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Increasing the conductivity of gas diffusion layers (GDLs) is an important way to improve the output performance of polymer electrolyte membrane fuel cells (PEMFCs). However, the complex porous fiber structures of GDLs significantly enhances the difficulty of quantitatively altering their conductivity which is determined by the carbon fibers and the conduction characteristics between fibers. In addition, the microstructures of various types of GDLs are different. Thus, it is a considerable challenge to explore the conductive mechanisms of these porous materials and optimize their structures to reduce their bulk resistances. In this work, a mathematical graph theory model that applies to the through-plane (T-P) bulk resistance prediction of two types of commonly used GDLs, carbon paper and carbon felt, is established to explain their different micro conduction mechanisms in depth. In addition to the number of fiber contact points, their distribution, as well as the resistance of the carbon fibers, are all important factors affecting the T-P conductivity. Optimizing fiber density and fiber diameter can significantly improve the T-P conductivity of carbon paper. In comparison, making the structure of carbon felt more compact so that the distribution of its contact points in the T-P direction can be more uniform will be more effective for the reduction of its T-P bulk resistance. Meanwhile, the T-P bulk resistance of carbon paper can also be effectively improved by optimizing the content and distribution of the binders. A method to decline the bulk resistance of carbon paper by aggregating the binders in the in-plane (I P) direction is proposed. The simulation results show that it can reduce the T-P bulk resistance of carbon paper by about 19.9% at a compressive stress of 1.5 MPa. This study provides further guidance for optimizing the structural designs of GDLs to optimize their conduction performance. • T-P bulk resistance prediction model suitable for different GDLs is constructed. • Micro conductivity mechanisms of carbon paper and carbon felt are compared. • Increasing fiber density and diameter improves carbon paper's T-P conductivity. • Making carbon felt's structure more compact can optimize its T-P conductivity. • To promote carbon paper's T-P conductivity by optimizing the binders inside. [ABSTRACT FROM AUTHOR]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

Innovations for near real-time common European balancing markets are underway to meet the flexibility needs induced by the deployment of renewables and new market agents. Never have markets and real-time network operations been run so closely on a continental scale. Our paper investigates a filtering method for integrating congestion management and near real-time markets. Reinforcement Learning is applied to add the cost of physical delivery to bid prices to advantage/disadvantage bids that reduce/create congestion. We assess the impact of this new method on market welfare and congestion management costs and show that it brings significant efficiency gains compared to no filtering or a baseline filtering methodology. • Real-time common European balancing markets are a new source of congestion. • Filtering is a pre-market congestion management method consisting of bid screening. • A continuous formulation of filtering, that modifies the merit order, is presented. • The cost of physical delivery is added to bid prices using Reinforcement Learning. • The proposed methodology increases net Social Welfare and helps maintain security. [ABSTRACT FROM AUTHOR]

The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents a newly developed methodology aimed at assessing at national level the techno-economic potential of district heating (DH) based on renewables and excess heat sources. The novelty of the model lies in the use of an optimization approach to match heat demand and heat sources at large scale level, while keeping a high degree of spatial detail. Areas suitable for DH adoption are identified by minimizing heat delivery costs, and therefore by choosing the most economical technology between district heating and the alternative individual solution. The optimization approach, usually applicable at limited analytical scope because of the computational burden, is here adapted to large scale analysis through the introduction of novel methodological elements with which the network topology is simulated nationwide. The methodology applies to preliminarily identified maps of available heat sources and eligible heat demand, with the quantification of the latter including retrofitting and low connection rate scenarios. It then consists in two steps: connecting elements in a graph through triangulation and routing algorithms and optimizing connections to minimize the overall heat delivery costs, either by adopting district heating or individual heating systems. The whole methodology is based on open-source data and tools for broad applicability. The paper presents the elaborated methodology together with the application of the entire model to Italy. The outcome is a map of the potential district heating systems identified with significant spatial detail nationwide. A four-fold expansion is envisaged, covering 12% of the national heat demand with renewables- and excess heat- based district heating. [Display omitted] • Novel methodology to assess techno-economic potential of district heating at large scale level. • Spatially detailed model based on open-source data and tools - replicable and adaptable model. • Optimization algorithm on delivered heat cost minimisation with no thresholds set a priori. • 38 TWh of district heating potential in Italy, equivalent to a four-fold expansion to 12% of the civil sector heat demand. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) is the most promising future energy source owing to its low operating temperature, high energy efficiency, high power density, and environmental friendliness. In this paper, a comprehensive PEMFC system control-oriented model with parameter identification is developed, which includes various subsystems and components such as the hydrogen supply subsystem, air supply subsystem, thermal management subsystem. To investigate temperature control strategy by the thermal management subsystem, the energy balance equation is used for the PEMFC stack, water tank and radiator to reflect the transient temperature variation during the operation. Four typical temperature control strategies are implemented on the thermal management subsystem and their performances are evaluated in terms of temperature profiles, overshoot and integral absolute error criterion (IAE). Besides, this paper also considers the parasitic power of the fan under different control strategies. It is found that among the four strategies, Case 4 has the best performance on the controlling both water pump and fan with the PID. To improve the control effect further, fuzzy control is introduced to optimize the PID controller of the pump, and the radial basis function (RBF) neural network is introduced to optimize the PID controller of the fan. The results demonstrate that Fuzzy-PID and RBF-PID can achieve a better control effect with 22.66% decrease in IAE of T st (stack temperature) and 77.56% decrease in IAE of T in (inlet cooling water temperature). • This paper establishes a comprehensive control-oriented PEMFC system model for control research. • The influence of water content λ is considered in the parameter identification of semi-empirical equations. • Fuzzy control and RBF neural network are introduced to optimize the temperature control effect. • The parasitic power of fan is investigated. [ABSTRACT FROM AUTHOR]

Discrete manufacturing systems (MSs) are prevalent across various sectors such as electronic production, food processing, and apparel manufacturing. However, their operation raises critical concerns regarding significant energy consumption and carbon emissions. Implementing local electricity and carbon allowance sharing among MSs presents a potential solution to these issues, which is explored in this paper. The main contribution of this paper lies in achieving distributed electricity and carbon allowance sharing among MSs while addressing challenges related to algorithmic and physical feasibility. Firstly, a local electricity and carbon allowance sharing problem for MSs is formulated, which involves numerous binary decision variables due to the operation of discrete manufacturing facilities and energy storage, rendering the problem challenging to solve in a distributed manner. To tackle this challenge, we propose an alternating optimization procedure (AOP)-based distributed method to solve the problem while ensuring algorithmic feasibility. Secondly, the second-order cone relaxation program (SOCP)-based power flow model is identified cannot guarantee the exactness of the distribution system model when conducting local electricity sharing. We tackle this challenge by employing a convex-concave procedure (CCP)-based feasibility recovery procedure (FRP) to recover the exactness of the SOCP relaxation, thereby ensuring physical feasibility. The numerical results demonstrate that conducting local electricity and carbon allowance sharing can effectively reduce energy costs and carbon emissions for MSs. Moreover, compared with the alternating direction method of multipliers (ADMM), the proposed distributed method can guarantee both algorithmic and physical feasibility when solving the problem. • Enabling electricity and carbon allowance sharing among manufacturing systems. • A local electricity and carbon allowance sharing problem is formulated. • An alternating optimization procedure-based distributed method is proposed. • A convex-concave procedure-based feasibility recovery procedure is introduced. • Guaranteeing algorithmic and physical feasibility when solving the problem. [ABSTRACT FROM AUTHOR]

Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we propose an enhanced YOLOv7-based deep learning framework for fast and accurate anomaly detection in PV cells. Our approach incorporates Partial Convolution, Switchable Atrous Convolution and novel data augmentation techniques to address the challenges of varying defect sizes, complex backgrounds. The Partial Convolution component effectively manages the irregularities in PV cell images, reducing false detections. On the other hand, the Switchable Atrous Convolution enhances the model's adaptability to different defect scales and spatial resolutions, leading to improved localization and classification performance. We evaluate our model on a large-scale dataset of PV cell images, demonstrating its superiority over existing methods in terms of detection accuracy, speed, and robustness. Our proposed framework offers a practical and reliable solution for real-time anomaly detection in PV cells, facilitating timely maintenance and maximizing the performance of solar energy systems. The integration of these advanced convolution techniques into the YOLOv7 model not only improves detection capabilities but also paves the way for further research and development in the field of deep learning-based anomaly detection. • The PSA-YOLOv7 proposed in this paper exhibits better interpretability than the baseline model. • The PSA-YOLOv7 shows better robustness under common corruptions. • PSA-YOLOv7 has improved anomaly detection performance while ensuring real-time capability. [ABSTRACT FROM AUTHOR]

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. This paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system. • A visibility-enhanced surrogate has been introduced, empirically demonstrating its effectiveness in enhancing deep reinforcement learning (DRL) performances. • A multi-fidelity neural network is employed as a surrogate model within the training environment, improving data utilization efficiency while ensuring the effectiveness of DRL training. • Realistic test feeders have been used for testing, further validating the feasibility of the proposed method in practical scenarios. [ABSTRACT FROM AUTHOR]

The construction of a virtual power plant (VPP) represents a powerful approach for harnessing the adjustable capacity of flexible resources. VPP actively participates in transmission network dispatching by aggregating these flexible resources. However, existing practical implementations and research cannot describe the influence of renewable energy's inherent unpredictability on the dispatching boundaries of VPPs. This shortfall leads to a lack of organic coordination between flexible resources and the power transmission network. Therefore, this paper suggests a way to characterize the dispatching boundaries of VPPs based on the concept of flexible operation region (FOR), aiming to better incorporate the flexible resources of decentralized access and lessen the issue of insufficient computing power caused by unified optimization. Initially, an analytical model for the VPP's FOR is presented, followed by dimensionality reduction of the FOR to the VPP's gateway. Subsequently, participation in transmission network scheduling is facilitated using an equivalent dimensionality reduction model. Based on standard example testing, the proposed method's effectiveness and accuracy have been verified in this paper. • This paper proposes a method to characterize the dispatching boundary of VPP based on the FOR. • Constructs the static voltage security region (SVSR) related to each node. • Thermal security region (THSR) related to each branch in the cluster in the power injection space. • This paper proposes a strategy to identify and eliminate the ineffective constraint • The boundary construction method of VPP is applied to the cooperative optimal dispatching. [ABSTRACT FROM AUTHOR]

Power system flexibility can be improved effectively, if the advantages of the peak shaving ability of molten salt solar tower power (STP) plant can be developed and utilized. In this paper, the heat transport and load response characteristics of the molten salt STP plant in the regulation process are studied, aiming at serving the development of the regulation method in the rapid peak regulation process. The steam generation system and turbine system models of a 50 MW STP station are established. Two load regulation methods and operation modes are used to investigate the inertia and delay effects of heat transfer and evaporation, as well as load response characteristics during the actual regulation operation, including steam generation system following (SF), turbine following (TF), constant pressure operation (CP), and sliding pressure operation (SP). The results show that when the plant load is reduced by 10% Pe at the rate of 3% Pe /min, the heat transfer of salt/water and the evaporation process of water in the whole regulation process have a 250 s delay compared with the load. The larger heat storage characteristics of the plant help to improve the load response speed. The load response speed of the plant is faster in SF mode. TF mode is beneficial to the safe operation of the plant. Considering the operational safety of thick-wall component, the recommended running mode is the collocation of SF and SP mode, TF and CP mode. The results show that the load change rate of 10% Pe /min is permittable under TF and CP modes. The conclusion of this paper lays a foundation for the formulation of a peak regulation strategy for molten salt STPs. • Dynamic model for STP plant as participated in grid peak-shaving is established. • Transient response characteristics of STP plant during real load regulation are analyzed. • Influences of load changing rate on SPT plant under SGS following control mode are compared. • Recommended operation mode and load adjusting rate of the 50MW el STP plant are given. [ABSTRACT FROM AUTHOR]

The rapid development of cyber-physical power system has highlighted the pressing issue of cyber attacks (CAs) faced by large-scale renewable energy power systems (LREPS). The injection of false data attacks on critical electrical equipment by attackers can lead to abnormal system frequencies, triggering cascade failures and causing widespread blackouts. This paper introduces a tri-level defense model specifically crafted for LREPS, comprising an upper, middle, and lower level (defender-attacker-operator). By promoting collaborative decision-making among these agents, the model aims to mitigate the rate of change of frequency (RoCoF) and effectively withstand CAs. The upper-level employs an economic-inertia optimization strategy, scheduling generator units to maximize system inertia while minimizing generation costs. By leveraging the strong duality theorem, the tri-level model is decomposed into a bi-level model consisting of a master problem and a subproblem, solved using the Benders decomposition method. Furthermore, this paper also considers augmenting the virtual inertia of renewable energy units when defense resources are insufficient, ensuring RoCoF remains within limits after CAs. The effectiveness and superiority of the proposed model and strategy are validated through case studies based on the improved IEEE 118-bus system. The results demonstrate that the model can reduce load shedding caused by the worst-case CAs, enhance system inertia, lower RoCoF , and improve the stability of LREPS. • Propose a defense model against frequency disturbances caused by cyber attacks. • Minimize load shedding under worst-case attack. • Enhance system inertia through economic-inertia optimization. • Model significantly reduces rate of change of frequency. [ABSTRACT FROM AUTHOR]

Most methods for creating an indoor thermal environment are based on controlling heating, ventilation, and air conditioning (HVAC) systems and do not consider the various needs of individuals in a multiperson space. Personal comfort systems (PCS) and personal comfort models (PCM) are popular technologies for achieving personal thermal comfort. This paper presents a thermal environmental collaborative control system (TECCS) that regulates environments at different spatial scales by leveraging the advantages of the HVAC system, PCS, PCM, and PCM-based automatic control to address the issue of individual differences in thermal demand in multiperson environments. The TECCS predicts thermal sensation votes (TSV) by combining facial skin temperature data obtained by an infrared sensor with environmental parameters. Subsequently, it performs the corresponding PCS control and adjusts the air conditioner according to the operating state of the PCS. This study proposes a collaborative control strategy with PCS at the core, enabling communication between thermal state recognition, HVAC system, and PCS. Twenty-eight adult males participated in the experiments testing the TECCS's performance. The results indicate that the TECCS can automatically regulate environments at different spatial scales based on thermal sensation prediction and that the operating state of the PCS can effectively guide air conditioning operations. Compared with constant setpoint control, the TECCS offers the advantage of improving thermal comfort. This paper also proposes future optimization directions based on the research results, focusing on recognition, equipment, and control. [Display omitted] • Skin-temperature-based comfort model and device collaborative control strategy. • Match occupant needs with environmental heat and cold supply. • Personal comfort system plays the role of both feedback and effector. • Realize the information interaction between occupants, PCS, and HVAC. • Prove the comfort improvement effect of the proposed system by subject experiments. [ABSTRACT FROM AUTHOR]

The dispatchable region (DR) describes the ability of a power system to adapt to the variability of distributed renewable energy (DRE) generation. Switchable devices such as soft open points (SOPs) and tie switches enable the AC–DC distribution network to flexibly cope with DRE power fluctuations. Currently, research on DRs seldom considers the effects of SOPs and reconfiguration with tie switches in AC–DC distribution networks, which prevents accurate evaluation of DR for this type of network. To bridge this gap, this paper formulates DRs for DRE generation in an AC–DC distribution network considering SOPs and reconfiguration with tie switches. The original model for deriving DRs is typically nonconvex, and the DR boundaries are intractable because of nonlinear power flow equations and binary variables of tie switches. Accordingly, this paper employs the outer polyhedral approximation and convex hull relaxation to approximate the original DR. An efficient adaptive constraint generation algorithm is developed to search for boundaries of the approximated DR. The numerical results show that the proposed model can effectively adapt to the DR of reconfigurable AC–DC distribution networks with SOPs and verify the accuracy and computational efficiency of the proposed method. • DRs of DRE generation are formulated in AC–DC distribution networks considering SOPs and tie switches. • Power flow equations and reconfiguration constraints in the AC-DC network are handled using polyhedral approximation and convex hull relaxation. • The accuracy and computational performance of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Battery thermal management systems play a crucial role in maintaining the optimal temperature range of lithium-ion batteries, ensuring their efficient operation and prolonged lifespan. Hybrid cooling (HC) techniques, which involve the utilization of hydrophilic fibers, have shown promise in enhancing the thermal performance of these systems. The present study employs an experimental approach to investigate the impact of selected fiber materials on the thermal performance of a hybrid cooling system designed for battery thermal management. Parameters such as the time required for complete wetting of the fiber and the coolant absorption capacity of the fiber are considered during the fiber selection process. Findings reveal that the tissue paper fiber displayed superior water transfer capabilities, achieving a minimum wetting time of 46 s for a 75 mm fiber length. This characteristic positions it as a promising candidate for the subsequent hybrid cooling experiment. Noteworthy is its water holding capacity per unit volume, which surpassed that of cotton fiber by 40%. HC demonstrates the superior temperature uniformity and average battery pack temperature compared to simple air cooling (SAC) under varying velocities, emphasizing the pivotal role of HC. A remarkable improvement of 41.86% in temperature uniformity is obtained in HC over SAC. [Display omitted] • Hydrophilic fiber-based hybrid cooling significantly enhances battery thermal management. • Tissue paper-based fibers exhibit superior water transport and 40% higher water holding capacity, influencing efficient cooling. • Hybrid cooling's thermal performance under various velocities underscores its adaptability to different operational conditions. • Hybrid cooling surpasses simple air cooling, achieving a notable 41.86% improvement in temperature uniformity. • Cumulative quantity of water evaporated indicates a stable and reliable evaporation rate, which leads to the sustained performance of the hybrid cooling system. [ABSTRACT FROM AUTHOR]

Under the background of green certificate trading (GCT)‑carbon emission trading (CET) market, the application of P2P trading in regional integrated energy system (RIES) has not been fully explored, and it is urgent to design a reasonable prosumer P2P trading mechanism to adapt to the development of diversified electricity market. Therefore, this paper proposes a prosumer P2P cooperative trading model based on generalized Nash bargaining. Specifically, based on the differential modeling of various types of prosumers, this paper constructs a prosumer P2P cooperative trading model based on generalized Nash bargaining theory, and quantifies the bargaining power of prosumers through the contribution value of interactive operation. The GCT-CET collaborative mechanism is introduced to promote the consumption of distributed energy by transforming carbon quotas through green certificates. In addition, this paper designs a warm start and adaptive step alternating direction multiplier method decentralized algorithm (WAS-ADMM) to improve the solution efficiency and protect the trading privacy of prosumers. Through a case study, it analyzes the impact of prosumers' P2P cooperative trading on the consumption of distributed energy, prosumers' GCT-CET costs and comprehensive costs in the context of GCT and CET market. The simulation results show that the proposed trading model reduces the GCT-CET costs of the prosumers' alliance by about 24.98%. • A P2P cooperative trading model of integrated energy is constructed based on GNB. • A GCT-CET collaborative mechanism is proposed to promote the consumption of renewable energy. • An improved decentralized algorithm based on WAS-ADMM is designed to solve the P2P trading model. [ABSTRACT FROM AUTHOR]

One of the major concerns of electric power utilities in recent years is mitigating wildfire risks. While different mitigations have been proposed and some are implemented, customer experience is less focused in some methods, and not even considered in some extreme methods. This paper focuses on minimizing such tradeoff: reliability vs. wildfire risk reduction. This paper first proposes a methodology to quantify the wildfire risk raised by electric distribution systems, utilizing the vegetation factor at proper locations as well as the probability of fault. An optimization framework is then developed in this work that considers system reliability improvement in decision-making while reducing the risk of wildfires. The means to achieve such optimization are reclosers and fuses: optimally locate them to minimize the wildfire risk while the negative impact on reliability is minimized. Both fuse-blowing and saving schemes are investigated in this work. Moreover, the temporary and permanent types of faults are taken into account by formulating their impact on the operation of reclosers and fuses in both fuse-blowing and saving schemes. The optimization framework utilizes the exchange market algorithm (EMA) to find optimal results. The effectiveness of the proposed approach is verified by utilizing a modified IEEE 123 node system. • A proposed index models the wildfire risk associated with conductors and fuses. • A novel optimization framework is presented for recloser and fuse siting. • This model minimizes the wildfire risk and its impact on the system's reliability. • The optimization approach considers the recloser-fuse coordination practices. • This method provides different formulas for the fuse-saving and blowing schemes. [ABSTRACT FROM AUTHOR]

China's carbon emissions from households have increased with the growth of the consumption scale. To accomplish the ambitious goals of reducing emissions, carbon emission reduction on the household side should not be neglected. Based on the 2012–2018 CFPS database, This paper examines the relationship between household carbon emissions(HCEs) and the digital economy by analyzing data on household consumption expenditure and carbon emission coefficients. Utilize instrumental variables and the "Broadband China" natural experiment to conduct robustness tests, and further clarify how the digital economy influences direct and indirect household carbon emissions. The study finds that (1) Direct HCEs witness an increase while indirect HCEs experience a decrease due to the digital economy. (2) The digital economy increases direct HCEs by expanding the consumption scale, but surprisingly, the digital economy has not made a significant impact on enhancing residents' environmental awareness. (3) When analyzing indirect HCEs, we introduced a macro perspective, and it is found that the digital economy results in a decline in indirect HCEs primarily due to a reduction in the energy intensity of the whole industry. This paper provides new evidence on the digital economy and its impact on carbon emissions at the household level, which is instructive for understanding the interaction between the production and consumption sides in achieving carbon neutrality and how to promote emission reduction on the consumption side. • Household carbon emissions are measured from direct and indirect perspectives. • The impact of digital economy on household carbon emissions is heterogeneous. • Digital economy increases household carbon emissions through consumption scale. • Digital economy has no significant impact on environmental awareness. • Digital economy reduces indirect household carbon emissions through energy intensity. [ABSTRACT FROM AUTHOR]

The accelerating urbanization, rapid industrial development, and excessive consumption of fossil fuels pose survival challenges such as energy depletion and environmental degradation for humanity. In the context of industrial park development, constructing a low-carbon energy system, increasing the proportion of renewable energy, enhancing energy-level matching, and utilizing heat pumps for deep waste heat recovery are effective approaches to reduce fossil energy consumption and alleviate environmental pressures. Addressing issues such as diverse thermal flows in industrial sites, complex electricity-heat-cooling energy demands, and unclear industrial-building energy coupling mechanisms, this paper proposes a two-stage planning framework, comprising the following five steps: (1) constructing a waste heat utilization system centered around heat pumps, (2) establishing a mechanism for matching heat sources, equipment, and thermal sinks, (3) establishing an energy conversion model and energy quality quantification system, (4) optimizing waste heat utilization path allocation, and (5) conducting a system energy flow analysis. Case studies demonstrate that the proposed system achieves optimized matching of multiple heat sources and sinks in industrial and building scenarios through thermal integration, meeting diverse energy demands in the park, including electricity, cooling, and multi-grade heat. After coupling with traditional steam pipeline networks, the annual total cost and annual steam power consumption decrease by 33% and 39% respectively, with a waste heat contribution rate of up to 26% and a renewable energy penetration rate of up to 25%, and a waste heat recovery system payback period of 6 years. By establishing an energy quality quantification system and conducting multi-objective optimization considering losses and economic costs, this paper provides Pareto frontier solutions, offering references for engineering proposals. The proposed networked waste heat recovery system is characterized by low energy consumption and high economic efficiency, effectively integrating the energy characteristics of industrial parks and demonstrating engineering applicability. [Display omitted] • Focusing on industrial-building energy conjugation issues • Proposing a low-carbon energy system planning framework for industrial parks • Establishing a networked matching mechanism for cold and hot flow streams • Short system recovery cycle, good economic performance • High contribution rates of waste heat and renewable energy, evident low-carbon effect [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the energy consumption and performance of fuel cell electric bicycles (FCEBs) under specific key input parameters. The paper applied an integrated method that includes an artificial neural network (ANN) and genetic algorithm (GA) to forecast and identify an optimal performance and energy consumption of FCEBs. The simulation model of FCEBs is established and simulated in MATLAB-Simulink environment to generate 1000 data points, that are used for training, validating, testing artificial neural network, the ANN architecture containing five input neurons, two hidden neurons and two output neurons, respectively. Furthermore, the GA is integrated to find the maximum performance and energy consumption once the ANN has exactly been trained. The study found that the FCEB configuration can achieve an effective performance at 30.3 km/h with required power of 210.4 W under speed level_5, radius of wheel 0.39 m, frontal area 0.423 m2, slope grade 0%. In order to validate and verify the simulated results, the experimental approach method was conducted in the same condition. The experimental results fit well with the simulated results in the same initial input parameters. • A method integrating the artificial neural network with genetic algorithm is applied. • The consumed power and fuel cell voltage are optimized by combining artificial neural network and genetic algorithm (ANN-GA). • The effective performance area of fuel cell electric bicycle under various a structure and operating parameters is identified. • Deep-learning results reflect major physical mechanism of fuel cell electric bicycle operation. [ABSTRACT FROM AUTHOR]

Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery's cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery's previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well. • Review of State of Health (SoH) estimation methods for lithium-ion battery pack translating from first life to second life. • Critical analysis of equipment's and test protocols subjected to cyclic ageing. • Classification of SoH estimation methods in the form of physical and chemical based approach, electrical and Artificial Intelligence (AI) based techniques. • Listed the parameters acquired from battery to be considered in second life SoH estimation. • Presented the challenges associated with SoH methods for Second Life Batteries (SLB). [ABSTRACT FROM AUTHOR]

In the current period, there has been a prominent and gradual upswing in the application of Internet of Energy (IoE) sensors in smart cities. These sensors play a vital role across diverse aspects of the energy sector, ranging from producing energy to haggling with the complexities of the smart grid. The IoE sensors use fiber optics technology that increases the speed and bandwidth of data transfer in smart grid applications. Incorporating IoE sensors, including fiber optics, is paramount to rehabilitating extensive IoE sensor data into practical information to distribute energy based on prices, availability, and demand for smart homes and electric vehicles. However, from energy generation to consumption, many nodes are incorporated. Therefore, security is a crucial challenge to processing accurate IoE sensor data for energy generation and consumption in smart cities. This paper presents blockchain-enabled, energy-efficient IoE smart grid architecture for smart homes and electric vehicle applications. The proposed architecture suggests blockchain-energy-efficient IoE sensors data scheduling (BEDS) algorithm schemes that consist of blockchain, smart grid, and vehicle and smart home schemes. The paper considers grid data based on sensors for different applications. The proposed system integrates fiber optics, collecting and offloading sensors to the grid for execution. This study aims to process IoE sensor data based on blockchain with a minimum processing time of 29% and less power consumption of 41%. Simulation results show that BEDS has less processing time and energy consumption than existing proof-of-work and proof-of-stake blockchain methods in smart grid networks. • A smart-grid architecture for energy production in smart cities. • Blockchain energy data scheduling (BEDS) algorithm framework. • An energy-efficient blockchain-enabled IoE smart grid architecture. [ABSTRACT FROM AUTHOR]

Most multi-period optimal power flow (MP-OPF) focuses on economic goals. However, with the increasing penetration of renewables, power systems face specific challenges to ensure secure and stable operation due to the fluctuation of renewables and the heavy loading demand. Hence, this paper proposes a multi-period voltage stability-constrained optimal power flow (MP-VCOPF) considering the uncertainty of the renewables and loads problem with both the economic and stable goals, i.e., minimizing the generation cost and ensuring a desirable static voltage stability margin. Unlike most models in the literature, the proposed problem formulation uses the nonlinear parameterized AC continuation power flow equations to model the voltage stability and exact AC power flow constraints. Furthermore, the thermal limits, voltage limits, ramping constraints, and security operation requirements are all considered in the proposed formulation. As another main novelty, the joint scenarios of renewables and loads are established, and a tailored scenario reduction technique is employed in this paper. To solve it reliably, the MP-VCOPF problem is decomposed into two subproblems, and a three-stage method is proposed to solve the large-scale stochastic nonlinear constrained programming problem. Computational and comparison results on IEEE 30-bus system, 118-bus system and 300-bus system verify the proposed method's effectiveness and accuracy under the short and long scheduling horizon and the robustness on different loading conditions. • A multi-period voltage stability-constrained optimal power dispatch problem with the constraints of AC power flow, AC continuation power flow, and the uncertainty from renewables is presented to account for both the economic and stable operation of power systems. • A three-stage approach is presented to solve the proposed MP-VCOPF problem reliably and quickly in a receding horizon. • A voltage stability problem-based scenarios reduction method is proposed to decrease the size of scenarios and obtain representative scenarios to reduce the difficulty of the proposed problem, which can avoid the loss of the extreme scenarios. [ABSTRACT FROM AUTHOR]

As the renewable energy consumption responsibility weight system is gradually implemented in various provinces, there is a closer intersection between the excess consumption and green certificates trading. Existing trading platforms can only meet the needs of green certificates trading. This single-platform solution is no longer sufficient to meet the trading requirements under this system. Consequently, this paper proposes a excess consumption and green certificates trading platform based on blockchain technology. This platform utilizes the decentralized, open, and anonymous characteristics of blockchain technology and rely on its smart contract capabilities to simultaneously meet the comprehensive trading needs of surplus consumption and green certificates trading through combining user-initiated transactions with platform-intelligent recommendations. It adapts to the complex excess consumption and green certificates trading market that has emerged following the issuance of the renewable energy consumption responsibility weighting policy. To conduct simulated transactions, 16 groups of buyers simulate a total of 215,792 excess consumption vouchers and 143,861 green certificates in this paper. The transaction results all meet the expected transaction scenarios under the conditions of smart recommendation, demonstrating the platform's transaction feasibility. [Display omitted] • Design a blockchain-based smart contract solution for volume consumption trading and green certificate trading. • Build a database containing 215,792 excess consumption vouchers and 143,861 green power certificates. • Simulate the purchase of 160 buyers by establish a transaction platform. • Analyzed the application direction of the data produced by the trading platform [ABSTRACT FROM AUTHOR]

Recently, the intent to use microgrid (MG) technology for urban, residential, and industrial applications has significantly increased. Thanks to the integration of shared storage technologies, these power systems allow for higher penetration of distributed renewable generation (DGs) and better mitigation of imbalances between demand and generation. This responds to social and environmental requirements in terms of decarbonizing energy and also contributes to strengthening smart cities. DC (Direct Current) microgrids offer several advantages compared to AC (Alternating Current) type microgrids, like superior efficiency, better control, stability, compatibility with the DC nature of renewables and storage sources, and the absence of reactive and synchronization problems. However, before fully exploiting the potential of microgrids in renewable-powered smart grids, it is necessary to conduct further research and discussion on critical technical and socio-economic challenges. This paper presents a review of the existing state-of-the-art research in DC microgrid development, relevant challenges related to security, communication, power quality, and operation, as well as the appropriate control and energy management strategies to handle them. As control and energy management strategies considerably impact other performance indicators such as operating cost, emissions, and power system safety, this paper offers a perspective on the potential improvement of such management solutions. • An overview of microgrids and their fundamentals is offered. • A comprehensive analysis of the classifications and applications of microgrids. • An in-depth analysis is conducted on the challenges related to the installation of microgrids. • An overview of potential future study topics that are worth investigating in the field of microgrids. [ABSTRACT FROM AUTHOR]

The growing adoption of renewable energy systems has created business opportunities for battery energy storage. Analytical tools and approaches to model the costs and benefits of energy storage have proliferated with the rapid growth in battery energy storage. This paper proposes a management system for energy storage (MSES) to analyze the costs and net benefits of battery energy storage. This paper establishes a general analysis model to describe the cost components of energy storage and define sources of battery energy storage benefits. Compared to traditional tools like Microsoft Excel used to compute the value of battery energy storage, results show that the proposed MESE is more friendly and more efficient in computation speed and operation. Furthermore, the comparison of the final evaluation results between MESE and the traditional tool confirms the validity of MESE. • This dissertation provides a practical analysis tool to improve the planning and operation of battery storage projects. • This paper establishes a cost model to describe the cost components of BESS and define sources of BESS benefits. • The paper analyzes the value and viability of BESS using Browser/Server (B/S) software architecture. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for achieving optimal energy management (OEM) in renewable-based hybrid microgrids (HMGs). The HMGs exhibit enhanced efficiency, minimizing power loss while maximizing generated power, making them advantageous over traditional MGs. This new approach consists of a modified model predictive control based improved Firefly1to3 algorithm (MMPC-IFA1to3). Here, MMPC adeptly regulates converter switching phenomena, ensuring stability during uncertain conditions in HMG. On the other hand, FA1to3 states that for each member movement of a firefly population, occurs towards three members. Here, the improved nature of FA1to3 is achieved by using the chaotic function to fine-tune the regulation parameter. This unified method generates the best power references for both active and reactive powers, making switching operations in voltage source converters simpler. This paper mainly emphasizes active and reactive power management through objective function minimization. The proposed IFA1to3 approach effectively incorporates constraints to minimize costs, ensure power availability, and mitigate voltage deviations in renewable-based HMGs. Moreover, charging/discharging of energy storage devices and power exchange between the utility grid and DC MG are carried out through the MMPC-IFA1to3 algorithm by monitoring active and reactive loads simultaneously. The corresponding converters are modeled based on the bidirectional power flow to cope with active and reactive power management. Further, the robustness of the proposed approach is verified under different operating conditions, and outcomes are compared with benchmark techniques in a MATLAB/Simulink environment to evidence its superior EM in HMG. Finally, the proposed MMPC-IFA1to3 approach is validated in a real-time environment through the dSPACE DS 1104 embedded processor to evidence its industrial applications through its robustness and OEM during various case studies. • In the hybrid MG, MPC regulates converter of the connected sources, while MMPC controls the VSC converter. • A novel FA, combining FA1to3 with chaotic logistic function, optimizes power references generation in this study. • Proposed MMPC-IFA1to3 approach ensures voltage stability and effectively manages ARP during uncertainties in MG. • The proposed IFA1to3 is more effective for multi-objective functions optimization. • Real-time validation of the proposed approach in dSPACE DS1104 evidences its practical applicability. [ABSTRACT FROM AUTHOR]

As the proportion of behind-the-meter (BTM) renewable energy resources (RERs) in the distribution network continues to rise, the precise role of RERs in the flexibility market, whether as "Recipients" or "Providers", is becoming less distinct. This observation has led us to explore the integration of flexible load resources and RERs into flexibility markets. Such integration facilitates resource sharing among multiple microgrids (MGs). This paper focuses on the potential emergence of a secondary flexibility (SecFlex) market in the power sector among high proportional BTM-RERs microgrids. Within the concept of SecFlex market, this paper outlines a comprehensive process supporting secondary flexibility trading, which includes "SecFlex market organization–trading volume collation–response schedule optimization". At first, the market mechanism of the SecFlex market is proposed, where players submit an empirical load curve to the market organizer. During the clearing stage, a social welfare equilibrium problem is solved. Subsequently, the equilibrium trading volume is adjusted in the collation stage to account for the distribution network (DN) voltage magnitude balance. Finally, a response schedule strategy is designed to analyze the behaviors of the players. The cvxpy framework is used to solve social welfare and competitive equilibrium in this paper. The results show significant cost reductions for two players, by 77.8% and 24.0% respectively. However, costs for Player 3 have increased substantially, up to 4.6 times the baseline when flexibility is limited. These findings provide theoretical guidance for the development of the SecFlex market. • A secondary flexibility market mechanism with high share of BTM RERS. • A comprehensive process including market, technical, and control aspects. • A trading volume collation method that supports the secondary flexibility market. • Discussion and control analysis of different behavior patterns of market players. [ABSTRACT FROM AUTHOR]

Underfrequency load shedding is the third level protection measure to ensure the safe and stable operation of power systems, which can effectively prevent the rapid decrease in system frequency caused by power system failure. To compensate for the power deficit resulting from faults during the island operation of a microgrid, a two-stage underfrequency load shedding strategy for microgrid groups considering risk avoidance is proposed in this paper. The proposed strategy divides underfrequency load shedding into a fast load shedding stage and a risk avoidance load shedding strategy. The first stage is fast underfrequency load shedding considering the load frequency characteristics and voltage characteristics; the fast underfrequency load shedding in the first stage reduces the fast frequency decrease before the second stage load shedding operation. The second stage of load shedding is risk avoidance under frequent load shedding. The load shedding in this stage accounts for the risk loss caused by the nondeterminacy of the demand side load to the system load shedding while accounting for the load frequency and voltage characteristics. First, the conditional value at risk (CVaR) theory is introduced in this paper to analyze and determine the risk loss caused by load nondeterminacy on load shedding, and the severity of load shedding (SoLS) is adopted as the CVaR value of load shedding. Second, the underfrequency load shedding optimization model is constructed by taking the risk value of the load shedding conditions of the microgrid as the optimization index of load shedding. Finally, the performance of the proposed strategy is verified based on the improved IEEE-37 node system microgrid group model. The results show that the proposed two-stage load shedding strategy can effectively prevent a rapid decrease in system frequency and effectively reduce the risk loss caused by load nondeterminacy during load shedding. • A novel two-stage underfrequency load shedding strategy is proposed. • The two-stage strategy consists of fast load shedding and risk avoidance strategy. • A quantitative method of load shedding risk loss based on CVaR theory is proposed. • The optimal load shedding ratio distribution quantity is obtained. [ABSTRACT FROM AUTHOR]

The most conventional control structure for Voltage Source Converters (VSC) is based on the stationary reference frame dq theory, which may result in varying levels of harmonic content in the AC currents and DC voltage oscillations under unbalanced voltage conditions. In the last two decades, numerous papers have proposed controllers with diverse levels of complexity to address these issues. However, a scientific gap exists in the current state of the art regarding the operational limits of conventional dq control in the face of these undesirable operational conditions. The primary contribution of this paper is a mathematical model capable of calculating the phase and magnitude of non-characteristic elements of AC currents, such as negative sequence and third harmonic components, present during unbalanced conditions. These components depend on the power handled by the VSC, the level of voltage unbalance and the bandwidths of the control loops, where the selection of the latter can either enhance the system performance or lead it towards instability. Thus, based on this model, it is now possible to quantitatively define the safe operating limits of a VSC driven by dq control under unbalanced voltages. The validation and effectiveness of the model are confirmed through real-time simulations and prototype experiments. Finally, the proposed novel framework holds potential utility for both industrial and academic applications. • THD in grid currents and DC voltage oscillations are influenced by controller response times and voltage unbalance. • PLL's influence in the generation of uncharacteristic harmonics can be rejected if the response time is long enough. • DC voltage oscillations varies based on the size of the capacitor but also maintains an exponential behavior determined by the ratio of response times. • The increase in the DC capacitor size has no influence on the reduction of harmonic content in the grid currents. • The appropriate selection of the ratio between the response times of the controllers makes it possible to maintain a safe operational limit of the VSC even under conditions of strong voltage unbalance using a conventional dq controller. [ABSTRACT FROM AUTHOR]

The construction sector is responsible for around 39% of global greenhouse gas emissions, with building envelopes playing a significant role in this carbon footprint. This is particularly relevant for building facades with large window-to-wall ratios (WWR), because higher proportions of glass increase both operational and embodied carbon. In light of the contemporary demand for such glazed envelopes, there is an emerging legislative trend that measures not only operational carbon, but also embodied via utilizing life-cycle assessment tools (LCA). To this effect, there is a great demand for techniques that can effectively lower the former carbon without increasing the latter. To contribute to this effort, this paper presents a whole LCA across seven cities for standard and advanced glazing solutions, including Water-Filled Glass (WFG). WFG is a novel technology that utilizes water in the cavity of insulated glass units (IGU), to improve thermal comfort, energy and acoustic performance of buildings. The paper utilizes energy simulation modelling through TRNSYS, and provides a comparative analysis of different glazing techniques. The significance of this paper is that a comprehensive cradle-to-grave LCA analysis of standard and advanced glass techniques is presented here for the first time, across all major Köppen-Geiger climates. The results show that WFG presents considerable carbon savings against all other options e.g., 31–53% compared to double glass, underlining the possibility of reducing operational and embodied carbon simultaneously. A second novel outcome of the paper is introducing the Glass Viability Index (GVI) for the first time, which presents the 'carbon payback' period required to offset an embodied carbon increase with operational savings. This is because the short lifespan of glass makes embodied carbon a large driver in LCA, and as such the prioritisation of operational carbon when selecting a technique can lead to a unsustainable practice. • Glass options should be ranked on LCA instead of just operational energy. • Paper presents life-cycle carbon for seven glass options across all major climates. • Viability of advanced glass options is affected by their high embodied carbon. • Glass option ranking depends on length of representative study period (RSP). • Glass Viability Index is created, which ranks carbon impact independently of RSP. [ABSTRACT FROM AUTHOR]

The objective of this paper is to explore how environmental protection expenditures (EPE) affect innovation and investigate the potential causal relationship between the two variables. The novelty and main contribution of our paper to the literature stems from the fact that we look at total environmental protection expenditures from both the public and private sectors, and distinguishing between corporations and households in the private sectors. We also consider both inputs and outputs of innovation and address potential endogeneity issues in this relationship, which are both lacking in the current literature. Using a panel of EU countries over the period 2006–2020, this study generally finds a positive impact of EPE on innovation after controlling for the effects of trade, credit, education and inflation on innovation, and using the System GMM estimator to treat endogeneity. Moreover, EPE from the government sector drives the bulk of this positive impact. The core results are robust to alternative measures of innovation, alternative estimation techniques that address endogeneity (i.e., the Lewbel 2SLS, Mundlak, and Hausman-Taylor estimators) and the moderation effects of Climate Change Performance (CCPI). This study also provides a useful guide to policy makers engaged in environmental planning. • We evaluate the impact of environmental protection expenditures (EPE) on innovation in mainly EU countries. • We use longitudinal dataset for the group of EU countries over the period 2006–2020. • We find a positive impact of EPE on innovation. • The results are robust for observable heterogeneity, alternative measures of innovation and estimation techniques. • This study provides a useful guide to policy makers engaged in environmental planning. [ABSTRACT FROM AUTHOR]

Hydrogen Direct Reduction of Iron (HDRI) combined with renewable electricity is an attractive option for low-carbon steel production. In this paper, we present a novel and computationally efficient techno-economic power-to-x-plant optimization model which is then applied to an HDRI plant located in Finland. Plant dimensioning was carried out in several current and future power market and regulatory scenarios. We predict a production cost of 373 €/t for hot briquetted iron, and 351 €/t for a future scenario of 2025–2030. When the recently introduced EU rules for renewable fuels of non-biological origin are applied, the production costs increased by 30–46 €/t. The rules also have a significant increasing effect on the required hydrogen storage. The flexibility of the direct reduction shaft emerged as an important parameter affecting the required hydrogen storage as well as total production cost. The results of this paper hold significance for the optimal design of future low-carbon steel plants. • Computationally efficient optimization model for Power-to-X operations and dimensions. • Hydrogen Direct Reduction Ironmaking (HDRI) plant using wholesales and wind power. • Significant amounts of intermittent wind power can be absorbed by the HDRI plant. • HDRI/HBI production costs analyzed for EU regulatory scenarios on green hydrogen. • Emission reductions over 96% compared to CO 2 emissions of fossil steelmaking. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid approach for switched reluctance motor (SRM) based water pumping system. The proposed hybrid method is combination of both the Northern Goshawk Optimization (NGO) and Finite Basis Physics-Informed Neural Networks (FBPINNs). Hence, it is named as NGO-FBPINNs. The NGO method is employed to better control among the three level boost converter (TLBC) and FBPINNs is predict the optimal control of the TLBC. In this system the water is pumped using a 4-phase SRM driven by a midpoint converter. An intermediary power conversion stage called a TLBC is positioned between the motor-pump and the solar PV array. The proposed method is very efficient because of the TLBC's small inductor size, wide operating voltage range, and low voltage stress across devices. The proposed method implemented in MATLAB platform and its efficiency is compared with various existing techniques like Heap-Based Optimizer (HBO), Wild horse optimizer (WHO) and Particle Swarm Optimization (PSO). The proposed approach NGO-FBPINNs obtains high efficiency than existing approaches. The proposed system's effectiveness is 95% it is higher efficiency than other system. • This Paper Proposes A Hybrid Approach For switched Reluctance Motor (SRM). • Hybrid Approach Is Combination Of Both The Northern Goshawk Optimization (NGO). • The NGO Method Is Employed To Better Control Among The Three Level Boost Converter. • TLBC Is Used As An Intermediate Power Conversion Stage. • The Proposed Method Implemented In MATLAB Platform. [ABSTRACT FROM AUTHOR]

With the increasing installation of battery energy storage systems, the safety of high-energy-density battery systems has become a growing concern. Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional information of voltage, current and temperature have gradually encountered bottlenecks. It is necessary to adopt a proactive approach by using mulitidimensional information to advance fault diagnosis techniques. This involves integrating advanced sensing technologies, collecting multidimensional data and uncovering subtle changes in battery behavior. This paper delves into the mechanisms and evolutionary paths of battery faults, with a specific focus on the multidimensional observable signals associated with different faults for enhanced safety strategy. Furthermore, the paper provides a comprehensive overview of potential applications of different sensors for multidimensional measurement in battery fault diagnosis. It also explores the future trends and research directions of the next generation of battery fault diagnosis techniques driven by multidimensional data collection and artificial intelligence algorithms. [Display omitted] • Enhanced safety through proactive, multidimensional fault diagnosis techniques. • Integration of advanced sensing tech for precise multidimensional data collection. • Uncovering subtle battery behavior changes for improved fault detection. • Specific focus on multidimensional signals to enhance safety strategies. • Future trends in battery fault diagnosis driven by AI and multidimensional data. [ABSTRACT FROM AUTHOR]

The fluctuation of oil futures price greatly influences the market investment sentiment, which can cause credit risk and capital pressure risk of various financial institutions, and ultimately affect the idiosyncratic risk and systemic risk of financial institutions. This paper adopts MES to measure the systemic risk of Chinese financial institutions and proposes a high-dimensional time-varying parameter spillover network matrix to identify the infection route and source of systemic risk. Finally, the paper investigates the mechanism by that oil futures affect the contagion effect of systemic risk across institutions. The results show that banking financial institutions are the main exporters of risk while insurance is the main absorber, and after the outbreak of COVID-19, the risk contagion capacity of institutions generally increased, the network structure became more complex, and the risk spillover level was pro-cyclical. The empirical results show that the rise of oil futures will promote the (net) risk spillover capacity of institutions and inhibit the risk absorption capacity, and the above process can be transmitted through the stock market, bond market, and foreign exchange market. • Banking institutions are transmitters of risk, while insurance are receivers; • Financial institutions' risk contagion network becomes more complex after COVID-19; • The systemic risk spillover level of Chinese financial institutions has obvious pro-cyclical; • Higher oil futures price promotes the (net) risk spillover capacity of institutions and inhibit the risk absorption capacity; • The above impacts involve three possible transmission mechanisms: the stock market, bond market and foreign exchange market; [ABSTRACT FROM AUTHOR]

The electricity required for air conditioning is soaring worldwide. Absorption chillers represent an alternative to classical vapor compression systems, using heat instead of electricity. However, absorption chillers powered by renewable geothermal heat have received little attention so far. This paper introduces a system using a hot geothermal fluid (typically in the range of 80–110 °C) to produce cooling through a single-effect absorption chiller, and Domestic Hot Water (DHW) through a heat exchanger. It considers a hotel located in Martinique, a French Caribbean island. The electric consumption of every subsystem has been thoroughly estimated. The originality of this paper is twice: i) the system is modelled in TRNSYS software considering dynamic conditions, ii) the system undergoes surrogate modelling and multi-objective optimization to minimize both the cost and the CO 2 content of the delivered thermal energy. Several scenarios are considered, depending upon the geothermal temperature, mass flow rate, well remoteness, and demand size. The studied system appears to be systematically more expensive than a combination of a classical vapor compression chiller and a boiler for DHW. However, it can significantly decrease the CO 2 content of the provided energy, especially in an island where most electricity is generated from fossil fuels. The proximity of the geothermal well and the use of the warm water leaving the absorption generator (here for DHW production) appear to be key factors for system relevance, along with a hotter geothermal fluid (e.g., 110 °C instead of 80 °C). • A geothermal absorption chiller in tropical climate is modelled. • The influence of design parameters on electricity consumption is investigated. • The system undergoes multi-objective optimization. • The well proximity and geothermal temperature are keys to the system relevance. [ABSTRACT FROM AUTHOR]

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance. • In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids. • Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window. • A dynamic detection window is obtained through optimization to transform electricity consumption data. • The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization. [ABSTRACT FROM AUTHOR]

CaO-CaCO 3 thermochemical energy storage is a promising technology for solar energy utilization and storage. Alkaline papermaking waste (APW) from paper mills, which is mainly composed of CaCO 3. Herein, TiO 2 /MnFe 2 O 4 co-modified APW was synthesized. The energy storage capacity of TiO 2 /MnFe 2 O 4 co-modified APW was studied during CaO-CaCO 3 thermochemical energy storage cycles in a duel fixed bed reactor. TiO 2 /MnFe 2 O 4 co-modified APW mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. The introductions of TiO 2 and MnFe 2 O 4 significantly improve the cyclic stability and optical absorption property of APW in 30 cycles. The co-modified APW (the mass ratio for CaO:TiO 2 :MnFe 2 O 4 = 100:5:5) possesses high sintering resistance, energy storage, and optical absorption capacities in 30 cycles. The energy storage conversion and capacity of the co-modified APW are 0.72 and 2288.57 kJ/kg after 30 cycles, respectively. The optical absorptance of the co-modified APW reaches 49%, which is 9.25 times as high as that of APW. CaTiO 3 and CaMnO 3 effectively mitigate the sintering of CaO grains. CaMnO 3 and Ca 2 Fe 2 O 5 significantly increase the optical absorptance of APW. The co-modified APW possesses stronger basicity and more oxygen vacancies, which is favourable for the carbonation reaction. Thus, TiO 2 /MnFe 2 O 4 co-modified APW is a good energy storage material. • TiO 2 /MnFe 2 O 4 co-modified papermaking waste is used for CaO-CaCO 3 energy storage. • The composite mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. • The composite has high and stable energy storage and optical absorption capacities. • The energy storage density of the composite is 2288.57 kJ/kg after 30 cycles. • The composite has high sintering resistance, stronger basicity, and oxygen vacancy. [ABSTRACT FROM AUTHOR]

In the context of the large-scale participation of renewable energy in market trading, this paper designs a cooperation mode of new energy power stations (NEPSs) and shared energy storage (SES) to participate in the power-green certificate market, which divides SES into physical energy storage and virtual energy storage. Secondly, combining the advantages of scenario generation and robust optimization (RO), a two-stage RO model with improved uncertainty interval is proposed to determine the optimal trading strategy. Then, to better align the distribution results of cooperative benefits with the actual contributions of NEPSs and SES, an entropy weight modified Shapley value benefit allocation strategy is constructed. Finally, the new energy base in Qinghai Province, China is chosen for simulation. The results show: (1) Adding energy storage and using two-stage RO are able to effectively improve the ability of NEPSs to resist uncertainty, which increases the revenue of the alliance by 22.8%. (2) The application of SES has better economic benefits than each member equipped with energy storage separately. Compared with the latter, the deviation penalty cost of the former is reduced by 66.4%, and the revenue is increased by 3.4%. (3) The proposed entropy weight modified Shapley value method embodies the important auxiliary role of SES more obviously. Based on this method, the overall satisfaction of the alliance increases by 12.6%. Generally speaking, the optimization model and benefit allocation strategy proposed in this paper can provide guidance for NEPSs and SES participating in power trading, and promote the low-carbon transformation of the power sector. [Display omitted] • Design a cooperation mode of new energy power stations and shared energy storage. • Divid the shared energy storage into physical energy storage and virtual energy storage. • Propose a two-stage robust optimization model with improved uncertainty interval. • Construct an entropy weight modified Shapley value-based benefit allocation strategy. • Select a new energy base as the simulation system for verifying the effectiveness and applicability. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

Access to decentralized energy solutions is critical for the development of rural communities in Sub-Saharan Africa (SSA). One form of decentralized energy technology, agrivoltaics (AV), has gained attention in the last decade as a means to tackle energy poverty in off-grid communities. The success of renewable energy (RE) projects of any kind, however, depends on existing challenges and risks that may be faced at different stages. Therefore, the aim of this paper is to identify and analyze the main risk factors when implementing agrivoltaics on a community level in SSA. Based on the PESTLE framework, the paper develops a first risk classification system for international agrivoltaic projects in rural farming communities in SSA. By making use of a mixed-methods approach, the research enables the prioritization of potential risks in terms of their occurrence and significance; furthermore, it discusses mitigation strategies. Results indicate that there are various multifaceted risks associated with agrivoltaics, primarily including financial challenges and regulatory barriers. In addition, the study emphasizes the importance of stakeholder involvement as well as a need for long-term community engagement and capacity building to ensure a successful implementation and sustainability of agrivoltaic projects. • Development of the first risk classification system for international agrivoltaic projects in rural communities in SSA. • A mixed-methods approach was used for identifying, classifying and rating potential risks. • Financial risks and regulatory barriers appear to be highly critical. • In addition, stakeholder involvement and long-term community engagement were considered essential. [ABSTRACT FROM AUTHOR]

With the high proportion of renewable energy connected to the grid, the problem of insufficient flexibility in the power system has emerged. Renewable energy and controllable distributed resources can be aggregated and managed through virtual power plants, reducing the need for flexibility to a certain extent. Although building new energy storage systems can compensate for the lack of flexibility, it requires high initial investment costs. To address this, this paper proposes a lease mechanism for coal-fired units based on the combination of carbon credits and prices, providing the right to use coal-fired units for virtual power plants. Subsequently, different demand response strategies are utilized to control the controllable loads of various users, thereby providing certain controllable resources for the virtual power plant. Additionally, to ensure the optimal decision-making of the virtual power plant operator, a cost model accurately describing the capacity degradation state of the energy storage system is adopted. Moreover, a multi-time scale scheduling strategy of virtual power plant is implemented to fully utilize controllable resources of different time scales, effectively dealing with the power imbalance caused by multiple uncertainties. The results show that utilizing the leasing mechanism of coal-fired units and employing demand response strategies from different users can provide flexibility to the virtual power plant. The accuracy of the capacity degradation model used by the operator significantly impacts the optimality of the scheduling plan. • The scheduling model of VPP consider capacity degradation of ESSs, carbon trading, and DR strategies. • A lease mechanism for coal-fired units based on the combination of carbon credits and prices is proposed. • This paper proposes ISBDR strategies for reduceable loads. • The ESSs capacity degradation model adopted takes into account DOD and SOC. • A multi-time scale scheduling strategy of VPP is adopted to deal with multiple uncertainties. [ABSTRACT FROM AUTHOR]

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper. • A network partition method based on affinity propagation algorithm considering the photovoltaic uncertainty is proposed. • The traditional probability density function (PDF) of PV was updated by the random simulation method. • LinDistflow equation is used to achieve the conversion of the active power loss minimization model. • The ADMM algorithm with penalty parameter adaptive control is used to realize the distributed control of the ADN system. •.The voltage/var control schemes of OLTC, CBs and PV inverters are presented through multi-time scale scheduling. [ABSTRACT FROM AUTHOR]

With the increasing installed capacity of energy storage and the rapid accelerating process of electricity marketization, grid-side independent energy storage are beginning to generate profit by participating in the ancillary service market and reducing the strain on the grid. Although energy storage are currently involved in only one auxiliary service, their low utilization and low revenue can make it difficult to recover costs quickly. A method is presented in this article for optimizing peak modulation (PM) and optimizing frequency modulation (FM) in the auxiliary services market by dynamically partitioning independent energy storage partitions. This paper focuses on developing a method for secondary power allocation that is combined with two-level nested optimization. For two different markets and two different methods, separate power allocations are required. The outer layer optimization depends on the inner layer optimization results to produce the optimal primary allocated power, and the inner layer optimization is constrained by the outer layer optimization results to produce the optimal secondary allocated power. Additionally, the method can not only be applied to fixed type energy storage, but also to pure PM stations and pure FM stations; this paper also proposes an improved method for energy storage containing two batteries and providing two auxiliary services. According to the results of the study, the proposed strategy has a better economic value as well as better engineering value as compared with a single auxiliary service and two different coordination methods. • Proposes a twice power allocation combined with a two-layer nested optimization method. • Proposed a method of prioritizing participation in different ancillary service markets. • The method considers multiple influencing factors at different time scales. • Proposed a method of prioritizing participation in different ancillary service markets. [ABSTRACT FROM AUTHOR]

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research. [Display omitted] • Modular-gravity energy storage (M-GES) power control system studied. • Two compensation modes and four control strategies are systematically studied and validated. • The center switching method with hysteresis control (CSM-H) excels in continuous compensation mode. • The center switching method with hysteresis control and dead zone (CSM-HD) excels in overlap compensation mode. • Based on the power control system proposed in this paper, the M-GES plant performs highly in natural power systems. [ABSTRACT FROM AUTHOR]

This paper is the second in a two-part series on the concavity-convexity of the distribution system security region (DSSR). In Part I, the concave-convex characteristics and mechanism of DSSR are revealed by observing many case grids. In Part II (this paper), the concave-convex principle of DSSR is revealed through mathematical deduction. Firstly, the relevant concepts of sub-regions are proposed, and a DSSR model based on the union of sub-regions is developed. Secondly, the concave-convex principle of DSSR is proposed. The proposed criteria show that the security region of a distribution system with a single load transfer path for each feeder is convex, while that with multiple load transfer paths for some feeders can be concave or convex. Thirdly, the proposed criteria are deduced mathematically. Fourthly, based on the criteria, a method is proposed to judge whether a DSSR is concave or convex. Fifthly, the proposed criteria and method are verified through several cases, and the DSSR concavity-convexity of real distribution systems in China is also analyzed. Finally, the applications of DSSR concavity-convexity are illustrated. This work lays a mathematical foundation for the research of the topological properties of DSSR and is also useful for the application of DSSR. • The DSSR mathematical model based on the union of sub-regions is developed. • Three criteria are proposed and proved, revealing the concave-convex principle of DSSR. • A judgment method for DSSR concavity-convexity is proposed based on these criteria. • The DSSR concavity-convexity of real distribution systems in China is analyzed. • The application values of DSSR concavity-convexity are demonstrated. [ABSTRACT FROM AUTHOR]

With deepening decarbonization and increased Renewable Energy Sources (RESs) integration, the power system's inertia has declined, affecting the network's ability to balance power at the distribution level. Concurrently, the proliferation of prosumers presents a regulatory opportunity for Distribution System Operators (DSOs), despite the complexity introduced by their high number and varied behaviors. This paper introduces a new co-scheduling model optimizing prosumers' capacities through Virtual Power Plants (VPPs) in local networks, enhancing DSO oversight and facilitating prosumer participation in regulation markets. The proposed model concurrently schedules energy provision alongside voltage and frequency regulation capacities. Recognizing prosumers' behavioral uncertainties, Data-Driven Distributionally Robust Optimization (DDRO) is employed to ensure adequate capacity for VPP engagement. Importantly, the paper outlines a mechanism allowing DSOs to partner with multiple privately-owned VPPs, ensuring privacy through an adaptive Alternative Direction Method of Multipliers (ADMM) method. This method avoids the exchange of sensitive information, ensuring confidentiality and scalability. Consequently, VPPs can proficiently manage scheduling and communicate their regulation capacities. The operator then dispatches control signals based on regulation needs and network flow. Results from the IEEE 33 bus test system confirm the model's efficacy in enhancing voltage support and frequency regulation, and generating revenue for both VPPs and prosumers. • This paper proposes a new co-optimization strategy for distribution-level VPPs. • A distributed coordination approach between DSO and VPPs is presented. • An adaptive consensus ADMM is developed to model the communications. • The uncertain nature of prosumer behavior is addressed by a DDRO model. • The effectiveness of the developed approaches is extensively illustrated. [ABSTRACT FROM AUTHOR]

Timely monitoring of abnormal vibration of machinery in highly harsh environments is essential to ensure the safe operation of mechanical systems. This paper uses a self-charging power module to harvest energy to provide sustainable power for monitoring multidirectional ultra-low frequency mechanical vibrations. The power generation unit consists mainly of a spherical electromagnetic triboelectric hybrid nanogenerator (SETE-HNG). It is equipped with advanced sensing functions to support deep learning to identify signals with different directions of vibration, different frequencies, and different amplitudes of vibration, thereby enhancing the high-precision perception function. The accuracy of the prediction results is as high as 98.6622%, 100%, and 99.3333%, respectively. A Power Management Circuitry (PMC) has been meticulously crafted to maximize the utilization of vibrational energy. It efficiently charges a 40 mAh lithium polymer battery to 3.3 V in just 26 min, all without the requirement of an external power source. This advancement facilitates self-powered Global Positioning System (GPS) tracking of vibrational signals. Moreover, the stored energy is harnessed to energize a microcontroller and a low-power Bluetooth module. This enables real-time monitoring of mechanical vibrations via a mobile phone. The design presented in this paper is a testament to the potential of self-powered multidirectional mechanical vibration monitoring, contributing significantly to the safety and efficiency of mechanical systems. The design in this paper enables self-powered monitoring of abnormal vibrations in mechanical systems in extremely harsh environments, thus ensuring the safe operation of equipment. [Display omitted] • Large-scale vibration energy harvesting provides an innovative and efficient method. • SETE-HNG has multidirectional ultra-low frequency mechanical vibration monitoring and energy harvesting functions. • A power management circuit is designed to store energy efficiently. [ABSTRACT FROM AUTHOR]