Search Results (314)

Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered. Full article

In the Latin American Pacific region, rivers are the primary transportation routes for isolated and non-interconnected areas; however, river transport relies heavily on fossil fuels, resulting in high operating costs, CO₂ emissions, and energy dependence. To address this challenge, this study proposes a methodology for the optimal sizing of renewable-based charging stations specifically adapted to the environmental and operational conditions of the Colombian Pacific coast. This research fills a critical gap in the literature by moving beyond urban-centric charging models and simplified theoretical assumptions, instead integrating real river navigation data with technical modeling of electric boat energy consumption. The methodology evaluates the technical, economic, and operational performance of photovoltaic and hybrid photovoltaic–hydrokinetic microgrids designed to ensure reliability under the region’s extreme resource seasonality and bimodal pluvial regime. Results indicate that while purely photovoltaic systems offer lower initial investment costs, hybrid configurations significantly enhance energy resilience by leveraging complementary renewable sources during periods of low solar irradiation. Crucially, the transition to electric propulsion reduces annual CO₂ emissions by more than 98%, mitigating approximately 3421 kg per vessel compared to conventional 20 HP gasoline engines. A comparative analysis shows that the 1.1 kW electric boat is a cost-effective solution, with a 1.76-year return on investment. In contrast, the 4 kW model offers operational performance comparable to conventional gasoline boats, with a 4.95-year payback. This study provides a foundational framework for sustainable mobility in high-vulnerability territories by adapting technological solutions to site-specific environmental realities. Full article

Extreme hydro-meteorological events are intensifying under climate change, disproportionately disrupting last-mile healthcare in flood-prone geographies. In this study, flood-adaptive primary care clinics (FAPCCs) integrated with islandable smart microgrids and a rapid-deploy medical technology stack (MedTech) are developed and evaluated to ensure continuity of essential services (triage, maternal and child health, vaccination cold-chain, minor procedures, diagnostics, and telemedicine) during fluvial, pluvial, and coastal flooding. Evidence on resilient health facilities, microgrid architectures, distributed energy resources, and modular clinical systems is presented in a multi-layer systems design: (1) a modular, amphibious, and elevatable clinic chassis; (2) a photovoltaic–battery–diesel hybrid system with demand-aware energy management; (3) redundant connectivity long-term evolution/fifth-generation, satellite, and very high frequency; (4) a rapid-deploy MedTech kit including point-of-care diagnostics, low-temperature cold-chain, negative-pressure isolation, and sterilization modules; and (5) flood-aware logistics using unmanned aerial vehicle/unmanned surface vehicle. A mixed-integer linear programming sizing is formulated and dispatched with a continuity-of-care reliability metric that couples energy availability to clinical throughput. Simulation across three archetypal sites (peri-urban delta, inland riverine, coastal estuary) shows that FAPCCs achieve the service availability of higher than 99.5% across 7-day grid outage scenarios while reducing fuel use by 62–81% relative to diesel-only baselines, maintaining vaccine temperatures within 2–8 °C with <0.1% thermal excursion time, and sustaining telemedicine quality of service with <150 ms median uplink latency in hybrid networks. A life-cycle cost analysis indicates a 7.1–9.8 year discounted payback from fuel displacement and avoided service loss. Deployment playbooks and policy guidance are also proposed for Ministries of Health and Disaster Agencies in monsoon-impacted regions. Full article

This paper introduces an enhanced load frequency regulation strategy for isolated renewable microgrids, leveraging an Active Disturbance Rejection Control (ADRC) framework optimized through Elephant Herding Optimization (EHO). A detailed microgrid model, encompassing a variety of energy generation and storage units, is implemented in a simulation environment. The effectiveness of the proposed ADRC-EHO method was assessed through comparative analysis with established control techniques: Particle Swarm Optimization (PSO)-tuned ADRC and H∞ control under diverse operational scenarios. These scenarios included deterministic and stochastic load disturbances, as well as variations in microgrid parameters. The findings demonstrate that the ADRC-EHO approach consistently yields superior performance, with improved robustness and a more rapid response to frequency fluctuations. The optimization of ADRC parameters using EHO effectively countered the challenges of intermittent renewable energy integration. Full article

Hybrid AC/DC microgrids (HMGs) are increasingly recognized as a solution for the transition toward future energy systems because they can combine the efficiency of DC networks with an AC system. Despite these advantages, HMGs still have challenges in protection, cybersecurity, and reliability. Conventional protection schemes often fail due to reduced fault currents and the dominance of power electronic converters in islanded or dynamically reconfigured topologies. At the same time, IEC 61850 protocols remain vulnerable to advanced cyberattacks such as Denial of Service (DoS), false data injection (FDIA), and man-in-the-middle (MITM), posing serious threats to the stability and operational security of intelligent power networks. Previous surveys have typically examined these challenges in isolation; however, this paper provides the first integrated review of HMG protection across three complementary dimensions: traditional protection schemes, cybersecurity threats, and artificial intelligence/machine learning (AI/ML)-based approaches. By analyzing more than 100 studies published between 2012 and 2024, we show that AI/ML methods in simulation environments can achieve detection accuracies of 95–98% with response times under 10 ms, while these values are case-specific and depend on the evaluation setting such as network scale, sampling configuration, noise levels, inverter control mode, and whether results are obtained in simulation, hardware in loop (HIL)/real-time digital simulator (RTDS), or field conditions. Nevertheless, the absence of standardized datasets and limited field validation remain key barriers to industrial adoption. Likewise, existing cybersecurity frameworks provide acceptable protection timing but lack resilience against emerging threats, while conventional methods underperform in clustered and islanded scenarios. Therefore, the future of HMG protection requires the integration of traditional schemes, resilient cybersecurity architectures, and explainable AI models, along with the development of benchmark datasets, hardware-in-the-loop validation, and implementation on platforms such as field-programmable gate array (FPGA) and μPMU. Full article

Accurate short-term forecasting of renewable energy sources is essential for stable and efficient microgrid operation. Existing models primarily focus on either solar or wind prediction, often neglecting their combined stochastic behavior within isolated systems. This study presents a comparative evaluation of three machine-learning models—Random Forest, ANN, and LSTM—for short-term solar and wind forecasting in microgrid environments. Historical meteorological data and power generation records are used to train and validate three ML models: Random Forest, Long Short-Term Memory, and Artificial Neural Networks. Each model is optimized to capture nonlinear and rapidly fluctuating weather dynamics. Forecasting performance is quantitatively evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Percentage Error. The predicted values are integrated into a microgrid energy management system to enhance operational decisions such as battery storage scheduling, diesel generator coordination, and load balancing. Among the evaluated models, the ANN achieved the lowest prediction error with an MAE of 64.72 kW on the one-year dataset, outperforming both LSTM and Random Forest. The novelty of this study lies in integrating multi-source data into a unified ML-based predictive framework, enabling improved reliability, reduced fossil fuel usage, and enhanced energy resilience in remote microgrids. This research used Orange 3.40 software and Python 3.12 code for prediction. By enhancing forecasting accuracy, the project seeks to reduce reliance on fossil fuels, lower operational costs, and improve grid stability. Outcomes will provide scalable insights for remote microgrids transitioning to renewables. Full article

This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation. Full article

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized by vulnerable grid infrastructure. A multi-objective optimization framework is developed to jointly minimize operational costs, load-not-served, and environmental impacts under both normal and abnormal operating conditions. The proposed strategy employs the Multi-objective JAYA (MJAYA) algorithm to coordinate photovoltaic generation, diesel generators, battery energy storage systems, and inter-microgrid power exchanges within a 20 kV distribution network. Using real load, generation, and electricity price data, we evaluate the NMG’s performance under five representative fault scenarios that emulate ND-induced outages, including grid disconnection and loss of inter-microgrid links. Results show that the interconnected NMG structure significantly enhances system resilience, reducing load-not-served from 366.3 kWh in fully isolated operation to only 31.7 kWh when interconnections remain intact. These findings highlight the critical role of cooperative microgrid networks in strengthening community-level energy resilience in vulnerable regions. The proposed framework offers a practical decision-support tool for planners and governments seeking to enhance energy security and advance sustainable development in disaster-affected areas. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

Rapid integration of renewable energy sources poses a serious problem to the functionality of microgrids since they are characterized by underlying uncertainties and variability. This paper proposes a multi-stage approach to energy management to overcome these issues in a virtual power plant that combines heterogeneous microgrids. The solution is based on multi-agent deep reinforcement learning to coordinate internal energy pricing, microgrid scheduling, and virtual power plant-level energy storage system management. The proposed model autonomously learns the optimal dynamic pricing strategies based on load and generation dynamics, which is efficient in dealing with operational uncertainties and maintaining microgrid privacy due to its decentralized structure. The efficiency of the proposed solution is tested on comparative simulations based on real-world data, which prove the superiority of the framework to the traditional operation modes, which are isolated microgrids and the energy sharing scenarios. The findings prove that the suggested solution has a dual beneficial impact on both virtual power plant operators and involved microgrids, as it leads to profit enhancement and, at the same time, system stability. This process facilitates the successful balancing of conflicting interests among the stakeholders at a time when the operation is low-carbon. The study offers an overall solution to dealing with complicated multi-microgrids and brings substantial changes in the integration of renewable energy, as well as the distributed management of energy resources. The framework is a scalable model that can be used in the future perspective of power systems with high-renewable penetration to address both economic and operational issues of the contemporary energy grids. Full article

In converter-dominated direct-current microgrids, severe load transients can cause large voltage deviations on the common direct-current bus. To mitigate this, an energy storage system is typically employed, and an isolated bidirectional dual active bridge converter is commonly used as the power interface. Therefore, the controller must ensure robust transient performance under step-load conditions. This paper proposes an active disturbance rejection control framework that adaptively adjusts the bandwidth of an extended state observer using fuzzy logic. The proposed observer increases its bandwidth during transients—based on the estimation error—to accelerate disturbance compensation, while decreasing the bandwidth near steady state to suppress noise amplification. This adaptive tuning alleviates the fixed-bandwidth trade-off between transient speed and noise sensitivity in ESO-based regulation. Hardware experiments under load-step conditions validate the method: for a load increase, the peak voltage undershoot and settling time are reduced by 22% and 48.9% relative to a proportional–integral controller, and by 20% and 36.1% relative to a fixed-bandwidth observer. For a load decrease, the peak overshoot and settling time are reduced by 27.9% and 49.5% compared with the proportional–integral controller, and by 20.5% and 25% compared with the fixed-bandwidth observer. Full article

Remote islands face persistent challenges in achieving secure, sustainable and affordable energy supply due to their geographic isolation, fragile ecosystems and dependence on imported fossil fuels. Hybrid renewable energy systems (HRES)—typically combining photovoltaics (PV), wind turbines and battery energy storage systems (BESS)—have emerged as the dominant off-grid solution, demonstrating their potential to reduce fossil fuel dependence and greenhouse gas emissions. Yet, empirical case studies from Zanzibar, Thailand, Malaysia, the Galápagos, the Azores and Greece confirm that current systems remain transitional, relying on oversized storage and fossil backup during low-resource periods. Comparative analysis highlights both technical advances and persistent limitations, including seasonal variability, socio-economic barriers and governance gaps. Future directions for PV—wind-based (non-dispatchable) island microgrids point toward long-term hydrogen storage, artificial intelligence (AI)-driven predictive energy management and sector coupling—alongside participatory planning frameworks that enhance social acceptance and community ownership. By synthesizing technical, economic and social perspectives, this study provides a roadmap for advancing resilient, autonomous and socially embedded hybrid off-grid systems for remote islands. Full article

With the rapid advancement of hydrogen-based direct current microgrid (H₂-DCMG) technology, multi-port converters (MPCs) have emerged as the pivotal interface for integrating renewable power generation, energy storage, and diverse DC loads. This paper systematically reviews the current research status and development trends of isolated and non-isolated MPC topologies within hydrogen-based DC microgrids. Firstly, it analyses the interface requirements for typical distributed energy sources (DER) such as photovoltaics (PV), wind turbines (WT), fuel cells (FC), battery energy storage (BESS), proton exchange membrane electrolyzers (PEMEL), and supercapacitors (SC). Secondly, it classifies and evaluates existing MPC topologies, clarifying the structural characteristics, technical advantages, and challenges faced by each type. Results indicate that non-isolated topologies offer advantages such as structural simplicity, high efficiency, and high power density, making them more suitable for residential and small-scale microgrid applications. Isolated topologies, conversely, provide electrical isolation and modular scalability, rendering them appropriate for high-voltage electrolytic hydrogen production and industrial scenarios with stringent safety requirements. Finally, the paper identifies current research gaps and proposes that future efforts should focus on exploring topology optimization, system integration design, and reliability enhancement. Full article

Cyber-physical power systems integrate sensing, communication, and control, ensuring power system resiliency and security, particularly in clustered networked microgrids. Software-Defined Networking (SDN) provides a suitable foundation by centralizing policy, enforcing traffic isolation, and adopting a deny-by-default policy in which only explicitly authorized flows are admitted. This paper proposes and experimentally validates a cyber-physical architecture that couples three DC microgrids through an SDN backbone to deliver rapid, reliable, and secure power sharing under highly dynamic conditions, including pulsed-load disturbances. The cyber layer comprises four SDN switches that establish dedicated paths for protection messages, supervisory control commands, and high-rate sensor data streams. An OpenFlow controller administers flow-rule priorities, link monitoring, and automatic failover to preserve control command paths during disturbances and communication faults. Resiliency is further assessed by subjecting the network to a deliberate denial-of-service (DoS) attack, where deny-by-default policies prevent unauthorized traffic while maintaining essential control flows. Performance is quantified through packet captures, which include end-to-end delay, jitter, and packet loss percentage, alongside synchronized electrical measurements from high-resolution instrumentation. Results show that SDN-enforced paths, combined with coordinated multi-microgrid control, maintain accurate power sharing. A validated, hardware testbed demonstration substantiates a scalable, co-designed communication-and-control framework for next-generation cyber-physical DC multi-microgrid deployments. Full article

The global transition toward sustainable mobility and renewable energy integration demands intelligent energy management frameworks capable of coupling electric mobility, distributed generation, and energy storage. This study presents a comprehensive evaluation of the SIMA Project (Sistema Inteligente Multimodal da Amazônia), an innovative mobility pilot implemented at the Federal University of Pará, Brazil. The SIMA consists of the monitoring building, photovoltaic systems, lithium-based energy storage systems, and electric transportation modes (including urban and intercity buses, as well as a solar-powered catamaran), all interconnected within a microgrid. Field monitoring, data processing, and simulation analyses were conducted to assess energy performance, consumption patterns, and the operational feasibility of these electric systems under Amazonian conditions. The results indicate that the PV systems supply most of the SIMA’s demand, with the laboratory building accounting for 70% of total consumption and electric vehicles for 30%. Simulated full operation scenarios reveal the potential for near net-zero energy balance when energy management strategies are applied to generation, storage and charging. The findings demonstrate the technical viability of integrated mobility–energy systems in tropical contexts and provide practical insights for future low-carbon transport infrastructures in isolated or city-scale networks. Full article