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This study investigated the integrated valorization of defatted rice bran (DRB) by converting it into lactic acid (LA) and subsequently utilizing the residues from LA production for biomethane generation through anaerobic digestion (AD). Processing 480 kg of DRB resulted in the production of 70 L of pure LA and generated significant waste streams, primarily consisting of 572 kg of decanted hydrolysate pellet (Pellet DEC) and 220 kg of microfiltration retentate (Retentate MF). Exceptionally high methane yields of 374‒434 LN kgVS-1 were observed for residues from LA fermentation in biochemical methane potential tests, indicating their high potential for biogas production. During long-term semi-continuous AD, varying organic loading rates (OLRs) from 0.5‒2.5 kgVS m-3 d⁻¹ demonstrated feedstock- and OLR-dependent methane production. Reactor failure at higher OLRs was attributed to the accumulation of total ammoniacal nitrogen (TAN). The co-digestion of Pellet DEC and Retentate MF proved to be more resilient, with OLRs up to 2 kgVS m-3 d-1, mitigating TAN inhibition. Methane yields, ranging from 265‒334 LN kgVS-1 before reaching inhibitory OLR levels, were higher than those found in the literature. Process integration has emerged as a promising approach because the biogas generated from residues could effectively offset the energy demands of LA production. Supported by life cycle assessment, the integrated processes showed a 67% lower environmental impact at the midpoint and a 71% lower environmental impact at the endpoint, along with an 80% reduction in energy costs compared to the standalone LA production. Results proved a significant enhancement of the sustainability and economic viability of this integrated biorefinery approach.

Achieving quantitative conversion of biomass-derived feedstocks under ambient environmental conditions (20°C, atmospheric air, 0.1 MPa) is a critical milestone for sustainability in chemical processes. Herein, the quantitative conversion of glucose to gluconic acid was accomplished under ambient environmental conditions without any additives using a Pt-Zn intermetallic nanoparticle-supported biochar catalyst prepared from raw rice straw (Pt-Zn/strawC) via a straightforward one-pot solvothermal reaction with ethylene glycol solvent. Spectroscopic analyses verified the formation of the Pt-Zn intermetallic alloy and confirmed strong electronic metal-support interactions. The Pt-Zn/strawC catalyst (Pt:Zn molar ratio of 1:6) was highly selective for the conversion of glucose to gluconic acid, whereas yields as high as 99.9% (98.9% gluconic acid, 1.0% glucaric acid) were reached at 20 oC under base-free and additive-free conditions. Isotope measurements and density functional theory revealed synergistic interactions in the Pt-Zn alloy, wherein the alloy tended to absorb glucose and active O2 into superoxide radical (O2·). This work demonstrates a chemocatalytic method that is practical for environmental conditions and provides a new avenue for sustainable conversion of lignocellulosic biomass to chemical products.

Cassava, a staple food crop, is widely used for starch production, but its inconsistent supply, price volatility, and substantial waste generation pose challenges to the cassava industrial market's growth. This study aims to identify a sustainable biorefinery pathway by optimizing conventional cassava starch plants (business-as-usual, BAU) for economic and environmental benefits. Four scenarios were evaluated: animal feed from peel waste (Scenario 1), fungal protein from thippi waste (Scenario 2), fish feed from digested wastewater (Scenario 3), and conversion of all waste streams into animal feed, fish feed, and fungal protein (Scenario 4). Scenario 4 emerged as the best pathway using the multi-criteria decision-making (MCDM) approach, with a performance score of 0.282. Despite the highest energy consumption (18.91 MWh), Scenario 4 was favored for producing four value-added products alongside starch, yielding the highest profit at USD 8.85 million. In contrast, profits for BAU, Scenario 1, Scenario 2, and Scenario 3 were 1.91, 2.30, 5.01, and USD 8.79 million, respectively. Waste valorization in Scenarios 1, 2, 3, and 4 resulted in CO2 avoidance of 36.5., 42.6., 21.7, and 57.45 t CO2eq., respectively. However, producing value-added products increased energy consumption by 13, 73, 7, and 74% compared to BAU (4.58 MWh). The global warming potential analysis showed negative values for scenarios 2 and 4, at -436 and -434 kg CO2eq./t root, respectively. This study highlights the potential of a biorefinery approach for sustainable cassava starch production, providing insights for future research and policy development.

We explore here, through reactive force field (ReaxFF) molecular dynamics simulations, oleic acid upgrading on beta zeolite (BEA) regulated with nine silica-to-aluminium ratios (SARs) to investigate their acid-catalyzed deoxygenation and coking susceptibility. The selected computational descriptors involved conversion, hydrodeoxygenation, and decarboxylation/decarbonylation selectivity, biofuels yield, and coke deposition characteristics. Simulations were validated by 20.3 SAR available experimental data and used for the systematic study. High deoxygenation selectivity was found to be related to the structural sensitivity of BEA(100) on the upgrading mechanism. ReaxFF simulations revealed that altering the Al-substitution could greatly promote biofuel formation. Specifically, SARs towards the mid-region (SAR 47) favored gasoline production, while 31 SAR exploited diesel-like hydrocarbons. An optimum ratio of 37.4 SAR achieved maximum oleic acid conversion (69.8%), with high yields of gasoline and diesel fuels (15.6 and 20.4 wt%, respectively) and moderate coking (8.6 wt%). Density functional theory screening of metallic dopants allowed investigating of their deoxygenation and coke susceptibility, obtaining that Cu-BEA structure favored the optimal carbon and O moiety adsorption (-3.89 and -1.8 eV, respectively). Furthermore, the economic and environmental analysis showed that Cu-dopped BEA displayed the lowest market price and global warming potential (71 USD/kg and 2.8 kg CO2-eq/kg).

This paper presents a novel approach that simultaneously enables photovoltaic (PV) inversion and flexible arc suppression during single-phase grounding faults. Inverters compensate for ground currents through an arc-elimination function, while outputting a PV direct current (DC) power supply. This method effectively reduces the residual grounding current. To reduce the dependence of the arc-suppression performance on accurate compensation current-injection models, an adaptive fuzzy neural network imitating a sliding mode controller was designed. An online adaptive adjustment law for network parameters was developed, based on the Lyapunov stability theorem, to improve the robustness of the inverter to fault and connection locations. Furthermore, a new arc-suppression control exit strategy is proposed to allow a zero- sequence voltage amplitude to quickly and smoothly track a target value by controlling the nonlinear decrease in current and reducing the regulation time. Simulation results showed that the proposed method can effectively achieve fast arc suppression and reduce the fault impact current in single-phase grounding faults. Compared to other methods, the proposed method can generate a lower residual grounding current and maintain good arc-suppression performance under different transition resistances and fault locations.

Wind-photovoltaic (PV)-hydrogen-storage multi-agent energy systems are expected to play an important role in promoting renewable power utilization and decarbonization. In this study, a coordinated operation method was proposed for a wind-PV- hydrogen-storage multi-agent energy system. First, a coordinated operation model was formulated for each agent considering peer-to-peer power trading. Second, a coordinated operation interactive framework for a multi-agent energy system was proposed based on the theory of the alternating direction method of multipliers. Third, a distributed interactive algorithm was proposed to protect the privacy of each agent and solve coordinated operation strategies. Finally, the effectiveness of the proposed coordinated operation method was tested on multi-agent energy systems with different structures, and the operational revenues of the wind power, PV, hydrogen, and energy storage agents of the proposed coordinated operation model were improved by approximately 59.19%, 233.28%, 16.75%, and 145.56%, respectively, compared with the independent operation model.

Biogas is a renewable and clean energy source that plays an important role in the current environment of low- carbon transition. If high-content CO2 in biogas can be separated, transformed, and utilized, it not only realizes high-value utilization of biogas but also promotes carbon reduction in the biogas field. To improve the combustion stability of biogas, an inhomogeneous, partially premixed stratified (IPPS) combustion model was adopted in this study. The thermal flame structure and stability were investigated for a wide range of mixture inhomogeneities, turbulence levels, CO2 concentrations, air-to-fuel velocity ratios, and combustion energies in a concentric flow slot burner (CFSB). A fine-wire thermocouple is used to resolve the thermal flame structure. The flame size was reduced by increasing the CO2 concentration and the flames became lighter blue. The flame temperature also decreased with increase in CO2 concentration. Flame stability was reduced by increasing the CO2 concentration. However, at a certain level of mixture inhomogeneity, the concentration of CO2 in the IPPS mode did not affect the stability. Accordingly, the IPPS mode of combustion should be suitable for the combustion and stabilization of biogas. This should support the design of highly stabilized biogas turbulent flames independent of CO2 concentration. The data show that the lower stability conditions are partially due to the change in fuel combustion energy, which is characterized by the Wobbe index (WI). In addition, at a certain level of mixture inhomogeneity, the effect of the WI on flame stability becomes dominant.

The launch of the carbon-allowance trading market has changed the cost structure of the power industry. There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch. In this study, a data-driven model of the uncertainty in the annual carbon price was created. Subsequently, a collaborative, robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation. The model is solved using the column-and-constraint generation algorithm. An operation and cost model of a carbon-capture power plant (CCPP) that couples the carbon market and the economic operation of the power system is also established. The critical, profitable conditions for the economic operation of the CCPP were derived. Case studies demonstrated that the proposed low-carbon, robust dispatch model reduced carbon emissions by 2.67% compared with the traditional, economic, dispatch method. The total fuel cost of generation decreases with decreasing, conservative, carbon-price-uncertainty levels, while total carbon emissions continue to increase. When the carbon-quota coefficient decreases, the system dispatch tends to increase low-carbon unit output. This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

The power grid, as the hub connecting the power supply and consumption sides, plays an important role in achieving carbon neutrality in China. In emerging carbon markets, assessing the investment benefits of power-grid enterprises is essential. Thus, studying the impact of the carbon market on the investment and operation of power- grid enterprises is key to ensuring their efficient operation. Notably, few studies have examined the interaction between the carbon and electricity markets using system dynamics models, highlighting a research gap in this area. This study investigates the impact of the carbon market on the investment of power-grid enterprises using a novel evaluation system based on a system dynamics model that considers carbon-emissions from an established carbon-emission accounting model. First, an index system for benefit evaluation was constructed from six aspects: financing ability, economic benefit, reliability, social responsibility, user satisfaction, and carbon-emissions. A system dynamics model was then developed to reflect the causal feedback relationship between the impact of the carbon market on the investment and operation of power-grid enterprises. The simulation results of a provincial power-grid enterprise analyze comprehensive investment evaluation benefits over a 10-year period and the impact of carbon emissions on the investment and operation of power-grid enterprises. This study provides guidelines for the benign development of power-grid enterprises within the context of the carbon market.

Single-phase 25 kV traction networks of electrified alternating current (AC) railways create electromagnetic fields (EMFs) with significant levels of intensity. The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground. Despite the short duration of exposure, they can adversely affect electronic devices and induce significant voltages in adjacent power lines, which is dangerous for operating personnel. Although numerous investigations have focused on modeling the EMF of traction networks and power lines, the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved. In this case, the field has a complex spatial structure that significantly complicates the calculations of intensities. This study proposes a methodology, algorithms, software, and digital models for determining the EMF in the described emergency scenarios. During the modeling process, the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields. This approach was implemented in the Fazonord software, and the modeling results show a substantial increase in EMF levels close to the support, with a noticeable decrease in the levels as the distance from it increases. The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length. Based on the proposed procedure, the EMF near the supports of overhead power lines and traction networks of various designs could be determined, the EMF levels at substations can be calculated, and the influence of metal structures located near traction networks, such as pedestrian crossings at railway stations, can be considered.

Energy storage systems (ESSs) operate as independent market participants and collaborate with photovoltaic (PV) generation units to enhance the flexible power supply capabilities of PV units. However, the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood. This study introduces a dual-timescale dynamics model that integrates a spot market clearing (SMC) model into a system dynamics (SD) model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems (IESSs) with that of an ESS integrated within a PV (PV-ESS). Furthermore, this study aims to ascertain the optimal allocation of the PV-ESS. First, SD and SMC models were set up. Second, the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem, a dual-timescale model was constructed. Finally, a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation. Additionally, negative electricity prices were considered during clearing processes. The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS, helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

In this study, the present situation and characteristics of power supply in remote areas are summarized. By studying the cases of power supply projects in remote areas, the experience is analyzed and described, and the applicability of related technologies, such as grid-forming storage and power load management, is studied, including grid-connection technologies, such as grid-forming converters and power load management. On this basis, three power-supply modes were proposed. The application scenarios and advantages of the three modes were compared and analyzed. Based on the local development situation, the temporal sequences of the three schemes are described, and a case study was conducted. The study of the heavy-load power supply mode in remote areas contributes to solving the problem of heavy-load green power consumption in remote areas and promoting the further development of renewable energy.

With the introduction of the “dual carbon” goal and the continuous promotion of low-carbon development, the integrated energy system (IES) has gradually become an effective way to save energy and reduce emissions. This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs. With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads, an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established. The role of flexible loads in improving the economy of an energy system was investigated using examples, and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios. The results showed that the total cost of the system in different scenarios was reduced by 18.04%, 9.1%, 3.35%, and 7.03%, respectively, whereas the total carbon emissions of the system were reduced by 65.28%, 20.63%, 3.85%, and 18.03%, respectively, when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously. Flexible electrical and thermal loads did not have the same impact on the system performance. In the analyzed case, the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account. Photovoltaics have an excess of carbon trading credits and can profit from selling them, whereas other devices have an excess of carbon trading and need to buy carbon credits.

Hydrogen production by proton exchange membrane electrolysis has good fluctuation adaptability, making it suitable for hydrogen production by electrolysis in fluctuating power sources such as wind power. However, current research on the durability of proton exchange membrane electrolyzers is insufficient. Studying the typical operating conditions of wind power electrolysis for hydrogen production can provide boundary conditions for performance and degradation tests of electrolysis stacks. In this study, the operating condition spectrum of an electrolysis stack degradation test cycle was proposed. Based on the rate of change of the wind farm output power and the time-averaged peak-valley difference, a fluctuation output power sample set was formed. The characteristic quantities that played an important role in the degradation of the electrolysis stack were selected. Dimensionality reduction of the operating data was performed using principal component analysis. Clustering analysis of the data segments was completed using an improved Gaussian mixture clustering algorithm. Taking the annual output power data of wind farms in Northwest China with a sampling rate of 1 min as an example, the cyclic operating condition spectrum of the proton-exchange membrane electrolysis stack degradation test was constructed. After preliminary simulation analysis, the typical operating condition proposed in this paper effectively reflects the impact of the original curve on the performance degradation of the electrolysis stack. This study provides a method for evaluating the degradation characteristics and system efficiency of an electrolysis stack due to fluctuations in renewable energy.

The global shift toward next-generation energy systems is propelled by the urgent need to combat climate change and the dwindling supply of fossil fuels. This review explores the intricate challenges and opportunities for transitioning to sustainable renewable energy sources such as solar, wind, and hydrogen. This transition economically challenges traditional energy sectors while fostering new industries, promoting job growth, and sustainable economic development. The transition to renewable energy demands social equity, ensuring universal access to affordable energy, and considering community impact. The environmental benefits include a significant reduction in greenhouse gas emissions and a lesser ecological footprint. This study highlights the rapid growth of the global wind power market, which is projected to increase from $112.23 billion in 2022 to $278.43 billion by 2030, with a compound annual growth rate of 13.67%. In addition, the demand for hydrogen is expected to increase, significantly impacting the market with potential cost reductions and making it a critical renewable energy source owing to its affordability and zero emissions. By 2028, renewables are predicted to account for 42% of global electricity generation, with significant contributions from wind and solar photovoltaic (PV) technology, particularly in China, the European Union, the United States, and India. These developments signify a global commitment to diversifying energy sources, reducing emissions, and moving toward cleaner and more sustainable energy solutions. This review offers stakeholders the insights required to smoothly transition to sustainable energy, setting the stage for a resilient future.

Global energy and environmental issues are becoming increasingly problematic, and the vibration and noise problem of 110 kV transformers, which are the most widely distributed, have attracted widespread attention from both inside and outside the industry. DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers. To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias, a multi-field coupling model of a 110 kV transformer was established using the finite element method. The electromagnetic, vibration, and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC, harmonic, and DC power sources. It was found that a DC bias can cause significant distortions in the magnetic flux density, force, and displacement distributions of the core and winding. The contributions of the DC bias effect to the core and winding are different at Kdc = 0.85. At this point, the core approached saturation, and the increase in the core force and displacement slowed. However, the saturation of the core increased the leakage flux, and the stress and displacement of the winding increased faster. The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics. When the DC bias coefficient was 1.25, the noise sound pressure level reached 73.6 dB.

Abstract Background Virtual power plants (VPPs) represent a pivotal evolution in power system management, offering dynamic solutions to the challenges of renewable energy integration, grid stability, and demand-side management. Originally conceived as a concept to aggregate small-scale distributed energy resources, VPPs have evolved into sophisticated enablers of diverse energy assets, including solar panels, wind turbines, battery storage systems, and demand response units. This review article explores the evolution of VPPs and their pivotal roles as major stakeholders within contemporary power systems. The review opens with a definition of VPPs that clarifies both their fundamental traits and technological foundations. A historical examination of their development highlights major turning points and milestones that illustrate their transforming journey. Main text The methodology used for this article entailed a thorough examination to identify relevant studies, articles, and scholarly works related to virtual power plants. Academic databases were used to gather relevant literature. The literature was organized into categories helping to structure and present information in a logical flow based on the outline created for the review article. The discussions in the article show that the various functions that VPPs perform in power systems are of major interest. VPPs promote the seamless integration of renewable energy sources and provide optimum grid management by aggregating distributed energy resources, which improves sustainability. One of the important components of this evaluation involves taking market and policy considerations. Examining worldwide market patterns and forecasts reveals that VPP usage is rising, and that regulatory frameworks and incentives have a bigger impact on how well they integrate. Conclusion Overcoming obstacles is a necessary step towards realizing full VPP potential. For VPPs to be widely adopted, it is still essential to address technological and operational challenges as they arise. Diverse stakeholders must work together to overcome market obstacles and promote the expansion of the VPP market. This analysis highlights the potential for VPPs to propel the evolution of contemporary power systems toward a more sustainable and effective future by highlighting areas for future research and development.

Abstract This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.

Abstract From the perspective of configuration, this paper takes the region of manufacturing efficiency as the explanatory variable, selects eight antecedent conditions, and applies fuzzy set qualitative comparative analysis (fsQCA) to study the paths and methods of improving manufacturing emission efficiency. The results of the study show that there are two configuration paths of carbon emission efficiency in manufacturing industry, namely, research frontier and technological innovation level and labour force structure, R&D investment, science and technology innovation level, manufacturing output value, and environmental regulation synergistic path.

Abstract Introduction Energy system simulation modeling plays an important role in understanding, analyzing, optimizing, and guiding the change to sustainable energy systems. Objectives This review aims to examine energy system simulation modeling, emphasizing its role in analyzing and optimizing energy systems for sustainable development. Methods The paper explores four key simulation methodologies; Agent-Based Modeling (ABM), System Dynamics (SD), Discrete-Event Simulation (DES), and Integrated Energy Models (IEMs). Practical applications of these methodologies are illustrated through specific case studies. Results The analysis covers key components of energy systems, including generation, transmission, distribution, consumption, storage, and renewable integration. ABM models consumer behavior in renewable energy adoption, SD assesses long-term policy impacts, DES optimizes energy scheduling, and IEMs provide comprehensive sector integration. Case studies demonstrate the practical relevance and effectiveness of these models in addressing challenges such as data quality, model complexity, and validation processes. Conclusions Simulation modeling is essential for addressing energy challenges, driving innovation, and informing policy. The review identifies critical areas for improvement, including enhancing data quality, refining modeling techniques, and strengthening validation processes. Future directions emphasize the continued importance of simulation modeling in achieving sustainable energy systems.

Abstract Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified.

Abstract Anion exchange membrane water electrolysis (AEMWE) has seen rapid advancements over the past decade due to its promising role in green hydrogen production. Ensuring long-term functionality is as crucial as optimizing performance to achieve commercial viability and industrial integration. However, few studies have systematically discussed the degradation issues of this technology. Therefore, a thorough understanding of AEMWE degradation is needed to guide the design, assembly, operation, and maintenance of the device over its lifetime. To address this gap, this review systematically overviewed the heterogeneous degradation of AEMWE across different material and interface levels, focusing on several key components including catalysts, ionomers, membranes, and gas diffusion layers. The influences of these components and their interfaces on the catalytic efficiency, active site density, and mass and electron transfer capabilities were discussed. Moreover, the impacts of operation conditions, including temperature, electrolyte composition, and clamping pressure, on the stable operation of AEMWE were assessed. Accordingly, current mitigation strategies to resolve these degradation phenomena were rigorously evaluated. By offering insights into optimizing operations, designing materials, and improving assessment protocols for AEMWE, this work will contribute to enhancing its stability for large-scale hydrogen production.

Abstract Packetized energy encapsulates energy into modulated, routable, and trackable energy packets, enhancing the flexibility of managing distributed energy resources and expediting prosumers’ participation in transactive energy markets. In the context of packetized energy trading (PET), energy prosumers are naturally deemed as self‐interested agents seeking to obtain their own benefits. To align with prosumers’ demand, supply, quality of service (QoS), and system‐level social welfare, it is necessary to explore the design of prosumers’ bidding strategies and the market clearing methods, considering prosumers’ utility and the best demand response to markets. This study addresses challenges arising from prosumers’ selfishness and asymmetric preferences by proposing a PET‐oriented iterative double auction (IDA‐PET) design, where prosumers are allowed to iteratively change the bids before the auctioneer clears the market. Moreover, IDA‐PET accommodates system capacity constraints, energy balance, and economic constraints, providing cooperative strategies for both prosumers and the auctioneer. To validate the effectiveness of IDA‐PET, a novel and dedicated co‐simulation platform based on the hierarchical engine for large‐scale infrastructure co‐simulation platform is developed and case studies are conducted within a residential microgrid. The simulation results demonstrate that IDA‐PET can efficiently enhance the revenue of the auction market while meeting prosumers’ QoS requirements.

Abstract Lithium‐ion battery state‐of‐health (SOH) monitoring is essential for maintaining the safety and reliability of electric vehicles and efficiency of energy storage systems. When the SOH of lithium‐ion batteries reaches the end‐of‐life threshold, replacement and maintenance are required to avoid fire and explosion hazards. This paper provides a comprehensive literature review of lithium‐ion battery SOH estimation methods at the cell, module, and pack levels. Analysis and summary of the SOH definition based on the resistance, capacity, and energy indices are presented at each battery hierarchy level. A Comparison of SOH indices in terms of modelling complexity, required measurement time, and accuracy is provided. To the best of knowledge, a comprehensive classification of SOH estimation methods at different battery hierarchy levels is presented for the first time in this review. In addition, SOH estimation methods are further classified based on the applied methodologies, including direct measurement, model‐based methods, data‐driven methods, and hybrid model‐data methods. Advantages and disadvantages of SOH estimation methods are summarized and compared across different battery hierarchy levels. A detailed summary of typical SOH estimation methods is presented along with the battery topology, operating conditions, and performance. The challenges and research prospects of lithium‐ion battery SOH estimation are discussed from the cell to pack levels.

Abstract Renewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Abstract To optimally harness the adjustable capabilities of reactive power sources for voltage control, a dynamic partitioning method that uses reactive power flow tracking for branch cutting through Binary Particle Swarm Optimisation (BPSO) is proposed for Active Distribution Networks (ADNs). Initially, the limitations of existing Voltage/Var Sensitivity (VVS) calculation methods are analysed, leading to the proposition of a novel VVS calculation method capable of capturing variations in source‐load timing characteristics. Subsequently, the fuzzification of the VVS matrix between nodes is used to derive the membership degree matrix. Next, based on the membership relationship between reactive power source nodes, these nodes are pre‐partitioned, and the number of leading nodes and zones alongside are preliminarily determined. Then, the range of the branch to be cut is established, guided by the reactive power flow direction of the branch. Employing the zonal comprehensive coupling degree as the objective function of the BPSO facilitates the identification of optimal branch cutting points, thereby determining the partitioning outcome. Finally, a reactive power reserve check is executed to rectify any non‐compliant zones. In this study, numerical simulations are conducted using the enhanced IEEE 33‐node power system to demonstrate the efficacy of the proposed method.

Abstract Accurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of Value‐at‐Risk and Conditional Value‐at‐Risk compared to original methods.

Abstract Background In a case study in Spain, the unequal proportion of men and women in a research organization in the energy sector is severe, and long-established dynamics that might determine differences in access to leadership positions and inequalities in research careers are evident. The gender gap in historically masculinized fields, such as energy engineering reflects more than simply the differences in male and female values and personalities. This study seeks to explore the gender gap in energy research centres and to identify barriers that potentially hinder the research careers of women. It proposes the development of a diagnostic tool, based upon indicators, to monitor and evaluate gender roles and inequalities in the management of research centres for identifying and addressing the dynamics and obstacles that hinder women's progress in the energy sector and their potential contribution to the field. This participatory multicriteria-based tool prioritizes the proposed indicators by their influence and importance in the context of energy research and applies it to the monitoring of a specific Spanish energy research centre. Results The results are threefold: (i) the methodology is adaptable to different research centres; (ii) the analysis of indicators’ prioritization could lead to recommendations that should be addressed first; (iii) the diagnostic tool used in this in-depth case study of an energy research centre in Spain allowed results to be achieved in terms of gender dynamics. Two indicators stand out as the most relevant in our analysis: gender diversity in leadership positions and uncomplicated application of work–life balance measures. In this case study, the measurement of the first indicator has drawn unsatisfactory results, and the research of the latter is considered still insufficient. In conclusion, this difference becomes a vicious or negative circle for attracting and retaining more women to the research centre. Despite these results, no gender gap seems to be recognized and thus, no measures are being taken to improve the situation. Conclusions Comprehensive data and contextualized monitoring are necessary to effectively study and enhance the presence and participation of women in the energy science sector. This approach, combining quantitative and qualitative techniques, is suitable for any research centre that would like to monitor its gender gap, identify potential sources of inequity and address them.

Abstract False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we compare several recent deep learning-based model that proven their high performance and accuracy in detecting the exact location of the attack node, which are convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models. We, then, compare their performance with baseline multi-layer perceptron (MLP)., All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Based on the results, CNN model exhibited consistently high performance in very short training time. LSTM achieved the second highest accuracy; however, it had required an averagely higher training time. The attention-based LSTM model achieved a high accuracy of 94.53 during hyperparameter tuning, while the CNN model achieved a moderately lower accuracy with only one-fourth of the training time. Finally, the performance of each model was quantified on different variants of the dataset—which varied in their $${\text{l}}_{2}$$ l 2 -norm. Based on the results, LSTM, CNN obtained the highest accuracy followed by CNN-LSTM and lastly MLP.

Abstract In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

Abstract To explore the optimization method of grid-connected voltage support technology in new energy stations, this study first analyzes and discusses this technology. Second, this study describes the deep learning model architecture and feature selection in detail and determines the framework used for the optimization model proposed here. Lastly, the development of optimization and control strategies is investigated, and the optimized model’s effectiveness is verified through experiments. The results reveal that the optimized model's accuracy, precision, recall, and F1 score are higher than those of the comparison model in the performance comparison experiment, reaching the highest values of 0.890, 0.888, 0.878, and 0.883, respectively. This reflects that the optimized model shows high performance on small datasets, and its performance benefits become more pronounced as the data volume increases. This feature is particularly significant because, in practical applications, power systems often need to process large amounts of data to achieve efficient voltage support. In simulation experiments, the optimized model demonstrates excellent performance in terms of response time, stability, robustness, and energy consumption. Moreover, this model effectively addresses various data challenges and uncertainties encountered in grid-connected voltage support technology for power systems, thereby providing robust support for stable and efficient voltage regulation. In light of the findings, this study offers substantial insights for advancing research in the realms of power systems and new energy technologies. The exploration into the application of deep learning and intelligent control strategies within power systems reveals significant potential for transforming grid optimization practices. This study accentuates how data-driven methodologies can revolutionize energy management, paving the way for smarter and more efficient energy systems. By enhancing both the responsiveness and operational efficiency of power grids, the study contributes to the acceleration of digital transformation within the energy sector, fostering innovation and laying a robust foundation for future advancements in energy informatics.

Abstract Catalytic transformation of renewable biomass into value-added chemicals is an appealing strategy to upgrade biomass resources. Due to the presence of abundant oxygen-containing groups such as hydroxyl and aldehyde, biomass and its derived platform molecules have been served as ideal starting feedstock to synthesize valuable N-containing chemicals through reductive amination. In this mini review, we overviewed the recent advances in the reductive amination of several key bio-platform molecules including hydroxyl carboxylic acids, furfural, 5-hydroxylmethyl furfural and levulinic acid, with a focus on the production of amino acids, furan amines and pyrrolidones using thermocatalysis, electrocatalysis or photocatalysis. Moreover, the functions of active sites and the reaction mechanisms in different catalytic systems are discussed to get insights into the key factors in the reductive amination of biomass resources.

Abstract A three-dimensional organic–inorganic hybrid halide perovskite (CH3NH3PbI2Br) nanoparticle film was synthesized using solution process in one step. Precursors’ stoichiometry was used to prepare methylammonium bromide and lead (II) bromide in dimethyl formamide. The synthesized CH3NH3PbI2Br solution was spin coated on cleaned ITO prepatterned glass substrate at 2000 rpm. In addition, the influence of post-thermal treatment on the deposited nanoparticles films was evaluated by studying the structural, optical and morphological properties using X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR) spectrometry, UV–visible spectrophotometry, Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The average crystallite sizes were calculated to be ~ 8.0, ~ 9.4, ~ 11.3 and ~ 13.1 nm of as-prepared and annealed films at 400, 500 and 600 °C, respectively. Besides, the bandgaps of 2.22 eV, 1.96 eV, 1.89 eV and 1.63 eV were extrapolated for as-prepared and annealed films at 400 °C, 500 °C and 600 °C, respectively. From J–V curves, the Jsc values of 9.4, 10.6, 11.7 and 12.8 mAcm−2, FF values of 56.99%, 57.25%, 58.94% and 59.26% and PCE values of 2.99%, 3.49%, 3.96% and 4.39% were calculated for as-prepared and annealed CH3NH3PbI2Br films at 400 °C, 500 °C and 600 °C, respectively. The percentage enhancement values of 14.32%, 24.49% and 31.89% compared to as-prepared CH3NH3PbI2Br film. Hence, post-thermal treatment of organic–inorganic halide perovskite material improved its photoconversion efficiency without sacrificing its dimensionality.

Abstract Photo-galvanic cell is a photochemical cell device whose performance depends on the combination of several factors like dye and reductant concentrations, dye stability, pH, light intensity, electrodes used, Pt electrode area, diffusion length, etc. An entirely new and unexplored combination of platinum with Allura Red photosensitizer, d-Galactose reductant, and didecyl dimethyl ammonium chloride (DDAC) surfactant has been explored in present research to upgrade the electrical performance of these cells. Allura Red is a water-soluble azo dye which shows good absorbance in region 501–507 nm. The Allura Red and DDAC is an anionic and cationic species, respectively, and therefore, the opposite charges of dye and surfactant molecules are expected to form a stable dye-surfactant complex enhancing the dye solubility and stability. d-Galactose has been used for its good reducing properties. This unexplored combination of Pt-Allura Red-d-Galactose-DDAC with these characteristics has encouraged further enhancement of the electrical output of the photogalvanic cells. The electrical output, stability, and spectral property of the photogalvanic cells have been studied in the present work. The observed power, current, potential, efficiency, and storage capacity (as half time) are of the order of 443.8 µW, 2400 µA, 721 mV, 11.61%, and 28 min, respectively. These observed results are higher than some reported data. In the spectral study, nearly the same band intensity of the pre-illuminated and post-illuminated electrolyte solution shows quite good photo-stability of the Allura Red dye in electrolyte form. This new combination of platinum-electrolyte still has the scope to achieve the enhanced cell performance of photogalvanic cells for future development.

Abstract Background Offshore wind energy (OWE) will play a significant role in achieving climate neutrality. For example, several scenarios for Germany (e.g., Kopernikus base, Kopernikus 1.5 degree, Prognos CN65, and CN60) depict substantial OWE annual installed capacity additions, especially after 2030. This tendency promotes OWE technology development as deployment expands, allowing manufacturers to gain expertise and optimize wind turbine construction. The global trend towards ever-larger components (e.g., hub height and rotor diameter) is critical to achieving higher-rated capacities. These aspects and others, such as wind quality, influence not only OWE annual electricity production but also its environmental performance. In addition, future supply chains might reduce their environmental impacts and enhance OWE climate change mitigation. In this paper, a prospective life cycle assessment (pLCA) is developed and applied exemplarily for a 9.5-MW offshore wind turbine (OWT) on the North Sea coast of Germany for the years 2030 and 2050. Considering that the current OWTs under construction in Europe have an average capacity of 10 MW, Germany plans to instal OWTs of 9.5-MW. This exemplary OWT describes the potential advances for offshore wind turbines in 2030 and 2050, considering component scale-up and learning effects. Yet, the methodology is adaptable to various installed capacities and regions. This approach allows us to analyse not only the potential future characteristics of wind turbines, but also future developments in OWE supply chains. Therefore, relevant parameters related to OWT construction and operation (e.g., rotor diameter, hub height, distance to the shore, lifetime, etc.) as well as prospective life cycle inventory data for background systems that reflect potential future developments in the broader economy are considered. In this way, scenarios (e.g., optimistic, moderate, and pessimistic) for OWE elucidate the expected environmental impacts, such as climate change, marine eutrophication, and abiotic depletion potential, in 2030 and 2050. Results The findings describe the variability of the environmental impacts of a 9.5-MW offshore wind turbine representing the technologies expected to be available in Germany in 2030 and 2050 and show that climate change impacts could vary between 7 and 18 g CO2-eq per kWh produced in 2030 and between 5 and 17 g CO2-eq per kWh in 2050. However, marine eutrophication could experience a significant increase (100% increase), depending on the consideration of hydrogen as a fuel in the electricity mix, as demonstrated in the climate-neutral scenarios adopted for Germany. Overall, construction efficiency improvements in 2050 might reduce the required materials, leading to a 6% decrease in abiotic depletion potential compared to 2030 values. Conclusions This paper highlights the need to consider temporal improvements in LCA studies, particularly when assessing the environmental impacts of offshore wind turbines. The complex nature and rapid growth of offshore wind technology require a comprehensive life cycle approach to deepen our understanding of its potential environmental impacts.

Abstract Background There is a close link between the lack of electricity access and poverty indicators such as illiteracy, high infant mortality, lack of access to health care and malnutrition among others. Most rural farming communities in Ghana lack access to electricity due to the high cost of extending the grid to these communities. This lack of access tends to worsen the gap between urban and rural inhabitants regarding access to education, healthcare and development. Methods This study assessed the technical and theoretical potential of agricultural residues in providing electricity to off-grid communities. The study used crop production figures of maize, cassava, millet and groundnut in the Soma and Goyiri farming communities in the Sawla-Tuna-Kalba District to conduct an assessment of the theoretical and technical potential of residues from the crops. The production figures of these crops were obtained from the District Office of the Ministry of Food and Agriculture. Expected electricity demand of households, schools and health centers in the study communities were collected and employed for the projected load demand estimates. Results The study found that 312.23 MWh/day of electricity could be generated from the combined residues of maize, cassava, millet and groundnut from the two communities. This amount of electricity is capable of providing ~ 202 to 263 times the peak electricity demand of the studied communities. Out of the total electricity demand of the two communities, only about 91 kWh/day is needed for use in a school and Community Health Promotion and Services (CHPS) compound, implying that the electricity from crop residues can also help to improve education and health provision in the rural communities. Conclusion It is concluded that the potential of crop residues in meeting the electricity demand of off-grid communities is enormous. Hence, it must be considered in Ghana’s energy development plans to achieve universal electricity access.

Abstract This study presents a major advance in grid management: the development and deployment of an integrated network command system for the main distribution network. The system integrates cutting-edge information technology, including modules such as command issuance, intelligent routing, security assurance and in-depth data analysis, opening a new era of refined and intelligent power grid management. The research focuses on the application of core technologies such as information communication technology, distributed control system, artificial intelligence and big data analysis, and strengthens the system operation foundation. The chapter on system architecture details the innovative integration of DDQN algorithm and attention mechanism, and carefully constructs intelligent scheduling engine and status monitoring and early warning system, which significantly improves real-time response, decision optimization and active security defense capabilities. Simulation experiments and actual case analysis verify the effectiveness of the system, specifically, the response time is reduced by 75.7%(from 2.1 s to 0.51 s in the traditional system), the data processing speed is still maintained at a high level under high load (100,000 data processing rate is 300/s), and the system stability is as high as 99.97%. The new system also achieved a high degree of automation, reducing annual operation and maintenance costs by 20%, and increasing user satisfaction to 90%, an increase of 28.6% over the previous period. These improvements not only optimize power quality and grid efficiency, but also further confirm that the fault response time is reduced by 30% and the user outage time is reduced by 25%. Therefore, this study not only highlights the innovation of the proposed system, but also demonstrates its significant contribution to accelerating the modernization of power grid management and ensuring safe operation with empirical data.

Abstract This study presents a comprehensive review of the primary distribution design of an advanced network control system, emphasizing its evolution from initial requirements to practical applications. The system solves complex problems of power management by combining real-time data analysis, intelligent decision making for resource allocation, rapid fault correction, remote monitoring and complex optimization methods, all aimed at ensuring stable and safe operation of the power grid. Its performance is geared towards fast response, efficient data processing and synchronous processing tasks, ensuring smooth operation even under heavy workloads. Security is enhanced through strict protocols, encryption methods, and controlled access systems. The system is divided into four layers-data collection, communication, decision-making and application management-using innovative tools such as Kalman filters and deep Q networks. The research showcases the integrated network command system’s prowess, achieving an average response time of 0.27 s, 98.5% dispatching accuracy, and 83.2% resource utilization, evidencing exceptional performance. It excels under various tests, including managing high loads with minimal accuracy loss, rapidly adapting to changes with a hydro model response time of 0.22 s, efficiently integrating renewables at 78.0% efficiency, and proving resilient in peak hours, affirming its capability to bolster grid efficiency, reliability, and integration of renewable energy resources. By outlining these specific achievements, this case study not only illustrates the complex design of the system, but also highlights its great potential for improving grid resilience and efficiency, attracting a wide audience interested in the future of energy management.

Abstract The conversion of biomass to chemicals/fuels has emerged as a valuable solution that offers both environmental and economic benefits, with the transformation of carbohydrate into formic acid garnering escalating scholar interest. However, the relative limited efficiency of catalyzed-oxidation or expensive cost of H2O2 and alkali in wet hydrothermal oxidation impose limitations on industrialization. This paper proposed a new idea for formic acid production by O2-H2O2 co-oxidation of carbohydrate. A two-step reaction method was developed, where the initial step is engineered to regulate the carbon chain cleavage of carbohydrates to augment the production of active intermediate. Oxygen was employed in the subsequent step as effective oxidant through free radical mechanism, resulting in a formic acid yield of 82.6%. Theoretical calculation, intermediates detection and real time EPR confirmed the reaction mechanism. Finally, the universality of the reaction was verified by using disaccharides and polysaccharides such as cellulose as substrates.

Abstract As building becomes more leak-tight, air refreshment to improve indoor air quality is needed for healthy living standards. In the quest to ensure comfortable indoor climatic conditions, energy recovery ventilators (ERV) are used to provide air refreshment and thermal comfort. In this study, a fixed plate air-to-air ERV was analysed to determine its effectivity in terms of energy recovery during air refreshment. A customized test bench was built and used to acquire the instantaneous surface temperatures of the heat exchanger plates contained in the ERV during air refreshment. Also, a numerical model of the ERV was developed and validated by comparing its output from simulation with experiment results under the same conditions. The model consisted of a single heat exchanger plate and the air across the plate surface. To capture the effect of heat transfer between the air and the heat exchanger plate in 3D, the model was discretized into numerous cells. From the experiment and simulation results, the air-to-air ERVs were more effective in recovering thermal energy at low flow velocity than at high flow velocity. At lower flow velocity, it will take a longer time duration before any significant impact is made on the thermal conditions of the building, comparatively. The prediction of the model was within acceptable margins and could be used to provide insight on the ERVs performance improvement.

Abstract Solar energy production is an intermittent process that is affected by weather and climate conditions. This can lead to unstable and fluctuating electricity generation, which can cause financial losses and damage to the power grid. To better control power production, it is important to predict solar energy production. Big data and machine learning algorithms have yielded excellent results in this regard. This study compares the performance of two different machine learning approaches to solar energy production prediction: regression and classification. The regression approach predicts the actual power output, while the classification approach predicts whether the power output will be above or below a certain threshold. The study found that the random forest regressor algorithm performed the best in terms of accuracy, with mean absolute errors and root mean square errors of 0.046 and 0.11, respectively. However, it did not predict peak power values effectively, which can lead to higher errors. The long short-term memory algorithm performed better in classifying peak power values. The study concluded that classification models may be better at generalizing than regression models. This proposed approach is valuable for interpreting model performance and improving prediction accuracy.

Abstract Background PV/diesel microgrids are getting more popular in rural areas of sub-Saharan Africa, where the national grid is often unavailable. Most of the time, for economic purposes, these hybrid PV/diesel power plants in rural areas do not include any storage system. This is the case in the Bilgo village in Burkina Faso, where a PV/diesel microgrid without any battery storage system has been set up. This power plant is composed of three diesel generators operating in parallel (two of 16 kW and one of 24 kW), coupled with a photovoltaic field of 30 kWp. It was observed that for such power plants, the grid management is not always efficient due to constantly fluctuating solar output and loads. This inconsistency in energy output raises the question if integrating battery energy storage systems could improve the grid's performance. While many studies in the literature focus on hybrid energy systems, only a few of them have tackled the optimization of existing and operational systems. Methods This study investigated three scenarios based on the existing microgrid's characteristics: conventional standalone diesel generators, PV/diesel without battery storage and PV/diesel with a battery storage system which are the main technologies used for off-grid rural electrification in Burkina Faso. The levelized cost of electricity (LCOE) was used to assess the economic performance of each scenario, and the calculations were made using the HOMER software. Results It was found that the best among the scenarios considered is the PV/diesel/battery configuration which has the lowest LCOE of US$ 0.524/kWh. The battery storage system for the optimal configuration has a capacity of 182 kWh with about 8 h of autonomy. Conclusions It can be inferred from this study that a storage unit is necessary for an optimal management of a PV/diesel microgrid. Indeed, the storage unit significantly reduces the operating and maintenance costs associated with running diesel generators, as well as the excess electricity. The storage system also allows for a greater reduction in CO2 emissions compared to systems without storage.

Abstract This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment.

Abstract In recent years, there has been growing interest in digital twin technology in both industry and academia. This versatile technology has found applications across various industries. Wind energy systems are particularly suitable for digital twin platforms due to the integration of multiple subsystems. This study aims to explore the current state of predictive digital twin platforms for wind energy systems by surveying literature from the past five years, identifying challenges and limitations, and addressing future research opportunities. This review is structured around four main research questions. It examines commonly employed methodologies, including physics-based modeling, data-driven approaches, and hybrid modeling. Additionally, it explores the integration of data from various sources such as IoT sensors, historical databases, and external application programming interfaces. The review also delves into key features and technologies behind real-time systems, including communication networks, edge computing, and cloud computing. Finally, it addresses current challenges in predictive digital twin platforms. Addressing these research questions enables the development of hybrid modeling strategies with data fusion algorithms, which allow for interpretable predictive digital twin platforms in real time. Filter methods with dimensionality reduction algorithms minimize the computational resource demand in real-time operating algorithms. Moreover, advancements in high-bandwidth communication networks facilitate efficient data transmission between physical assets and digital twins with reduced latency.

Abstract The effective management of reactive power plays a vital role in the operation of power systems, impacting voltage stability, power quality, and energy transmission efficiency. Despite its significance, suboptimal reactive power planning (RPP) can lead to voltage instability, increased losses, and grid capacity constraints, posing risks to equipment and system reliability. Rigorous RPP methodologies can mitigate these challenges, resulting in tangible improvements in voltage profiles, system stability, and reduced losses. A comprehensive review of 20 technical articles published between 2020 and 2023 was conducted to compare and synthesize contributions to the field of RPP. The review highlighted the efficacy of strategic RPP approaches in reducing power losses, minimizing equipment malfunctions, and improving power quality, leading to substantial economic benefits—strategic planning approaches and integrating emerging technologies. For instance, examples include renewable energy sources and energy storage systems, which offer promising avenues for enhancing RPP and ensuring stability, reliability, and efficiency in power systems.

Abstract Green technology innovation is a critical factor in ensuring the long-term stable development of renewable energy enterprises. Based on the super network theory, this paper constructs a network model of green technology innovation influencing factors of renewable energy enterprises, which includes the knowledge sub-network of green technology innovation of renewable energy enterprises, the research and development member sub-network of green technology innovation team of renewable energy enterprises and the policy sub-network of green technology innovation of renewable energy enterprises. It explores the mechanism of its influence on innovation in the preparation stage. Simulation analysis by Netlogo software concludes that innovation knowledge sharing, R&D membership, and innovation policy all have a significant positive impact on green technology innovation in renewable energy companies.

Abstract This study presents a novel method for planning the expansion of transmission lines and energy storage systems while considering the interconnectedness of electricity and gas networks. We developed a two-level stochastic planning model that addresses both the expansion of transmission and battery systems in the electrical grid and the behavior of the gas network. This research explores the challenges and effects of integrating high levels of renewable energy sources while ensuring security within both networks. Our model uses a stochastic mixed-integer non-linear programming approach. To solve this complex model, we applied the Human Evolutionary Model (HEM). We tested our approach on two case studies: a simple 6-node network and the more complex IEEE RTS 24-bus network for the electricity grid, combined with 5-node and 10-node gas networks, respectively. The results demonstrate the effectiveness of our model, particularly in scenarios where connections in the power and gas networks are disrupted, preventing load shedding even when integrated network connections are cut.

Abstract A growing number of industries have started to adapt to the circular economy since the concept's introduction. Therefore, in order to accurately evaluate the development level of circular economy, the circular economy prediction model based on support vector machine-Gaussian K-nearest neighbor is proposed. This model first uses the improved K-nearest neighbor algorithm based on Gaussian function to classify the index data of various levels, and then uses Support Vector Machine to make predictions based on relevant data. According to the experimental findings, the model's average prediction accuracy for each level of indicator was approximately 98.1%, 98.8%, 94.9%, and 95.9% for the levels of industrial development, resource consumption, ecological protection, and resource recycling and reuse, respectively. This prediction accuracy was higher than that of the multi-vector autoregressive model and the grey prediction model. The average prediction accuracy of the multi-vector autoregressive model, the grey prediction model, and the support vector machine-Gaussian K-nearest neighbor-based model in predicting the overall development level of the circular economy were about 94.3%, 96.2%, and 99.3%, respectively, with average recalls of about 86.6%, 87.7%, and 89.1%, and the average F1-measure was about 0.88, 0.89, and 0.92. Moreover, the average relative error based on the support vector machine-Gaussian K-nearest neighbour model was only approximately 0.6%, which was lower than the 3.7% and 2.8% for the multi-vector autoregressive model and the grey prediction model, respectively. Meanwhile, compared with the existing time series analysis techniques, the proposed SVM-Gaussian K nearest neighbor model fitted up to 0.95, which achieved good prediction performance. According to the above data, the support vector machine-Gaussian K-nearest neighbour model has the highest accuracy in predicting the amount of development of the circular economy.

Abstract Background Large-scale renewable energy projects are increasingly being rolled out across rural Kenya, with the government playing a frontline role in attracting energy investors through various state-led and state-centric policies and investment incentives such as feed-in-tariffs and power purchase agreements. While these policies are commendable, and are indeed attracting many private investors, existing studies document how social and environmental justice concerns are often overlooked—sometimes causing local contestations against energy projects. However, to date, there has been less attention given to cases where procedural justice elements (e.g., access to information, access to meaningful participation, access to justice, and respect for local culture) led to a successful land negotiation for energy development without outright conflict. Using a case study in Kenya, this article aims to bridge this gap by showing how a fair application of various elements of procedural justice in land consultation has facilitated the establishment of the Kipeto wind farm. This qualitative research is based on semi-structured interviews that took place from February to March 2023, with a follow-up visit in December the same year, supplemented with review of secondary data sources. Results The results indicate that for energy projects to be accommodated in and by communities, access to land must be properly negotiated, particularly with the actual landowners whose livelihoods are most likely to be implicated by the project. Second, local people’s perception of what they regard as a ‘just’ or ‘fair’ process of land consultation constitutes the basis for their acquiescence and compliance. Conclusion Ensuring a ‘just’ procedure in land consultation with the actual landowners is a key strategy to avoid conflicts. Land investors, governments, and policy-makers who interface and negotiate with communities must ensure the provision of procedural justice, particularly in contexts where local livelihood is tied to land and where land is individually owned. Although the findings suggest a positive case of wind energy development in Kenya, the project is barely 4 years old; things may change overtime if agreed conditions are not met as specified in the MoU. Therefore, additional follow-up research is needed to ascertain the extent to which both KEL and landowners live up to their promises.

Abstract The study investigates the weighting and hierarchization of renewable energy sources in specific geographical regions of Colombia using the TOPSIS and Diffuse TOPSIS metaheuristic models. 5 regions were analyzed, two of them with different scenarios: Caribbean 1 and 2, Pacific 1 and 2, Andean, Amazonian and Orinoquia. The results reveal significant differences in the evaluation of technologies between the two models. In the Caribbean 1, Diffuse TOPSIS gave a higher score to Solar Photovoltaics, while TOPSIS favored Hydropower. In the Caribbean 2, Solar Photovoltaic obtained similar scores in both models, but Wind was rated better by TOPSIS. In the Pacific Region 1, Biomass and large-scale Hydropower led according to both models. In the Pacific 2, Solar Photovoltaic was better evaluated by TOPSIS, while Wind was preferred by Diffuse TOPSIS. In the Andean Region, large-scale hydroelectric and Solar photovoltaic plants obtained high scores in both models. In the Amazon, Biomass led in both models, although with differences in scores. In Orinoquia, Solar Photovoltaic was rated higher by both models. The relevance of this research lies in its ability to address not only Colombia's immediate energy demands, but also in its ability to establish a solid and replicable methodological framework. The application of metaheuristic methods such as TOPSIS and TOPSIS with fuzzy logic is presented as a promising strategy to overcome the limitations of conventional approaches, considering the complexity and uncertainty inherent in the evaluation of renewable energy sources. By achieving a more precise weighting and hierarchization, this study will significantly contribute to strategic decision-making in the implementation of sustainable energy solutions in Colombia, serving as a valuable model for other countries with similar challenges.