Search Results (185)

As data centers become major and schedulable loads of the new power system, connecting them to multiple microgrids offers a promising route to absorb local renewable energy through cross-domain coordination. However, when the microgrids belong to competing operators, coordinated scheduling forces each party to disclose its data-center load curve, storage state, and pricing strategy, which constitutes a core operational secret that no microgrid is willing to reveal. This paper develops a secure and privacy-preserving distributed scheduling scheme for data-center-integrated microgrids built on blockchain. A “data-stays-local, energy-crosses-centers” model is established that elevates privacy from an add-on feature to a first-order architectural constraint, defining a “three-no” principle and a two-layer architecture in which each microgrid optimizes its interior in plaintext and exposes only encrypted matchable factors. On this basis, a decentralized ciphertext scheduling-negotiation algorithm is designed on blockchain smart contracts, performing cross-microgrid matching under secure multi-party computation entirely in the encrypted domain, committing auditable encrypted digests on-chain, and dynamically allocating scheduling priority through an on-chain reputation mechanism. Case studies on a cluster of interconnected microgrids show that the proposed scheme attains cost and renewable accommodation within about three-tenths of a percent of the centralized optimum while reducing operational data-leakage risk from 96.7 percent to 3.8 percent, at the manageable expense of a few seconds of negotiation latency. Benchmarking against an exact mixed-integer solver on small-scale systems bounds the mean optimality gap of the decomposed scheme at 0.74 percent, with a worst case of 2.54 percent over sixty instances. Full article

In DC microgrid (DC-MG) clusters, distributed generation units rely on electronic communication networks to exchange voltage measurements, current information, and coordination signals for voltage recovery and current sharing. As the degree of system clustering and communication coupling increases, nodal denial-of-service (DoS) attacks may interrupt the information exchange of leaders and followers, resulting in communication topology switching and degraded cooperative control performance. Accordingly, this paper proposes an event-triggered (ET) resilient control scheme with high communication efficiency for networked DC-MG clusters under nodal DoS attacks. First, a distributed secondary control model with a cross-layer communication mechanism is constructed in accordance with the requirements of the system’s overall power distribution, which incorporates the two-layer node architecture of leaders and followers in DC-MG clusters. Second, a statistical multimode nodal DoS attack model is developed to characterize heterogeneous communication interruptions through topology-dependent attack modes and their occurrence probabilities. Finally, an exponential threshold ET mechanism based on bus-voltage recovery errors is designed within the distributed secondary control framework to reduce redundant information transmission while preserving resilience against nodal communication attacks. Simulation results demonstrate that the proposed method can maintain accurate voltage recovery and current sharing in networked DC-MG clusters under large-scale DoS attacks, while improving communication efficiency through ET updates. Full article

Industrial microgrids are emerging as important providers of demand-side flexibility for active distribution networks. However, their participation in source–load coordination is hindered by complex production constraints, limited dispatch executability, and the widespread reliance on baseline-based demand response mechanisms. To address these challenges, this paper proposes a baseline-free source–load coordination framework for industrial microgrid clusters. A linear state–task network (LSTN) model is employed to characterize industrial production processes while preserving equipment operation, material balance, buffer storage, and production target constraints. Based on the feasible operating regions of individual microgrids, a simplified optimal adjustable load model (OALM) is developed at the aggregator level to identify and aggregate cluster-level flexibility boundaries without disclosing detailed production information. Building upon the aggregated flexibility region, a baseline-free target power tracking strategy is established, in which the distribution network issues absolute power targets and the aggregator coordinates multiple industrial microgrids to achieve their realization. The proposed framework simultaneously ensures production feasibility, scalable flexibility aggregation, and practical dispatch implementation. Case studies demonstrate that the aggregated industrial load can accurately track dispatch targets while satisfying all production constraints, thereby enhancing the capability of industrial microgrid clusters to participate in large-scale source–load interaction and renewable energy accommodation. Case study results show that when the number of industrial microgrids increases from 10 to 200, the total computation time increases from 5.08 s to 337.40 s, while the target tracking error remains within numerical tolerance. Compared with the conventional baseline-based virtual-battery (VB) method, whose root mean square error (RMSE) with respect to the intended absolute target reaches 241.30 kW under a ±10% baseline estimation error, the proposed method achieves near-zero tracking error. In addition, the production-agnostic aggregation model expands the flexibility boundary by 21.84% and generates targets that are not exactly executable under the full LSTN production constraints. Full article

The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids. Full article

Distributed energy systems are increasingly important for strengthening critical infrastructure resilience under conditions of technological, climatic, geopolitical, and cyber disruption. However, existing research on energy resilience is still dominated by technical approaches focused on reliability, renewable energy integration, microgrid control, and storage optimisation, while the role of distributed energy systems in the full crisis-management cycle remains insufficiently conceptualised. This article addresses this gap by combining a scoping review, lexicographic and semantic analysis using IRaMuTeQ version 0.7 alpha 2, and a conceptual-methodological framework for assessing distributed energy systems as instruments of crisis management. The main contribution of the study is the M_ZK-DES model, which integrates technological-infrastructural, decision-operational, legal-institutional, and socio-organisational dimensions with four crisis-management phases: prevention, preparedness, response, and recovery. The model distinguishes distributed energy systems, distributed energy resources, distributed generation, microgrids, prosumers, energy communities, and energy clusters and links them to measurable resilience indicators. These include SAIDI, SAIFI, energy not supplied, restoration time, share of critical load served, islanding capability, voltage and frequency stability, storage autonomy, procedural readiness, and local coordination capacity. The analysis shows that distributed energy systems may reduce vulnerability to cascading failures, support islanded operation, protect vulnerable consumers, improve emergency power continuity, and strengthen local energy autonomy. The proposed scoring and weighting logic enables future empirical validation, scenario testing, and comparative assessment across regions and crisis types, including extreme weather events, cyberattacks, and supply-chain disruptions. The article contributes to energy resilience and crisis-management studies by offering an integrated and operational framework for evaluating distributed energy systems as practical tools for critical infrastructure protection and continuity of essential public services. Full article

The increasing frequency of high-impact, low-probability climate events has highlighted the limitations of conventional reliability criteria, including N-1 planning assumptions, and the need for dynamic resilience architectures in electrical systems. This article analyzes the evolution, trends, and technological challenges associated with microgrid resilience under extreme-weather disruptions. The study adopts a hybrid research design that combines quantitative bibliometric mapping of 283 Scopus-indexed article records for 2016–2025 using CiteSpace version 7 with a structured technical synthesis of the selected literature. The structural analysis identified nine thematic clusters and indicated a transition from service restoration and component-level recovery toward multi-energy energy-management systems, resilient distribution-system planning, and mobile restoration resources. The technical synthesis shows that modern resilience is increasingly associated with hierarchical control architectures: optimization and forecasting methods support tertiary-level energy management, while grid-forming inverters can provide primary-layer voltage references that support islanded operation and black-start sequences under appropriate design, protection, and validation conditions. The article concludes that future research should bridge stochastic planning and real-time physical operation through standardized interoperability frameworks, reproducible dynamic metrics, and experimentally validated control strategies for autonomous critical microgrid operation under extreme-weather conditions. Full article

Traditional control strategies, such as droop-frequency and PI controllers, often show limited adaptability when the system operates under highly variable renewable generation and load conditions. In order to overcome this limitation, this study proposes the design and simulation of a clustered microgrid supported by reinforcement learning (RL)-based virtual inertia control. Three continuous-control RL algorithms were evaluated: Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC). The SAC agent provided the most robust training performance, reaching stable convergence after approximately 400 episodes and a final reward close to 86 units after 600 episodes. DDPG presented the second-best behavior, whereas TD3 achieved the lowest final reward, approximately 43 units. The proposed Agent SAC-Reinforcement Learning control was tested on a two-area microgrid cluster, which demonstrated greater frequency stability. Results indicate a frequency nadir of 59.82 Hz and ROCOF of 0.1484 Hz/s, with 6.78% nadir deviation improvement and 37.23% ROCOF reduction compared to PID-based strategies. Full article

To address the issues of intensified source–load mismatch and peak-shaving pressure in microgrids under conditions of high-penetration distributed photovoltaic integration, this paper proposes a multi-objective optimization configuration method for microgrid clusters that integrates energy storage systems (ESSs) with demand response mechanisms. A mixed-integer linear operational model is constructed to characterize charge–discharge exclusivity and state-of-charge constraints. Based on this, a three-dimensional objective function encompassing economic efficiency, low carbon emissions, and reliability is established, and the Pareto-optimal solution set is solved using the NSGA-II algorithm. Through case studies in typical county-level scenarios, operational characteristics are compared between scenarios without ESSs and those with coordinated optimization, revealing the law of marginal returns for energy storage capacity and the mechanism by which source–grid–load–storage coordination mitigates the “duck curve.” The results demonstrate that the proposed method enables full accommodation of renewable energy, enhances peak-shaving capability by over 32%, reduces the cost per kilowatt-hour by approximately 11%, and identifies the economically optimal capacity point. This research provides a quantitative basis for energy storage planning and multi-objective decision-making in county-level microgrid clusters. Full article

Interlinking converters (ILCs) are critical interfaces for coordinating power exchange in hybrid ac/dc microgrid clusters. Practically, different microgrids have varying inertia capacities and load priorities, it is urgent to design flexible power exchange control of interlinking converters for priority-driven dynamic sharing. To achieve optimal inertia inter-support among microgrids, a decentralized optimal dynamic control of ILCs is proposed for priority-driven inertia sharing among microgrid clusters. Firstly, an inertia interaction optimization model is established, incorporating subgrid priority weights and inertia-support capacity. Secondly, the established optimization model is implemented in a decentralized manner by deriving a local ILC control law from the optimality condition. Furthermore, a quantitative analytical framework based on a whole equivalent circuit model is constructed to reveal the impact of control parameters on key dynamic indicators. The proposed strategy features high scalability and less-communication requirements of decentralized control, enabling global optimization of transient performance and priority support for critical loads. Finally, the proposed method is validated through five representative cases in a Hardware-in-the-loop (HIL) platform. Full article

This paper investigates AC microgrid clusters with high penetration of DFIG-based wind power. Conventional separated control of wind power output and stability support may cause abrupt power command variations during transitions between grid-connected and islanded operating modes, thereby aggravating system power imbalance. To address this issue, a unified DFIG control strategy is proposed in which an operating objective coordination factor continuously coordinates stability support and wind power output within a unified reference generation framework. A continuity preservation mechanism is then developed to suppress transient impacts through phase angle coordination and DC link power correction. Simulation results show that, compared with conventional MPPT + PQC and FSC methods, the proposed strategy mitigates transition induced fluctuations during grid island transitions. RTDS experiments based on a modified Banshee microgrid model further verify that the proposed strategy improves system stability during grid-connected to islanded transition and islanded to grid connected reconnection. Full article

To address fault-induced boundary variations and conflicting commands among heterogeneous controllers in microgrid clusters with high distributed generation penetration, this paper proposes a multilayer multiagent control strategy based on cross-coordinated multiagent control and conflict coordination. The method uses a hierarchical distributed hybrid architecture. Local grid-forming (GFM) energy storage and photovoltaic (PV) converters provide autonomous voltage source support, microgrid coordination controllers generate distributed candidate commands, and the system-level coordination controller performs event-triggered arbitration. Unlike consensus-based cooperative control with fixed exchanged variables, the proposed method enables overlapping supervisory authority, weighted command fusion, explicit conflict classification, and feasible command projection under resource, state-of-charge (SOC), ramping, and load priority constraints. Direction, capacity, and objective conflicts are resolved through system-level arbitration, which converts multiple candidate commands into a single executable command. Comparative simulations show that the proposed method reduces frequency and voltage deviations, shortens power recovery time, improves SOC balancing among energy storage units, and enhances constrained hydropower coordination compared with conventional droop control and one-to-one hierarchical control. These results verify its effectiveness in improving dynamic stability and coordinated support capability in microgrid clusters. Full article

This study presents a field-validated, scenario-based two-layer dispatch framework for sustainable rural electrification, demonstrated at the Khlong Ruea hybrid microgrid (50 kW micro-hydro, 20 kWp PV, 48 kWh LiFePO₄ BESS, 48 kW diesel) in Chumphon Province, southern Thailand. The framework combines an offline mixed-integer linear program (MILP) with scenario-based uncertainty handling (k-medoid clustering, N = 8; CVaR penalty at α = 0.9) and an operator-assisted execution layer implementing source transitions via manual changeover switches. A Fluke 435 IEC 61000-4-30 Class-A field campaign with stationary block-bootstrap inference (B = 2000 resamples, 10 min blocks) documented substantial power quality improvements under BESS supply: the three-phase average THD-V reduced from 5.4% to 2.9% with 95% confidence intervals that do not overlap between the two supply modes; the THD-I dropped from 55.8% to 4.9% (Phase A; 91.2% reduction; three-phase average 64.0% → 7.8%); the voltage unbalance fell from 0.86% to 0.03%; and the displacement power factor improved from 0.92 to 0.95. IEEE Std 1459-2010 decomposition reveals that 93% of the non-fundamental apparent power under diesel supply is attributable to current-distortion volt-amperes (Dᵚ = 4737 VA vs. 283 VA under BESS). A composite power quality index confirms that diesel operation fails the IEEE 519-2022 current-distortion limits while BESS supply satisfies all EN 50160 and IEEE 519-2022 thresholds (PQI: 0.75 vs. 3.89). A 365-day closed-loop simulation confirmed an 18.4% reduction in annual operating cost and a 27.6% reduction in diesel runtime relative to a rule-based baseline, while maintaining LPSP at or below 0.53%. Techno-economic projection from field-verified HOMER inputs reduced the levelized cost of electricity from approximately 0.69 USD/kWh (diesel-only) to 0.36 USD/kWh for the proposed PV + BESS + Hydro + Diesel configuration, which retains diesel as a low-utilization backup at a near-100% renewable energy share. The same configuration delivered a 47.9% net present cost advantage over diesel-only operation and a 12.8 t (82%) annual CO₂ reduction. Manual source-transfer interruptions of 1–3 min are fully characterized, and a cost-estimated ATS + SCADA upgrade roadmap is defined. Full article

With the large-scale integration of distributed PV, wind power, small hydropower, ESSs, and flexible loads, microgrid clusters exhibit stronger coupling across multiple voltage levels and more complex dynamic interactions. Existing ESS allocation methods mainly focus on economy or total capacity, but insufficiently distinguish the stability support functions of ESSs at the station, feeder, and distribution transformer area levels. To address this issue, this paper proposes a coordinated capacity and siting allocation method for ESSs in multi-voltage level microgrid clusters. The proposed method quantifies the effects of transformers, feeder switches, and power electronic interfaces on disturbance propagation, optimizes station-level and feeder-level ESS capacities, and determines distribution transformer area-level ESS siting according to voltage sensitivity, recovery capability, and stability margin. Simulation results under PV fluctuation, load disturbance, external grid disturbance, and local islanding verify that the proposed method improves voltage recovery, power support, and operational stability while supporting renewable energy utilization, storage resource efficiency, and resilient clean power supply. Full article

Mobile emergency generators and mobile energy storage clusters are core flexible resources for the rapid recovery of critical loads in post-disaster distribution networks and the enhancement of resilience in isolated microgrids. However, due to the strong random changes in end-load loads, heterogeneous units are prone to problems such as large transient inrush currents, unstable phase-locked loops (PLLs), and reliance on manual synchronization adjustments when connected under load. To address these issues, this paper proposes a multi-timescale smooth grid-connected control architecture that combines data-driven feedforward and physical feedback. The architecture extracts spatiotemporal load features based on a CNN-BiLSTM-Attention model to achieve capacity optimization and baseline allocation. Predicted load voltage drop is converted into feedforward compensation to construct a virtual internal potential for coarse pre-grid connection adjustment. This is then combined with a closed-loop PLL to achieve fine-tuning of phase angle and voltage errors and autonomous decoupling of transient power after grid connection. Simulation and experimental results show that the proposed method suppresses voltage overshoot from 0.3 p.u. to within 0.03 p.u., increases the minimum frequency to above 49.8 Hz, reduces inrush current, and shortens synchronization time by 15.4%, significantly improving the system’s rapid connection and recovery capabilities. Full article

The large-scale integration of renewable energy and electric vehicles introduces grid-connected power fluctuations in microgrids. To address this, this paper proposes a novel two-stage optimization scheduling strategy that balances economic efficiency and grid compatibility. In the first stage, a multi-objective optimization model is formulated to minimize both operating costs and power fluctuations, and the Improved Multi-Objective Grey Wolf Optimization algorithm—incorporating the Bernoulli chaotic map—is employed to solve it efficiently. In the intra-day phase, a rolling tracking strategy based on model predictive control is proposed to address ultra-short-term forecasting errors, and a multi-unit hierarchical error compensation mechanism is designed. This mechanism prioritizes the use of supercapacitors to absorb high-frequency fluctuations, followed by the coordinated use of batteries, electric vehicle clusters, and micro gas turbines to mitigate residual deviations, thereby effectively reducing the operational burden on individual energy storage devices. Finally, a comparative analysis of six simulation cases was conducted using a weighted evaluation metric that integrates average power deviation values and interconnection line power fluctuations. The results confirm that this strategy not only significantly smooths grid-connected power fluctuations but also demonstrates exceptional robustness and adaptability under extreme forecast error scenarios. Full article