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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]

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]