Search Results (274)

This paper introduces a reverse demand response scheme that uses electric vehicles in an isolated microgrid system, aiming to solve the renewable energy curtailment issue. We focus on an off-grid system where the system operator faces a stabilization problem due to surplus energy production, while electric vehicles seek to charge energy at a lower price. In our system model, the operator determines the incentive to encourage more charging facilities and electric vehicles to participate in the reverse demand response program. Charging facilities, acting as brokers, use a portion of these incentives to further encourage electric vehicle engagement. Electric vehicles follow the decisions made by the broker and system operator to determine their charging strategy within the system. Consequently, charging energy and incentives are allocated to the electric vehicles in proportion to their decisions. The paper investigates the economic benefits of individual participants and the contribution of power stabilization by implementing a hierarchical decision-making heterogeneous multi-leaders multi-followers Stackelberg game. By demonstrating the existence of a unique Nash Equilibrium, we show the effectiveness of the proposed model in an isolated microgrid environment. Full article

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article

Decentralized desalination systems driven by renewable energy sources have surfaced as a feasible way to alleviate water scarcity in arid and rural areas. This bibliometric study aims to clarify the research trends, conceptual frameworks, and cooperative dynamics in the scientific literature on decentralized renewable-powered desalination techniques. Using a thorough search approach, 1354 papers were found. Duplicates, thematically unrelated works, and entries with poor information were removed using the PRISMA 2020 framework. A selected 832 relevant papers from a filtered dataset were chosen for in-depth analysis. Quantitative measures were obtained by means of Bibliometrix; network visualisation was obtained by means of VOSviewer (version 1.6.19) and covered co-authorship, keyword co-occurrence, and citation structures. Over the previous 20 years, the data show a steady rise in academic production, especially in the fields of environmental science, renewable energy engineering, and water treatment technologies. Author keyword co-occurrence mapping revealed strong theme clusters centred on solar stills, thermoelectric modules, reverse osmosis, and off-grid systems. Emphasizing current research paths and emerging subject borders, this paper clarifies the intellectual and social structure of the field. The outcomes are expected to help policy creation, cooperative projects, and strategic planning meant to hasten innovation in sustainable and decentralized water desalination. Full article

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, a native species of the Colombian Caribbean. The system integrates three control strategies: a classical Proportional-Integral-Derivative (PID) controller, a fuzzy logic–based PID controller, and a neural network predictive controller. All strategies were evaluated in simulation using a third-order transfer function model identified from real pond data. The fuzzy PID controller reduced the mean squared error (MSE) by 66.5% compared to the classical PID and showed faster settling times and lower overshoot. The neural predictive controller, although anticipatory, exhibited high computational cost and instability. Only the fuzzy PID controller was implemented and validated experimentally, demonstrating robust, accurate, and stable regulation of potential hydrogen (pH), dissolved oxygen, and salinity under dynamic environmental conditions. The system operated in real time on embedded hardware powered by a solar kit, confirming its suitability for rural or off-grid aquaculture contexts. This approach provides a viable and scalable solution for advancing intelligent, sustainable aquaculture practices, particularly for sensitive native species in tropical regions. Full article

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article

For a new distribution network with energy storage and a flexible multi-state switch (FMSS), several problems of multi-mode operation and switching, such as the unbalance of feeder loads and feeder faults, among others, should be considered. This paper forwards a coordination control strategy to address the above challenges faced by the FMSS under grid-connected operations. To tackle the multi-mode operation problem, the system’s operational state is divided into multiple working modes according to the operation states of the system, the positions and number of fault feeders, the working states of the transformers, and the battery’s state of charge. To boost the system’s operational reliability and load balance and extend the power supply time for the fault load, the appropriate control objectives in the coordination control layer and control strategies in the equipment layer for different working modes are established for realizing the above multi-directional control objectives. To resolve the phase asynchrony issue among the fault load and other normal working loads caused by the feeder fault, the off-grid phase-locked control based on the V/f control strategy is applied. To mitigate the bus voltage fluctuation caused by the feeder fault switching, the switching control sequence for the planned off-grid is designed, and the power feed-forward control strategy of the battery is proposed for the unplanned off-grid. The simulation results show that the proposed control strategy can ensure the system’s power balance and yield a high-quality flexible power supply during the grid-connected operational state. Full article

Transitioning to sustainable energy systems is crucial for reducing greenhouse gas (GHG) emissions, especially in remote industrial operations where diesel generators remain the dominant power source. This study examines the feasibility of integrating a redox flow battery (RFB) storage system to optimize wind energy utilization at the Raglan mining site in northern Canada, with the goal of reducing diesel dependency, enhancing grid stability, and improving energy security. To evaluate the effectiveness of this hybrid system, a MATLAB R2024b-based simulation model was developed, incorporating wind energy forecasting, load demand analysis, and economic feasibility assessments across multiple storage and wind penetration scenarios. Results indicate that deploying 12 additional E-115 wind turbines combined with a 20 MW/160 MWh redox flow battery system could lead to diesel savings of up to 63.98%, reducing CO₂ emissions by 68,000 tonnes annually. However, the study also highlights a key economic challenge: the high Levelized Cost of Storage (LCOS) of CAD (Canadian dollars) 7831/MWh, which remains a barrier to large-scale implementation. For the scenario with high diesel economy, the LCOS was found to be CAD 6110/MWh, and the corresponding LCOE was CAD 590/MWh. While RFB integration improves system reliability, its economic viability depends on key factors, including reductions in electrolyte costs, advancements in operational efficiency, and supportive policy frameworks. This study presents a comprehensive methodology for evaluating energy storage in off-grid industrial sites and identifies key challenges in scaling up renewable energy adoption for remote mining operations. Full article

Due to the intermittent and uncertain nature of emerging renewable energy sources in the modern power grid, the level of dispatchable power sources has been reduced. The contemporary power system is attempting to address this by investing in energy storage within the context of standalone microgrids (SMGs), which can operate in an island mode and off-grid. While renewable-rich SMGs can facilitate a higher level of renewable energy penetration, they also have more reliability issues compared to conventional power systems due to the intermittency of renewables. When an SMG system needs to be upgraded for reliability improvement, the cost of that reliability improvement should be divided among diverse customer sectors. In this research, we present four distinct approaches along with comprehensive simulation outcomes to address the problem of allocating reliability costs. The central issue in this study revolves around determining whether all consumers should bear an equal share of the reliability improvement costs or if these expenses should be distributed among them differently. When an SMG system requires an upgrade to enhance its reliability, it becomes imperative to allocate the associated costs among various customer sectors as equitably as possible. In our investigation, we model an SMG through a simulation experiment, involving nine distinct customer sectors, and utilize their hourly demand profiles for an entire year. We explore how to distribute the total investment cost of reliability improvement to each customer sector using four distinct methods. The first two methods consider the annual and seasonal peak demands in each industry. The third approach involves an analysis of Loss of Load (LOL) events and determining the hourly load requirements for each sector during these events. In the fourth approach, we employ the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. The annual peak demand approach resulted in the educational sector bearing the highest proportion of the reliability improvement cost, accounting for 21.90% of the total burden. Similarly, the seasonal peak demand approach identified the educational sector as the most significant contributor, though with a reduced share of 15.44%. The normalized average demand during Loss of Load (LOL) events also indicated the same sector as the highest contributor, with 12.34% of the total cost. Lastly, the TOPSIS-based approach assigned a 15.24% reliability cost burden to the educational sector. Although all four approaches consistently identify the educational sector as the most critical in terms of its impact on system reliability, they yield different cost allocations due to variations in the methodology and weighting of demand characteristics. The underlying reasons for these differences, along with the practical implications and applicability of each method, are comprehensively discussed in this research paper. Based on our case study findings, we conclude that the education sector, which contributes more to LOL events, should bear the highest amount of the Cost of Reliability Improvement (CRI), while the hotel and catering sector’s share should be the lowest percentage. This highlights the necessity for varying reliability improvement costs for different consumer sectors. Full article

To explore the feasibility of renewable hybrid energy systems for expressway infrastructure, this study proposes a scenario-based design methodology integrating solar, wind, and hydropower resources within the expressway corridor. A case study was conducted on a highway service area located in southern China, where a solar/wind/hydro hybrid energy system was developed based on the proposed approach. Using the HOMER Pro 3.14 software platform, the system was simulated and optimized under off-grid conditions, and a sensitivity analysis was conducted to evaluate performance variability. The results demonstrate that the strategic integration of corridor-based natural resources—solar irradiance, wind energy, and hydrodynamic potential—enables the construction of a technically and economically viable hybrid energy system. The system includes 382 kW of PV, 210 kW of wind, 80 kW of hydrokinetic power, a 500 kW diesel generator, and 180 kWh of battery storage, forming a hybrid configuration for a stable and reliable energy supply. The optimized configuration can supply up to 1,095,920 kWh of electricity annually at a minimum levelized cost of energy of USD 0.22/kWh. This system reduces CO₂ emissions by 23.2 tons/year and NO_x emissions by 23 kg/year. demonstrating strong environmental performance and long-term sustainability potential. Full article

Off-grid renewable energy hydrogen production is a crucial approach to enhancing renewable energy utilization and improving power system stability. However, the strong stochastic fluctuations of wind and solar power pose significant challenges to electrolyzer reliability. While hybrid energy storage systems (HESS) can mitigate power fluctuations, traditional power allocation rules based solely on electrolyzer power limits and HESS state of charge (SOC) boundaries result in insufficient energy supply capacity and unstable electrolyzer operation. To address this, this paper proposes a two-stage power optimization method integrating rule-based allocation with algorithmic optimization for wind–solar hydrogen production systems, considering reserved energy storage. In Stage I, hydrogen production power and HESS initial allocation are determined through the deep coupling of real-time electrolyzer operating conditions with reserved energy. Stage II employs an improved multi-objective particle swarm optimization (IMOPSO) algorithm to optimize HESS power allocation, minimizing unit hydrogen production cost and reducing average battery charge–discharge depth. The proposed method enhances hydrogen production stability and HESS supply capacity while reducing renewable curtailment rates and average production costs. Case studies demonstrate its superiority over three conventional rule-based power allocation methods. Full article

The adoption of wind energy is pivotal for advancing sustainable power systems, particularly in off-grid microgrids where infrastructure limitations hinder conventional energy solutions. The inherent variability of wind generation, however, challenges grid reliability and demand–supply balance, necessitating accurate forecasting models. This study proposes a hybrid framework for short-term wind speed prediction, integrating deep learning (Long Short-Term Memory, LSTM) and ensemble methods (random forest, Extra Trees) to exploit their complementary strengths in modeling temporal dependencies. A multivariate approach is adopted using meteorological data (including wind speed, temperature, humidity, and pressure) to capture complex weather interactions through a structured time-series design. The framework also includes a feature selection stage to identify the most relevant predictors and a hyperparameter optimization process to improve model generalization. Three wind speed variables, maximum, average, and minimum, are forecasted independently to reflect intra-day variability and enhance practical usability. Validated with real-world data from Cuenca, Ecuador, the LSTM model achieves superior accuracy across all targets, demonstrating robust performance for real-world deployment. Comparative results highlight its advantage over tree-based ensemble techniques, offering actionable strategies to optimize wind energy integration, enhance grid stability, and streamline renewable resource management. These insights support the development of resilient energy systems in regions reliant on sustainable microgrid solutions. Full article

The use of solar photovoltaic systems for power generation requires efficient battery energy storage systems to ensure a steady and constant supply for self-sufficient power generation and off-grid areas. “Vision 2030” is Saudi Arabia’s strategy for reducing the country’s dependence on oil by 50% through investment in clean, renewable resources by 2030. This paper reviews the latest advancements in battery technologies designed for solar photovoltaic panels through a detailed comparative analysis of performance, energy storage capacity, efficiency, lifespan, cost, safety, and environmental impact for residential applications in the Kingdom of Saudi Arabia and those available in the United States of America. The performance of the advanced lithium-ion battery technology available in the USA surpasses that in the Kingdom of Saudi Arabia. The findings underscore the need for investments by the Kingdom of Saudi Arabia in advanced battery manufacturing technologies to improve the availability of different battery types and capacities and achieve the objectives outlined in the Kingdom of Saudi Arabia’s Vision 2030. Full article

Aimed at the large disturbance of a power system caused by frequent new energy clusters going off-grid, we propose a cooperative optimization strategy of variable-speed and constant-speed pumped-storage units to address power oscillation due to significant power shortages following the clusters going off-grid. From a multi-time-scale perspective, we first investigate the fast power support control strategy of variable-speed pumped-storage (VSPS) units during new energy cluster off-grid scenarios. Using a consensus algorithm, the VSPS acts as the primary unit, while the constant-speed unit provides long-term power support. We present a rapid power control method for VSPS to prioritize frequency stability in mainland grids with high new energy penetration. This ensures stable power support for large-scale new energy clusters under large disturbances across multiple time scales. Simulation analysis on a high proportion of new energy power networks with new energy clusters confirms the effectiveness of our proposed method. Full article

The Raglan mining site in northern Quebec relies on diesel for electricity and heat generation, resulting in annual emissions of 105,500 tons of CO₂ equivalent. This study investigates the feasibility of decarbonizing the site’s power generation system by integrating a renewable energy network of wind turbines and a pumped hydro storage plant (PHSP). It uniquely integrates PHSP modeling with a dynamic analysis of variable wind speeds and extreme climatic conditions, providing a novel perspective on the feasibility of renewable energy systems in remote northern regions. MATLAB R2024b-based simulations assessed the hybrid system’s technical and economic performance. The proposed system, incorporating a wind farm and PHSP, reduces greenhouse gas (GHG) emissions by 50%, avoiding 68,500 tons of CO₂ equivalent annually, and lowers diesel consumption significantly. The total investment costs are estimated at 2080 CAD/kW for the wind farm and 3720 CAD/kW for the PHSP, with 17.3 CAD/MWh and 72.5 CAD/kW-year operational costs, respectively. The study demonstrates a renewable energy share of 52.2% in the energy mix, with a payback period of approximately 11 years and substantial long-term cost savings. These findings highlight the potential of hybrid renewable energy systems to decarbonize remote, off-grid industrial operations and provide a scalable framework for similar projects globally. Full article

The charging station (CS) is generally directly off-grid under a grid fault, which has become a key technical bottleneck that restricts the sustainable development of new energy transportation systems. During a grid fault, the CS under the vehicle-to-grid (V2G) mode experiences a reduction in active power due to the current limitation of the voltage source converter (VSC), which may cause the DC voltage to exceed its limitations under unbalanced power. The effect of the active and reactive power of CS in low- and medium-voltage distribution networks on supporting the PCC voltage under the limitation of DC voltage and VSC current has not been analyzed, and a control method for PCC voltage support for CS has not been established. Therefore, a power boundary that avoids the DC overvoltage and AC overcurrent of the CS is defined. A power feasible region for the CS considering fault duration is established. The characteristic that the power feasible region shrinks with the increase in duration is found, and a calculation method for the critical clearing time of a fault to avoid DC overvoltage is proposed. The relationship between PCC voltage and power injected by the CS is analyzed. The property that the control point of maximum voltage support lies at the boundary of the power feasible region is revealed. A control method of PCC voltage support that considers the limitation of DC voltage and VSC current for the CS is proposed. Simulation verification shows that the support capability of CS for PCC voltage during a fault is significantly enhanced by the proposed method while securing the DC voltage. Full article