Search Results (1,245)

The control of micro energy grids (MEGs) is characterized by volatility, uncertainty, and decentralization. Traditional power distribution algorithms, designed for centralized, dispatchable generators, are inadequate for MEG environments. Controllable load management provides peak shaving, load balancing, frequency regulation, and voltage stability, as well as fast balancing services for renewable energy grids in distributed power systems. A non-grid-tied inverter costs a fraction of its grid-tied counterpart for the same capacity. In the initial setting, one or more inverters are used. As the demand grows, more non-grid-tied inverters are added to the mix. Non-grid-tied inverters cannot be connected in parallel. There is no practical solution available in the market for the optimum utilization of this type of setting. Unlike a grid-tied microgrid, in non-grid-tied mode, a microgrid uses grid power only when needed, prioritizing renewable sources. This paper explores autonomous strategies for controlling and coordinating multiple renewable energy sources in MEG settings. It reviews and develops an algorithmic framework for optimal load distribution among multiple renewable sources, including solar photovoltaic (PV), wind turbines, and battery energy storage systems (BESSs). The proposed framework integrates resource forecasting, multi-objective optimization, and adaptive supervisory control to ensure stability, maximize renewable penetration, and minimize operational costs. Performance considerations, mathematical modelling, and potential implementation architectures are discussed. A hybrid approach, combining multiple algorithms, is therefore proposed. In this paper a real-life solution is proposed to a real-life problem. Full article

Amid the global transition toward sustainable energy systems, the hybrid energy storage system (HESS) plays a vital role due to its combined advantages of high energy density and high power density. However, distributed HESSs in islanded microgrids still lack effective management strategies for handling complex and abnormal operating conditions, which may compromise system stability. Therefore, this paper proposes a coordinated control and management strategy for distributed HESSs based on grid-forming (GFM) converters. First, a dynamic following decoupling algorithm based on actual power anchoring is proposed to eliminate the reverse active power regulation phenomenon during the initial transient period while enabling the frequency restoration process and the power transfer process to be completed independently. Second, to address communication interruptions in the multi-agent system, a communication weight update mechanism and a local degraded control strategy are designed to ensure that the system can still operate stably when communication is disconnected. Furthermore, through an information relay mechanism, a faulty converter is redefined as an information relay node to maintain the global communication topology of the multi-agent system under converter fault conditions. Finally, hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed control strategy, demonstrating its ability to enhance microgrid resilience and sustainability. Full article

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. 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

Microgrids are important technologies for increasing the penetration of renewable energy sources (RESs). Compared with AC microgrids, DC microgrids avoid frequency regulation and reactive-power compensation. Moreover, many RES interfaces and energy storage systems (ESSs) are DC or DC-link based; therefore, they can be integrated into DC buses with fewer conversion stages, reducing conversion losses. Consequently, DC microgrids have attracted increasing attention. This paper reviews DC microgrid topologies, hierarchical control methods, and protection schemes. First, the representative topologies are compared from the perspectives of structural features, control implications, protection requirements, and application scenarios. Next, primary, secondary, and tertiary control strategies are analyzed, with emphasis on droop control, virtual impedance, virtual inertia, fractional-order control, communication delay, and energy management. Protection issues, including fault detection, fault interruption, and ground-fault protection, are then discussed with respect to topology–control interactions. Finally, future research trends and challenges for DC microgrids are summarized. Full article

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and voltage spikes. To avoid expensive poles and wires upgrades, Battery Energy Storage Systems (BESS) are increasingly being deployed as Non-Network Solutions (NNS). However, the current literature reveals a distinct gap between the macro-scale economic planning of these storage assets and the micro-scale dynamic control actually required to keep the grid resilient. To address this gap, this review proposes a multi-layer deterministic synthesis framework that links physical renewable modelling, degradation-aware techno-economic planning, deterministic forecasting, and EMS dispatch through offline time-domain control validation for AC-microgrid energy storage integration. The research examines how advanced central control units within battery management systems can rigorously and jointly estimate State of Charge (SoC) and State of Energy (SoE) to ensure accurate grid-aware dispatch. Furthermore, the study explores the integration of degradation-aware economic modelling in HOMER Pro with dynamic transient control in MATLAB/Simulink R2025b, driven by hybrid metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). This analysis demonstrates that integrating energy storage must be treated as a tightly coupled multidimensional optimization problem to successfully deliver the secure and sustainable infrastructure needed to solve the modern energy trilemma. Full article

This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations. Full article

The increasing integration of renewable energy introduces uncertainty in microgrid operation, making effective energy management more challenging. Rolling-horizon optimization is used to address this challenge by enabling periodic decision updates; however, most existing approaches rely on fixed optimization horizons and predetermined update frequencies. When prediction accuracy decay (PAD) is considered in adaptive rolling-horizon approaches, it is represented using a fixed decay value, not an online indicator that compares forecasted and actual renewable generation during operation. This leads to suboptimal re-optimization timing, unnecessary computational effort, excessive battery switching, or delayed corrective actions. To address these limitations, this paper proposes a PAD-driven adaptive rolling horizon (ARH) approach, in which re-optimization is triggered using an online PAD indicator computed from the percentage deviation between forecasted and realized renewable generation data. Re-optimization is activated when the PAD indicator exceeds a predefined threshold, enabling adaptive scheduling updates according to real-time forecasting degradation. The problem is formulated as a robust mixed-integer linear programming (MILP) model of a high renewable penetration microgrid, including battery degradation and switching penalties. The energy self-sufficiency ratio (SSR) is used as a key sustainability performance indicator to assess the extent to which local renewable generation and storage satisfy microgrid demand. The proposed approach is first compared with a fixed rolling-horizon approach using a fixed re-optimization interval of 1 h, where the results show a profit improvement of 10.5%. A sensitivity analysis tested the proposed approach under bounded renewable forecast uncertainty levels up to ±15 and different battery capacities. The results indicate that performance is strongly influenced by forecast accuracy and battery capacity, with higher economic gains under low uncertainty and more conservative operation under high uncertainty. Full article

Microgrids are increasingly recognized as transformative and crucial constituents within advanced smart grid systems. This study introduces a decentralized energy management approach for interconnected microgrids that leverage renewable energy sources such as wind and solar, alongside distributed energy generators and storage mechanisms. An energy coalition manager (ECM) plays a key role in facilitating each microgrid’s integration to optimize power exchanges, enhance data communication, and reduce costs. The alternate-direction multiplier method is adapted to address optimization challenges, incorporating modifications to develop a censored version that enhances communication efficacy. This refined approach involves the exchange of information among neighboring entities, evaluated against a preset threshold. Through this precise comparison, ECMs strategically reveal their local variables to ensure convergence towards an optimal solution. A detailed case study was conducted to assess the performance, efficiency, and scalability of both methodologies comprehensively. Full article

This paper presents a power-based dynamic programming (DP) method for day-ahead battery scheduling in a grid-connected photovoltaic (PV) microgrid under time-of-use (TOU) tariffs. The proposed formulation optimizes battery power directly, rather than SOC setpoints, so the dispatch is easier to apply in practical inverter control and remains computationally tractable over a 48 h horizon. The model includes battery degradation through a linear wear-cost term based on a 200 USD/kWh replacement cost, while also enforcing SOC and charging/discharging power limits. The case study uses a 250 kWh battery and evaluates two power limits, 0.1C and 0.2C, together with two degradation cases, 200 and 400 USD/kWh. The simulation considers two different operating days to test the controller under unequal renewable and demand conditions. Day 1 has stronger PV generation and lower load demand, whereas Day 2 has lower PV output and higher demand. Under the baseline 0.1C limit, DP reduces the net operating cost to 97.47 USD, compared with 122.95 USD for the TOU-aware rule-based benchmark. When the power limit increases to 0.2C, the net operating cost falls further to 78.35 USD because export revenue rises substantially. When the battery replacement cost doubles from 200 USD/kWh to 400 USD/kWh, the optimizer reduces cycling and the net operating cost increases to 129.21 USD. Overall, the results show that power-based DP provides a practical and transparent framework for balancing tariff arbitrage and battery preservation in grid-connected microgrids. Full article

In this work, a multi-objective quantum particle swarm optimization (QPSO) algorithm is proposed to address the optimal scheduling of non-dispatchable sources in a microgrid energy management system (MGEMS) for residential areas under utility-induced demand-side management (DSM) programs. While taking economic and environmental aspects into account, the goal is to maximize energy management by integrating a variety of distributed generation (DG) units with an energy storage device. Using real-time meteorological data, two case studies were analyzed and simulated using MATLAB/Simulink R2025b. The simulation results reveal that the optimum optimization outcome among the case studies is obtained at a higher DSM load participation level of 10%. Without the involvement of DSM, MG’s producing units in the first case had the highest carbon emissions of 797.110 kg and an overall operating cost of 267.10 €. Similarly, with the involvement of DSM, the second case had the lowest overall operating cost of 155.01 € and the lowest carbon emissions of 748.731 kg. The second case, which has optimal DG scheduling, is the suggested way to improve microgrid efficiency and provide a dependable power supply with low operating costs and emission reduction. Full article

There are significant difficulties with power quality and stability as a result of active cooperation between renewable energy sources and load demand. To maintain power stability between renewable energy supplies and the microgrid/utility grid, novel solutions must be implemented. By using an artificial and computational intelligence controller to schedule power from multiple sources (photovoltaic, wind, grid, and battery) under a set of constraints, such as weather, load-shedding hours, and peak pricing hours, this paper introduces a novel approach for power management in grid-connected hybrid renewable systems with PV–wind and energy storage systems. The approach involves using an artificial neural network (ANN) to process all of the inputs and creating an ANN rule set from a modelled hybrid renewable system. A rule-based power scheduler is developed, and simulations are run for a full day. The suggested fuzzy control approach can detect ongoing variations in grid load-shedding patterns, PV–wind power generation, load demands, and battery state-of-charge to enable prompt and accurate decision-making. The proposed ANN rule-based scheduler can handle nonlinearity by integrating metaheuristics into computer-assisted decision-making and can function effectively with imprecise inputs, negating the need for an exact numerical model. The MATLAB/Simulink R2023a software was used for simulation, and the system operated as efficiently as possible. The simulation results suggested that an ANN offers a foundation for extension to handle numerous particular scenarios. Full article

In this study, we present an integrated surrogate-assisted multi-objective optimization for an energy management system, ensuring physical feasibility with real system constraints. The hybrid framework incorporates knee-guided selective physical replay and a stochastic survival strategy to maintain both convergence and diversity of the search process. The method is used to evaluate grid-forming and grid-following modes using the OPAL-RT and Lucas-Nülle platforms in three different stages to address the technical and economic performance, and the reliability of the system. The proposed method reduces 116 generated surrogate candidates to 7 physically feasible non-dominated solutions based on physical replay. In the direct evaluation stage without replay, the system achieves high renewable utilization ($PV$ ≈ $97\%$), reliable load coverage (>99%), and minimal supply–demand mismatch (≈1 W), supported by controlled battery usage. In the extended EMS evaluation, the proposed method reduces the number of true evaluations from approximately 54,600 to 16,895 (≈69% reduction) while maintaining stable performance. Despite the reduction in the number of evaluations, the method preserves stable convergence behavior and a consistent Pareto spread (≈0.0124). Statistical tests, such as Wilcoxon ($p\approx 1.9\times {10}^{-6}$) and Friedman ($p\approx 2.9\times {10}^{-7}$), show a significant difference and consistent performance across runs. This demonstrates the framework’s ability to provide a compact, decision-relevant set of feasible operating solutions under real system constraints and its practical applicability to real-world EMS decision making. Full article

The increasing penetration of distributed energy resources and the growing vulnerability of centralized power systems to natural hazards, terrorist attacks, acts of sabotage, technical incidents, and operational uncertainties have intensified the need for resilient and secure energy infrastructures. Microgrids have emerged as a promising solution to enhance energy security by enabling the localized generation, autonomous operation, and flexible integration of renewable energy sources. However, their effective deployment introduces complex risks related to technical, economic, and operational uncertainties. This paper presents a comprehensive framework for risk management in microgrids within modern power systems, aiming to improve the overall security and resilience of Romania’s power system. The study systematically identifies and evaluates the main risk scenarios affecting the power system: natural disasters, terrorist attacks, acts of sabotage, and technical incidents. In addition, to achieve an in-depth analysis, the paper also discusses the SWOT and PESTEL analyses of the Romanian power system, as well as its resilience. A multi-level risk assessment methodology is proposed, combining probabilistic analysis with severity (impact) analysis. The proposed approach is validated through case studies based on risk scenario assessments, demonstrating its effectiveness in improving microgrid performance under diverse disturbance conditions. The results highlight the critical role of proactive risk management in supporting energy security objectives, while ensuring stable and resilient operation of the Romanian power system. This research contributes to the development of adaptive and sustainable power systems, capable of addressing future challenges in an increasingly decentralized energy landscape, and can be adapted to any modern power system worldwide. Full article

This paper addresses the operational challenges introduced by the growing share of intermittent renewable energy sources in islanded microgrids. Traditional unit commitment (UC) methods struggle to manage the continuous variations in demand and renewable generation effectively because dispatch setpoints remain fixed between scheduling intervals. To overcome these limitations, a dynamic voltage and frequency controller (DVFC) is proposed. The DVFC uses adaptive critic control and approximate dynamic programming to update mid-level control actions based on measured microgrid states, technical constraints, and look-ahead utility functions. The proposed method is applied to short-term UC, ensuring frequency and voltage regulation while maintaining microgrid stability. Simulation results on the modified CIGRE test system demonstrate that the DVFC reduces frequency deviations by up to 40–50% and voltage deviations by 60–65% compared to conventional UC. In addition, the method lowers operating costs by up to 6% and extends the effective battery lifecycle by nearly twofold by reducing stress and cycling. These results confirm that the DVFC significantly outperforms conventional UC algorithms in both technical performance and economic efficiency. Full article