Search Results (106)

This study examines the constrained social acceptance of small-scale geothermal energy in a rural sub-Saharan region, a critically understudied area, characterised by high energy poverty, heavy dependence on biomass, and suitable for geothermal energy exploration. Small-scale geothermal energy may offer an additional option for decentralised power supply through mini grids. The study investigates public awareness and knowledge level of geothermal energy technologies among the residents of Eka Awoke, Ikwo, Ebonyi State, Nigeria, to assess the potential of the deliberative process to enhance the social acceptance of geothermal energy technologies and the development of an improved participatory framework to aid the discussion. Citizen jury and survey methods, combining qualitative and quantitative research techniques, were employed. This study presents the first in-depth analysis of the social acceptance of small-scale geothermal energy for electricity supply in a rural African context. Pre-deliberative assessments revealed that 36% of the jurors had limited knowledge and expressed environmental concerns. The post-deliberative assessment revealed that over 80% of jurors reported improved understanding and views. The study demonstrates that citizen jury, when combined with surveyed results can serve as a powerful and scalable tool for advancing social acceptance of geothermal energy. These findings provide a solid foundation for policymakers, stakeholders, and energy providers to design more effective communication engagement strategies for sustainable energy transition in the community. Full article

This paper presents two innovative procedures developed for rural electrification planning. To address the challenges of processing vast geospatial data, handling complex and computationally intensive network design, and making detailed yet accessible economic assessments, this work introduces a Buffering plugin for community identification and a Grid Routing and Cost Allocation plugin for network design and economic assessment, both integrated into the open-source QGIS platform. The first enables the identification of potential electrification zones through dual methodologies, while the second introduces three key processes: hierarchical clustering, a modified minimum spanning tree, and a novel cost allocation methodology that provides village-specific LCOE calculations. Testing in Zambia has proven that this approach is not only effective but also—compared to existing tools—offers significant advantages in terms of computational efficiency and accessibility, while providing practical solutions to large-scale challenges. This synergistic approach enables planners to move from granular geospatial data to actionable electrification decisions through a streamlined process. The analysis covered over 3 million buildings, grouped into 162,142 settlement clusters, and subsequently determined optimal electrification strategies for 3025 villages—40.4% connected to grid extensions and 59.6% to mini-grids—serving a total population of 18 million people. Full article

In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived from high-quality station time series. For the first task, we used a mini statistical ensemble of gridded datasets of the daily maximum air temperature (TX). The ensemble included the following: ClimUAd and E-OBS (Ukrainian and European observation-based gridded data, respectively), reanalyzes ERA5, ERA5-Land, NOAA-CIRES 20CR V2c and V3, and NCEP-NCAR R1. For the second task, the same gridded data were used along with 178 quality-controlled and homogenized TX station time series from Ukraine. HWs and their metrics were defined according to the approach summarized by Perkins and Alexander (2013). All calculations were performed for the period 1950–2014. Our results showed that, depending on the gridded dataset, the calculated values of HW metrics might differ significantly. Even after averaging over the study period and the territory of Ukraine, the ranges between the max and min values of HW metrics remain large. For instance, the spread in HW number per year may be up to six events. However, the differences in the trend slopes of HW metrics are less pronounced. In addition, the comparison of HW calculations derived using gridded and station data showed that E-OBS, ERA5, and ERA5-Land provide similar verification statistics. The evaluation statistics for 20CRV3 are worse compared to E-OBS, ERA5, and ERA5-Land, but significantly better than for 20CRV2c and NCEP-NCAR R1. Our findings can aid in selecting gridded datasets for calculating reliable HW climatology and, consequently, contribute to developing climate adaptation strategies for extreme temperature events in Ukraine, its neighboring countries, and potentially across Europe. Full article

Standards describing the test procedures recommended to investigate the shielding effectiveness of enclosures have two major issues: they generally prescribe the assessment of the electromagnetic field of empty cavities, and they do not deal with very small enclosures. However, the dimensions of some very common shielded apparatus are smaller than those considered in the standards and the electromagnetic field distribution inside the shielded structure is strongly affected by the enclosure content. In this paper, both issues have been investigated for two components commonly used in medium voltage/low voltage (MV/LV) substations: a mini personal computer used to store, process, and transmit relevant data on the status of the electric network, with these aspects being essential in smart grids, and an electronic relay which is ubiquitous in MV/LV substations. Both components are partially contained in a metallic enclosure which provides a certain amount of electromagnetic shielding against external interferences. It is observed that an electrostatic discharge may cause a failure and/or a loss of data, requiring an improvement of shielding characteristics or a wise choice of the positions where the most sensitive devices are installed inside the enclosure. Since the dimensions of very small enclosures, fully occupied by their internal components, do not allow for the insertion of sensors inside the protected volume, numerical analysis is considered as the only way for the appraisal of the effects induced by a typical source of interference, such as an electrostatic discharge. Full article

Reinforcement learning is one of the most prominent research areas in the field of artificial intelligence, playing a crucial role in developing agents that autonomously make decisions in complex environments. This study proposes a method to optimize agent behavior in the MiniGrid-Empty-5x5-v0 environment using large language models (LLMs). By leveraging the natural language processing capabilities of LLMs to interpret environmental states and select appropriate actions, this research explores an approach that differs from traditional reinforcement learning methods. Experimental results confirm that LLM-based agents can effectively achieve their goals, and it is anticipated that maximizing the synergy between LLMs and reinforcement learning will contribute to the development of more intelligent and adaptable AI systems. Full article

Electromobility contributes to decreasing environmental pollution and fossil fuel dependence, as well as increasing the integration of renewable energy resources. The increasing interest in using electric vehicles (EVs), enhanced by machine learning (ML) algorithms for intelligent automation, has reduced the reliance on. This shift has created an interdependence between power, automatically, and transportation networks, adding complexity to their management and scheduling. Moreover, due to complex charging infrastructures, such as variations in power supply, efficiency, driver behaviors, charging demand, and electricity price, advanced techniques should be applied to predict a wide range of variables in EV performance. As the adoption of EVs continues to accelerate, the integration of ML and especially deep learning (DL) algorithms will play a pivotal role in shaping the future of sustainable transportation. This paper provides a mini review of the ML impacts on mobility electrification. The applications of ML are evaluated in various aspects of e-mobility, including battery management, range prediction, charging infrastructure optimization, autonomous driving, energy management, predictive maintenance, traffic management, vehicle-to-grid (V2G), and fleet management. The main advantages and challenges of models in the years 2013–2024 have been represented for all mentioned applications. Also, all new trends for future work and the strengths and weaknesses of ML models in various aspects of mobility transportation are covered. By discussing and reviewing research papers in this field, it is revealed that leveraging ML models can accelerate the transition to electric mobility, leading to cleaner, safer, and more sustainable transportation systems. This paper states that the dependence on big data for training, the high uncertainty of parameters affecting the performance of electric vehicles, and cybersecurity are the main challenges of ML in the e-mobility sector. Full article

Sub-Saharan Africa, especially its rural areas, faces significant challenges in achieving universal electrification despite its abundant renewable energy resources. The region has the highest population without access to electricity, largely due to economic, infrastructural, and geographical barriers. Energy poverty is a critical issue that hinders sustainable development and exacerbates inequalities. Namibia’s sustainable energy policy aligns with the global Sustainable Development Goals (SDGs), particularly SDG 7, which aims to provide affordable and reliable modern energy access for all. The policy emphasizes mini-grids and decentralized power systems as key strategies for rural electrification. However, despite increased deployment of mini-grids, these solutions often struggle with long-term sustainability. This research explores cost-effective electrification strategies through scenario-based modeling to reduce energy poverty and expand energy access in Namibia’s rural communities, focusing on the existing mini-grids in Tsumkwe and Gam. Using a comprehensive methodology that incorporates HOMER Pro for mini-grid capacity expansion and MS Excel for evaluating main-grid extensions, this study aims to identify the most feasible and economical electrification solutions. The analysis compares electricity supply, total net present cost, and the levelized cost of electricity across these systems. The findings will offer insights into addressing energy poverty in Namibia and provide recommendations for sustainable and scalable rural electrification across Sub-Saharan Africa. Full article

The central inverter topology presents some advantages such as simplicity, low cost and high conversion efficiency, being the first option for interfacing photovoltaic mini-generation, whose shading and panel orientation studies are evaluated in the project planning phase. When it uses only one power converter, its control structures must ensure synchronization with the grid, tracking the maximum power generation point, appropriate power quality indices, and control of the active and reactive power injected into the grid. This work develops and contributes to mathematical models, the principles of formation of control structures, the decoupling process of the control loops, the treatment of nonlinearities, and the tuning of the controllers of a single-stage photovoltaic system that is integrated into the electrical grid through a three-phase voltage source inverter. Using the parameters and configurations of an actual inverter installed at the power plant CRESP (Reference Center for Solar Energy of Petrolina), mathematical modeling, implementation, and computational simulations were conducted in the time domain using MatLab^® software (R2021b). The results of the currents injected into the grid, voltages, active powers, and power factor at the connection point with the grid are presented, analyzed, and compared with real measurement data during one day of operation. Full article

The energy transition toward Net Zero Emission by 2060 hinges on the renewable energy power plants in Indonesia. Good practices in several countries suggest a peer-to-peer (P2P) energy trading system using blockchain technology, supported by renewable energy (solar panels), an innovation to provide equal access to sustainable electricity while reducing the impact of climate change. The P2P energy trading concept has a higher social potential than the conventional electricity buying and selling approach, such as that of PLN (the state-owned electricity company in Indonesia), which applies the network management concept but does not have a sharing element. This model implements a solar-powered mini-grid system and produces a smart contract that facilitates electricity network users to buy, sell, and trade electricity in rural areas via smartphones. This study aims to measure the stakeholders’ perceptions of the peer-to-peer (P2P) energy trading model using blockchain technology in the Gumelar District, Banyumas Regency, Central Java Province, Indonesia. The stakeholders in question are representatives of Households (producers and consumers), Government, State Electricity Company (PLN), Non-Governmental Organizations, Private Sector and Academician. Measurement of perception in this study used a questionnaire approach with a Likert scale. The results of filling out the questionnaire were analyzed using four methods: IFE/EFE matrix; IE matrix; SWOT matrix; and SPACE matrix to assess the results and their suitability to each other. The results of the stakeholder perception assessment show that there are 44 internal factors and 33 external factors that can influence this model. We obtained an IFE and EFE score of 2.92 and 2.83 for the internal and external results using the IE matrix. These place the model in quadrant V, meaning the P2P model can survive in the long term to generate profits. Based on the SWOT analysis results, this model is located at the coordinate point −0.40, 0.31, placing it in quadrant II. This means that the P2P model is in a competitive situation and faces threats but still has internal strengths. Based on the SPACE matrix, stakeholder perception states that the P2P model is at coordinate point 1, −0.3. This shows that the P2P model has the potential to be a competitive advantage in its type of activity that continues to grow. In conclusion, our findings show that stakeholders’ perceptions of P2P models using blockchain technology can be implemented effectively and provide social, economic, and environmental incentives. Full article

Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization of the operation of power systems and their components. Digital twins are used to address different issues, including the management of equipment reliability and efficiency, integration of renewable energy sources, and increased flexibility and adaptability of power grids. Digital twins can be developed with the use of specialized software solutions for designing, prototyping, developing, deploying, and supporting. The existing diversity of software requires systematization for a well-informed choice of digital twin’s development tool. It is necessary to take into account the technical characteristics of power systems and their elements (equipment of power plants, substations and power grids of power systems, mini- and microgrids). The reviews are dedicated to tools for creating digital twins in the power industry. The usage of Digital Twin Definition Language for the description data of electromagnetic, thermal, and hydrodynamic models of a power transformer is presented. Full article

This study focused on optimizing a model house for different locations and types of thermal systems to understand better how heating system type affects thermal envelope design, operational greenhouse gas emissions, and life-cycle cost. The study investigated six different thermal system configurations in separate optimizations for five locations. Optimization implies reducing energy consumption, minimizing greenhouse gas emissions (GHG), lowering operational costs, ensuring regulatory compliance, enhancing resilience, and improving occupant comfort and health. The Pareto front, multi-objective optimization, is used to identify a set of optimal solutions, considering multiple goals that may conflict with each other. In determining the least-cost building design envelope, the design balances costs with other goals, such as energy efficiency and environmental impact. The optimizations determine the life-cycle cost versus operational GHG emissions for a single-detached house in Canadian locations with varying climates, emissions factors, and energy costs. Besides natural gas, the study evaluated four electricity-heated options: (a) an air-source heat pump, (b) a ductless mini-split heat pump, (c) a ground-source heat pump, and (d) an electric baseboard. A net-zero-carbon design with grid-tied photovoltaics was also optimized. Results indicate that the heating system type influences the optimal enclosure design. In each location, at least one all-electric kind of design has a lower life-cycle cost than the optimized gas-heated model, and such designs can mitigate the majority of operational GHG emissions from new housing in locations with a low carbon electricity supply. Full article

The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for creating maps of dominant risks and unsupervised pricing grids. This research article aims to adapt well-established clustering methods to complex insurance datasets containing both categorical and numerical variables. To achieve this, we propose a novel approach based on Burt distance. We begin by reviewing the K-means algorithm to establish the foundation for our Burt distance-based framework. Next, we extend the scope of application of the mini-batch and fuzzy K-means variants to heterogeneous insurance data. Additionally, we adapt spectral clustering, a technique based on graph theory that accommodates non-convex cluster shapes. To mitigate the computational complexity associated with spectral clustering’s $O\left(n^3\right)$ runtime, we introduce a data reduction method for large-scale datasets using our Burt distance-based approach. Full article

In mini-grids and marine-isolated grids, power generation gas turbines are subjected to rapid start-up, shutdown, and acceleration/deceleration. This sudden load change can pose a significant impact on the power grid, severely affecting the operational characteristics of gas turbines. To understand the dynamic characteristics of the gas turbine in the transitional processes, this testing takes twin-shaft medium-sized power generation gas turbines as the test object, and goes through the process of startup, acceleration, deceleration, acceleration, shutdown in one hour, and repeats this process 40 times continuously. With fuel flow as the control parameter and power turbine outlet temperature and high-pressure turbine speed as the controlled parameters, the parameter response rate of the gas turbine under various transition processes is analyzed and the effect of thermal inertia on the gas turbine mass temperature as well as speed is studied. Research findings: During the transition processes, the gas temperature exhibited an axial gradient distribution in the channel. In both the acceleration and deceleration processes, the working fluid temperature gradually decreased along the flow direction. And thermal inertia posed different extents of impact on the dynamic characteristics of the gas turbine under different transitional processes. In the same transition process, the impacts of thermal inertia on the response speeds of temperature and rotational speed varied. The results of this study help to more accurately predict the operating state of the gas turbine during the transition process and lay the foundation for the dynamic simulation model of the non-adiabatic gas turbine. Full article

Renewable energy mini-grids are considered a cost-effective way to provide electricity for a large proportion of the population in developing countries who do not have access to it. Compared with standalone home systems and national grid systems, mini-grids can potentially offer a better service. They can be deployed faster, making them essential for sustainable development, especially in rural and semi-urban areas of developing countries. However, mini-grids often face challenges regarding their resilience, and many fail to survive beyond their pilot phases. This paper aims to identify the factors contributing to the success of mini-grids and to identify common themes that can help existing and future mini-grid developments become more resilient and influence policy decision making. To achieve this goal, we developed a database of the status of mini-grids in Malawi, with the energy generation resource(s) of their installed capacity, enabling factors, and challenges. We undertook a more detailed investigation of two hydro mini-grid systems—Bondo and Chipopoma. We collected qualitative and quantitative data through literature reviews, site visits, interviews, and observations. The study identified 19 mini-grids with a combined installed capacity of 26 MW. Of these, seven had been abandoned, and one was under development. Several factors that affect successful mini-grid efficacy in Malawi were identified, including financial resourcefulness, technical resourcefulness, policies and regulations, community engagement and capacity building, cross-sector linkages, and institutional organisational frameworks. These factors need to be integrated into decision making by all stakeholders to ensure the enhancement of resilience and the sustainable development of mini-grids. Full article

This paper explores energy poverty and its distribution among households in the Upper Blinkwater community, a typical remote South African community. Its selection was based on being the first identified to benefit from the pilot project implementing a decentralized hybrid mini-grid. We utilize the Foster–Greer–Thorbecke technique, which identifies households below the energy poverty line, measures the depth, and identifies those most vulnerable to energy poverty. A total of 53 households were interviewed by means of a questionnaire. The findings indicate a reliance on diverse energy sources such as wood for heating and LPG for cooking, which has enhanced community resilience and control over energy consumption, with greater proportions not affected by energy poverty. However, about 38% still experience energy poverty. The findings show that energy poverty is unevenly distributed within the community. Older individuals tend to have greater energy security, likely due to the stability provided by social grants. In contrast, female-headed households and lower-income families face the most significant challenges. The study concludes that there are substantial gender disparities and that lower-income households are particularly vulnerable to energy poverty. Therefore, we recommend gender-sensitive interventions to reduce the financial burdens on these vulnerable households, thereby improving their energy security. Full article