Energy Reports最新文献

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

Amid the bloom of Renewable energy (RE) integrated into the grid, an accurate Photovoltaic(PV) power forecast is considered to be a crucial task in maintaining the reliability and stability of the power systems since this technology strongly depends on various external factors, causing the fluctuation in the output power. However, the poor quality of input data, which is very common in practical circumstances owing to the low-cost measurement and data acquisition devices, poses an enormous challenge for the predictive model to deeply extract the spatial and temporal correlation of the input data. This study proposes a Multi Two-Dimensional Convolutional Neural Network (2D-CNN) for short-term PV power forecast embedded with Laplacian Attention mechanism. By viewing the input sequences in a 2D form, the input map is constructed, and the interconnected feature among variables can be captured by convolution operation. Moreover, with the multiple CNN layers working in parallel architecture, different representations hidden inside the input map can be detected, enabling the proposed model to bring out promising performance across forecast time-step without modifying its initial parameters. In order to reduce the decay impact of irrelevant variables existing inside the input data, the Laplacian Attention mechanism is employed. The Attention matrix is dynamically modified during the training process to produce an accurate attention matrix, which represents the correlation between variables. Therefore, the model is able to focus on informative features and ignore negative ones. The experiments conducted on two different datasets with opposite characteristics provide deep insights into the strength of the proposed model over the baseline model, which strongly demonstrates the efficiency of the proposed model, especially when dealing with datasets bearing tough characteristics.

Africa has enormous potential to produce low-cost e-fuels, e-chemicals and e-materials required for complete defossilisation using its abundant renewable resources, widely distributed across the continent. This research builds on techno-economic investigations using the LUT Energy System Transition Model and related tools to assess the power-to-X potential in Africa, for meeting the local demand and exploring the export potential of power-to-products applications. In this context, we analysed the economic viability of exporting green e-fuel, e-chemicals and e-materials from Africa to Europe. We also present the core elements of the Power-to-X Economy, i.e., renewable electricity and hydrogen. The results show that hydrogen will likely not be traded simply due to high transport costs. However, there is an opportunity for African countries to export e-ammonia, e-methanol, e-kerosene jet fuel, e-methane, e-steel products, and e-plastic to Europe at low cost. The results show that Africa's low-cost power-to-X products backed by low-cost renewable electricity, mainly supplied by solar photovoltaics, is the basis for Africa's vibrant export business opportunities. Therefore, the Power-to-X Economy could more appropriately be called a Solar-to-X Economy for Africa. The Power-to-X Economy will foster socio-economic growth in the region, including new industrial opportunities, new investment portfolios, boost income and stimulate local technical know-how, thereby delivering a people-driven energy economy. Research on the topic in Africa is limited and at a nascent stage. Thus, more studies are required in future to guide investment decisions and cater to policy decisions in achieving carbon neutrality with e-fuels, e-chemicals, and e-materials.

The power system has experienced significant changes due to digitalization, decarbonization, and decentralization, leading to the emergence of the smart grid concept. Metaverse is a virtual realm that improves the efficiency and cost of smart power system operation and development in various ways that are rarely considered in the literature. This paper aims to integrate the metaverse into the smart grid architecture model and explain its various applications through different use cases. It also examines the challenges and issues related to the metaverse deployment within the smart grid.

In this paper, an accurate model of proton exchange membrane fuel cell (PEMFC) for optimal identification of PEMFC parameters has been developed. The optimization methodology is based on the modified version of Manta Ray Foraging Optimization (MMRFO) technique for minimizing the sum of squared errors (SSE) between the Experimentally measured stack voltage and the estimated voltage produced by the optimized model. In the modified methodology, the sine-cosine method has been utilized to enhance the global searching capability in the exploration phase and the local searching capability in the exploitation phase of the MRFO algorithm. In order to validate the effectiveness of the suggested methodology, four different case studies comprising standard benchmark 250 W PEMFC, BCS-500 W PEMFC, SR-12 500 W FC, and 1 kW Temasek stacks were utilized, and the attainments have been compared with the measured polarization characteristics. The attainments have been intensively compared with several metaheuristic algorithms (MA) including Tree growth Algorithm (TGA), Grey wolf optimizer (GWO), Whale optimization algorithm (WOA), Salp swarm algorithm (SSA), and original Manta Ray Foraging Optimization (MRFO), to confirm the superiority of the MMRFO against the compared techniques. The obtained results give a satisfactory agreement between the MMRFO-based model and the experimentally measured data. Finally, the achievements confirmed the effectiveness of the MMRFO over the basic MRFO algorithm and other novel metaheuristic algorithms in identifying PEMFC parameters.

Parameter identification for a proton exchange membrane fuel cell (PEMFC) entails employing optimisation techniques to discover the best unknown parameter values required to generate an accurate fuel cell performance prediction model. This technique, known as parameter identification, is important since manufacturers' datasheets do not usually disclose these values. To address this, the manuscript examines five optimisation strategies, including the suggested algorithm, Enhanced Tunicate Swarm Optimizer (ETSO), for predicting these parameters in PEMFCs. Each technique uses the six unknown parameters as decision variables, aiming to reduce the sum squared error (SSE) between anticipated and observed cell voltages. The data reveal that the suggested strategy outperforms existing approaches and cutting-edge optimizers. The two models are used to assess the dependability and performance of the PEMFC. The results are also compared to the non-parametric tests, and it is found that the suggested method outperforms the other algorithms in both suggested models.

Flat-plate solar collectors are the leading product in the production of low-temperature hot water market, and are also one of the most widely used technologies for utilizing solar energy to generate low-temperature heat. In this study, a specific type of flat-plate collector optimized using field synergy theory was introduced, with the main change being the use of S-bend structure instead of traditional direct current channel. The study applied numerical simulation to analyze and evaluate the performance of the collector. The results showed that introducing an S-bend with a curvature radius of 90 mm can improve the efficiency of a single block collector by 0.1 %, reduce the temperature of the absorption plate by 0.33℃, and drive the angle between the velocity vector and temperature gradient (β angle) close to zero. Compared with the relatively uniform vortex structure observed in the cavity of the straight tube collector, the S-bend structure generates more complex and irregular vortex patterns. It is worth noting that the introduction of S-bend produces a unique butterfly shaped temperature distribution on the glass surface. The changes that occur in the cavity area are driven by the temperature difference between the upper and lower plate surfaces, and these new phenomena induced by S-bending deserve further in-depth discussion. These findings emphasize the importance of continuous research to develop optimized designs, improve efficiency, and promote wider applications, and provide certain ideas and references for subsequent work.

This work shows the design and validation of a Shunt Active Power Filters (SAPF) using Controller Hardware-In-the-Loop (CHIL) Simulations by using a OP5707XG Real-Time Simulator module provided by OPAL-RT, an external OP8666 controller, a host PC and an oscilloscope for visualization. A novel methodology for the modelling of real non-linear electrical loads by making use of MATLAB/SIMULINK is presented. This allows, in conditions like real physical systems, an evaluation of the behavior of active filters before their prototyping, allowing improvements to be made in their design. For the compensation strategy, the calculation of a compensation current from the estimation of the ideal current is used. This strategy is implemented in a microcontroller system for validation with a CHIL configuration simulation. The results have demonstrated significant progress in harmonic mitigation, with the effectiveness of the SAPF in reducing the current Total Harmonic Distortion (THD) across various load types firmly established. As demonstrated in the test cases, the SAPFs significantly reduced THD from significant double-digit percentages to values well below 3 %. This confirms their significant impact on maintaining the integrity and quality of the power system.

Approximately 60 % of Beninese lack access to electricity, relying heavily on traditional energy sources, electricity imports, and low renewable energy integration. This study aims to forecast the energy demand for Benin while reducing greenhouse gas (GHG) emissions and propose alternative solutions to clean energy deployment barriers. The Low Emissions Analysis Platform (LEAP) is used to explore the future energy demand for Benin and associated GHG emissions. Four scenarios have been developed, namely Business as Usual (BAU), High Economic Development (HED), Electrification and Urbanization Growth (EUG), and High Efficient Energy Application (HEEA). In addition, the Fuzzy AHP and TOPSIS techniques have been employed to rank and prioritize alternative solutions to Renewable Energy (RE) deployment barriers in the country. A total of eighteen sub-barriers were identified and classified into five main categories under the four pillars of sustainability (technical, social, economic, and environmental), and nine strategies were identified and proposed to overcome the barriers. The results show that under the BAU scenario, the total energy demand is expected to reach 445 PJ in 2050 from the current demand of around 164 PJ. A total GHG emission of 21 MtCO_2e is estimated under the BAU scenario in 2050. However, the HEEA scenario indicates that energy demand would considerably decrease resulting in low GHG emissions from the energy sector. The findings from the Fuzzy AHP ranking show that technical barriers, political and regulatory barriers, and financial and economic barriers, are the top three barriers hindering RE deployment in Benin. Moreover, adequate policy and regulatory framework emerges as the most weighted solution to eliminating barriers to RE deployment in the country. These results encourage the application of effective policy to support sustainable energy transition. The study's approach is applicable to other developing countries outside Benin, offering insights into RE barriers and potential alternative solutions.

Although onshore wind energy is a key pillar of renewable energy systems, installation targets in Europe have not been met. One contentious issue is its distribution, involving trade-offs between economic costs, environmental impact, public acceptance, and equity considerations. In this study, we evaluate different distribution strategies that meet Germany’s national onshore wind power target of utilizing 2 % land area, breaking it down to subordinate levels such as federal states. Therefore, we define key indicators for energy policy objectives to comprehensively analyze these strategies. We employ an energy system optimization model to address the system integration of spatial onshore wind power distribution, an aspect often overlooked in previous studies. Our results indicate that the impact of different distribution strategies on the overall energy system design is moderate, with the highest sensitivity observed in the allocation of electrolyzers, which closely align with renewable energy surpluses. However, our analysis shows that concentrating onshore wind power in areas with high energy yield can lead to an increase in electricity transport by up to 38 %, whereas more evenly distributed scenarios are preferred for environmental sustainability and distributive justice. In conclusion, we argue that energy system analysis can enhance the accuracy of assessment of onshore wind power distribution, but it must consider non-techno-economic criteria within spatially-distributed energy systems itself to address policymakers.