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Abstract Traffic information is crucial for managing transportation and city planning, but obtaining national-scale data is difficult due to privacy concerns. Consequently, most current traffic datasets have limitations in terms of time and location coverage, leading to a lack of comprehensive public access to national traffic data. To address this issue, a multi-source highway traffic dataset has been created, featuring 2042 sensors in New Zealand over a 9-year period with 15-minute intervals and accompanying metadata. The dataset includes data of both light-duty and heavy-duty vehicles, as well as weather information like temperature and precipitation. This dataset has diverse potential research applications such as traffic flow prediction and congestion management.

Abstract Peer‐to‐peer energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real‐time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two‐stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game, considering that prosumers are self‐interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy‐preserving algorithm to guide prosumers the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33‐power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

Abstract The involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi‐energy complementary system, the hydropower‐photovoltaic‐hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed renewable energy system to improve the flexibility of energy management and reduce power curtailment. However, the intra‐day scheduling of HPH system brings challenges due to the time‐related nonlinear hydropower generation process, the complex energy conversion process and the uncertain natural resource supply. Faced with these challenges, an improved deep deterministic policy gradient (DDPG)‐based data‐driven scheduling algorithm is proposed. In contrast to the prevalent DDPG, two sets of actor‐critic networks are properly designed based on prior knowledge‐based deep neural networks for the considered complex uncertain system to search for near‐optimal policies and approximate actor‐value functions. In addition, customized reward functions are proposed with the consideration of interactions among different energy supplies, which helps to improve convergence speed and stability. Finally, the case study results demonstrate that the proposed system model and the optimal energy management strategy based on the improved DDPG algorithm can guide the electricity‐hydrogen system to achieve rapid response and more reasonable energy management.

Summary: Artificial intelligence has substantially improved the efficiency of data utilization across various sectors. However, the insufficient filtering of low-quality data poses challenges to uncertainty management, threatening system stability. In this study, we introduce a data-valuation approach employing deep reinforcement learning to elucidate the value patterns in data-driven tasks. By strategically optimizing with iterative sampling and feedback, our method is effective in diverse scenarios and consistently outperforms the classic methods in both accuracy and efficiency. In China’s wind-power prediction, excluding 25% of the overall dataset deemed low-value led to a 10.5% improvement in accuracy. Utilizing just 42.8% of the dataset, the model discerned 80% of linear patterns, showcasing the data’s intrinsic and transferable value. A nationwide analysis identified a data-value-sensitive geographic belt across 10 provinces, leading to robust policy recommendations informed by variances in power outputs and data values, as well as geographic climate factors. The bigger picture: In the era of big data, the surge in volume is matched by the challenges of data quality, which resonates across all domains of data-driven and artificial-intelligence-related technologies. This research proposes a method to navigate these challenges by introducing a paradigm based on deep reinforcement learning, capable of discerning value in data across varied contexts. By enabling the strategic selection of optimal data samples, we envision a future where analytics are not just smarter but also more adaptable, allowing decision makers to harness the full potential of their data assets. The implications of this work extend beyond technical realms, offering insights that could shape policies and provide fresh perspectives in data-driven industries.

In this study, the heat transfer model of a radiation–conduction–convection coupled lithium-ion battery pack is established through theoretical analysis. The temperature distribution and flow field distribution inside the battery pack are obtained by simulation using ANSYS Fluent software 2022 R1, and the reasonableness of the simulation model is verified with an experiment. This study also analyzes in detail the improvement effect of adding heat dissipation ribs, applying heat dissipation coatings, and adjusting the fan speed on the heat dissipation performance of the system. Under the same heat sink rib height conditions, the relationship between its thickness and total heat dissipation and thermal efficiency is studied in depth, and the temperature distribution of the cell under different rib thicknesses is obtained. At the same time, the emissivity of the heat sink coating under different coating thicknesses was measured by infrared thermography, and the relevant design values were determined through simulation experiments. Finally, based on the experimental test results of fan performance, a corresponding control strategy is proposed to construct an efficient and high-performance multiple-input multiple-output (MIMO) battery thermal management system. The experimental results show that optimizing the structure of the forced air cooling system through the above measures can ensure that the Li-ion battery operates within the efficient operating temperature range, thus extending its cycle life, improving its stability, and reducing the risk of thermal runaway. Meanwhile, the problem of excessive temperature difference between different modules is improved, and the output capacity of the energy storage system is increased.

Abstract Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested.

The current electricity system has a large number of carbon emissions. As an environmentally friendly and low-carbon electricity generation source, distributed energy sources gradually get more and more attention. However, the participation of a large number of distributed energy sources brings uncertainty to the operation of the electricity market. Prosumers could effectively avoid the instability caused by the participation of distributed energy sources in the electricity market. At present, the strategy of the prosumer participating in the electricity market has been thoroughly studied. However, equilibrium analysis for prosumer’s participation in joint electricity and carbon markets still needs to be studied in depth. Therefore, we propose a method for the prosumer to participate in the joint electricity and carbon markets, which could effectively reduce carbon emissions. First, we build a carbon market model. Then, the equilibrium model of the prosumer’s participation in joint electricity and carbon markets is constructed. The equilibrium model can realize the equilibrium between the coordination of prosumers and joint markets. Finally, we adopt an iterative algorithm to solve the equilibrium model. Prosumers and joint electricity and carbon markets transmit marginal information to each other. According to the marginal information. The equilibrium between Prosumer and joint markets is solved through iteration by self-optimization and self-decision. Case studies based on the IEEE 30-bus system verify the effectiveness of the model and method proposed in this paper.

Summary: Our research addresses the critical intersection of communication and power systems in the era of advanced information technologies. We highlight the strategic importance of communication base station placement, as its optimization is vital for minimizing operational disruptions in energy systems. Our study introduces a communications and power coordination planning (CPCP) model that encompasses both distributed energy resources and base stations to improve communication quality of service. This model facilitates optimal resource distribution, ensuring communication reliability over 96% and downlink transmission rates above 450 Mbps, enhancing network resilience and cost-effectiveness. Through case studies, we demonstrate CPCP’s potential to significantly reduce planning costs, particularly with increased renewable energy integration, supporting the transition to low-carbon energy systems. Our findings contribute to a comprehensive understanding of the symbiotic relationship between communication and power networks, emphasizing the need for coordinated planning in building future-proof energy infrastructures.

Abstract With an increasing frequency of natural disasters and security attacks, the safe and stable operation of smart grid has been challenged unprecedently. To reduce the economic loss and social impact caused by power outage accidents, it is urgent to develop and improve the smart grid technology, and strengthen the disaster resistance and recovery capability of smart grids when faced with extreme events. As an efficient and flexible secondary energy source, hydrogen is crucial in improving the resilience of smart grid and supporting energy security. To further promote the deep integration of hydrogen systems and smart grid and improve the energy system resilience, the resilience of smart grids supported by hydrogen is assessed here. First, a technical framework of hydrogen‐powered smart grid resilience is established, and the value of hydrogen‐powered smart grid resilience is analysed considering different time frames (before, during and after an extreme event) of smart grids facing extreme events. Then, hydrogen‐powered smart grid resilience is investigated from perspectives of pre‐prevention regulation, in‐process correction regulation, and post‐recovery regulation. Finally, future direction for hydrogen‐powered smart grid resilience is investigated and related policy suggestions are provided.

Summary: The increasing demand for chemical raw materials has provided opportunities for the ammonia (NH3) industry. However, little attention has been devoted to the economic feasibility of renewable-to-ammonia (RE2A). Therefore, this paper proposes a technoeconomic model to research the optimal capacity configuration and quantify the levelized cost of ammonia (LCOA) for RE2A, which is a retrofitted plant based on coal-to-ammonia (C2A). A cost model of C2A is established as a benchmark to evaluate the economic feasibility of RE2A. A case study in Inner Mongolia is adopted, which shows that the monthly NH3 output is 7–11×103t, which satisfies actual industrial production. The LCOA of RE2A is 469$/t, with investment in wind turbines accounting for 58%, which is lower than the NH3 market price (605$–650$/t). The LCOA of RE2A will equal that of C2A with a carbon tax of 47.1$/t CO2, which confirms the economic advantages of RE2A in the future.

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of “access over ownership”, the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

Digitalization and decarbonization are projected to be two major trends in the coming decades. As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern. This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles. This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems.

Abstract With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM.

Abstract The 2050 carbon‐neutral vision spawns a novel energy structure revolution, and the construction of the future energy structure is based on equipment innovation. Insulating material, as the core of electrical power equipment and electrified transportation asset, faces unprecedented challenges and opportunities. The goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first discussed. The engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are investigated. Insulating materials, including intelligent insulating material, high thermal conductivity insulating material, high energy storage density insulating material, extreme environment resistant insulating material, and environmental‐friendly insulating material, are categorised with their scientific issues, opportunities and challenges under the goal of carbon neutrality being discussed. In the context of carbon neutrality, not only improves the understanding of the insulation problems from a macro level, that is, electrical power equipment and electrified transportation asset, but also offers opportunities, remaining issues and challenges from the insulating material level. It is hoped that this paper envisions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality rules. The authors also hope that this paper can be helpful in future development and research of novel insulating materials, which promote the realisation of the carbon‐neutral vision.

Big data has been advocated as a dominant driving force to unleash the great waves of the next-generation industrial revolution. While the ever-increasing proliferation of heterogeneous data contributes to a more sustainable energy system, considerable challenges remain for breaking down the barrier of data sharing across monopolistic sectors and fully exploiting data asset value in a trustworthy environment. Here, we focus on a global aspiration and interest regarding the challenges, techniques, and prospects of data sharing in energy systems. In this paper, a conceptual framework for data sharing is designed, in which we introduce the commodity attribute of data assets and explain the bottlenecks of data trading. Two critical issues, i.e., right confirmation and privacy protection, are then systematically reviewed, which provide a fundamental guarantee for credible data openness. A detailed data market is conceived by elaborating on market bids, data asset valuation and pricing strategy, and game-based clearing. Finally, we conduct a discussion about some low-hanging fruit of data sharing in energy systems.

Abstract To rapidly obtain the complete characterization information of pulse signals and to verify the sensitivity and validity of pulse signals in the clinical diagnosis of related diseases. In this paper, an improved PNCC method is proposed as a supplementary feature to enable the complete characterization of pulse signals. In this paper, the wavelet scattering method is used to extract time-domain features from impulse signals, and EEMD-based improved PNCC (EPNCC) is used to extract frequency-domain features. The time–frequency features are mixed into a convolutional neural network for final classification and recognition. The data for this study were obtained from the MIT-BIH-mimic database, which was used to verify the effectiveness of the proposed method. The experimental analysis of three types of clinical symptom pulse signals showed an accuracy of 98.3% for pulse classification and recognition. The method is effective in complete pulse characterization and improves pulse classification accuracy under the processing of the three clinical pulse signals used in the paper.

The seed metering process of a fluted force-feed seeder was simulated using the Discrete Element Method and its parameters optimized using the Box–Behnken Design of Experiments and the Response Surface Method. The rotational speed of the feed roller, the lead (helix) angle of the flutes, and the number of flutes were the independent variables, while the response value was the seeding uniformity index. Two regression models were investigated, and the following conclusions drawn. For the flute lead angle between 0 and 10 degrees, and the number of flutes between 10 and 14, it was found that the number of flutes and the lead angle influenced the seeding performance the most, with the order of importance being the (i) number of flutes, (ii) lead angle and (iii) roller speed. For the flute lead angle between 5 and 15 degrees, and the number of flutes between 12 and 16, it was found that the roller speed and the number of flutes influenced the seeding performance the most, with the order of importance being the (i) roller speed, (ii) number of flutes and (iii) flute lead angle. The two regression models were then minimized for the seeding uniformity index and the corresponding optima verified experimentally on a conveyor belt test stand fitted with an image recognition system.

The joint optimization of power systems, mobile energy storage systems (MESSs), and renewable energy involves complex constraints and numerous decision variables, and it is difficult to achieve optimization quickly through the use of commercial solvers, such as Gurobi and Cplex. To address this challenge, we present an effective joint optimization approach for MESSs and power grids that consider various renewable energy sources, including wind power (WP), photovoltaic (PV) power, and hydropower. The integration of MESSs could alleviate congestion, minimize renewable energy waste, fulfill unexpected energy demands, and lower the operational costs for power networks. To model the entire system, a mixed-integer programming (MIP) model was proposed that considered both the MESSs and the power grid, with the goal of minimizing costs. Furthermore, this research proposed a highly efficient deep reinforcement learning (DRL)-based method to optimize route selection and charging/discharging operations. The efficacy of the proposed method was demonstrated through many numerical simulations.

Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability. In this paper, we propose a method to evaluate the reliability value of a photovoltaic (PV) energy system with a battery storage system (BSS) by considering the probability of grid outages causing household blackouts. Considering this reliability value, which is the economic profit and capital cost of PV+BSS, a simple formula is derived to calculate the optimal planning strategy. This strategy can provide household-level customers with a simple and straightforward expression for invested PV+BSS capacity. Case studies on 600 households located in eight zones of the US for the period of 2006 to 2015 demonstrate that adding the reliability value to economic profit allows households to invest in a larger PV+BSS and avoid loss of load caused by blackouts. Owing to the differences in blackout hours, households from the 8 zones express distinct willingness to install PV+BSS. The greater the probability of blackout, the greater revenue that household can get from the PV+BSS. The simulation example shows that the planning strategy obtained by proposed model has good economy in the actual operation and able to reduce the economic risk of power failure of the household users. This model can provide household with an easy and straightforward investment strategy of PV+BSS capacity.

Due to the shortage of fossil energy and the pollution caused by combustion of fossil fuels, the proportion of renewable energy in power systems is gradually increasing across the world. Accordingly, the capacity of power systems to accommodate renewable energy must be improved. However, integration of a large amount of renewable energy into power grids may result in network congestion. Hence, in this study, optimal transmission switching (OTS) is considered as an important method of accommodating renewable energy. It is incorporated into the operation of a power grid along with deep peak regulation of thermal power units, forming an interactive mode of coordinated operation of source and network. A stochastic unit commitment model considering deep peak regulation and OTS is established, and the role of OTS in promoting the accommodation of renewable energy is analyzed quantitatively. The results of case studies involving the IEEE 30-bus system demonstrate that OTS can enable utilization of the potential of deep peak regulation and facilitate the accommodation of renewable energy.

Abstract With the rapid development of vehicle‐to‐grid (V2G) technology, the use of the internet of things and advanced information technology to achieve the optimal dispatch of large‐scale plug‐in electric vehicles (PEVs) has become an important focus of scientific research and engineering practice worldwide. In order to achieve a balance between mass distributed energy resources and the secure and economic operation of the power grid, the authors propose an optimal market bidding strategy for a virtual power plant considering the feasible region of V2G. A detailed battery model considering the V2G mode of PEVs is established. A two‐stage stochastic optimisation model for the virtual power plant considering massive volumes of PEVs is built, taking into account the day‐ahead market revenue and the costs brought about by real‐time market deviations. Three bidding strategies—PEVs charging immediately after arrival, without V2G, and considering V2G—are compared using case studies to demonstrate the efficiency and effectiveness of the proposed strategy. Through the complete interaction between PEVs and the virtual power plant, this V2G‐based strategy can coordinate distributed energy resources, promote the accommodation of renewable energy, and significantly improve the economy of the virtual power plant operation.

China has enacted Electric Heating Policy to substitute electricity for in-home combustion for rural residential heating. Here the authors show that this shift would greatly increase national carbon emissions by 101.69–162.89 megatons in 2015 while impeding China’s carbon mitigation process in the future.

As renewable energy resources increasingly penetrate the electric grid, the inertia capability of power systems has become a developmental bottleneck. Nevertheless, the importance of primary frequency response (PFR) when making generation-expansion plans has been largely ignored. In this paper, we propose an optimal generation-expansion planning framework for wind and thermal power plants that takes PFR into account. The model is based on the frequency equivalent model. It includes investment, startup/shutdown, and typical operating costs for both thermal and renewable generators. The linearization constraints of PFR are derived theoretically. Case studies based on the modified IEEE 39-bus system demonstrate the efficiency and effectiveness of the proposed method. Compared with methods that ignore PFR, the method proposed in this paper can effectively reduce the cost of the entire planning and operation cycle, improving the accommodation rate of renewable energy.

To improve the controllability and utilization of distributed energy resources (DERs), distribution-level electricity markets based on consumers' bids and offers have been proposed. However, the transaction costs will dramatically increase with the rapid development of DERs. Therefore, in this paper, we develop an energy sharing scheme that allows users to share DERs with neighbors, and design a novel incentive mechanism for benefit allocation without users' bidding on electricity prices. In the energy sharing scheme, an aggregator organizes a number of electricity users, and trades with the connected power grid. The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads. A novel index, termed as sharing contribution rate (SCR), is presented to evaluate different users' contributions to the energy sharing. Then, based on users' SCRs, an efficient benefit allocation mechanism is implemented to determine the aggregator's payments to users that incentivize their participation in energy sharing. To avoid users' bidding, we propose a decentralized framework for the energy sharing and incentive mechanism. Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme, and the incentive mechanism allocates the benefits according to users' contributions.

An approach of transmission network expansion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper. The problem brought on by the strong nonlinearity property of short circuit currents is solved with a linearization method based on the DC power flow. The model can be converted to a mixed-integer linear programming problem, realizing the optimization of planning model that considers the constraints of linearized short circuit currents and N-1 security. To compensate the error caused by the assumptions of DC power flow, the compensation factor is proposed. With this factor, an iterative algorithm that can compensate the linearization error is then presented. The case study based on the IEEE 118-bus system shows that the proposed model and approach can be utilized to: optimize the construction strategy of transmission lines; ensure the N-1 security of the network; and effectively limit the short circuit currents of the system.