Search Results (147)

In recent years, distributed energy power supply systems have been widely used in remote areas and extreme environments. However, the intermittent and uncertain output power may cause power grid fluctuations, leading to higher harmonics in electromechanical equipment, especially motors. For permanent magnet synchronous motor (PMSM) systems, an electromagnetic (EM) vibration can cause problems such as energy loss and mechanical wear. Therefore, it is necessary to design control algorithms that can effectively suppress EM vibration. To this end, a vibration suppression algorithm for fractional-slot permanent magnet synchronous motors based on a d-axis current injection is proposed in this paper. Firstly, this paper analyzes the radial electromagnetic force of the fractional-slot PMSM to identify the main source of EM vibration in fractional-slot PMSMs. Based on this, the intrinsic relationship between the EM vibration of fractional-slot PMSMs and the d-axis and q-axis currents is explored, and a method for calculating the d-axis current to suppress the vibration is proposed. Experimental verification shows that the proposed algorithm can effectively suppress EM vibration. Full article

The energy consumption of current vertical-lifting underwater monitoring devices mainly falls into two categories: one fully supplied by battery packs; and the other partially by battery packs, with the rest from ocean thermal energy. Constrained by battery capacity, their operation time is limited, making long-term remote operations difficult. This study focuses on a device powered entirely by ocean thermal energy, which realizes the absorption and storage of energy through a phase change heat-exchange system, significantly extending its operation cycle and working area. A composite phase change material of n-hexadecane and graphite with a volume ratio of 9:1 is used. The Fluent software 2022 R1, based on the enthalpy-porosity method, simulates the phase change process of the device to analyze the effects of different structures and seawater temperatures. Results show that with the same phase change material volume and inner diameter of the cylindrical heat exchanger, a smaller outer diameter yields better phase change performance. Lower seawater temperature facilitates solidification. Due to natural convection in the liquid phase, the melting time is 520 s and solidification time is 4800 s, with the melting rate far exceeding the solidification rate. 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

In the context of rapid urbanization in China, many farmers still live in areas far away from urban energy supply networks. To meet the multi-level energy demands of rural communities, this study proposes a combined heat, power, and electricity (CCHP) supply system that uses solar and biomass energy as inputs, tailored to the natural resources and climatic conditions of the northwestern region. A theoretical model of this system was established in Nanan Community, Wuwei City, and its dynamic performance throughout the year was simulated and analyzed using TRNSYS software. The system was also evaluated for its economic viability, energy efficiency, and environmental impact. The results show that compared with the original and traditional energy supply systems, the CCHP system achieves average primary energy saving rates of −9.87% and 41.52% during the heating season, annual cost savings of 50.35% and 64.19%, carbon dioxide emission reduction rates of 32.89% and 66.86%, and a dynamic investment payback period of 3.14 years. This study provides development ideas for constructing modern integrated energy systems in rural areas that are remote from urban energy supply networks and offers references for investors. Full article

In areas such as remote, rural, and mountainous regions, supplying low-voltage three-phase power has traditionally required distribution line extension and transformer installation. However, these areas often yield low electricity revenues, making cost recovery difficult for utilities. To address this challenge, this paper proposes a Single-to-Three-Phase Converter (STPC) capable of converting single-phase low-voltage input into three-phase output for use in low-voltage distribution systems. The STPC topology employs a single-phase half-bridge AC–DC stage and a three-phase full-bridge inverter stage using SiC-MOSFETs. To validate the system, simulations and experiments were conducted under various load conditions, including unbalanced, nonlinear, and motor loads. The results show that STPC maintains output stability while minimizing impact on the existing grid. The findings demonstrate STPC’s feasibility as an alternative to conventional line extension and transformer installation, with potential for application in grid-forming and low-voltage distribution current (LVDC) systems. Full article

Quantitative assessment of the ability of the ecosystem service (ES) and its driving forces is of great significance for achieving regional SDGs. In view of the scarcity of existing research that evaluates the sustainability of multiple ES types over a long time series at the township scale in a typical Watertown Region, this study aims to address two key scientific questions: (1) what are the spatiotemporal changes in the ecosystem service supply–demand index (ESSDI) and ecosystem service sustainability index (ESSI) of a typical Watertown Region? and (2) what are the key factors driving the changes in ESSI? To answer the above two questions, this study takes the Yangtze River Delta Integrated Demonstration Zone (YRDIDZ) as the study area, utilizing multi-source remote sensing and other spatiotemporal geographical datasets to calculate the supply–demand levels and sustainable development ability of different ES in the YRDIDZ from 2000 to 2020. The main findings were as follows: (1) From 2000 to 2020, the mean ESSDI values for habitat quality, carbon storage, crop production, water yield, and soil retention all showed a declining trend. (2) During the same period, the mean ESSI exhibited a fluctuating downward trend, decreasing from 0.31 in 2000 to 0.17 in 2020, with low-value areas expanding as built-up areas grew, while high-value areas were mainly distributed around Dianshan Lake, Yuandang, and parts of ecological land. (3) The primary driving factors within the YRDIDZ were human activity factors, including POP and GDP, with their five-period average explanatory powers being 0.44 and 0.26, whereas the explanatory power of natural factors was lower. However, the interaction of POP and soil showed higher explanatory power. The results of this study could provide actionable ways for regional sustainable governance: (1) prioritizing wetland protection and soil retention in high-population-density areas based on targeted land use quotas; (2) integrating ESSI coldspots (built-up expansion zones) into ecological redline adjustments, maintaining high green infrastructure coverage in new urban areas; and (3) establishing a population–soil co-management framework in agricultural–urban transition zones. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article

Standalone wind–solar–diesel–storage microgrids serve as a crucial solution for achieving energy self-sufficiency in remote and off-grid areas, such as rural regions and islands, where conventional power grids are unavailable. Addressing scheduling optimization challenges arising from the intermittent nature of renewable energy generation and the uncertainty of load demand, this paper proposes an adaptive optimization scheduling method (DQN-PSO) that integrates Deep Q-Network (DQN) with Particle Swarm Optimization (PSO). The proposed approach leverages DQN to assess the operational state of the microgrid and dynamically adjust the key parameters of PSO. Additionally, a multi-strategy switching mechanism, incorporating global search, local adjustment, and reliability enhancement, is introduced to jointly optimize both clean energy utilization and power supply reliability. Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. These findings validate the strong adaptability of the proposed algorithm to dynamic environments. Further, a parameter sensitivity analysis underscores the significance of the dynamic adjustment mechanism. Full article

This study demonstrates that small-scale liquefied natural gas (SS LNG) is a viable and cost-effective alternative to High-Speed Diesel (HSD) for power generation in remote areas of Indonesia. An integrated supply chain model is developed to optimize total costs based on LNG inventory levels. The model minimizes transportation costs from supply depots to demand points and handling costs at receiving terminals, which utilize Floating Storage Regasification Units (FSRUs). LNG distribution is optimized using a Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP), formulated as a Mixed Integer Linear Programming (MILP) problem to reduce fuel consumption, CO₂ emissions, and vessel rental expenses. The novelty of this research lies in its integrated cost optimization, combining transportation and handling within a model specifically adapted to Indonesia’s complex geography and infrastructure. The simulation involves four LNG plant supply nodes and 50 demand locations, serving a total demand of 15,528 m³/day across four clusters. The analysis estimates a total investment of USD 685.3 million, with a plant-gate LNG price of 10.35 to 11.28 USD/MMBTU at a 10 percent discount rate, representing a 55 to 60 percent cost reduction compared to HSD. These findings support the strategic deployment of SS LNG to expand affordable electricity access in remote and underserved regions. Full article

As global energy demand increases, ensuring a reliable electricity supply in rural or semi-remote areas remains a significant challenge. Hybrid energy systems, which integrate renewables, generators, storage, and grid connections, offer a promising solution for addressing energy reliability issues. In this context, the rural community of Aras de los Olmos, Spain, serves as the focal point because of its frequent power outages despite being connected to the main grid. This study investigates innovative solutions tailored to the community’s unique needs. It highlights critical challenges in achieving reliable energy access and bridges the gap between existing limitations and sustainable, future-oriented energy systems. This is achieved by analyzing the current energy setup and evaluating potential alternatives. Two scenarios were evaluated: one optimizing the existing configuration for economic efficiency while retaining the grid as the primary energy source, and another introducing a biomass generator to enhance reliability by partially replacing the grid. Detailed technical, financial, and environmental assessments were performed using HOMER. These assessments identified an optimal configuration. This optimal configuration improves reliability, enhances stability, reduces disruptions, and meets growing energy demands cost-effectively. As will be indicated, the first scenario can reduce total costs to approximately USD 90,000 compared to the existing setup, whereas the second scenario can lower grid dependence by approximately 70%. In addition, introducing renewable energy sources, such as solar and biomass, significantly reduces greenhouse gas emissions and reliance on fossil fuels. Additionally, these solutions create local job opportunities, promote community engagement, support energy independence, and align with broader sustainability goals. Full article

To address the stability of the power supply to agricultural facilities and greenhouses in remote areas, this paper proposes a solution based on the bus voltage fluctuation issue in an islanded photovoltaic-storage DC microgrid. Traditional power supply methods often struggle to meet demand due to significant fluctuations in solar irradiance and load. To resolve this, an improved sliding-mode linear active disturbance rejection control (ISMLADRC) strategy is designed, significantly enhancing the response speed of the microgrid control system while improving its adaptability in complex agricultural environments. The system integrates a hybrid energy storage system and photovoltaic power generation to optimize microgrid power compensation, ensuring the stability of the power supply to agricultural facilities and greenhouses. Simulation results demonstrate that the proposed control scheme enhances the robustness and efficiency of the original system, ensuring a reliable power supply for crop production in remote areas, advancing smart agriculture, and promoting the sustainable development of green agriculture. Full article

In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different areas. Spatiotemporal heterogeneity means that vacancy rates vary in different geographical locations and over time. The complexity of data affected by various factors means that the vacancy rate is jointly affected by macroeconomic factors, policy regulatory factors, market supply and demand factors and individual resident factors. These factors are intertwined, increasing the complexity of data and the difficulty of analysis. In view of the diversity of data sources, we discuss multi-source data fusion technology, which aims to integrate different data sources to improve the accuracy of vacancy rate observation. The diversity of data sources, including geographic information system (GIS) (Geographic Information System) data, remote sensing images, statistics data, social media data and urban grid management data, requires integration in format, scale, precision and spatiotemporal resolution through data preprocessing, standardization and normalization. The multi-source data fusion algorithm should not only have the ability of intelligent feature extraction and related analysis, but also deal with the uncertainty and redundancy of data to adapt to the dynamic needs of urban development. We also elaborate on the optimization methods of algorithms for different data sources. Through this study, we find that algorithms play a vital role in improving the accuracy of vacancy rate observation and enhancing the understanding of urban housing conditions. Algorithms can handle complex spatial data, integrate diverse data sources, and explore the social and economic factors behind vacancy rates. In the future, we will continue to deepen the application of algorithms in data processing, model building and decision support, and strive to provide smarter and more accurate solutions for urban housing management and sustainable development. Full article

One of the biggest challenges, both from a technical and economic point of view, of the Distribution Network Operators refers to identifying the locations (electric distribution substations) integrated into a supervisory, control, and acquisition (SCADA) system to perform on-site measurements used in the state estimation of the electric distribution networks (EDNs). In response to this challenge, a robust and resilient three-layer methodology has been proposed to solve the state estimate issue of the EDNs based on an optimal placement algorithm of the remote terminal units integrated into the SCADA system at the level of the EDSs. The first layer allows a clustering algorithm-based determination of the classes of the EDSs with similar features of the load profiles. The second layer identifies the “candidate” classes and decides the pilot EDSs with on-site SCADA measurements. The third layer allows the state estimation of the EDN based on the load values measured in the pilot EDEs. The framework was tested and validated using a medium voltage EDN of a Romanian DNO supplying an urban area. The results obtained highlighted that the accuracy had been ensured for on-site measurements in 12 of 39 EDSs (representing approximately 30% of EDSs integrated into the SCADA system), leading to a mean average percentage error of 2.6% for the load estimation and below 1% for the state variables determined by a power flow calculation at the level of the EDN. Consequently, the framework can significantly decrease the investments associated with integrating the SCADA system by the DNOs, with great benefits regarding the state estimation of the EDNs. Full article

Combining wind and hydropower facilities makes it possible to solve the problems caused by power supply shortages in areas that are remote from the central energy system. Hydropower plants and highly manoeuvrable hydroelectric units successfully compensate for the uneven power outputs from wind power plants, and the limitations associated with them are significantly reduced when they are integrated into the regional energy system. Such an integration contributes to increasing the efficiency of renewable energy sources, which in turn reduces our dependence on fossil resources and decreases their harmful impact on the environment, increasing the stability of the power supply to consumers. The results of optimisation calculations show that a consumer load security of 95% allows the set capacity of RESs to be used in the energy complex up to 700 MW. It is shown here that the joint operation of HPPs and WPPs as part of a power complex and hydraulic energy storage allows for the creation of a stable power supply system that can operate even in conditions of variable wind force or uneven water flow. The conclusions obtained allow us to say that the combination of hydro- and wind power facilities makes it possible to solve the problem of power supply deficits in the regions of Kazakhstan that are remote from the central power station. At the same time, hydroelectric power plants and highly manoeuvrable hydroelectric units successfully compensate for the uneven power output from wind power plants and significantly reduce the limitations associated with them during their integration into the regional energy system. Full article

Remote agricultural regions in desert areas, such as Ghardaïa in southern Algeria, face significant challenges in energy supply due to their isolated locations and harsh climatic conditions. Harnessing solar energy through photovoltaic (PV) systems offers a sustainable solution to these energy needs. This study aims to identify suitable areas for PV power installations in Ghardaïa, utilizing a geographic information system (GIS) combined with the fuzzy analytical hierarchy process (AHP). Various environmental, economic, and technical factors, such as solar radiation, land use, and proximity to infrastructure, are incorporated into the analysis to create a multi-criteria decision-making framework. The integration of fuzzy logic into AHP enables a more flexible evaluation of these factors. The results revealed the presence of ideal locations for installing photovoltaic stations, with 346,673.30 hectares identified as highly suitable, 977,606.84 hectares as very suitable, and 937,385.97 hectares as suitable. These areas are characterized by high levels of solar radiation and suitable infrastructure availability, contributing to reduced implementation costs and facilitating logistical operations. Additionally, the proximity of these locations to agricultural areas enhances the efficiency of electricity delivery to farmers. The study emphasizes the need for well-considered strategic planning to achieve sustainable development in remote rural areas. Full article