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This study delves into the dynamic evolution of solids content in a pilot-scale through air drying tissue machine, aiming to enhance comprehension and refine optimization strategies for tissue manufacturing. It focuses on development of solids content throughout the process. Analyzing the interaction between process parameters and solids content provides deeper insights into water removal dynamics. The research employs solid contents monitoring techniques in pilot scale, offering a detailed view of solids content evolution from the wet web's inception to the final tissue product. It significantly highlights the impact of key parameters, particularly pulp refining, on solids content across all positions of the pilot machine. Additionally, the study emphasizes the influence of vacuum system configurations, identifying the relationship between energy expended in the vacuum system and evaporation energy. Furthermore, the results indicate that compression and air displacement dewatering in vacuum boxes are less speed-sensitive compared to heat-driven evaporation in the TAD- and Yankee cylinders. Solid contents at all positions in the tissue machine correlates between various dewatering elements. Optimizing site-specific dewatering and evaporation strategies can potentially save drying energy in the Yankee drying phase which hold promise for enhanced energy and resource utilization, aligning with demands for sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

Overwintering insects are facing energetic challenges because of food shortage, low temperature, and desiccation stress. Paper wasps of the genus Polistes overwinter as mated adults (gynes) in hibernacula protecting them from predation, snow, and rain but barely from low environmental temperature. In different climates, they face differing overwintering temperature regimes, and therefore they may differ in their energy use. We investigated how much of energy resources built up until autumn is used during diapause dormancy in natural hibernacula by measuring lipid, glycogen, and free carbohydrate content in autumn and early spring in Polistes dominula from temperate European (Austrian) and warm Mediterranean (Italian) climate and Polistes gallicus from Mediterranean climate. Winter energy consumption amounted to ~ 339 and ~ 310 J per wasp in the Austrian and Italian Polistes dominula populations. The smaller Italian Polistes gallicus consumed ~ 247 J. This amounts to 2.62, 2.35, and 1.79 J per day. Of this, the energy demand was mainly fuelled by lipids (84%, 93%, and 90%, respectively), but glycogen stores contributed also considerably (16%, 6%, and 9%). Free carbohydrates decreased only by 0.7%, 1%, and 0.8%. While fat stores seem still sufficient in spring, the wasps depleted most of their carbohydrates. The energy reserves of 396, 400, and 147 J per wasp remaining in spring in the three populations seem sufficient to fuel rest or simple brood care activities for a whole summer but restrict foraging flights to a few hours (~ 3.5–6 h). Results suggest that energy supply might become challenging in expected future climate scenarios. [ABSTRACT FROM AUTHOR]

This paper examines the potential of solar and hydrogen (H 2) energy-based hybrid energy systems for residential applications. The growing need for energy and the demand for sustainable energy sources have led to the development of integrated energy systems that combine renewable energy resources to meet energy needs. This paper overviews recent studies on hybrid energy systems for on-grid and off-grid residential utilizations. It discusses the system configuration and components of hybrid energy systems, including solar panels, electrolyzers, fuel cells (FC), and batteries. It also covers the technical optimization of integrated energy systems, including sizing, control strategies, and economic analysis. The key findings of this review paper indicate that hybrid energy systems can offer dependable and sustainable energy for residential applications. Through numerical analyses, the optimal dimensioning of the system elements and control strategies can significantly enhance the system's performance and lower the cost of energy. This study also highlights the challenges and opportunities for integrating hybrid energy systems within residential applications. Overall, it provides in-depth perspectives on the possibilities inherent in solar and hydrogen energy-based hybrid energy systems for residential applications. The findings can guide future research and the advancement of hybrid energy systems for sustainable energy solutions. • Comprehensive review on solar and hydrogen-based residential applications. • Holistic approach to system configuration, optimization, and economics. • Detailed analysis of interconnected components enhancing system efficiency. • Unique focus on real-world residential applications and scenarios. • Broad applicability across diverse geographical regions. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous derivative of the Internet of Things (IoT) business, the use of various types of information sensing equipment needs to continue to increase. However, power supply conditions are getting tougher. On the other hand, as the function of the devices itself increases, the power consumption also increases, and the lack of energy demand is more obvious. To solve these problems, a dual-frequency RF energy harvesting system is designed in this paper. First of all, in order to expand the effective receiving range of RF energy, this paper designed a 920 MHz hexagonal windmill circular-polarized antenna, and added an energy receiving circuit in the 2.45 GHz band on this basis, which fully alleviated the problem of insufficient energy collection for small devices in the Internet of Things while ensuring a small volume wavelength ratio. In addition, in order to improve the energy conversion efficiency, this paper also designed a dual-frequency rectifier circuit based on conjugate separation matching, using the harmonic signal of 920 MHz signal to broaden the bandwidth of 2.45 GHz. The experimental results show that the peak conversion rates of 920 MHz and 2.45 GHz channels are 72.8%@0dBm and 38.7%@-2dBm, respectively. Overall testing in a Wireless Fidelity (Wi-Fi) room with a commercial Radio Frequency Identification (RFID) reader. The experimental results show that the system can steadily output 3.29 V–3.3 V voltage within 6 m charging distance, which is 20% higher than the output voltage of the single-channel receiving system, and the receiving angle is 36.84% larger. [ABSTRACT FROM AUTHOR]

The research problem of this paper is related to numerous open questions in the field of energy, its understanding, its use, and the challenges of the energy future. After the introductory part, in which a brief historical overview of energy literacy is provided, the paper focuses on energy literacy, its emergence, and the different approaches to its definition and measurement. The paper analyzes the energy literacy of students at the Faculty of Economics in Rijeka based on their cognitive, affective, and behavioral skills. Their knowledge about energy, energy processes, and the energy efficiency of the objects they encounter on a daily basis is examined. Their personal attitudes and values are examined through various forms of personal initiatives and active participation in energy sustainability projects. Finally, the habits and behavioral patterns that the respondents have in their daily lives and their tendencies to save energy resources and find energy-efficient solutions are examined. Despite some positive findings, the current results are not satisfactory and point to the creation of adequate public policies with a particular focus on education and the role of the corrective mechanisms of the state, but also the need for joint negotiations between policy makers, regulators, scientists, representatives of civil society, and the business community. The particular contribution of this work is reflected in being the first research of its kind conducted among Croatian economics students, as well as among Croatian students in general; in the creation of a research instrument that is conceptually consistent with the findings from the existing literature, but with an original set of questions within each energy literacy skill adapted to the cultural and sociological background of the respondents; and in a kind of progress from previous research by taking into account the quantitative aspect (in addition to the qualitative) assessment of the respondents' energy literacy. [ABSTRACT FROM AUTHOR]

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a wireline serial link transceiver for on‐package die‐to‐die links based on 12‐nm fin field‐effect transistor (FinFET) technology. The link is crucial for improving the computational performance of larger systems built from chiplets. To achieve high pin/energy efficiency and prevent traditional single‐ended signaling disadvantages, the transceiver adopts single‐ended ground‐reference signaling (GRS) to transfer information. To precompensate for the frequency‐dependent loss, retain the advantages of GRS, and avoid the disadvantages of traditional AC‐coupled equalizer, the transmitter in the transceiver adopts a DC‐coupled switched‐capacitor charge pump equalizer that can transmit GRS and its equilibrium value can be adjusted according to the channel attenuation. This paper proposes a method for timing de‐skew using digital control delay lines to eliminate the timing skew between data and the clock. In addition, this paper presents a duty cycle corrector to avoid duty cycle distortion, with an adjustment range of 44.5% to 52% and a power dissipation of only 30 μW. The transceiver has an area of 1.016 × 0.676 mm2, including one clock lane link and four data lane links. At a nominal 0.8‐V power supply voltage, the aggregate eye‐opening of the measured 25 Gb/s data on an organic substrate channel with an attenuation of −2.2 dB over 6 mm is 0.675 UI, with a bit error rate (BER) of less than 10−12 and an energy efficiency of 1.01 pJ/bit. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an ultra‐low‐power and area‐efficient demodulator circuit for wireless downlink SCM/CWM modulation schemes is proposed. The proposed demodulator offers high performances and allows simultaneous power and downlink data transmission over a single 13.56‐MHz inductive link. The proposed method requires only two carrier cycles per data bit period, and also, a power index of 75% is obtained. Although the proposed demodulator can be applicable to other wireless systems, here, it is intended for implantable medical devices. To prove the performance of the proposed demodulator circuit, it has been designed and post‐layout simulations are provided using a standard 65‐nm CMOS technology and occupies 27 × 32 μm2 of active area. Post‐layout simulation results indicate that with a 1‐V power supply, the proposed circuit achieves an ultra‐low‐power consumption of 8.51 μW at a data rate of 6.78 Mb/s corresponding to an energy efficiency of 1.25 pJ/bit. [ABSTRACT FROM AUTHOR]

Amid a war in Southeast Asia, fomenting campus radicalism, and a looming energy crisis, a number of physicists shifted from the big science endeavors of post-war physics toward a new and little science of energy efficiency. These moves were actively supported by the American Physical Society, which took various ongoing crises as an opportunity to create employment opportunities and harness enthusiasm for more socially engaged physics. The Society's 1974 Summer School on efficient energy use was illustrative. Participants came from universities, national laboratories, industry organizations, and utility companies. Together, they estimated efficiency savings believed achievable at certain points in the US energy system. The summer school attendees argued energy efficiency should be redefined according to the second law of thermodynamics rather than the first. This approach allowed energy-using appliances to be reconceived of as sources of energy supply as well as demand. Rigorous estimates of potential savings were made. However, to the ire of more radical physicists, the school placed the onus to conserve on the consumer, ignored industrial energy use, and had avoided drastic measures. In revisiting these events, their lead-up and afterlife, this paper historicizes a now-central tenet of energy policy. [ABSTRACT FROM AUTHOR]

• Novel damped conformable fractional accumulation method. • Prediction of energy related series with the uncertainties. • Design of damped conformable fractional grey bernoulli model. • Improved prediction accuracy, robustness and applicability. • Prediction of world coal consumption and production in 2025–2026. Energy resources, such as oil, coal are important to people's life and the development of the economy. Predicting the energy consumption and production with uncertainties can help the government and policymakers make the reasonable energy strategy and give constructive guidance. In this paper, a novel damped conformable fractional accumulation operator with two parameters are proposed to have control of both accumulation generation and new information involvement. The new accumulation can be adjusted from linear approximation to high order accumulation. A structure adaptive grey Bernoulli model based on proposed accumulation method is established, with the parameters optimized by particle swarm optimization algorithm. For testifying the effectiveness and the robustness of the model, two validation cases and two application cases are used to build the prediction model of diverse series with unexpected trends or fluctuations in energy fields. Compared with other existing fractional grey models and non-grey models, the proposed model has best prediction accuracy, better model stability and greatly improved applicability. The global coal consumption by industry is predicted as 30.85×106 TJ in 2025 and 30.81×106 TJ in 2026. The world coal production could be 182.93–187.73 exajoules in 2025 and 180.85–182.16 exajoules in 2026. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

With the urgent global need to limit warming to 2 °C as well as a localized need in our case study to address rising energy demand amid electrical and thermal network limitations, a critical examination of demand-side energy reductions and the concept of energy sufficiency is needed. This paper contributes to the sparse literature on bottom-up analysis by utilizing Iceland—a leader in renewable energy generation—as a case study to explore the socio-economic factors influencing energy footprints. Our findings reveal significant energy footprints across various consumption domains, particularly housing and mobility, influenced by income levels, urbanization, and lifestyle choices. The study highlights the paradox of a high renewable energy supply leading to potential misconceptions regarding abundant and low-cost energy, resulting in substantial energy consumption-related environmental impacts. Using detailed household consumption survey data, this research provides insights crucial for developing sustainable energy policies that not only target technological advancements but also address the need for a reduction in energy demand and a shift towards energy sufficiency. This work marks a contribution to the literature through the provision of a case study of low income inequality and high energy footprints in a highly renewable energy system context. Further, this work is useful for Icelandic and international policymakers to understand in such high-demand contexts which consumption domains would be most relevant for sufficiency policies. This comprehensive analysis opens pathways for future research to further explore the intersections of energy consumption, socio-economic factors, and well-being, offering a nuanced understanding necessary for crafting sufficiency and demand-side policies aimed at a sustainable energy future. [ABSTRACT FROM AUTHOR]

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users' social network interactions to gain popularity. By incorporating the users' mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users' electricity charges. [ABSTRACT FROM AUTHOR]

As the goal of "double carbon", integrated energy systems aiming at the development of low-carbon economy are developing rapidly, and carbon capture and other emission reduction technologies are gradually gaining more extensive development space. For controlling carbon emissions and enhance the consumption of renewable energy. This work proposes to introduce carbon capture technology in the framework of integrated energy system and optimize the energy dispatching of integrated energy system in multiple time scales, and design a multi-time scale optimization model of integrated energy system with carbon capture. Based on the basic architecture of a low-carbon integrated energy system, this study analyzes the power characteristics of each unit of the integrated energy system, which consists of thermal power units, gas turbines, electric boilers, batteries, gas storage, heat storage, etc. By studying the energy conversion and storage processes of each unit, a power model of each unit of the integrated energy system is established. On this basis, the relationship between carbon emissions and unit output of thermal power units and gas turbines was studied, and a carbon emission model for the energy supply unit in the comprehensive energy system was established. At the same time, in order to solve the problem of carbon emission reduction under the day ahead scheduling plan of the integrated energy system, considering the emission reduction goals and system operation security factors, the study analyzed the economic model and carbon emission model of the integrated energy system, established the day ahead low-carbon scheduling model of the integrated energy system, and reasonably planned the output of each unit that can achieve the carbon emission reduction goals on the premise of meeting the balance of supply and demand. The innovation of the research method of this paper is that this paper establishes a multi time scale rolling optimization model under the emission reduction goal of the integrated energy system. Based on the day ahead scheduling scheme obtained in the day ahead low-carbon scheduling phase, the day ahead plan is first revised through 4 h rolling scheduling in the day; Then, with the goal of minimizing the adjustment amount, fine tune the unit output within 15 minutes to provide a daily output plan for subsequent low-carbon emission reduction targets. The outcomes indicate that in the practical application, the carbon emission of the optimized model in the peak hour 11:00 to 12:00 phase is 118 tons, which is 7 tons less than the 125 tons of the traditional model. In summary, it demonstrates that the studied multi-timescale optimization model of integrated energy system with carbon capture has good application. We have studied and analyzed the low-carbon implementation mechanism of coordinated cooperation in multiple time scales, and constructed a multi time scale rolling optimization model, laying a theoretical foundation for subsequent low-carbon scheduling research. This enables the system to formulate more accurate and reasonable scheduling plans, while improving the low-carbon performance and economic benefits of the system, providing reference for the low-carbon development of the power system. [ABSTRACT FROM AUTHOR]

With the continuous development of large-scale wind and photovoltaic power worldwide, the net load fluctuation of systems is increasing, thereby imposing higher demands for power supply assurance and new energy consumption capacity within emerging power systems. It is imperative to establish a quantifiable and efficient model for planning new power systems, to propose an analytical approach for determining optimal evolutionary paths, and to advance research on flexible resource planning across wide areas. In this paper, based on the simplified operating characteristics of multi-type flexible resources, a source-grid-load-storage collaborative planning and evolution analysis framework is established. Secondly, the lowest total cost of the whole life cycle of the system is taken as the optimization goal, the multiple flexible resource investment decisions and production operation constraints of various flexible resources on all sides of the system are considered, and the source-grid-load-storage planning model is established. Then, through the investment decision-making strategy setting of the system in different planning level years, the evolutionary path analysis method of the whole life cycle economy and weighted environmental protection benefit of the system is given. Finally, by taking the sending-end power grid in Gansu Province as an example, a case study is carried out. Calculations of new energy, key channels within the province, energy storage capacity, and load-side response capacity requirements for 2025, 2030, and 2060 are optimized. Based on the above analysis, the optimal evolution path of the power grid is proposed. When considering the weighted benefits of economy and environmental protection, the greater the weight of environmental protection benefits, the greater the possibility of choosing a radical scheme. The model and method proposed in this paper can provide technical reference for the future development planning and evolution analysis of new power systems. [ABSTRACT FROM AUTHOR]

Increasing energy security is a crucial component of achieving the Sustainable Development Goals (SDGs). Three main factors influence energy security: (1) the efficiency of resource use in energy production, (2) the extent of energy losses, and (3) the use of new energy sources. Novel food products can impact these factors, and this paper explores whether they are being studied in the context of reducing energy consumption. Specifically, we investigate the role of technical progress and know-how in the creation and development of novel food products and whether novel methods of food production using artificial intelligence aim to reduce energy expenditures while improving product quality, variety, and the use of new energy sources. This paper seeks to examine the impact determinants of novel foods on energy security, considering economic, technological, social, and environmental aspects of knowledge about new food. To implement the study, the relevant international literature published in the past ten years have been reviewed and methods of modeling, visualization, and descriptive statistics applied. The review is structured into three sections: the first section presents ways to save energy and other resources in the food production chain through the intensive use of artificial intelligence tools; the second section presents the development of novel food products; and the last section presents marketing challenges for novel foods. The findings show that the topic addressed by this paper is currently critical, with many authorities, research centers, food producers, and energy producers interested. However, the research problem remains open, as a systematic review of secondary sources revealed little knowledge of the topic under study, and each author's study presents a new solution. The conclusion is that utilizing new foods and innovative production techniques that require less energy not only enhances production diversity but also improves its quality. [ABSTRACT FROM AUTHOR]

This paper presents a novel off-grid hybrid renewable energy system integrated with hydrogen production and retired electric vehicle (EV) batteries for combined power and heat supply to a small rural community. The system consists of photovoltaic (PV) panels, evacuated tube solar collectors, a proton exchange membrane fuel cell (PEMFC), an alkaline electrolyzer, compressed hydrogen storage tanks, and reused lithium-ion batteries from EVs. A new battery model is developed in the Transient System Simulation Program (TRNSYS) to capture the capacity degradation of retired batteries. A two-stage energy management strategy is proposed to regulate power flow and maximize solar energy utilization. The system is optimized for minimum power supply disruption, hot water supply disruption, energy waste, and annualized costs over a 20-year lifetime using a multi-objective NSGA-II algorithm coupling MATLAB and TRNSYS. Optimization results reveal tradeoffs between the competing objectives. Dynamic simulations show the water tank temperature fluctuates between 20 and 100 °C with mean values dropping slightly in later years due to battery degradation. Energy waste from components rises in later years for the same reason. The replacement interval for a retired EV battery module is around 2.5 years. The integrated system proves feasible for off-grid combined power and heat supply using solar energy and retired EV batteries as storage, though battery degradation impacts long-term performance. • Integrates solar, hydrogen, retired EV battery for off-grid power and heat supply with optimized storage and backup. • Utilizes multi-objective optimization to balance system reliability, waste minimization, and costs through Pareto solutions. • Implements long-term performance over twenty years to provide insights into component lifetimes and replacement needs. [ABSTRACT FROM AUTHOR]

In order to achieve the flexible and efficient utilization of distributed energy resources, microgrids (MGs) can enhance the self-healing capability of distribution systems. Conventional primary droop control in microgrids exhibits deviations in voltage and frequency and lacks research on voltage–frequency control during network reconfiguration. Therefore, this paper investigates the control strategy of secondary control for voltage and frequency during the process of reconstructing distribution networks to operate in the form of microgrids after faults. Firstly, the mathematical model of three-phase voltage-source inverters with droop control is analyzed. Then, inspired by the generator's secondary frequency control strategy, an improved secondary control method based on droop control is proposed, and the effectiveness of the improved droop control strategy is verified. Finally, simulation examples of distribution networks with various dynamic boundary topology changes are designed to simulate a relatively stable control result, validating the proposed strategy. [ABSTRACT FROM AUTHOR]

Summary: The non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS. [ABSTRACT FROM AUTHOR]

In pursuit of the "Dual Carbon Goals" and to mitigate the adverse effects of "power supply restrictions," a microgrid scheme integrating wind and solar power with hydrogen energy storage is proposed. This paper introduces the principles of system capacity configuration and establishes a mathematical model. This research offers a novel method for configuring wind and solar hydrogen storage systems called quantum-enhanced multi-objective collaboration. This work intends to address the complicated issues of achieving effective energy storage, reducing prices, and maximising renewable energy utilisation by leveraging the capabilities of quantum computing. A multi-objective capacity optimization configuration model for wind–solar–hydrogen energy storage is developed using Homer Pro software and an enhanced BAS-GA algorithm. Under off-grid operating conditions, the wind–solar–hydrogen energy storage system's capacity optimization configuration model is validated through practical examples. The results indicate that, in comparison to wind storage, solar storage, wind–solar storage, and wind–solar–diesel storage systems, the net present value and levelized cost of electricity of the wind–solar–hydrogen energy storage system decrease to 14.25 million RMB and 1.529 RMB/(kW h), respectively. The renewable energy utilization rate increases to 98.7%, and the load shedding rate decreases to 5.50%. [ABSTRACT FROM AUTHOR]

The green transition is a challenge for the Polish economy and energy sector. In this paper, the expert method of analysis was applied and findings revealed that the challenges and prerequisites for an effective transformation of the power sector mainly include technical, technological and organizational issues of energy production and use. The provision of electricity at competitive prices and with a low carbon footprint, for individual consumers and industry, is a prerequisite for maintaining the well-being of the population and ensuring the competitiveness of domestically produced goods. The ambitious climate policy goals of the European Union require immediate action and call for radical changes in the Polish energy sector; in the coming years, it must drastically reduce the amount of energy produced from fossil fuels and replace it with so-called green energy from renewable sources. The main purpose of this article was to highlight the need to modify Poland's energy policy until 2040 in order to make it more consistent with the ambitious climate goals of the European Union. This article also shows that Poland's energy transition must include a shift from fossil fuels to renewables, while ensuring that energy security is stabilized by the current energy and generation resources. To this end, we discuss the issues of creating reserves in the national energy system for the entire period of Poland's energy transition. [ABSTRACT FROM AUTHOR]

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error. [ABSTRACT FROM AUTHOR]

Reducing the heat demand of existing buildings is an essential prerequisite for achieving a greenhouse gas-neutral energy supply. Numerous studies and open-source tools deal with heat demand mapping. It is not uncommon that estimated heat demands deviate from real heat consumption, so existing approaches should be improved by including in-depth building information. Some tools have recognised this problem and offer built-in functions for factoring various parameters into their assessments. Nevertheless, the necessary information is usually missing and should be obtained first. In this paper, we analyse the impact of thermal refurbishment and climate on building heat demand; hence, generate public datasets with corresponding key figures for each building type in different efficiency states and years. Accounting for already performed refurbishments in methodologies for assessing the actual state heat demand for cities will result in a reduction of at least 8% up to more than 21%, depending on whether conventional or passive house components were installed. As a result of climatic differences within Germany, a building's heat demand can be up to 39% higher or up to 21% lower than the heat demand of an identical building in the reference climate of Germany. By further developing the approaches of the tools Hotmaps and Heat Cadastre Hamburg, we could improve the estimated heat demand of Hamburg to a value approximating the real consumption. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station. [ABSTRACT FROM AUTHOR]

In this paper, wind resource availability was determined by analyzing the meteorological wind energy potential at the hub height of wind turbines. The wind resource potential was identified in specific geographic locations. Technical factors, such as siting requirements and resource availability, were taken into account in the investigation. The economic potential was calculated for each area of Algeria, considering overall losses. Subsequently, the potential for reducing fossil fuel usage was determined through wind to hydrogen estimation. The main components utilized in this work were a Vestas-90 wind turbine (V90–2.0 MW) and an alkaline electrolyzer. The analysis employed a Geographical Information System (GIS) tool. The results demonstrate Algeria's significant potential for wind to hydrogen production, estimated at 1.093 Gt/year without constraints, and reduced to 1.066 Gt/year when considering constraints. The levelized cost of wind to hydrogen production in Algeria varies between 1.51 and 15.37 US $/kg, depending on the chosen wind turbine. The wind to hydrogen production potential identified has the capacity to replace the entire current fossil fuel consumption in the transportation sector, which amounts to 5.28 Mt H 2 /year. The utilization of wind to hydrogen in the transport sector results in a significant reduction of CO 2 emissions, with a calculated avoidance of 45.69 Mt compared to the use of gasoline and diesel. Hydrogen produced from wind energy resources also could be used to support other sectors, including residential, agricultural, and industry in Algeria. • GIS-Based approach is used to evaluate the potential for wind to hydrogen production in Algeria. • The total wind to hydrogen potential in Algeria, when considering constraints, is estimated at 1.066 Gt/year, covering an area of 730,719 km2. • The levelized cost of wind to hydrogen production in Algeria ranges between 1.51 and 15.37 US $/kg, depending on the selected wind turbine (V90–2.0 MW). • The CO 2 emissions avoided in the transport sector are 45.69 Mt compared if the same amount of gasoline and diesel used. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained. [ABSTRACT FROM AUTHOR]

In recent times, the research in sustainable energy resources was integrated with the control approaches of various hybrid renewable energy systems for balancing the electricity generation. The accelerated hybrid energy storage systems and steadily growing energy consumption make the variability and the predictable behavior occur in the renewable energy sources, which requires an energy management system. This paper aims to control hybrid renewable energy systems for power management. The systems consist of photovoltaic (PV) arrays, diesel generators, wind systems with boost converter DC-DC, and inverters. The optimum of the real-time process is less due to prediction errors of multiple renewable sources, which track the output voltage and load current. Model predictive control (MPC) is proposed for problem-solving time operation to address this issue and compensate for the prediction error. The major impact factors, such as optimal split power, stability, reliability of energy, and desired dynamic responses, have been identified by applying the MPC technique. A simulation model has been constructed using MATLAB/Simulink environment to confirm the effectiveness of the MPC technique model. [ABSTRACT FROM AUTHOR]

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

Challenges and future perspectives of desalination integration are presented. [Display omitted] • Different optimized models for desalination technologies integration are summarized. • The control strategies of RES-desalination for reducing environmental are discussed. • Impact of desalination process innovation on energy saving and emission reduction. In the present framework, desalination, as an energy-intensive industry, has garnered increased attention due to the imperative need for reducing energy consumption. In this paper, various challenges in the integration and optimal design of desalination processes have been presented. The energy consumption, energy supply method, water production method and improvement measures of various desalination are studied in detail, of which energy consumption is a major aspect. This article summarizes the integration modes of different desalination technologies. Low integration density, inadequate energy utilization and unsystematic theory guidance have been the focus of research. Operational strategies and optimal design methods for the integration of renewable energy and desalination technologies are studied in detail. The integration of renewable energy emerges as a pivotal trend, contributing significantly to the reduction of carbon footprints and costs. Additionally, the significance of recently developed desalination processes, such as pre-treatment process, equipment enhancements and advanced materials have been discussed. Furthermore, various unresolved issues in de integration desalination technologies system have been investigated in the paper along with the possible solutions to address the reported open issues. By exploring the integration of desalination technologies, the paper aims to provide valuable insights for enhancing the efficiency of commercial desalination plants. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the performance and fuel-saving effect of a velocity control algorithm on modern 50 cc scooters (Euro 5). The European Parliament has adopted major CO 2 emission reductions by 2030. But modern combustion-powered scooters are inefficiently restricted and emit unnecessary amounts of CO 2. Replacing the original restriction method with the system presented in this paper, the engine's operating point is being improved significantly. A Throttle-by-Wire-System senses the rider's throttle command and manipulates the throttle valve. A redundant wheel speed sensor measures the precise vehicle velocity using the Hall effect. The entire system is managed by a central ECU, executing the actual velocity control, fail-safe functions, power supply and handling inputs/outputs. For velocity control, an adaptive PI-controller has been simulated, virtually tuned and implemented, limiting the max. velocity regulated by legal constraints (45 km/h). In this way, the environmentally harmful restrictors used today can be bypassed. By implementing a human–machine interface, including a virtual dashboard, the system is capable of interfacing with the rider. For evaluation purposes a measurement box has been developed, logging vehicle orientation, system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro 5) is serving as test vehicle. Finally, the system has been evaluated regarding performance and fuel efficiency both through simulation and road testing. Fuel savings of 13.6% in real-world test scenarios were achieved while maintaining vehicle performance. • Efficiency is increased and the throttle valve opening is reduced by 50%. • The controlled air supply minimizes the fuel injected by up to 25%. • Significant improvements of fuel consumption and CO 2 emissions by 14%. • Consistent performance is demonstrated by using energy more effectively. [ABSTRACT FROM AUTHOR]

• A new control method for DER is suggested. • The proposed control method balances the load between feeders. • DER connects to feeder through SST. • DER-controlled circuit shared the load between two feeders like ring connection. • Voltage profiles are improved. The multi-purpose use of distributed energy resources (DER) can bring a lot of income to its owners. For example, the load imbalance of the radial feeders in the distribution network is an old problem that occurs due to the simple structure of the radial topology and the unidirectional power flow in traditional networks. With emerging DERs and the development of power electronic technology, previous concepts are changing continuously. This paper aims to balance the load between two feeders whilst, it yields better voltage profiles and more appropriate utilization of distribution feeders. This paper proposes a new control method for DERs (including PV panels plus battery storage) that makes the radial system pretends a ring topology properties. It is done with the help of a solid-state transformer (SST). The relationships of the DER current controller are formulated based on current passes through the ring connection. Case studies are carried out on improved IEEE 33-node and 13-node systems with DERs and SSTs. The results show that the suggested control method can simulate the real ring topology and operating objective namely, load balancing is reached. [ABSTRACT FROM AUTHOR]

Nowadays, climate change is a major global societal challenge that significantly increases environmental stress. Most international organizations and policies have promoted initiatives to minimize emissions, reduce fossil fuel dependence and increase renewable energy resource integration into different sectors. An energy transformation towards more renewable systems is thus a priority. Under this scenario, the present paper describes and evaluates an alternative energy conversion matrix-based model to combine sector electrification, power generation units from renewables, and new clean technologies. The proposed green matrix-based model allows analyzing future scenarios, including electricity participation in end-use consumption and electric power generated by renewables – potentially integrated into different sectors –. The proposed model is evaluated in the state of Maine (United States). This case study is focused on decarbonizing both residential heating and transport sector through the integration of large offshore wind power plant. Results and discussion is also included in the paper, providing expected energy demand reductions and decreasing emissions through the integration of renewables. This energy transition integration case study is proposed in three road-maps with different penetration rates and time scales. The proposed green matrix-based model can be also applied to other areas and energy resources, as an alternative way to analyze and estimate renewable integration into different sectors. • Green Electrical Matrix to link electricity supply and end-use for energy transition. • Assesses renewable potential and electrification tech for end uses, e.g., transport. • Applies model to Maine, USA to show reductions in fossil fuels and emissions. • GIS and MCDM for optimal offshore wind location, maximizing electricity generation. • Short, medium, and long-term projections of renewable integration into energy system. [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

• This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. • The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. • An interval security region model of integrated energy system is constructed, according to the N-1 safety constraints of equipment and pipeline. • An engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. • A dimension reduction observation method based on observation variable optimization is proposed, which can enlarge the volume unaffected by renewable energy uncertainty. This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. Reason: Integrated energy system is conducive to achieving low-carbon transition and efficient utilization of energy. As the penetration of renewable energy increases, the security of integrated energy system faces severe challenges. It holds significance for comprehensively studying N-1 security test method in the operation scenario of integrated energy system, the impact of renewable energy on the IES security region model, and the optimal selection for observation variables. Methods: Firstly, a model for interval security region of integrated energy system is constructed, according to the N-1 safety constraints of energy hub key equipment and pipeline. The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. Then, an engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. On the basis, a dimension reduction observation method based on observation variable optimization is proposed, the selection of observation variables help to expand the volume of area unaffected by renewable energy uncertainty. Results: To the existed problem in current research, an example is presented to verify the effectiveness of proposed method, which brings forward N-1 security test method in the operation scenario of integrated energy system, analyzes the impact of renewable energy on the security region model, and realizes the optimal selection for observation variables of simplified security region. Conclusions: Interval security region of integrated energy system can intuitively estimate the security status of operating points and observe the security boundaries. The simplified model can satisfy the needs of calculating precision. Optimal selection of observation variables is necessary, and the volume of area unaffected by renewable energy uncertainty can be effectively increased. [ABSTRACT FROM AUTHOR]

• A new DPV-5G BS-ES communication and power mutual coupling system is developed. • A BSSCP model is proposed to minimize the number of 5G base station deployments. • The ISOM model is used to optimize the microgrid connection lines in 3D space. • Economically optimal simulation experiments are conducted for different PV systems. The emergence of ultra-dense 5G networks and a large number of connected devices will bring with them significant increases in energy consumption, operating costs, and CO 2 emissions. At the same time, the deployment of distributed photovoltaic (DPV) in megacities plays an important role in promoting the integration of "building-photovoltaic", adjusting the city's clean energy supply system, and building a green, and low-carbon smart city. The development of a new "DPV-5G Base Station-Energy Storage (DPV-5G BS-ES)" coupled DC microgrid system and its pre-deployment investment costs are fundamental factors to be considered when the problem of large-scale DPV and BS deployment in cities has to be addressed. There are almost no published papers on this issue in the literature, which only consider the problem of maximizing the deployment of 5G BS service coverage or only perform optimization analyses for the performance of microgrid systems without considering the economic optimality and low-carbon green issues in the construction of urban utilities. In order to solve the problem of 5G BS deployment and economic optimization of topology in the "DPV-5G BS-ES" coupled DC microgrid system, a novel deployment strategy is proposed in this paper. The strategy is divided into: (a) The Geographic Information System (GIS) for extracting and analyzing 3D data from all buildings in the area. (b) A GIS-based formulation of a mixed integer quadratic constrained planning model (BSSCP) under discrete fixed radius coverage constraints is used to delineate building clusters within the area and determine the optimal deployment location of 5G BS under each sub-cluster. (c) An Improved Self-Organizing Mapping (ISOM) model combines GIS and microgrid performance parameters to address the economically optimal connection of grid lines for DPV in 3D space. (d) Simulation experiments were conducted for the power output and profitability of eight different PV panels and tracking systems to calculate the economically optimal type of DPV generation hardware. The experimental results show that the BSSCP model proposed in this study reduced the 5G BS deployment cost input by up to 35.8974 % compared to that of the control experimental model. Meanwhile, the ISOM model obtained an average reduction of 52.3741 % in the cost of connecting cables for the optimized 25 DPV planning and DC microgrid topology. [ABSTRACT FROM AUTHOR]

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

China's rural solar photovoltaic projects (SPVPs), as an innovative initiative to improve livelihoods through clean energy, have played a positive role in poverty reduction. However, whether this measure can enhance energy services for rural families remains unconfirmed. This paper employs a quasi-experimental method from the perspectives of energy service accessibility and affordability to quantify the net contribution of China's rural SPVPs to enhancing family energy services. The results indicate: (1) SPVPs can significantly improve family energy services, with a net effect of 14.7 % on the energy service accessibility and a net effect of 44.5 % on the energy service affordability. (2) SPVPs can increase the energy consumption capacity, energy supply capacity and energy equipment utilization rate by 2.6 %, 7.1 % and 5 %, respectively. Furthermore, these initiatives can effectively slash the energy costs of rural families by 4.3 %, bolster the energy access affordability by 3.9 %, and catapult energy investments by an impressive 44.9 %. (3) These projects also perform well in the family energy consumption cleanliness. (4) The effect of SPVPs in providing energy services presents a regional difference, with the best performance observed in 'Type III' solar resource areas. Meanwhile, the implementation of China's rural SPVPs does not involve "elite capture." • We analyze the changes of energy accessibility and affordability in rural China. • We quantify the energy services effect of solar PV projects (SPVPs). • SPVPs perform well in improving family energy consumption cleanliness. • The energy services effect of solar PV projects has spatial heterogeneity. • The implementation of China's rural SPVPs does not involve "elite capture.". [ABSTRACT FROM AUTHOR]

Vision-based displacement measurement has become increasingly popular in bridge health assessment. However, this technology faces challenge that a complicated power-consuming supply system must be installed in the darkness, making it unable to meet the needs of low energy consumption and low cost. This paper proposes a bridge displacement measurement method using power-free target and thermography to address it. First, a thermography measurement system for bridge displacement in the darkness is developed. The power-free target with a hollowed-out circular feature is adopted to form the contrast to the background, and graphene is coated on the target surface to enhance the infrared emissivity and contrast further. Second, a sub-pixel elliptic center positioning algorithm based on thermography enhancement and data optimization is proposed to improve the accuracy and the ability to resist occlusion. The feasibility of the method is verified by laboratory tests and bridge field tests. When the measurement distance is within 15 m, the results meet the engineering error requirements of 5%, with the prospect of popularization in small and medium-sized bridges displacement measurement. • A bridge displacement measurement method in the darkness using thermography is proposed for the first time to address the power supply problem in bridge measurement. • A thermography measurement system for bridge displacement is developed and power-free target with graphene coated is adopted to form the contrast to the background. • A two-step parameter optimization strategy for elliptic center positioning is proposed. [ABSTRACT FROM AUTHOR]

Urban transportation is a significant contributor to energy consumption and environmental degradation, while solar electric bikes (SE-bikes) represent a viable solution for alleviating both problems. In this paper, the charging and discharging properties of the SE-bike are investigated. An SE-bike is constructed for short and long term experiments. Experimental results indicate that the SE-bike can increase endurance by 20.5 km on a sunny day, and can meet daily cycling needs of 10 km without charging in both summer and autumn in Hefei. To assess the annual performance of the SE-bike, a theoretical model is developed, which includes the effects of environmental factors, vehicle shapes, daily electricity usage, etc. Simulation results reveal that the SE-bike only needs to be charged 5 times per year to meet daily demands in Hefei, which is a relative reduction of 93.0 % compared to 71 times per year for the regular bike without solar power supply. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

As the number of transistors on a chip surface increases, power consumption becomes more and more a serious concern. A promising solution to bridge the gap between resource-constrained gadgets and computation-intensive applications could be the approximate computing paradigm. This paper presents four efficient approximate full adder cells based on dynamic logic and carbon nanotube field-effect transistors (CNFETs). To the best of our knowledge, dynamic logic has never been deployed in the design of approximate full adders before. Comprehensive simulations and analyses are conducted to study the efficacy of the new circuits. Simulation results indicate remarkable improvements compared to state-of-the-art circuits. For instance, at 0.9 V power supply, our final proposed design improves the power-delay-area product (PDAP) metric by at least 63% compared to its peers. Moreover, the applicability of the proposed adders in the image sharpening application is examined by measuring peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) using the MATLAB tool. The proposed designs have also a reasonable performance in this regard. • Deploying dynamic logic (DL) in the design of approximate full adders for the first time. • Designing ultra-low area and energy consumption full adder structures. • Applying the proposed adders in the image processing application. [ABSTRACT FROM AUTHOR]

The transfer of energy-intensive projects to areas abundant in clean energy resources holds promise for optimizing the energy structure. After obtaining specific information on transferred projects of listed energy-intensive firms through search or phone contact, this paper innovatively constructs a three-dimensional panel containing 1,859,220 observations to empirically examine the attraction of clean energy to energy-intensive projects under the carbon intensity constraint policy (CICP). Our findings reveal that: (1) the CICP promotes the transfer of gray energy-intensive projects, leading to the transfer structure trending towards graying. (2) Furthermore, we observe a positive correlation between the proportion of thermal power in transfer-out areas and the outflow of gray energy-intensive projects. Conversely, areas with higher wind power and photovoltaics attract a greater inflow of gray energy-intensive projects. (3) Notably, clean energy predominantly attracts the inflow of gray projects rather than green projects. Our findings provide micro evidence and valuable insights into the transfer of energy-intensive industries, informing the design and implementation of future environmental policies for policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively. [Display omitted] • Priority-ordered incentive-based DR program for the EV and non-EV loads is proposed. • Fast dynamic reconfiguration of the off-grid microgrid is presented. • Multi-objective energy scheduling problem for the off-grid microgrid is modeled. • Utopia-tracking approach is used to solve the conflicting multi-objective problem. [ABSTRACT FROM AUTHOR]

Given the stochastic nature of solar energy, integrating PV arrays with energy storage systems (ESSs) is crucial for a consistent power supply. Effective maximum power point tracking (MPPT) control is of great importance in this context. The significance lies not just in achieving high energy efficiency but also in ensuring overall system safety and reliability, as rapid and stable power tracking control alleviates power stress on ESSs. Challenging factors like parameter uncertainty from the PV array linearization process, dynamic DC bus variations induced by pulsed power loads, and sudden irradiance variation under cloudy weather, make the maximum output power of PV arrays largely dependent on the effectiveness of the control laws. Therefore, this paper presents a comparative study for MPPT control using PV-interfaced DC/DC converters. The aim is to demonstrate the advantages of employing average large-signal model-based sliding mode-type controllers in PV system integration with health-conscious objectives. A comprehensive system-level control-oriented modeling process is presented, followed by the theoretical background of three types of controllers (classical proportional–integral (PI)-type controller, μ -synthesis-based robust controller, and sliding mode controller) for practical applications. Extensive tests with real experimental data reveal sliding mode-type controllers' superiority in various integration scenarios. For example, with random solar irradiance variation over 0.5 s, they achieve a 54.75% reduction in energy loss, outperforming the commonly used PI-type counterparts. [ABSTRACT FROM AUTHOR]

Understanding the connection between water supply and energy consumption in a circular economy is crucial for managing the demands of these resources. Sankey diagrams have the potential to enhance the visualisation of resource flows. However, there is a knowledge gap regarding the effective use of Sankey diagrams to capture the water-energy nexus and circularity of water. This study focused on effectively utilizing Sankey diagrams to model the nexus of water and energy flows and to assess the circularity of water. A comprehensive literature review was conducted on major studies on water material flows and modelling. This was supported by a review of approaches for modelling circularity, which have evolved from the material circularity index. The newly developed framework in this paper models water resource flows at all stages of the water supply chain, embedding the nexus as an interaction and considering resource intensity and material circularity index at each node. A city-level case study in South Africa was then used to apply the recommendations, model circularity, and the water-energy nexus using Sankey diagrams. In terms of circularity, the results indicate that water loss in the study area was very high, accounting for up to 40% of the total water supply and the water recycle rate was still very low, ranging from 3% to 7%. In terms of the nexus, the residential sector was found to have the highest energy intensity for water supply, primarily due to energy demand for water heating. Exploring the use of renewable or solar energy for water heating will be of interest and value. The new approach presented in this study allows for the simultaneous consideration of objectives, enabling a more robust analysis of energy-effective water use and water circularity along the water supply chain. [Display omitted] • A new framework for modelling the water energy nexus enriched by circularity options, energy intensity factors and confidence modelling. • Options for embedded circularity through the water supply chain. • The urgent challenge to address massive water loses in water supply systems for water scarce regions. • New Sankey diagrams showcasing how to model nexus and circularity. [ABSTRACT FROM AUTHOR]

The 2023 revision of the Energy Performance of Building Directive underlines the need to consider the whole life cycle emissions for buildings. The decarbonization targets could not be achieved if reduction of energy demand in the operational phase is accompanied by a high energy resource use in the whole life. This paper proposes a novel investigation of an energy renovation project, involving both building envelope components and space heating system. The adopted method is based on the life cycle assessment, in the variant cradle-to-gate with options, to analyze and compare the design data achieved through an analysis of product (embodied) and use (operational) stages. Different solutions for energy improvement of the building performance are identified, and the scenario with the lowest embodied energy was found to be more energy efficient globally. The most effective scenario, with energy and CO 2 savings of 39% and 40% is characterized by a weight of embodied energy and emissions of 6.5% and 6.9%, respectively. Given the rise of the weight of the embodied energy in the whole life cycle of future buildings, the life cycle assessment will become crucial for sustainable design and in the transition to an energy-efficiency built environment. [Display omitted] • The impact of life cycle analysis in the process of energy efficiency is investigated. • The "cradle-to-gate" with options approach is implemented. • The efficiency of walls and windows involves the greater use of energy resources. • The most energy-efficient scenarios from an operational and non-operational point of view are different. • The scenario identified in the analysis of A1-A4 phases allows a higher total energy saving. [ABSTRACT FROM AUTHOR]