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This paper investigates the challenges associated with constructing wireless chargers at the megawatt level using inductive power transfer (IPT) technology for marine energy storage systems. For marine applications, charging can only take place at a quay, meaning that only static charging is possible. In this paper, the specifications of the MF Ampere ferry have been used for the feasibility study. Through analytical calculations and PSIM simulation, it was determined that several low-power (100 kW) IPT systems need to be paralleled to achieve 1 MW power transfer. Building a single unit of 1 MW is not feasible due to the voltage and current stress across the components and limited space available onboard and onshore. However, a 100-kW power level can be implemented, despite the involved misalignments, if the primary system is kept stationary with respect to the secondary. Two proposed solutions for implementing a fast charger at the 1 MW level are presented in this paper: 10 units of 100 kW without mechanical mooring assistance or 5 units of 200 kW with mechanical mooring assistance. [ABSTRACT FROM AUTHOR]

In complex environments such as mines and pipelines, wireless power transfer (WPT) technology stands as a safe and convenient method for supplying power. However, in practical applications, the unavoidable angular misalignment between the sending and receiving coils results in decreased power. To address this issue, this paper proposes a variable direction WPT design method based on auxiliary coils. The mutual inductance of the system is analyzed with coils placed at different positions and incorporating multiple auxiliary coils. This paper conducts simulation and experimental analysis based on a 45° angle between the horizontal shaft and the slant shaft, showing a 14.92% increase in received power. The effectiveness of the proposed design method validates the feasibility of the technology and offers substantial support for practical applications. [ABSTRACT FROM AUTHOR]

With the rapid development of hydrogen production by water electrolysis, the coupling between the electricity-hydrogen system has become closer, providing an effective way to consume surplus new energy generation. As a form of centralized management of distributed energy resources, virtual power plants can aggregate the integrated energy production and consumption segments in a certain region and participate in electricity market transactions as a single entity to enhance overall revenue. Based on this, this paper proposes an optimal scheduling model of an electricity-hydrogen coupling virtual power plant (EHC-VPP) considering hydrogen load response, relying on hydrogen to ammonia as a flexibly adjustable load-side resource in the EHC-VPP to enable the VPP to participate in the day-ahead energy market to maximize benefits. In addition, this paper also considers the impact of the carbon emission penalty to practice the green development concept of energy saving and emission reduction. To validate the economy of the proposed optimization scheduling method in this paper, the optimization scheduling results under three different operation scenarios are compared and analyzed. The results show that considering the hydrogen load response and fully exploiting the flexibility resources of the EHC-VPP can further reduce the system operating cost and improve the overall operating efficiency. [ABSTRACT FROM AUTHOR]

Concerning energy waste and rational use, this paper studies the optimal scheduling of day-ahead energy supply and the community's demand with a combined cooling, heating, and power (CCHP) system in summer. From the perspective of bilateral costs and renewable energy use, this paper examines the impact of energy storage systems integrated into cogeneration systems. The Gurobi solver is used to optimize the residential community's supply and demand sides of the traditional CCHP system (T-CCHP) and the CCHP system with energy storage (CCHP-ESS) under insufficient solar power. Subsequently, two optimal arrangements for energy consumption on the user side under these systems are suggested. In the optimization model, energy storage is added to the T-CCHP system on the energy supply side. On the user side, the energy use scheme is optimized considering the user's comfort. The innovation point of this study is that the optimization of comprehensive energy in the park involves both supply and demand. The impact of increasing energy storage is discussed on the energy supply side, and the impact of optimization of the energy use plan on costs is discussed on the user side. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power. [ABSTRACT FROM AUTHOR]

Energy storage technology can well reduce the impact of large-scale renewable energy access to the grid, and the liquid carbon dioxide storage system has the characteristics of high energy storage density and carries out a variety of energy supply, etc. Therefore, this paper proposes an integrated energy system (IES) containing liquid carbon dioxide storage and further exploits the demand-side regulation potential on the basis of which an integrated demand response model is proposed to consider the cooling, heating, and electricity loads. On this basis, an IES optimal scheduling model with the lowest total system operating cost as the objective function is established, the Yalmip toolbox and Cplex commercial solver are used to solve the algorithms, and the optimal scheduling results are obtained for electricity, heat, and cold under four scenarios, and it is proved through comparative analyses that the model and scheduling strategy established in this paper can optimize the load profile, realize peak shaving and valley filling, and have good economic benefits. Then, by analyzing the impact of the initial pressure of the high-pressure storage tank and fluctuating electricity price on the liquid carbon dioxide energy storage system, the system model established in this paper has good stability. Finally, for the comprehensive demand response model established in this paper, the impact of the demand response of different types of loads on the economy of the system is analyzed in depth from the perspective of economic benefits. [ABSTRACT FROM AUTHOR]

The country has experienced industry shutdowns, decreased productivity, educational setbacks, increased unemployment rates, and negative healthcare outcomes. Load-shedding in South Africa is primarily caused by inadequate maintenance, internal mismanagement, corruption, and a lack of skills. This issue has had a significant impact on small businesses, as they often lack the necessary resources to handle power surges, equipment damage, and the costs associated with replacing stock. This paper explores how load-shedding has impacted the South African economy from an entrepreneurial perspective. The research is qualitative and exploratory in nature, and it was found that load-shedding has had a significant impact on businesses' finances, resources, and performance. Load-shedding in South Africa has had a severe impact on the daily lives of people, causing disruptions in businesses, households, and the overall economy. Eskom, the primary supplier of energy in South Africa, has been facing significant challenges in meeting the growing demand for electrical energy, leading to job losses, decreased productivity, and a loss of billions of rands. Small businesses have been particularly affected, with an estimated 75% of them failing after being operational for less than three years due to load-shedding. To ensure small businesses have electricity to operate during load-shedding it is recommended that small businesses adopt alternative energy sources like solar power to increase the businesses' independence and mitigate the effects of load-shedding. The South African government should also encourage and support small businesses to collaborate with successful renewable energy companies. [ABSTRACT FROM AUTHOR]

In this paper, a double-pole double-throw analog switch with n-channel architecture driven by a charge pump is described. The architecture proposed in this paper not only can reduce the on-resistance of the complementary metal oxide semiconductor (CMOS) switch, but also can realize a stable on-resistance within the full swing range of the input signal. This analog switch has the characteristics of high-speed, low-voltage, and high linearity, which is suitable for high-speed USB (Universal Serial Bus) 2.0 applications and meets low-speed and full-speed USB requirements. The test chip using a 0.25 μm BCD (bipolar–CMOS–DMOS) process confirms the characteristics of the switch. It uses + 2.8 to + 5.5 V single power supply, and the normal operating temperature range is − 40 to + 85 °C. This switch has an over-voltage protection function, which can prevent damage to the switch from the high input voltage. Besides, the switch port can realize dual-direction transmission. The analog switch designed in this paper solves some problems of traditional analog switches, such as large on-resistance, large supply current, and large parasitic capacitance. The on-resistance of the analog switch architecture in this paper is 5 Ω and the on-resistance flatness is 0.1 Ω. The supply current is as low as 0.87 μA while the whole chip area is 0.65 mm2. [ABSTRACT FROM AUTHOR]

In order to solve the problem of optimal allocation of general heat resources in high permeability renewable energy distribution network, an optimal allocation scheme of general heat resources in renewable energy distribution network is proposed. The optimal allocation scheme in renewable energy distribution network is proposed. In this paper, the internal and external two-layer structure is designed. The outer layer uses genetic algorithm to search the structural parameters of general energy system, and the inner layer uses dynamic programming method to get the optimal performance of general energy system. The internal and external alternate adjustment methods are used to optimize the general-purpose power distribution capability of the continuous-wave power supply network with different permeability and controllability, and the proposed method is verified in IEEE 33 node distribution network. The experimental results show that the general strength storage capacity is 26.6% and 45.7% higher than that of the process with a continuous strength renewable permeability of 30% and 40%, respectively. The utility rates were increased by 18.4% and 27.8%, respectively. Under the condition of high permeability renewable energy, the proposed method can increase the efficiency of resource management of distribution network by regulating load, decrease the distribution capacity of energy stability, and reduce the operation cost of the system. [ABSTRACT FROM AUTHOR]

Technological innovation is the key to develop wind power and photovoltaic power industries. The feed-in tariff (FIT) policy, as a demand-pull policy, is important to support renewable energy technological innovation. Using the "difference-in-differences" method, this paper investigates the impact of FIT policy of wind power and the impact of the FIT policy designed according to differences in the distribution of resources on wind power technological innovation. The findings show that the FIT policy can drive patenting in wind power technologies during the implementation period, but may play a relatively weak promoting role in technological innovation in the latter term, and the FIT policy designed according to differences in the distribution of resources also stimulates more patent counts. Finally, based on the fixed effect negative binomial regression model, this paper finds that the higher feed-in tariffs can increase the patent counts in photovoltaic power technologies. [ABSTRACT FROM AUTHOR]

Ensuring an adequate electric power supply while minimizing redundant generation is the main objective of power load forecasting, as this is essential for the power system to operate efficiently. Therefore, accurate power load forecasting is of great significance to save social resources and promote economic development. In the current study, a hybrid CEEMDAN-TCN-ESN forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and higher-frequency and lower-frequency component reconstruction is proposed for short-term load forecasting research. In this paper, we select the historical national electricity load data of Panama as the research subject and make hourly forecasts of its electricity load data. The results show that the RMSE and MAE predicted by the CEEMDAN-TCN-ESN model on this dataset are 15.081 and 10.944, respectively, and R2 is 0.994. Compared to the second-best model (CEEMDAN-TCN), the RMSE is reduced by 9.52%, and the MAE is reduced by 17.39%. The hybrid model proposed in this paper effectively extracts the complex features of short-term power load data and successfully merges subseries according to certain similar features. It learns the complex and varying features of higher-frequency series and the obvious regularity of the lower-frequency-trend series well, which could be applicable to real-world short-term power load forecasting work. [ABSTRACT FROM AUTHOR]

Changing technology mixes will change market outcomes, but they do not change the fundamental economic principles behind market design. Market-clearing prices in such a market are not necessarily mostly zero even in an energy-only market, especially with grid scale storage, an active demand side of the market, and scarcity pricing. 6 Incorporating new power system security paradigms into low-carbon electricity markets Ben Skinner, Pierluigi Mancarella, Maria Vrakopoulou and Ian Hiskens Australian Electricity Markets are integrating variable renewable energy sources at rates faster than anywhere in the world. [Extracted from the article]

Nuclear power is the ulitimate polariser of the energy debate. Greens generally hate it. Most engineers love it. And successive British governments have blown hot and cold since the 1960s. What's different this time is that economists, financiers and energy security hawks are coming round to the case for a nuclear renaissance. The sea change that has occurred in just a few short years is nothing short of extraordinary. No question, concerns over energy security, the rising cost of fossil fuels and high-profile hand-wringing about climate change have put nuclear power firmly back on the agenda. [ABSTRACT FROM AUTHOR]

Modeling of fluid flow and mass/heat transfer and prediction of resources and energy production (e.g., geothermal systems in urban environment) Advances of reactive transport modeling (e.g., integrating information across scales, data - model integration, machine learning, and physical model coupling). [Extracted from the article]

Energy management for energy harvesting-based embedded systems (EHES) is an emerging field, which aims to collect renewable energy from the environment to power an embedded system. In this work, we use the systematic mapping method to study the relevant literature, with the objective of exploring and analysing the state of the art in energy management for EHES, as well as to provide assistance for subsequent literature reviews. To this end, we conducted extensive searches to find articles related to energy harvesting, embedded systems, energy consumption, and energy management. We searched for papers from January 2005 to July 2019 from three mainstream databases, ACM, IEEE Xplore, and Web of Science, and found more than 3000 papers about EHES. Finally, we selected 142 eligible papers. We have completed the system mapping research from five aspects, namely, (1) research type (validation research, evaluation research, solution proposal, philosophical paper, opinion, and experience), (2) research goals (application or theory), (3) application scenarios, (4) tools or methods, and (5) paper distribution, such as publication year and authors' nationality. The results showed that the major research type of the EHES papers is validation research, accounting for 65%, which indicated research is still in the theoretical stage and many researchers focus on how to improve the efficiency of harvesting energy, develop a reasonable energy supply plan, and adapt EHES for real-world requirements. Furthermore, this work reviews the tools used for EHES. As the future development direction, it is indispensable to provide tools to EHES for research, testing, development, and so on. The results of our analysis provide significant contributions to understanding the existing knowledge and highlighting potential future research opportunities in the EHES field. [ABSTRACT FROM AUTHOR]

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact. [ABSTRACT FROM AUTHOR]

To address challenges in locating high-impedance grounding faults (HIGFs) and isolating fault areas in resonant grounding systems, this paper proposes a novel fault identification method based on coordinating a Peterson coil and a resistance grounding system. This method ensures power supply reliability by extinguishing the fault arc during transient faults with the Peterson coil. When a fault is determined to be permanent, the neutral point switches to a resistance grounding mode, ensuring regular distribution of zero-sequence currents in the network, thereby addressing the challenges of HIGF localization and fault area isolation. Fault calibration and nature determination rely on recognizing neutral point displacement voltage waveforms and dynamic characteristics, eliminating interference from asymmetric phase voltage variations. Fault area identification involves assessing the polarity of zero-sequence current waveforms attenuation during grounding mode switching, preventing misjudgments in grounding protection due to random initial fault angles and Peterson coil compensation states. Field experiments validate the feasibility of this fault location method and its control strategy. [ABSTRACT FROM AUTHOR]

South Africa is currently experiencing an energy crisis because of a mismatch between energy supply and demand. Increasing energy demand necessitates the adequate operation of generation and transmission facilities to maintain the reliability of the power system. Transmission line compensation is used to increase the ability to transfer power, thereby enhancing system stability, voltage regulation, and reactive power balance. Also, in recent years, the introduction of renewable energy sources (RES) has proven to be effective in supporting the grid by providing additional energy. As a result, the dynamics of power systems have changed, and many developing nations are adopting the integration of renewable energy into the grid to increase the aspect ratio of the energy availability factor. While both techniques contribute to the grid's ability to meet energy demand, they frequently introduce technical challenges that affect the stability and protection of the systems. This paper provides a comprehensive review of the challenges introduced by transmission line compensation and the integration of renewable energy, as well as the various techniques proposed in the literature to address these issues. Different compensation techniques, including fault detection, classification, and location, for compensated and uncompensated transmission lines, including those connected to renewable energy sources, are reviewed. This paper then analyzes the adaptive distance protection schemes available in the literature to mitigate the impact of compensation/integration of RES into the grid. Based on the literature reviewed, it is essential for protection engineers to understand the dynamics introduced by network topology incorporating a combination of RES and heavily compensated transmission lines. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a systematic approach, various factors, such as renewable energy integration, demand–response strategies, and investment costs, are evaluated to formulate an efficient and sustainable energy community model. Time-of-use pricing schemes are incorporated to reflect the dynamic nature of energy markets and consumer behavior. By integrating techno-economic analyses, this study aims to optimize energy resource allocation while ensuring cost-effectiveness and environmental sustainability. The influence of optimized sizes of photovoltaics (PV), battery storage, and electrical vehicles (EVs) on self-sufficiency rates, self-consumption rates and CO2 savings is analyzed. The findings offer valuable insights into the design and implementation of energy communities in urban settings, highlighting the importance of adaptive strategies in the transition towards a resilient and low-carbon energy future. The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability. [ABSTRACT FROM AUTHOR]

Featured Application: The main application of this paper is to review and improve a model that enables the use of water heaters by small electricity customers in electricity markets and services in the short and medium term. For this goal, it is necessary to review the service of loads to avoid customer discomfort and the way control mechanisms work, usually thermostats. To guarantee a minimum level of accuracy in the flexibility of the response, the understanding of heat and electric processes in the load is a cornerstone for the participation of these appliances. This must be done by comprehensive validation of the model in different conditions, including water draws, temperature, and load control. To exemplify the potential of the model, an application in a prosumer scenario is presented. Residential segments are of the greatest interest from the point of view of Demand-Side Resources and Decarbonization. Main end-uses such as water heaters, heating, and cooling have interesting opportunities: first, they can store energy, and this is relevant for the integration of renewables. Second, they are candidates for efficiency and electrification, increasing their demand share and the flexibility of demand. This paper aims to formulate an elemental Physical-Based Heat Pump Water Heater model that will enable the use of these energy-efficient appliances through aggregation in complex products, considering the advantages for demand and supply sides. Simulation results show that the individual performance is quite accurate and that the proposed model is flexible enough to be used to take more profit from energy markets or to easily respond to fast-occurring events. The model can be easily aggregated and used to obtain baselines, an important point for Demand Response evaluation. Results also demonstrate that demand–supply coordination and balance can be improved using these models to reduce or mitigate the risks and volatility of renewables without inducing a noticeable loss of service. Consequently, the contribution of this responsive load can be modelled through this methodology, making the engagement of more customer segments in Demand Response policies more credible and deploying new segments, such as prosumers. [ABSTRACT FROM AUTHOR]

As the understanding of natural gas hydrates as a vast potential resource deepens, their importance as a future clean energy source becomes increasingly evident. However, natural gas hydrates trend towards secondary generation during extraction and transportation, leading to safety issues such as pipeline blockages. Consequently, developing new and efficient natural gas hydrate inhibitors has become a focal point in hydrate research. Kinetic hydrate inhibitors (KHIs) offer an effective solution by disrupting the nucleation and growth processes of hydrates without altering their thermodynamic equilibrium conditions. This paper systematically reviews the latest research progress and development trends in KHIs for natural gas hydrates, covering their development history, classification, and inhibition mechanisms. It particularly focuses on the chemical properties, inhibition effects, and mechanisms of polymer inhibitors such as polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap). Studies indicate that these polymer inhibitors provide an economical and efficient solution due to their low dosage and environmental friendliness. Additionally, this paper explores the environmental impact and biodegradability of these inhibitors, offering guidance for future research, including the development, optimization, and environmental assessment of new inhibitors. Through a comprehensive analysis of existing research, this work aims to provide a theoretical foundation and technical reference for the commercial development of natural gas hydrates, promoting their safe and efficient use as a clean energy resource. [ABSTRACT FROM AUTHOR]

With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to develop a precise reliability model that captures system functions and fault logic. By classifying APS components into five distinct structural patterns and mapping them to corresponding nodes in the APS-RPNM, the model is successfully constructed. Specifically, None-Input-to-Multiple-Output components are transformed into two-state nodes, while Multiple-Input-to-None-Output, Single-Input-to-Multiple-Output, and Multiple-Input-to-Single-Output components are mapped to three-state nodes. For Multiple-Input-to-Multiple-Output components, a novel approach employing multiple two-state sub-nodes is adopted to capture their complex functional logic. A case study comparing the performance of the APS-RPNM with the traditional minimal path set method in reliability analysis was conducted. The results demonstrate that the APS-RPNM not only simplifies the model construction process and eliminates errors stemming from subjective engineering judgments but also enables the efficient computation of power supply reliability for all load points in a single inference by integrating all of the components. This significantly improves computational efficiency and system dependency analysis capabilities, highlighting the APS-RPNM's tremendous potential in optimizing the reliability design of APS. [ABSTRACT FROM AUTHOR]

This paper presents a charge pump circuit with a wide output range and low current mismatch applied to phase-locked loops. In this designed structure, T-shaped analog switches are adopted to suppress the non-ideal effects of clock feedthrough, switching time mismatch, and charge injection. A source follower and current splitting circuits are proposed to improve the matching accuracy of the charging and discharging currents and reduce the current mismatch rate. A rail-to-rail high-gain amplifier with a negative feedback connection is introduced to suppress the charge-sharing effect of the charge pump. A cascode current mirror with a high output impedance is used to provide the charge and discharge currents for the charge pump, which not only improves the current accuracy of the charge pump but also increases the output voltage range. The proposed charge pump is designed and simulated based on a 65 nm CMOS process. The results show that when the power supply voltage is 1.2 V, the output current of the charge pump is 100 μA, the output voltage is in the range of 0.2~1 V, and the maximum current mismatch rate and current variation rate are only 0.21% and 1.4%, respectively. [ABSTRACT FROM AUTHOR]

In recent years, the "power restriction" measures were taken in many localities, industrial electricity consumption was interrupted, mainly in the "two high" (high energy consumption and high pollutant emission) enterprises. Take the "power restriction" event in 2021 as an example, this event affected a wide range of regions and disrupted people's livelihood. It not only refers to the power switch and rationing measures taken by individual regions, but also includes differentiated policies implemented by over 20 provinces to limit power supply and industrial production. This paper investigated in detail and summarized the different requirements of power restriction across the country, through the comparison of annual growth rates of power generation and industrial added value，the comparison results of energy intensity, electricity production, power consumption and the industrial growth rate in each province are analyzed. Industrial enterprises and related industrial chains were most heavily affected by the "power restriction" event，mainly in steel, cement, electrolytic aluminum, industrial silicon, textile printing and dyeing, chemical fiber, chemical and other enterprises with high energy consumption and high pollutants emission. The measures to limit electricity will affect the normal production of enterprises to a certain extent, leading to not only the decrease of production efficiency, but also the increase of energy consumption and pollutant emission per unit product. Energy management measures such as differentiated electricity price have limited effect on the output of "two high" industrial products, and the economic leverage of differentiated electricity price is gradually being diluted by the price rise of end products. This paper analyzed the reasons and explored several key problems that need special attention. then recommended certain countermeasures for its appropriate application in the future. [ABSTRACT FROM AUTHOR]

This paper explores the significant role of Wastewater Treatment Plants (WWTPs) in achieving environmental sustainability, with a particular focus on enhancing energy efficiency, resource recovery, and water reuse. WWTPs are crucial for removing pollutants and recovering resources from wastewater, thereby protecting public health and biodiversity. However, they are also associated with high operational costs, substantial carbon footprints, and energy-intensive processes. This article delves into various strategies and technologies to overcome these challenges, aiming to transform WWTPs from energy consumers to energy-efficient resource recovery hubs. Techniques such as anaerobic digestion and the use of advanced oxidation processes and microbial fuel cells are investigated for their potential in energy recovery and efficiency enhancement. Success stories from around the globe are highlighted to demonstrate the feasibility of transitioning to energy-positive WWTP operations. The integration of water reuse systems is also discussed, highlighting recent advancements that enable treated wastewater to be repurposed for agricultural, industrial, and potable uses, thereby promoting sustainability and water conservation. This paper emphasizes the importance of integrating cutting-edge energy management practices to minimize environmental impacts, reduce operational costs, and contribute to a more sustainable water sector. [ABSTRACT FROM AUTHOR]

Power converters enhance the DC-AC power supply by using multi-level inverters based on switched capacitor technology. The voltage stress on the switches in switched capacitor-based multi-level inverters decreases as the number of switches rises, and vice versa when the number of switches falls. This paper suggests a quadruple boost nine-level inverter based on a switched capacitor with a single voltage source to address this issue. Only nine switches are used in the suggested architecture to implement the inverter with two diodes and two capacitors. Since the diodes and capacitors are linked to the low voltage input side, the total voltage stress of the inverter is kept low even at the elevated for quadruple boost output voltage. The theoretical study, computations, and design parameters of the nine-level inverter are presented in this paper. The switching specifics of the seven-level boost extension topology are also reported in this work. Simulations and laboratory tests on a 120 W inverter prototype have confirmed the viability of the suggested topology. [ABSTRACT FROM AUTHOR]

Logistics today has been an integral part of the world, responsible for major trades of goods, medicines, products, and services. However, logistics face a big problem when it comes to goods that require a constant or a near about temperature to be maintained while being on the road. Using a temperature control system can help largely reduce the problem, thus saving time, money, and resources. The proposed container can be used to store temperature sensitive items such as medicines or general purpose/day to day items or food and beverages. In this paper, we discuss a temperature-controlled container that can be used to transport such temperature sensitive items. The main technology being used in the container would be thermoelectric Peltier (TEC). The container shall be user controlled via a keypad accompanied with several safety features such as over temperature protection, emergency shutdown button, display to view the current/set temperatures and have a docked/portable power supply system. The power supply and power converter for the board has been made completely in-house using detailed PCB's. Calculations for the power requirement and its efficiency have been discussed in depth. Such a product shall provide great help to logistics and medical industry. Material of the box (HDPE) could sustain wide temperature changes and the efficiency of the overall system was 70 percent. [ABSTRACT FROM AUTHOR]

To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in advance. In this paper, the data are preprocessed by data merging and format processing, and then the association rules are mined by association rules analysis. Finally, the data are utilized to train the behavioral prediction model of the short‐duration memory network via Bayesian optimization. The experimental results showed that the average running time of the research model was 1.682 s, the average accuracy was 96.77%, the average root‐mean‐square error was 0.382, and the average absolute error was 0.315. The designed behavior prediction model is capable of effectively predicting the user behavior of the Internet of Things, thereby enabling the reasonable allocation of energy and resources in the Internet of Things system. [ABSTRACT FROM AUTHOR]

Photovoltaic inverters are widely utilized in microgrid systems working as the key equipment for converting solar energy into usable electricity. This paper presents a fuzzy sliding mode control (FSMC) method for the photovoltaic inverter in a microgrid. The inverter module uses voltage control to achieve stable AC output voltage. Moreover, to deal with system uncertainty and non-linearity in the photovoltaic inverter, a fuzzy controller was designed to realize real-time adaptation of the gains of the constant term and the reaching term in the sliding mode control law, which serves as a compensating controller for traditional sliding mode control. The gains of the exponential reaching law can be adjusted according to the system state instead of fixed gains, which can effectively reduce the chattering phenomenon and improve the robustness of the photovoltaic microgrid. Finally, the Lyapunov stability theory was used to ensure the stability of the entire control system, achieving a high-performance independent power supply for loads in a microgrid. Simulation results show that the designed control system is more robust to load disturbances and has superior dynamic performance. [ABSTRACT FROM AUTHOR]

Deploying renewable generation to replace conventional fossil-fuel-based energy supplies provides an important pathway for the decarbonization of steelmaking plants. Meanwhile, it is also crucial to improve the flexibility of blast-furnace-gas-fired combined-cycle power plants (BFGCCs) to ease the accommodation of uncertain renewable generation. To this end, this paper proposes the deployment of a molten salt heat storage (MSHS) system in BFGCCs to store the heat of gas turbine flue gas so that the power–heat coupling of these BFGCCs can be unlocked to enhance the flexibility of the energy supply. A stochastic capacity optimization of an integrated BFGCC–MSHS–wind–solar (BMWS) energy system is presented to determine the optimal installed capacities of a BFG holder, MSHS, wind turbine, and PV panel, aiming to achieve an economic and safe energy supply for the entire system. Multiple scenarios considering uncertain fluctuations in load demands and renewable generation are generated with the Monte Carlo method based on a typical scenario. These scenarios are then reduced to representative scenarios using the synchronous substitution and reduction method for stochastic capacity optimization to enhance the reliability of the results. The case study results demonstrate that configuring MSHS reduces the total annualized cost of the BMWS system by 2.28%. Furthermore, considering the uncertainties of the power/heating load and wind/PV generation can reduce the expected annualized total cost of the BMWS system and the corresponding standard deviation by 5.66% and 81.45%, respectively. The BMWS system can achieve 730.68 tons of equivalent CO2 reduction in 24 h due to the successful utilization of renewable energy. This paper provides an effective approach for the decarbonization of energy generation systems in steelmaking plants. [ABSTRACT FROM AUTHOR]

With the development of big data, our lives have gradually entered the information age, which has changed and reshaped the behavior of enterprises and consumers. In this paper, a user portrait clustering model based on big data is proposed to implement business model design for specific groups after clustering, target potential user groups for active marketing, and promote actual purchase behavior. In this paper, cost, risk, and contribution factors are introduced to improve the basic cooperative game allocation method. The improved model considers the operating cost of the main body, the level of risk, and the contribution proportion of the actual energy supply. In order to verify the effectiveness and applicability of the benefit distribution strategy based on the cooperative game proposed by the project, the research results provide a certain reference for precision marketing in relevant industries and enterprises. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

This paper is concerned with a hydraulic cylinder directly controlled by a variable speed motor-driven fixed-displacement pump. The considered configuration is referred to as a one-motor-one-pump (1M1P) motor-controlled hydraulic cylinder (MCC). A 1M1P MCC with a hydraulically driven passive load-holding function enabled by controlled system pressures is proposed. The proposed load-holding functionality works if there is a standstill command, a loss of power supply, or a hose rupture. Additionally, this paper conducts a comprehensive analysis of the proposed system's operation and load-holding function across four quadrants. The simulation results demonstrate four-quadrant operation and good load-holding performance under the aforementioned scenarios. In conclusion, the proposed 1M1P MCC can be successfully used on practical applications characterized by overrunning loads and four quadrant operation. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the existing literature on firms' power through the lens of the supply chain and highlights some gaps that could be covered by future research. Design/methodology/approach: This study uses a systematic framework-based review combining the insights of the antecedents, decisions and outcomes (ADO) and theories, contexts and methods (TCM) frameworks. The review was carried out using a sample of 108 articles published between 1984 and 2022 in 25 prestigious journals. Findings: The ADO framework maps out the state of the art of the antecedents of power (i.e. sources and types of firm power), the decision to use power and the effect that exercising power over other firms may have on firm performance and the quality of inter-firm relationships. In addition, this framework highlights factors that mediate or moderate the decision to exercise power and the factors that mediate or moderate the outcomes of exercising power or power asymmetry. The TCM framework provides insights into the theories, contexts (i.e. countries, industries, level of analysis and sources of data) and methods used by the existing literature. The content analysis using the aforementioned frameworks provides the basis to elaborate propositions for future research on power in the supply chain from the perspective of gender differences. Research limitations/implications: This systematic literature review offers a comprehensive guide for researchers to understand the antecedents, decisions and outcomes of firm power in the supply chain, as well as the TCM used in the literature. The content analysis using frameworks provides a road map to investigate the proposed factors that might moderate the decision to exercise power and the outcome of exercising power or power asymmetry from the perspective of gender differences. In addition, based on content analysis, the authors make propositions about TCM that could be applied in future research. Practical implications: From a practical perspective, this systematic literature review may help managers to better understand the sources and consequences of their firm's power. This would allow managers to make better decisions when negotiating with their supply chain parties, which could potentially lead to better performance for their firms and the whole supply chain. Originality/value: To the best of the authors' knowledge, this study is the first to conduct a comprehensive systematic literature review of the different dimensions of firms' power in the supply chain. [ABSTRACT FROM AUTHOR]

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise. [ABSTRACT FROM AUTHOR]

In the active magnetic bearing (AMB)-rotor system, a displacement sensor is one of the indispensable components. It detects the displacement of the rotor in real time and feeds back to the control system to realize the stable suspension of the rotor. However, due to electromagnetic interference, unstable power supply, poor grounding, and uneven rotor surface, the signal runout of the displacement sensor can often occur, resulting in rotor vibration. Given the above problems, this paper establishes the control model of the AMB-rotor system, lists three typical sensor runout signals, then analyzes the influence of different types of sensor runout on the system, and discusses the control methods under different sensor runout conditions. The research results have a specific guiding significance for vibration control of the maglev rotor under sensor runout influence. [ABSTRACT FROM AUTHOR]

In complex environments, such as mines, ports, and pipelines, wireless power transfer technology is a convenient and safe power supply method. However, the offset and change in direction of the receiving coil greatly weaken the power transfer performance. To address this issue, this paper proposes a variable direction wireless power transfer device for mining based on metamaterials. According to the relationship between incident angle, refracted angle, and permeability, three different metamaterials are selected to fabricate a metamaterial slab arranged in 3 × 3 using printed circuit board technology. Experimental results show that when the transmitting coil and receiving coil are perpendicular to and deviate from each other, the designed power transfer system with the metamaterial slab can effectively increase power transfer by 117.9%. The proposed variable direction transfer system only needs a metamaterial slab without an additional power supply or control structure, and it is convenient for large-scale manufacturing and application. [ABSTRACT FROM AUTHOR]

The DC distribution network is a low-inertia system, which is very sensitive to load disturbance, system failure, and other factors. By adding virtual capacitance to the converter connected to the power supply, the virtual inertia of the DC power grid can be improved. Firstly, this paper proposes a strategy for adjusting virtual capacitance based on the voltage change rate to achieve adaptive control of virtual inertia, which enables the converter to quickly absorb or release energy during power fluctuations. Secondly, the adaptivity of the strategy is improved and the main control parameters in the proposed control method are qualitatively analyzed. Finally, the four-terminal photovoltaic storage DC distribution network system is constructed. Through Simulink, the adaptive virtual inertia control is incorporated on the battery side to simulate and validate the effectiveness of the strategy and the rationality of parameter analysis. The results show that this method can provide flexible and adjustable inertia support for DC grids and improve the voltage stability of DC grids. [ABSTRACT FROM AUTHOR]

In this paper, the notion of a cohesive and self-sufficient grid is proposed. Based on a cohesive and self-sufficient virtual microgrid, an active distribution network is optimally planned, and an optimal configuration of demand-side resources, distributed generations, and energy storage systems are generated. To cope with stochastic uncertainty from forecast error in wind speed and load, flexibility reserves are needed. In this paper, the supply relation between flexibility and uncertainty is quantified and integrated in an innovative index which is defined as cohesion. The optimization objectives are a minimized operational cost and system net-ability cohesion as well as self-sufficiency, which is defined as the abilities both to supply local load and to deal with potential uncertainty. After testing the optimal configuration in the PG&E 69 bus system, it is found that with a more cohesive VM partition, the self-sufficiency of VMs is also increased. Also, a case study on uncertainty-caused system imbalance is carried out to show how flexibility resources are utilized in real-time operational balance. [ABSTRACT FROM AUTHOR]

The development of DC microgrids is reliant on multi-input converters, which offer several advantages, including enhanced DC power generation and consumption efficiency, simplified quality, and stability. This paper describes the development of a multiple input supply based modified SEPIC DC–DC Converter for efficient management of DC microgrid that is powered by two DC sources. Here Multi-Input SEPIC converter offers both versatility in handling output voltage ranges and efficiency in power flow, even under challenging operating conditions like lower duty cycle values. These features contribute to the converter's effectiveness in managing power within a DC microgrid. In this configuration, the DC sources can supply energy to the load together or separately, depending on how the power switches operate. The detailed working states with equivalent circuit diagrams and theoretical waveforms, under steady-state conditions, are shown along with the current direction equations. This paper also demonstrates the typical analysis of large-signal, small-signal, steady-state modeling techniques and detailed design equations. The proposed configuration is validated through the conceptual examination using theoretical and comprehensive MATLAB simulation results. Detailed performance analysis has been done for different cases with various duty ratios. Finally, to show the competitiveness, the multi-input SEPIC topology is compared with similar recent converters. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction. [ABSTRACT FROM AUTHOR]

In the built heritage studies, the intricate web of social and selective processes that define heritage is evident. These processes are, in many cases, intertwined with the notion of scale, examplified through the production of heritage sites at the local, national, and transnational scales. While heritage and geography scholars have articulated the role played by scale in heritage-making and argue against a rigid, fixed, and hierarchical understanding of scale, they highlight the constant reproduction of scale. There is, so far, limited explanation of how the perception of scale gets reproduced and how crucial actors manipulate scalar power and resources for heritage making and the reproduction of scale. To fill this gap, this paper delves into mainland China's heritage-making, using the southern Anhui historical villages as an example. Based on intensive 5-month field research, this paper has three findings: 1) The nomination process for a World Heritage Site is notably influenced by politics and selectivity; 2) Diverse stakeholders are pivotal in shaping heritage narratives; 3) Individual contributions to heritage creation directly interact with, and subsequently reshape, 'scale', an entity that is simultaneously discursive and tangible. By integrating the notion of 'scale' into heritage discussions, we illuminate two concurrent processes: creating hierarchies through rule assimilation by interpreting the UNESCO standard internally and evolving socio-spatial dynamics via the manifestation of individual agency with resource manipulation, scale jumping, and reproduction of scale. This approach aligns with the material orientation in human geography and repositions 'scale'. Here, it's not just an epistemological framework but also a tangible force that steers individual perceptions and actions and yields measurable material impacts. [ABSTRACT FROM AUTHOR]

This paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms. [ABSTRACT FROM AUTHOR]