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Abstract Background Fossil fuels can be replaced with electricity and hydrogen. However, the implementation and use of these low-carbon energy carriers require a sociotechnical transition. This transition might not be completed in time. Main text $$\text {CO}_2$$ CO 2 -based methane is a substitute for natural gas that is less carbon-intensive. This methane is synthesized by capturing $$\text {CO}_2$$ CO 2 from air and by performing water electrolysis to produce hydrogen. $$\text {CO}_2$$ CO 2 -based methane is compatible with our current fossil-based society. An analysis of the substitution of natural gas with different energy carriers will be performed, and the results will be compared. The effects of $$\text {CO}_2$$ CO 2 -based methane, hydrogen, and electricity will be evaluated for energy storage, high-temperature level heat production, and residential heating. The multi-level perspective will be applied to assess these energy carriers in the context of our society. Conclusions $$\text {CO}_2$$ CO 2 -based methane is the least energy efficient energy carrier among those analyzed. Nevertheless, this type of methane supports the acceleration of the energy transition. Highlights CO2-based methane is a valuable, renewable, and carbon-neutral energy carrier that supports a timely energy transition. The implementation of hydrogen and electricity requires more modifications to our current sociotechnical society than the implementation of CO2-based methane. The urgency of reducing CO2 emissions is not being considered adequately in the current societal discussion, and a multi-level perspective analysis should provide valuable results that account for the temporal aspect.

Abstract Energy-efficient building design is often challenged by multiple optimization problems due to contradictory objectives that are often hard to balance, so an effective optimization method should be thoroughly considered. Accordingly, a multi-objective evolutionary algorithm is then proposed. Firstly, the multi-agent auxiliary objective evolutionary algorithm for building energy efficiency model is established. According to model result analysis, the proposed algorithm runs fastest for 1640s with the average running time of 1710s in a single-room building, comparing to the least running time of 1680s for the multi-objective particle swarm optimization algorithm. In multi-room buildings, the proposed algorithm runs from 3350s to 3650s, with the average running time of 3500s. In conclusion, the model proposed in this study can comprehensively consider multiple objectives such as energy consumption, cost, comfort, etc. No matter in single-room or multi-room buildings, the model demonstrates superior performance and stability to realize comprehensive optimization of energy conservation design.

Abstract With the development of sustainable economy, new energy materials are widely used in various industries, and many cars also adopt new energy power batteries as power sources. However, it is currently not possible to accurately diagnose faults in power batteries, which results in the safety of power batteries not being guaranteed. To address this issue, this study utilizes the Whale Optimization Algorithm to improve the Long Short-Term Memory algorithm and constructs a fault diagnosis model based on the improved algorithm. The purpose of using this model for fault diagnosis of power batteries is to strengthen the safety management of batteries. This study first conducted experiments on the improved algorithm and obtained an accuracy of 95.3%. The simulation results of the fault diagnosis model showed that the diagnosis time was only 1.2s. The analysis of the power battery showed that after using this model, the safety performance has been improved by 90.1%, while the maintenance cost has been reduced to 20.3% of the original. The above results verify that the fault diagnosis model based on the improved algorithm can accurately diagnose faults in power batteries, thereby improving the safety of power batteries.

Abstract This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.

Abstract For coordinated regional growth and the development of high-quality manufacturing, China must narrow its regional energy efficiency gap and catch up inter-regionally. This paper focuses on whether China’s inter-provincial manufacturing energy efficiency has technological diffusion and a catch-up effect and explores its possible influencing factors, which are important for narrowing the differences in China’s manufacturing energy efficiency and promoting the improvement of the overall level of efficiency. Between 2011 and 2020, 30 Chinese manufacturing industries will be evaluated using a non-radial distance function model under environmental conditions. By employing the Dagum Gini coefficient method, regional disparities were analyzed, with hyper-variable density and efficiency discrepancies between regions making a noteworthy contribution. This paper evaluated a catch-up effect by constructing a frontier productivity model that considered the influence of China’s manufacturing energy efficiency. Results show a general rise in energy efficiency, particularly in coastal regions, higher than inland ones. The Gini coefficient of energy efficiency in manufacturing experienced a slight increase; however, when comparing it to the regional efficiency frontier, the catch-up effect and technology diffusion effect of China’s provincial manufacturing energy efficiency become more pronounced when taking into account the national efficiency frontier; the sub-regional manufacturing energy efficiency catch-up effect has different performances; the catch-up and technology diffusion effect is more evident after controlling for Economic development, innovation levels, the environmental regulation, and the proportion of high-energy-consumption output value and other influencing factors.

Abstract With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Abstract Background Energy systems, as critical infrastructures (CI), constitute Cyber-Physical-Social Systems (CPSS). Due to their inherent complexity and the importance of service continuity of CIs, digitization in this context encounters significant practical challenges. Digital Twins (DT) have emerged over the recent years as a promising solution for managing CPSSs by facilitating real-time interaction, synchronization, and control of physical assets. The selection of an appropriate architectural framework is crucial in constructing a DT, to ensure integration of enabling technologies and data from diverse sources. Objectives This study proposes a Systematic Literature Review (SLR) to examine technological enablers, design choices, management strategies and Computational Challenges of DTs in Smart Energy Systems (SES) by also analyzing existing architectures and identifying key components. Methods The SLR follows a rigorous workflow exploiting a multi-database search with predefined eligibility criteria, accompanied by advanced searching techniques, such as manual screening of results and a documented search strategy, in order to ensure its comprehensiveness and reliability, More specifically, research questions are first defined and then submitted as queries to scientific digital libraries (i.e., IEEE Xplore, Scopus, and WoS) selected due to their coverage and reliability (Google Scholar was excluded for the presence of grey literature and non-peer-reviewed material). Then, inclusion and exclusion criteria are established to filter the results and shortlist the significant publications. Subsequently, relevant data are extracted, summarized, and categorized in order to identify common themes, existing gaps, and future research directions, with the aim of providing a comprehensive overview of the current state of DTs for SESs. Results From the proposed DT-based solutions described in the selected publications, the adopted architectures are examined and categorized depending on their logical building blocks, microservices, enabling technologies, human–machine interfaces (HMI), artificial intelligence and machine learning (AI/ML) implementations, data flow and data persistence choices, and Internet-of-Things (IoT) components involved. Additionally, the integration of edge-cloud computing and IoT technologies in literature are studied and discussed. Finally, gaps, opportunities, future study lines, and challenges of implementing DTs are thoroughly addressed. The results achieved also pave the way for a forthcoming design pattern catalog for DTs in CPSSs capable of supporting the engineering and research communities, by offering practical insights on implementation and integration aspects. Conclusion The proposed SLR provides a valuable resource for designing and implementing DTs of CPSSs in general and of SESs in particular. Furthermore, it highlights the potential benefits of adopting DTs to manage complex energy systems and it identifies areas for future research.

Abstract This study proposes a distinct coordination control and power management approach for hybrid residential microgrids (MGs). The method enhances the feasibility of hybrid MGs by reducing power loss on ILBCs. The MG has been modeled with solar and wind generators. The MG comprises multiple direct current (DC) and alternating current (AC) sub-microgrids (SMGs) with varying voltage levels. The coordination control and power management strategies for autonomous hybrid MGs with primary and secondary control levels. A novel technique is proposed to ensure seamless and precise power transfer among SMGs while minimizing the constant operation of ILBCs in islanded mode, with a focus on the secondary control level. The study uses MATLAB/Simulink to analyze on-grid, off-grid, and transient mode power transfer among MG. The MG has been operative during transient/faulty conditions. The results indicate that the proposed method demonstrates excellent adaptability in managing power flow.

Abstract This paper adopts the “baseline scenario method” to construct a comprehensive model for calculating and reducing carbon emissions generated by employee travel, including the accounting of carbon emissions from commuting and business travel, as well as the assessment of green travel for carbon reduction. The study employs methods such as questionnaires and on-site interviews to collect travel data from employees of a research institute in Beijing as a case study. The results show that employees’ commuting methods are diverse, with the subway being the primary mode of travel; however, business travel generates higher carbon emissions, particularly among employees with higher education levels. The research concludes that the model proposed in this paper provides a framework for preliminary carbon emission estimation, but to improve the accuracy of the estimates, more variables and factors need to be considered, and the limitations of the model are pointed out. The research findings have significant implications for policy and institutional practices, suggesting the adoption of more targeted measures to reduce the use of high-carbon-emission travel methods and to encourage the use of green travel options. With the continuous advancement of data collection technologies in the future, it will be possible to further establish a more refined carbon emission accounting model and obtain more accurate and comprehensive travel data, thereby providing solid data support for the development of more effective carbon reduction strategies and policies.

Abstract This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, “PV” specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model’s efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model.

Abstract As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy Systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems.

Abstract With the accelerated advancements in artificial intelligence and the increasing emphasis on sustainable supply chain management, the integration of multimodal artificial intelligence (AI) into green supply chains has emerged as a critical research frontier. This study delves into the synergistic potential and challenges of combining multimodal AI, which leverages diverse data types such as text, images, and numerical data, to enhance decision-making processes in green supply chains. Through the meticulous design of a data strategy and model framework, this research establishes a sophisticated and efficient data processing and model training pipeline. The experimental results reveal that the comprehensive analysis and fusion of multimodal data significantly improve the prediction accuracy of key supply chain metrics, with observed increases in accuracy and recall rates by 12.4% and 9.8%, respectively. Additionally, the model's limitations are critically assessed, and targeted improvement strategies are proposed. The practical implications of this study are profound, offering actionable insights for the application of multimodal AI in real-world energy sector scenarios. The findings underscore the potential of this technology to optimize operations, reduce environmental impact, and drive sustainable growth in the energy industry.

Abstract Thermal Energy Storage (TES) systems are pivotal in advancing net-zero energy transitions, particularly in the energy sector, which is a major contributor to climate change due to carbon emissions. In electrical vehicles (EVs), TES systems enhance battery performance and regulate cabin temperatures, thus improving energy efficiency and extending vehicle range. The enhanced efficiency reduces overall energy consumption in EVs. Consequently, this reduction in energy demand can lead to decreased infrastructure needs, minimising the scale and investment required in energy production and distribution systems. Furthermore, the integration of TES with existing infrastructure allows for the simultaneous charging of thermal and electrical energy, leveraging waste heat or renewable energy sources. This not only cuts costs by optimizing resource use but also bolsters sustainability by minimising reliance on non-renewable energy sources. The widespread adoption of TES in EVs could transform these vehicles into nodes within large-scale, distributed energy storage systems, thus supporting smart grid operations and enhancing energy security. Strategic investments and regulatory updates are essential to realise a sustainable, carbon-neutral transportation future, underpinned by robust, cost-efficient infrastructure.

Abstract Nigeria currently relies on 80% thermal energy generation. However, studies have shown that less than 60% of the population have access to power. To address this issue, Nigeria has developed an energy transition plan to achieve net-zero emissions by utilizing eco-friendly and sustainable renewable energy sources. However, the effectiveness of renewable energy resources is often hampered by seasonal variations, which limit the amount of energy that can be produced to meet growing demand. One effective solution to this challenge is long-term energy storage, particularly during periods of low demand. Power-to-X (PtX) technology offers a promising approach by enabling long-term sustainable energy generation and storage for future use when renewable energy availability decreases during peak demands. This study critically reviews the latest advancements in renewable PtX technology and evaluates its potential application within Nigeria's energy sector. Furthermore, it explores the potential obstacles to the widespread adoption of PtX technology in Nigeria. Despite Nigeria’s significant potential for implementing PtX initiatives, the country currently falls behind in technology deployment and viable production pathways for sustainable PtX implementation. This shortfall is primarily due to lack of policies, frameworks, and financing schemes to support infrastructural development, especially for long-term energy storage. Given the intermittent nature of renewable energy, a transition strategy that includes adequate storage capacity is crucial. Although green hydrogen, a key component of PtX, has substantial potential as an energy carrier in Nigeria, its immediate use is limited by high production costs. Nonetheless, ongoing efforts to diversify Nigeria’s energy mix through infrastructure and policy developments could eventually establish a roadmap for PtX implementation, promoting long-term energy sustainability and distribution efficiency.

Abstract Background The post-pandemic period, military conflicts, and geopolitical instability have all contributed to concerns regarding the world's sustainable and stable development, and small national economies are particularly vulnerable to these challenges. The five countries of the Western Balkan region (Republic of Serbia, Republic of North Macedonia, Bosnia and Herzegovina, Montenegro, and Albania) have established their national interests and formulated security policies in line with their strategic commitment to the green transition and integration into the European Union. The region has been grappling with a multitude of challenges for several decades, and further instability may be expected beyond 2022. This paper aims to analyze the impact of sustainable development politicization on the security policies of Western Balkan countries and identify critical challenges to the region’s sustainable development. Main text The main research findings suggest that the security strategy of the Western Balkan countries was distinct from their economic and energy development until the beginning of the twenty-first century. Sustainable development is a strategic commitment of the Western Balkan countries, but it necessitates intricate changes, particularly in energy and economy. However, the Western Balkan countries have been confronted with new challenges due to the intricate geopolitical developments that emerged after 2022. The following are particularly noteworthy: frequently conflicting regional initiatives, the usage of energy resources and essential minerals, and significant impacts on how individuals form their opinion on these matters. The factors mentioned above are distinct security threats that require the Western Balkan countries to pursue solutions. Conclusions A politicized approach to sustainable development is a novel phenomenon that has to be incorporated into current security policy. Specifically, the present approach, which prioritizes the state's security, often conflicts with the emerging concept of individual security (which focuses on the stability of the economy and the environment). This situation adds a layer of complexity to the position of the Western Balkan countries. They have a limited capacity to impact geopolitical developments, and in order to make progress, maintain stability, and foster social peace, they must make concessions and cooperate with influential economies that frequently have conflicting geopolitical interests.

Abstract Coal is currently the most important energy source in most countries. With the advent of information intelligence, more and more intelligent technologies are being applied in coal mine detection. A new model for coal mine drilling detection, which combines improved YOLOv5 and Gaussian filtering, is proposed to address the low efficiency and poor accuracy in manual detection of coal mine drilling. This new model incorporates attention mechanism and multi-object detection model on the basis of traditional YOLOv5. Due to factors such as equipment vibration and electrical interference in drilling detection, random noise is often mixed into the image signal data obtained. In order to effectively reduce the impact of noise on data and improve signal-to-noise ratio, Gaussian filtering method is studied for data denoising. This new model’s border regression loss value was 0.004 lower than the YOLOv5 loss value. This new optimization method’s accuracy was improved from 0.966 to 0.982. This new model improved the detection accuracy of small cracks by about 0.05. The detection depth of the coal seam in this new model was 9.54 m, which was closer to the true value than other methods. Therefore, using the new model to detect coal mine boreholes can effectively improve the accuracy of borehole detection images, which has a good effect on the analysis of coal mine rock layers. This new model has a good guiding role in the detection images and rock analysis research of future coal mine boreholes. The research has good research value in oil drilling inspection, natural gas pipeline monitoring, and quality inspection of industrial automation systems. This provides important technical support for future coal mine drilling image detection and rock analysis research.

Abstract This study proposes a smart grid model named “GridOptiPredict”, which aims to achieve efficient analysis and processing of power system data through deep fusion of deep learning and graph neural network, so as to improve the intelligent level and overall efficiency of power grid operation. The model integrates three core functions of load forecasting, power grid state sensing and resource optimization into one, forming a closely connected and complementary framework. Through carefully designed experimental scheme, the practical value and effectiveness of “Grid OptiPredict” model are fully verified from three aspects: accuracy of load forecasting, sensitivity of power grid state sensing and efficiency of resource allocation strategy. Experimental results show that the model has significant advantages in prediction accuracy, model stability and robustness, resource optimization, security, information security, social and economic benefits and user experience.

[Introduction] Realizing peak carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060 have been identified as important strategic goals for fostering high-quality economic and social development in China. Carbon sink is one of the crucial approaches to achieving carbon neutrality. Effective carbon sink incentive policies are the guarantee for achieving "carbon neutrality". [Method] By sorting out the development of ecosystem carbon sinks both domestically and internationally, the main problems in the development of carbon sinks in China were revealed, and a comprehensive understanding of China's carbon sink projects was established. By studying the mechanism for carbon sink projects to participate in carbon market transactions, channels for reaping benefits were clarified. Furthermore, through comparative analysis and research on pilot experiences, relevant conclusions were drawn by analyzing specific issues. [Result] As shown by the results, the development of carbon sink projects in China is mainly focused on forestry carbon sink, involving fewer other carbon sink projects. With middle-and-short term as the main project cycle, the growth rate of project development has slowed down. There are mainly three problems faced by China's carbon sink development: (1) the limited variety of methodologies and more limitations for developing carbon sink; (2) the long development process and imperfect mechanism; (3) the insignificant incentive effect of the carbon market after development. [Conclusion] The research indicates that China's future work on ecosystem carbon sink still faces huge challenges. To promote the high-quality development of carbon sinks, it remains necessary to expedite the adjustment and upgrading of carbon sink support policies, increase investment in carbon sink technology, and actively develop the “carbon sink +” industry.

[Introduction] The rapid development of new energy vehicles (NEVs) brings higher requirements for the power demand of highways. Based on the analysis of the power loads of highways, the photovoltaic endowment, and the energy storage technologies suitable for highway service areas in China, this paper explores the self-consistency of the highway transportation and energy integration mode of the PV-Storage-Charging integrated development path, and combines practical engineering project to demonstrate and analyze the feasibility of the highway transportation and energy integration mode. [Method] By analyzing the total mileage of highways in China, the market penetration rate of NEVs, and the PV & energy storage resources of highway transportation, based on the potential increase in highway power load due to the growth of NEVs and the utilization space of photovoltaic resources in highway transportation, a comprehensive energy utilization mode of PV-Storage-Charging for highway service areas was proposed. Combined with existing projects of self-consistent modes of transportation and energy integration, suggestions were proposed for the integrated development mode of highway PV-Storage-Charging. [Result] Data indicates that by 2025, the national stock of NEVs will exceed 25 million, consuming 37.5 TWh of electricity, showing tremendous potential for electricity demand. At the same time, the abundant photovoltaic resources along and around roads in China have a photovoltaic generation potential of approximately 1022.8 TW, while the calculated annual energy consumption of highway infrastructure is approximately 17.99 TW. [Conclusion] The main path of integration between transportation and energy should start with developing wind and photovoltaic natural resource endowments based on the infrastructure of road transportation itself, and forming a self-consistent supply of transportation energy systems, thereby constructing a distributed, green, renewable, and net zero-emission energy supply system for transportation systems. The integrated development path of PV-Storage-Charging transportation and energy integration can consume renewable energy locally, alleviate grid pressure while promoting the clean energy utilization of highways, showing immense potential. This paper can provide theoretical support and reference for the development of highway transportation and energy integration in China.

[Introduction] This paper aims to deeply analyze and improve China's carbon pricing mechanism to more effectively promote the social transition to green and low-carbon, and at the same time enhance China's leadership and initiative in the process of global carbon peaking and carbon neutrality. [Method] By comprehensively sorting out the current situation and future trends of the global carbon pricing mechanism, this article deeply analyzed the shortcomings of China's existing carbon pricing mechanism, such as insufficient correlation with the overall climate policy goals, unclear nature of property rights in the carbon market, and inactive market entities. This paper further discussed the potential impact of rising carbon price on China's emission reduction effect and energy supply guarantee. [Result] Based on China's national conditions, this paper gives the strategic direction and corresponding measures to construct China's carbon pricing mechanism by using carbon market carbon quota, carbon tax, coordination between carbon market and carbon tax, and carbon finance. [Conclusion] On the basis of clarifying the development ideas of China's carbon pricing mechanism, it provides a countermeasure reference for China to win more voice in the international carbon pricing mechanism, such as strategies for synergizing the domestic electricity certificate and carbon markets, improving the "integrity" of China's green certificates, engaging in the promotion of mutual recognition in international carbon markets, and collaborating to establish coordinated carbon pricing mechanisms among nations.

[Introduction] Carbon dioxide capture, utilization and storage is an important measure to achieve the goal of net zero greenhouse gas emission. The synthesis of methanol by carbon dioxide hydrogenation is one of the effective ways to use carbon, which can not only convert CO2 into methanol, an valuable fuel and chemical product, but also combine with producing hydrogen by electrolytic water, making methanol become the storage carrier of hydrogen. [Method] The methanol producing process by carbon dioxide hydrogenation was modelled by simulation software Aspen Plus. For the methanol production scale of 300,000 t/a, the hydrogen producing by water electrolysis was considered as the hydrogen source, and the influence of temperature, pressure and CO2/H2 feed ratio on methanol yield was analyzed in detail. [Result] The simulation results show the methanol yield is the highest when the synthesis temperature is around 250 °C and the CO2/H2 feed ratio is 7.33. Considering the direct combination with water electrolysis process, the synthesis pressure at around 5 MPa is reasonable. The water content of hydrogen has a certain effect on the methanol yield. The methanol yield only reduces 0.1 percent with the water content increase 100 kg/h in the raw H2. [Conclusion] The simulation determined the selection basis of key process parameters of methanol synthesis system by carbon dioxide hydrogenation. In addition, the feasibility of directly combining H2 obtained from water electrolysis with methanol synthesis process without drying and compression treatment is proved. In conclusion, hydrogen production from renewable sources combined with methanol synthesis by carbon dioxide hydrogenation is a technology with application prospect.

[Introduction] Carbon utilization technologies, which convert CO2 into valuable products such as fuels, chemicals, and building materials, represent an innovative approach to mitigating climate change and promoting resource recycling. The analysis of climate benefits and economic viability is a significant challenge in current research on carbon utilization technologies and is key to their development and implementation. [Method] This study developed a methodology for quantifying the emission abatement benefits and economic viability of carbon utilization technologies, based on background analysis, baseline selection, boundary definition, and indicator calculation. Using this methodology, key indicators such as emission abatement per unit, levelized cost of emission abatement, and abatement benefit per unit for four typical CO2 utilization technologies were analyzed. [Result] The study shows that CO2 enhanced oil recovery (CO2-EOR) achieves a unit emission abatement of 1.62～5.85 t CO2/t oil. When the price per barrel of oil is around 390 yuan, the abatement benefit for CO2-EOR varies from 2 to 1152 yuan/t CO2 under different injection-production ratios. The CO2 and green hydrogen-based methanol production achieves a unit emission abatement of 4.89 t CO2/t methanol, with a negative abatement benefit, requiring subsidies or equivalent carbon pricing support of 18 to 833 yuan/t CO2 to break even on abatement. The carbon capture and utilization of steel slag for building material production yields a unit emission abatement of 0.94 t CO2/t steel slag powder, with an abatement benefit of 36 to 164 yuan/t CO2. The microalgae cultivation for biodiesel production achieves a unit emission abatement of 0.36 t CO2/t algae powder, and the abatement benefit can reach 30,000 to 90,000 yuan/t CO2. Beyond the key indicators emphasized in this paper, actual project decisions must also consider the impact of technological stability, market environment, and resource conditions on economic feasibility. [Conclusion] The carbon abatement effectiveness and applicability of different carbon utilization technologies vary across different scenarios. To ensure their positive contribution to emission abatement, it is necessary to improve institutional frameworks, facilitate the integration of these technologies into carbon markets, and assess their actual climate impact, thereby supporting technologies that provide genuine climate benefits.

[Introduction] China's transportation sector is a significant contributor to carbon emissions. To achieve carbon peaking and neutrality, developing a new transportation-energy integrated system based on energy Internet is a crucial step for the low-carbon transition of this sector. [Method] Focusing on low-carbon transition, this paper provided an overview of the development goals, policy background, innovations, and insights of China's new highway transportation-energy integrated system. [Result] Centered on the multi-objective demand of "energy-saving transportation – economical transportation – low-carbon transportation", various transportation-energy integrated service business models has been developed for China's new highway transportation-energy integrated system, such as battery charging & switching systems, PV-storage-charging integration systems, and oil-gas-hydrogen-electricity-PV charging & storage systems. Corresponding collaborative planning and construction, operation and control, advanced energy storage, multilevel coordination of energy management, and other key technologies are also being gradually developed. However, challenges related to planning, operation and environmental protection remain. To facilitate the rapid advancement of this integrated system, efforts should focus on expanding market awareness, streamlining regulations and strengthening technical collaboration in the next stage. [Conclusion] This paper discussed China's new future-oriented highway transportation-energy integrated system, offering valuable perspectives on enhancing the quality development of China's transportation sector.

[Introduction] The green and low-carbon transformation and industrial restructuring under the premise of energy security is a serious challenge faced by Zhejiang Province to achieve its "carbon peaking and carbon neutrality" goals. Given the high proportion of coal-fired power generation in Zhejiang, carbon capture, utilization and storage (CCUS) technology, as an important choice to realize the low-carbon utilization of fossil energy, is vital to the "double carbon" technology system in the energy sector and industrial circle of the province. [Method] This paper presents a research review of CCUS technology from multiple aspects, including CO2 capture, transportation, utilization and storage, and introduces the development of CCUS demonstration projects in Zhejiang. This study summarizes the features of the major technologies throughout the full CCUS chain, and briefly analyzes the existing challenges amid the ongoing technological advancements. Besides, the prospect of CCUS technology in Zhejiang is explored along with corresponding suggestions. [Result] Zhejiang boasts a solid technical foundation in the field of CCUS, especially in CO2 capture, and has numerous full-chain demonstration projects employing various process routes, including pre-combustion and post-combustion capture (CO2 capture), biological, mineral, and chemical utilization (CO2 utilization), as well as geological storage (CO2 storage). However, technology readiness level differs largely across the process chain, in addition to other challenges, such as small scale, generally high initial capital and operating costs, and dispersed spatial distribution, which hinder the development of industrial clusters. [Conclusion] It is important to promote CCUS integration projects to adapt to local conditions, based on a thorough understanding of Zhejiang's potential and source/sink conditions related to CCUS in key areas. Additionally, continuous investment in technological research and development is necessary, along with guarantees in terms of incentive mechanisms, policies, regulations, and business models.

[Introduction] The mangrove ecosystem has a high carbon sequestration capacity and is an important part of the global carbon cycle. Exploring its carbon sink potential can promote the mitigation and adaptation to climate change, providing a potential marine solution for China's carbon peak and carbon neutralization. [Method] In this study, the methodology of mangrove blue carbon assessment was studied to estimate the carbon reserve and carbon sink potential in Guangdong Province. The carbon reserve in mangroves in Guangdong Province was about 3 222.035 kt C, of which the carbon reverse in vegetation was about 1 094.465 kt C and the carbon reserve in soil was about 2 127.57 kt C. Among different mangrove communities, the vegetation carbon reserve of Aegiceras corniculatum was the highest, reaching 1 508.931 kt C, followed by Avicennia marina, Kandelia obovata and Sonneratia apetala, with carbon reserve of 1 508.931 kt C, 859.115 kt C, 193.296 kC and 660.693 kt C respectively. [Result] According to the " Special Action Plan for Mangrove Protection and Restoration (2020-2025)", by 2025, 55.00 km2 of mangroves will be planted in Guangdong Province, and the carbon sink potential can increase by 36.171 kt CO2-e ~99.891 kt CO2-e, with great carbon sink potential. [Conclusion] This study can provide scientific basis for the afforestation of mangrove carbon sink in Guangdong Province, and is of great significance for China's carbon peak and carbon neutrality cause.

[Introduction] This paper aims to explore the development status, application cases, and economic evaluation of direct air capture (DAC) technology in the context of carbon neutrality, thus providing references for achieving carbon emission reduction targets in China. [Method] This paper reviewed the working principles, types, and application cases of DAC technology and analyzed its development status both at home and abroad. By comparing cost data from different studies, it assessed the economy of DAC technology and discussed current challenges and potential solutions. [Result] The study finds that DAC technology effectively captures CO2 from the air and offers advantages such as flexibility in deployment and compatibility with renewable energy. However, its commercialization is still constrained by high costs, high energy consumption, and technical challenges related to large-scale deployment. Case analysis at home and abroad reveal the urgent need to address the efficiency and cost issues in practical applications, while also showing potential for optimization through technological improvements and policy support. [Conclusion] Despite the existing challenges, DAC technology remains a potential reserve technology for achieving carbon neutrality goals, especially for countries facing severe carbon reduction pressures like China. Research efforts should focus on developing more efficient and low-cost absorbents and adsorbents, improving system design, reducing energy consumption, and exploring combination with renewable energy sources. Government policy support and broad social acceptance are also key factors for the commercialization of DAC technology. These measures can drive the development and application of DAC technology, contributing to both carbon emission reduction and environmental protection goals.

[Introduction] As an important carbon capture method in CCUS, pressure swing adsorption (PSA) CO2 capture technology has been widely used. However, the excessive capture energy consumption and operation cost restrict the promotion and implementation of the technology. How to accurately select the appropriate capture technology according to the actual situation and reduce the capture energy consumption is particularly important. [Method] This paper discussed the basic research and technical application of PSA technology at home and abroad, and analyzed the economy and prospect of PSA technology in petrochemical industry based on a practical application case of PSA technology for CO2 capture in a petrochemical enterprise.[Result] In this case, the project using PSA CO2 capture technology captured and stored about 800000 tons of medium-concentration carbon sources produced by the purification unit and low-temperature methanol washing unit of the coal-to-hydrogen plant. For 73.9% concentration of CO2 raw gas, the device achieved 96% CO2 recovery rate and 98% capture purity. H2S, CH4 and CH3OH are all controlled below 0.015%, which can achieve about 56 kWh/t CO2 capture power consumption. It is found that pressure swing adsorption technology has the advantages of low energy consumption, low piezoresistivity, continuous process and strong stability of adsorbent, which shows the technical and economic feasibility. Since pressure swing adsorption is mainly physical adsorption, PSA may face the problem of high energy consumption and insufficient enrichment concentration for the treatment of low concentration of CO2 feed gas. [Conclusion] In summary, PSA CO2 capture technology is suitable for the treatment of medium concentration carbon sources, and has potential in thetreatment of exhaust emissions in petrochemical, cement and other industries in the future.

[Introduction] The construction process of pile insertion and removal for installing offshore wind turbines on mobile platforms is risky, which can easily affect the working performance of adjacent infrastructure, and even lead to its instability and failure. [Method] In order to clarify the mechanism of the influence of pile shoe insertion and removal on adjacent foundation, this paper used CEL large deformation method to simulate the insertion and removal process of similar rectangular pile shoe in homogeneous and heterogeneous clay, and focused on the analysis of the evolution mechanism of additional overturning angle caused by pile insertion process on adjacent jacket foundation; based on the simulation results of large deformation, the influence of soil softening effect on the ultimate bearing capacity of adjacent buckets after pile shoe removal was further studied by using small deformation. [Result] The results show that under the action of pile shoe compaction, the rotational displacement of the three-cylinder jacket foundation will take place, and it will decrease gradually with the increase of net spacing. At the same time, the soil softening caused by compaction will reduce the bearing capacity of adjacent cylinder. [Conclusion] The overturning angle of three-cylinder jacket in heterogeneous soil is larger and the corresponding penetration depth is deeper due to the influence of pile shoe insertion and removal. The affected area of softening area caused by pile shoe insertion and removal is larger in horizontal direction and smaller in depth direction in homogeneous clay. In homogeneous clay, the average strength loss is small, and the horizontal and angular bearing capacity loss of the three-cylinder jacket is small. In homogeneous and heterogeneous clays, the vertical bearing capacity is reduced obviously, and the maximum reduction factor can reach 0.72.

[Introduction] Aiming at the characteristics of offshore converter station, this paper proposes the design scheme for auxiliary power system of offshore converter station. [Method] By analyzing the requirements for auxiliary power system at offshore converter station both at home and abroad, in combination with the construction situation and actual needs of offshore wind farms, a design scheme for the auxiliary power system at offshore converter station was proposed. [Result] The scheme of setting up off-site power supply for offshore converter station is proposed for the first time. A simplified wiring scheme can improve equipment utilization and conversion efficiency of auxiliary power system, and its control protection is analyzed to verify the practicality and reliability of the wiring scheme. Comparing with the scheme of low-voltage distribution system of offshore booster station, the optimization scheme of not setting emergency section bus and connecting diesel generator in parallel is proposed. Through analyzing the load characteristics of offshore converter station, the method of selecting the capacity of diesel generator, the capacity of fuel tank and the diesel generator incoming circuit breakers is proposed. [Conclusion] Therefore, it has been optimized while ensuring the reliability of auxiliary power system, which has strong practicality and applicability, and provides guidance for the design of auxiliary power system for offshore converter station.

[Introduction] Facing the advancement of the national "carbon peaking and carbon neutrality" strategy, energy in various regions of China will develop towards low-carbon utilization based on its unique resource endowment. In order to understand the development level of low-carbon energy, it is necessary to conduct evaluation research on low-carbon energy utilization. [Method] This paper first analyzed the evaluation objects of energy low-carbon utilization; then classified and organized the evaluation indicators of energy low-carbon utilization. This paper sorted out and introduced the characteristics of each evaluation method and model, and explained the role of energy low-carbon utilization evaluation research. [Result] The evaluation objects of energy low-carbon utilization can be divided into energy system evaluation and regional energy evaluation. The evaluation indicators are summarized from three aspects: energy low-carbon indicators, energy utilization indicators and energy security indicators, and the subjective and objective weighting evaluation methods are rationally used. It is possible to more comprehensively and accurately assess and understand the situation of low-carbon energy utilization. [Conclusion] The current energy low-carbon utilization evaluation is usually only reflected as a part of the evaluation index system. With the development of low-carbon energy and the increase in the proportion of clean energy, the evaluation of low-carbon energy utilization will become more and more important.

[Introduction] In order to meet the national goals of "30·60" - peak carbon emissions by 2030 and achieving carbon neutrality by 2060, an influx of new energy sources such as wind and photovoltaic power is integrated into the system via converter interfaces and progressively supplants coal-fired synchronous generators, significantly impacting the power system's inertia characteristics and the associated frequency dynamic characteristics. [Method] Firstly, the study reviewed the inertia equation of motion for traditional power systems, and the power-frequency dynamic process during the inertia response phase following a disturbance in traditional generators, and then qualitatively discussed the mechanism of system inertia loss due to the extensive integration of new energy sources. [Result] The study considers evaluation indicators used at home and abroad for broadly recognized centralized inertia, and elucidates the reason for employing the system's total kinetic energy and inertia constant as the inertia evaluation indicators for the power grid. [Conclusion] Combined with the planning of new energy development and consumption of a provincial power grid, an engineering evaluation of the grid's inertia level is carried out, and the corresponding suggestions for inertia improvement are put forward.

[Introduction] In the face of energy scarcity and carbon reduction imperatives, the transition to clean energy centered on electricity is crucial. To achieve this, power system development must prioritize stability, automation and intelligence. Artificial intelligence (AI) technology is a key means to realize this goal. [Method] This paper introduced the carbon emissions of domestic and foreign power systems at first, and pointed out the difficulties and feasible solutions for low-carbon development of the power system in China on this basis. Then, the application and prospect of AI technology in power system development were discussed. [Result] In response to the transition to low-carbon and low-energy power systems, a series of low-carbon development routes, such as integration of capital and resources in the thermal power industry, low-carbon transition (such as the application of carbon capture technology), and promoting the consumption of and smooth replacement by clean energy, were proposed in this paper. It was also noted that AI technology will play a crucial role in automating, intelligentizing and optimizing power systems, with wide-ranging applications in power dispatching, relay protection, power equipment management, power system stability evaluation, and decision-making processes. [Conclusion] As third-generation AI technology continues to evolve and fourth-generation AI technology emerges, the application of AI technology in the power system will become increasingly widespread.

[Introduction] In order to achieve the strategic goal of "carbon peaking and carbon neutrality", it is imperative for the transportation industry to transition towards greening and low-carbon development. The integration of transportation and energy is one of the important technological paths to achieve the strategic goal in the transportation sector, and the investment decision-making model and business model enhance the momentum of the development of the transportation and energy integration.[Method] Therefore, this paper analyzed the composition of investment estimation and decision-making points, proposed economic evaluation indexes and environmental benefit indexes, constructed an investment decision-making model, and explored the business model of transportation and energy integration in terms of the development path, framework, and the conception of typical scenes.[Result] By analyzing the investment feasibility of typical projects of transportation and energy integration, the validity of the transportation and energy integration investment model presented in this paper is verified. Based on the classification of regions according to China's solar resource conditions, the promotion of China's transportation and energy integration model is divided into three types: single-industry integration promotion region, multi-industry integration promotion region, and industry cluster integration promotion region. The development of the business model of transportation and energy integration is divided into three stages of cultivation, upgrading and innovation, and the market positioning, investment and financing subjects and methods, operation methods and profit mechanism of different stages are analyzed, so as to summarize and form the typical scenarios of the business model at different stages, and to realize the continuous upgrading of the business model.[Conclusion] The investment decision-making model for transportation and energy integration proposed in this paper helps to improve the efficiency and effectiveness of investors' decision-making, and the business model helps to promote the market-oriented development of transportation and energy integration.

[Introduction] With the proposal of the "carbon peaking and carbon neutrality" and the increasing scale of the carbon market, how to manage carbon assets and realize the preservation and appreciation of carbon assets has become an important issue facing the green and low-carbon development of large-scale energy enterprises. This paper summarizes and analyzes the situation faced by large-scale energy enterprises, the status quo of management mode and the construction method of management system, and puts forward relevant suggestions for large-scale energy enterprises in China on carbon asset management.[Method] This paper first analyzed the situation faced by energy enterprises under the background of the start of the national carbon market and the upcoming restart of the national certified voluntary emission reduction CCER, and then analyzed the cost difference between the current unified management mode and the decentralized management mode of enterprise carbon assets, and then put forward the principle of "four unification" of carbon asset management, the three-level system of "enterprise headquarters-carbon asset management company-control enterprise", and the five principles of "transaction risk control", an ecological chain of carbon asset management business was constructed by building a unified carbon asset information management platform for large energy enterprises, gradually deepening the three major business segments of data management and development, low-carbon analysis and consulting, market transactions and carbon finance.[Results] Through the analysis of the current situation, development status and policy requirements of energy enterprises, the optimization method of carbon asset management system and the future business development direction are clarified. [Conclusion] This paper summarizes the carbon asset management system and business structure of large energy enterprises under the background of carbon peaking and carbon neutrality. At present, there are still a series of problems brought by decentralized management in the management mode. It is necessary to gradually optimize the management from the top-level management, organization and implementation, capital guarantee, risk control management, talent guarantee, incentive assessment and other ways, so as to maximize the preservation and appreciation of carbon assets, and help the local region and even the whole country to carry out the "carbon peaking and carbon neutrality" work smoothly.

[Introduction] As an important carrier of China's industrialization and urbanization, industrial parks not only provide a large amount of infrastructure and public services, but also become the main source of carbon emission in China and an important entry and focus point for achieving China's goals of carbon peaking and carbon neutrality. The paper proposes a theoretical research framework for analyzing the transfer process of carbon emission in industrial parks and optimizing the decision-making process of the "dual carbon" path, in order to achieve the refined management of carbon emission in industrial parks. [Method] Firstly, we analyzed the carbon emission characteristics of energy, industry, construction, transportation, and infrastructure in industrial parks, and established a carbon emission accounting model for industrial parks based on inventory analysis method. Secondly, considering the impact of factors such as gross industrial output value, industrial structure, energy structure, and energy consumption on carbon emission, we established a decomposition model of factors influencing carbon emission based on the STIRPAT method. Finally, we analyzed the mechanism and impact path of digital technology empowering green and low-carbon transformation, and proposed the optimization plan for decision-making of the "dual carbon" path. [Result] Taking Ganzhou economic and technological development zone in Jiangxi Province as the research object, we calculate the carbon emission and carbon emission intensity from 2020 to 2022, identify the main factors influencing the increase of carbon emission, and plan the "dual carbon" transformation path from four aspects: industrial structure, energy structure, policy system, and platform construction. [Conclusion] Research suggests that the proposed model can fully explore the information contained in statistical data of industrial parks and is scientific and effective, providing theoretical support for the decision-making of "dual carbon" transformation path and the construction of industrial internet platform for digital carbon management of industrial parks.

[Introduction] The main shafting structure with double supports of single row tapered roller bearings has been widely used in wind turbines, and determining the appropriate bearing operating clearance is crucial for the reliable operation of bearings and the main shafting.[Method] A dynamics analysis model was established focusing on the main shafting of a medium speed permanent magnet wind turbine, incorporating the hub, main shaft, and both upwind and downwind bearing. The variation trends of bearing raceway life, load area, and maximum contact stress under different axial clearances were calculated. [Result] The ideal axial operating clearance range of matched single row tapered roller bearings has been identified as −0.74 ~ −0.3 mm. [Conclusion] The research results have significant implications for the reliable operation of bearings and main shafting, providing a method and reference for determining the axial operating clearance of matched single row tapered roller bearings.

[Introduction] The widespread adoption of electric vehicles (EVs) enhances environmental sustainability during travel, yet it simultaneously elevates strain on the mains supply. To assess the impact of EV charging on the power grid, a specialized charging load calculation method is developed for large EV cluster on the basis of the Monte Carlo method. [Method] In this approach, EVs were categorized into six groups based on their usage: private EVs, electric buses, electric taxis, online ride-hailing EVs, official EVs, and logistics EVs. Typical battery performance parameters for each group were identified, and probability models were established to characterize the variability in their travel and charging patterns. By integrating group scale forecasts, daily charging schedules for each group were simulated through random sampling. Subsequently, the daily charging load for each group was calculated, culminating in the total charging load for the EV cluster through an aggregation method. [Result] The EV cluster in a southern province in 2030 was taken as the simulation case. [Conclusion] The case analysis reveals that the proposed approach can provide the daily charging load of various EV groups and the entire EV cluster. Furthermore, among all types of EVs, electric buses have the highest peak charging load at 4 639.5 MW, followed by that of private EVs (not higher than 70% of the electric buses' peak load), and electric taxis have the lowest peak charging load. The charging peak of the entire EV cluster occurs between 19:00 and 23:00 at night, and the peak load can reach 10.0927 GW.

随着风电、光伏等新能源发电渗透率的不断提高，电力系统等效惯量水平不断下降且空间分布不均的特性日益凸显。而电力系统维持频率稳定的能力与系统等效惯量水平息息相关，因此明确电力系统等效惯量时空演变趋势具有重要意义。基于系统动力学模型，对电力系统等效惯量的时空演变趋势进行了分析。首先，明确新能源电力系统等效惯量概念及有效惯量组成，并分析惯量响应过程；其次，基于系统动力学模型，在考虑地域、技术进步率、综合成本等多因素影响下，对中国各地区光伏、风电、火电、水电、核电2025—2060年间的装机容量进行预测；最后，基于预测数据，对电力系统等效惯量时空演变趋势进行分析。所预测装机容量结果符合长期发展目标，计算所得电力系统等效惯量水平直观体现了在考虑虚拟惯量与否的情景下中国整体与各区域的变化趋势，可为规划各区域新能源发展，确定低惯量区域惯量补偿措施，保障频率安全等方面提供参考。

宽频振荡紧急控制是保障风电系统安全运行的“兜底”措施之一。由于风电系统非线性强度大及运行工况多变，难以建立宽频振荡在线风险评估的解析模型。为此，提出了基于数据驱动的宽频振荡风险在线评估方法及紧急控制策略。首先，设计了能够应对宽频振荡多模态和时变特征的宽频相量检测算法，为后续宽频振荡特征评估提供数据支撑；其次，利用最大李雅普诺夫指数作为宽频振荡动态演化趋势的量化指标，并根据风电机组模态阻抗的负电阻效应在线辨识宽频振荡源；然后，融合宽频振荡特征辨识结果，提出宽频振荡风险在线评估策略，进一步将评估结果应用于宽频振荡紧急控制；最后，通过RTDS硬件在环平台验证所提方法的有效性和可靠性。

整流站采用二极管整流器能够提升远距离大容量海上风电直流送出系统的经济性和可靠性。提出了基于24脉动二极管整流器的海上风电并网系统中交流滤波器的优化配置方法。首先，明确了基于24脉动二极管整流器的海上风电送出系统结构，阐述了24脉动二极管整流器的基本原理。然后，计算了24脉动二极管整流器的无功功率需求，对于24脉动二极管整流器，可以将设计复杂的交流滤波器简化为电容器，通过对有功功率变化范围内不同容量电容器滤波效果进行扫描，提出了综合考虑滤波效果和滤波器容量的交流滤波器无功容量优化方法。最后，在PSCAD/EMTDC中对基于24脉动二极管整流器的海上风电送出测试系统进行了谐波分析和交流滤波器优化设计。

实现全球“碳中和”，核心是推动能源绿色低碳转型，重要举措是推动新能源大规模基地化开发和高效消纳。针对偏远、水电不足地区新能源基地开发面临的低碳调节手段不足、电网配置和消纳能力有限、“风光火”开发模式降碳难等问题与挑战，提出了基于电-氢-碳协同的新能源基地发展思路和开发模式，将氢基产业发展与新能源开发消纳、煤电灵活低碳转型深度融合，同时结合绿氢、绿氨、绿色甲醇等氢基产品制取工艺的技术发展和调节能力提升，量化分析绿电与绿色氢/氨/甲醇在终端消费市场的竞争力及经济效益，研判不同时期新能源基地开发与氢基产业协同发展的适用模式及应用时序，展望新能源基地电-氢-碳协同开发模式在中国三北地区以及北非等海外地区的应用前景。

针对现有架空配电线路故障离线定位方法定位可靠性与精度不足的问题，建立适用于阻抗特征分析的线路等效模型，通过分析故障线路不同检测点处的零序阻抗角差异，提出一种基于卫星对时的故障离线式定位方法。该方法向停电隔离后的故障线路首端注入频率、相位恒定的交流电压信号，并手持信号接收装置进行人工巡线，利用卫星对时完成信号接收与注入装置间的同步采样，在不采集电压的情况下，即可实现检测点零序阻抗角的计算，进一步通过比较不同检测点处零序阻抗角差异，确定故障分支及故障点精确位置。分析并明确了注入信号频率、故障点过渡电阻与时钟同步误差对故障定位精度产生的影响。数字仿真结果表明该方法可有效实现故障的精确定位。

为提升高比例新能源渗透的配电网在小概率-高损失极端事件下的弹性，提出了一种基于分布鲁棒优化的多端口智能储能软开关（E-SOP）配置模型。首先，构建含多端口E-SOP的配电网运行模型；进而，采用KL散度构建新能源机组出力模糊集，并以投资成本与配电网失负荷成本之和最小为目标，建立E-SOP双层三阶段分布鲁棒规划模型；采用列与约束生成算法实现分布鲁棒模型的拆分求解。最后，在IEEE 33节点算例中对模型和算法进行了验证，结果表明E-SOP可有效提升储能对不同馈线的功率支撑能力，从而增强配电网弹性。

基于数据驱动的方法在电力系统暂态稳定评估的效率和精度提升上已经取得了一些研究成果。然而电力系统暂态过程中涉及多维度时序特征的变化，常规算法对特征的提取能力不足且缺乏可解释性，难以反映系统暂态过程中的动态行为。因此，构建了一个具有双塔结构的Transformer模型，以Transformer编码器作为特征提取器，考虑同一时刻不同维度的特征以及每一维度特征在不同时间步对系统暂态稳定的影响，并将其分别作为双塔结构Transformer模型的输入，训练和学习各特征通道和时间步对系统暂态稳定性的影响。通过融合机制，建立了由系统特征到系统稳定性的端到端的映射模型，实现了暂态稳定高精度的评估，并通过注意力热图可视化解释模型的决策过程。最后，在IEEE-39节点系统验证了所提方法的有效性。

提出城区配电系统自适应鲁棒应急检修计划编排方法，旨在提高检修计划的灵活性和适应性，应对各种不确定性和突发事件。首先，基于“防御-攻击-防御”思想建立配电网元件检修模型。灾前防御决策人员根据有限的检修预算，考虑攻击场景，选择配电系统中的线路与电源进行检修；灾中攻击者考虑防御者能采用的优化运行策略，根据有限的攻击预算选择线路与电源进行攻击；灾后运行人员利用优化运行策略，应对攻击者所采取的攻击方案，最小化攻击造成的影响。所构建的三层两阶段自适应鲁棒优化模型基于列约束生成算法进行求解，在同时考虑配电网的灾前强化策略和灾后运行策略的前提下解决了配电网元件强化的问题。最后基于IEEE RTS-96高压配电网测试系统验证了所提模型和算法的有效性，为城区配电系统的安全稳定运行提供了支持。

孤岛检测技术是分布式光伏规模化接入配电网的关键技术。正反馈主动移频法 （active frequency drift with positive feedback，AFDPF） 是目前应用最广泛的主动式孤岛检测法之一，针对其存在的对电能质量影响大、可靠性差的问题，提出一种基于谐波电压突变与盲区识别的自适应混合式孤岛检测法。首先，提出以突变量检测加延时的方式替代传统谐波电压检测法的总谐波失真 （total harmonic distortion，THD） 值整定，构建孤岛保护启动判据；其次，基于过/欠压及过/欠频法的检测盲区识别，构建扰动自适应注入判据；最后，基于判据系数以及频率波动，完成传统AFDPF的自适应改进，并推导检测无盲区时正反馈系数的取值范围。仿真结果表明，该方法可有效过滤非孤岛工况，避免因不必要扰动注入引起的电能质量下降问题，通过选择合适的正反馈系数，改进AFDPF检测快速且不存在盲区。所提方法可兼顾快速性、可靠性、实用性和电能质量等多方面因素，对于分布式光伏规模化接入配电网的孤岛检测问题研究具有重要意义。

针对传统高斯混合模型（Gaussian mixture model，GMM）聚类算法中计算复杂、收敛速度慢和人为确定聚类数目时存在盲目性和主观性等不足，提出了一种基于改进GMM的变电站负荷聚类算法。以传统GMM聚类算法为基础，采用k均值（k-means）算法确定初始聚类中心。减少了GMM聚类算法迭代步骤，提高了输出结果的稳定性。输出不同聚类数下聚类结果的Davies-Bouldin（DB）指标、Calinski-Harabasz（CH）指标和轮廓系数（silhouette coefficient，SC），应用熵权法确定不同评价指标所占权重，构建聚类评价混合指数（cluster evaluation mixed index，CEM）。将聚类评价混合指数最大值对应的聚类个数作为最佳聚类数目，再次输入到改进GMM聚类算法中，得到变电站负荷聚类结果和聚类中心。结果表明，所提方法增强了传统GMM聚类算法的计算速度和稳定性，对变电站负荷具有良好的聚类综合能力，有助于实现聚类结果最优化。

近年来致灾台风频率呈增加趋势，以2023年影响广东电网约百万用户的第4号台风“泰利”为例，分析广东电网遭台风侵袭受灾情况，建立输配电杆塔受损预测模型，识别关键特征变量与因素，为电网防灾减灾提供支持。首先，分析台风“泰利”气象特征，具有“台前对流活跃，风力强度大，降水范围广”等特点，对输配电设备均产生一定程度破坏。其次，利用随机森林、支持向量机、梯度决策树、神经网络等4种机器学习算法建立输配电杆塔受损预测模型，并对比部分算法针对不平衡样本优化前后模型表现。算例表明，随机森林优化后提升最大，综合考虑时间指标及预测质量指标，梯度决策树为最优算法。最后，基于shapley additive explanations法等对最优模型解释性进行评估，分析表明最大风速、温度、杆塔数量等对预测结果有显著影响。所得结论有助于深入理解台风对广东电网的影响机制，为提升区域电网抵御复杂自然灾害能力提供参考。

在当前“双碳”目标的背景下，中国大力提倡能源转型，可再生能源发展迅速，其反调峰增大了系统的调峰压力。抽水蓄能电站作为重要调峰资源，是解决上述问题的有效手段之一，综合量化评估其在大规模风光并网下的效能，是对抽蓄电站的建设投资、运营设计和经济优化等工作提供科学决策的前提。为此，首先分析实际系统运行中抽蓄电站效能发挥的典型场景，并根据抽蓄电站的特性从不同维度建立了电站效能评价指标。然后采用模糊综合评价法构建了一套综合效能量化评估体系，运用改进层次分析法-CRITIC法的综合赋权法对各层指标赋权，并结合评价指标设置多层评价集，实现了对抽蓄电站从局部到整体的效能评估。最后，通过实例对综合效能评估模型验证分析，结果表明：构建的评价体系能较好地评估抽水蓄能电站的综合效能，研究结果可为中国蒙西地区抽水蓄能电站的建设提供指导，为其他地区抽水蓄能电站的效能评估提供指标和方法参考。