance in the continuous control of these systems, mitigating the overestimation bias and improving the training stability of the intelligent agents. The efficiency of the proposed method is verified via simulations based on real data, achieving a beneficial trade-off between the energy cost and the thermal comfort compared to FedAvg and Fedprox baselines. The results show that the proposed pFedMe framework consistently outperforms FedAvg and FedProx in both convergence speed and overall reward, achieving an energy cost reduction of approximately 10% compared to the other schemes, while exhibiting marginal thermal comfort behavior. Full article

The accelerating global transition toward sustainable energy necessitates a more profound comprehension of the behavioral and contextual determinants influencing household adoption intentions, particularly in policy-driven economies. This research analyses household renewable energy adoption in Saudi Arabia employing a Theory of Planned Behavior-informed, attitude-centric framework. Awareness, environmental concern, and perceptions of governmental policy are evaluated as contextual precursors of attitude, while perceived financial cost is conceptualized as a moderating constraint on the attitude–intention nexus. Data were amassed through a cross-sectional survey of 300 household decision-makers in Saudi Arabia and analyzed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). The outcomes reveal that perceptions of governmental policy, environmental concern, and awareness are positively correlated with attitudes toward renewable energy, with policy perception demonstrating the most robust relationship. Attitude, in turn, is strongly correlated with adoption intention. Furthermore, perceived financial cost negatively moderates the attitude–intention relationship, indicating that financial apprehensions diminish the transference of favorable evaluations into adoption intentions. By situating the Theory of Planned Behavior within a policy-oriented energy framework, this study underscores the pivotal roles of institutional perception and affordability in shaping household renewable energy intentions through attitudinal mechanisms. The findings furnish practical insights for policymakers by accentuating the significance of policy credibility, financial accessibility, and targeted communication strategies aligned with Saudi Vision 2030’s sustainability objectives. Full article

Automated demand response in residential sectors is critical for grid stability, but centralized control strategies fail to address the unique energy profiles of individual households. This limitation becomes more pronounced in districts where buildings differ in load demand, photovoltaic (PV) production and battery energy storage system (BESS) behavior, while electricity prices and grid carbon intensity vary hourly. Conventional rule-based controllers can exploit patterns, but they require tuning and do not generalize across heterogeneous buildings. Existing centralized reinforcement learning methods improve adaptivity, yet they often learn compromise policies and scale poorly as the number of buildings increases. To address these issues, this paper proposes an AttentionKAN-based multi-agent reinforcement learning controller for district-level BESS scheduling. The method uses centralized training with decentralized execution, where each building is controlled by its own actor and a centralized critic models cross-building interactions through a multi-head query-key-value attention mechanism. To improve approximation accuracy under nonlinear and constrained battery dynamics, multilayer perceptron (MLP) blocks in the actor and critic are replaced with Kolmogorov-Arnold Networks (KANs), whose spline-parameterized univariate functions capture saturation effects, tariff discontinuities and couplings among state of charge, PV availability and carbon intensity. Implemented in CityLearn and evaluated on a residential net-zero community dataset, the proposed controller is assessed using building-level and district-level indicators for cost, ${\mathrm{CO}}_2$ emissions, peak demand, ramping and load shape. The learned policy charges during solar-rich hours and discharges during evening peaks, achieving the strongest performance among benchmark controllers, including an approximately 50% cost reduction versus the reference case and emissions reduction. From a sustainability perspective, the results indicate that coordinated multi-building BESS control can support low-carbon residential electrification through emission reduction, lowering electricity expenditure and improving renewable-energy utilization and providing grid-supportive flexibility through reduced peaks and ramping. Full article

Large-scale renewable energy bases increasingly employ automatic voltage control (AVC) to coordinate heterogeneous reactive-power resources. The resulting voltage regulation process inherently involves sampling, communication delay, and nonlinear device characteristics, which may induce nontraditional voltage oscillations and stability degradation that cannot be adequately captured by conventional continuous-time or small-signal analysis. This paper proposes a discrete-time nonlinear voltage stability analysis framework for renewable energy bases with multi-reactive-power-resource coupling under AVC-based coordinated control. The voltage regulation dynamics are formulated as a discrete-time nonlinear closed-loop system by incorporating sampled AVC actions, delayed voltage feedback, and nonlinear voltage–reactive-power coupling. An incremental system representation is constructed, and a strong-contraction-based stability criterion is derived using sector-bounded nonlinearity descriptions and linear matrix inequalities, providing a sufficient condition for global voltage convergence without local linearization. Extensive numerical studies are conducted on a representative renewable energy base with parallel and series coupling topologies. A total of 2916 randomized configurations are evaluated. The proposed criterion achieves consistency rates exceeding 96% for the parallel topology and 99% for the series topology when compared with time-domain simulations, while the probability of dangerous misjudgment remains below 1%. Scenario-based simulations further demonstrate that coupling topology plays a critical role in shaping voltage stability behaviors, and state-space analysis further supports the observed stability behaviors. These results indicate that nonlinear strong contraction offers an effective and practical stability notion for AVC-based voltage regulation in renewable energy bases. Full article

With urban renewal accelerating and carbon constraints tightening, retrofit of old residential communities has become an important pathway for building-sector decarbonization. This study compares four typical retrofit measures in a single old residential community in Zhejiang Province, China, under a unified life cycle accounting framework: energy-efficient lighting, greening enhancement, rooftop photovoltaics, and end-use electrification. For each measure, the assessment includes measure-related material production, transport and construction, operation, replacement, and end-of-life processes, and the corresponding unretrofitted condition is reported as the baseline for comparison. The novelty of this study lies in placing heterogeneous retrofit measures within the same community boundary and in jointly examining service life, implementation intensity, electricity pathway, and retrofit timing, which provides a replicable screening framework and reference values for similar preliminary assessments. Results show that lighting retrofit, greening enhancement, and rooftop photovoltaics reduce life cycle carbon emissions, but through different mechanisms and on different time scales. Under the baseline scenario, lighting retrofit reduces lighting-related carbon emissions by 77.8%, rooftop photovoltaics reduce carbon emissions by 295.9 t CO₂-eq/a, and 34% additional greening yields a cumulative reduction of 4666.0 t CO₂-eq over 50 years. End-use electrification does not outperform other options under a static carbon-intensive grid, but its life cycle carbon footprint under the average electricity scenario for 2025–2075 is 66.9% lower than that under the static baseline scenario at full adoption of the modeled end uses. Earlier retrofit more effectively releases cumulative mitigation benefits. This study provides a systematic comparison of the carbon reduction potential of typical retrofit measures for old residential communities and offers evidence for low-carbon retrofit planning. Full article

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article