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This mini‐review summarises and discusses recent findings form the literature on the degradation of carbon‐based electrodes for vanadium redox flow batteries (VRFBs). It becomes evident that the focus of current investigations is on carbon paper, carbon felt and graphite felt electrodes, which is understandable from a practical point of view. However, the structural complexity of these materials often prohibits doubtless attribution of observed performance reduction (or increase) to changes in the electrode materials. Among the discussed major causes for degradation are formation or change of surface functional groups, changes in the carbon sp2/sp3 ratio, intercalation of ions as well as formation of inhibiting adsorbates. In order to gain deeper insight into the changes of carbon electrodes in VRFBs under relevant operation conditions, the authors suggest reducing complexity of the investigated materials and applying in situ‐studies under well‐defined and controllable conditions on model electrodes. These studies then should be extended towards more practical systems and may finally help to reduce degradation phenomena including enhanced overvoltages and thus could improve cycling and energy efficiency as well as long‐term stability of vanadium redox flow batteries. [ABSTRACT FROM AUTHOR]

A 0.25 m3 laboratory repulper was built for the purpose of determining which variables (pulp type, rotor tip speed, pulp suspension temperature, rotor design, rotor to extraction plate clearance, power to vat volume ratio, and pulp suspension consistency) affect the specific energy (kW-h/ton) required for repulping. Scale replicas of 3 commercial repulper rotors were constructed to test the effect of rotor geometry on repulping specific energy. The pulp types used included kraft aspen, C-flute corrugated cardboard, office printing paper, and unbleached paper towel. The energy required for repulping was found to vary with pulp type, temperature, consistency, and rotor design. It was found that a given material at a given temperature and consistency requires a unique quantity of energy to be repulped independent of the rate of energy addition. It was also found that repulper rotors show Reynolds independence at rotor tip speeds greater than 12m/s. It was found that the specific energy consumption in a 0.25 m3 laboratory repulper was identical to that for a 15 m3 repulper given the comparison was made with identical pulp types, temperatures, consistencies, and rotor designs. [ABSTRACT FROM AUTHOR]

With the diversification of users' energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%. [ABSTRACT FROM AUTHOR]

An editorial is presented on 12th international IET Mediterranean Conference on power generation, transmission, distribution and energy conversion (MEDPOWER conference). Topics include discussing online the latest research achievements in academia and industry; and power system operation and planning, and Energy efficiency and reduction of greenhouse emissions.

Residential cooking with non‐renewable energy sources, such as firewood, charcoal, natural gas, participate in the emission of more than a gigaton of CO2 per year, which represents 2% of the global CO2 emissions. Additionally, toxic particles including sulfur dioxide, carbon monoxide, and mercury are released leading to elevated levels of indoor air pollution, and adversely affecting the health of the inhabitants. The residential sector's non‐renewable energy cooking devices also pose significant problems, consuming approximately 30%–40% of global energy usage, with over 80% dedicated to cooking applications. To mitigate the negative impacts of traditional cooking on health and the environment, various renewable energy‐based cooking technologies have been developed recently. The primary contributions of our paper are to: (a) present a comprehensive review of concentrated solar thermal cooking technologies, assessing their social, economic, and environmental impact across different climatic zones in developing countries like India; (b) classify and compare different solar cooking technologies, highlighting their advantages and limitations in various scenarios; (c) evaluate the energy efficiency of diverse solar cooking technologies; (d) analyze the impact of solar cookers on communities in developing countries; and (e) identify the challenges and future directions for solar cooker technologies, particularly in solar community kitchens. Our novel findings demonstrate that using solar cooking devices can reduce energy consumption by up to 56% in Indian schools. Moreover, the payback period ranges from 3 to 6 years, contingent on the technology's cost, climatic conditions, and available subsidies. Consequently, significant positive impacts on society, the economy, and the environment are observed when traditional cooking devices are replaced by solar cooking devices. This study provides a unique and thorough analysis, contributing to the growing body of knowledge on sustainable cooking solutions and their potential to transform energy consumption patterns in developing regions. [ABSTRACT FROM AUTHOR]

Carbon reduction is an important issue of global concern today, and with the construction industry accounting for a significant portion of global carbon emissions, carbon reduction research for the civil engineering industry is becoming increasingly important. The current research on carbon emission of buildings mainly focus on energy consumption, choice of building materials, and energy‐saving technologies. While different construction programs can affect the construction sequence and have a significant impact on carbon emissions, especially for ultra‐high‐rise steel structures. In this paper, carbon emission is introduced as the optimization objective, and a nodal optimization procedure based on carbon emission is given to explore the optimization scheme of ultra‐high‐rise steel structure construction based on carbon emission. Calculations using the carbon emission factor method were carried out to analyze the differences in carbon emissions between different construction schemes and to explore the impact of the method of carbon emission optimization. Results show that the combined lifting scheme can save 463.4 kg carbon emissions and reduce the number of lifts by 780 times. [ABSTRACT FROM AUTHOR]

Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy. [ABSTRACT FROM AUTHOR]

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking. [ABSTRACT FROM AUTHOR]

Following the systematic review and bibliometric analysis of current literature, this paper attempts to investigate whether the wealth generated through cryptocurrency trading can assist in attaining the United Nations' (UN) sustainable development goal (SDG) 7, affordable and clean energy and UN SDG 13 related to climate action. The critical analysis of literature indicates a growing interest in cryptocurrency, the UN's SDGs and the negative effect that crypto mining has on the use of enormous energy. However, there is a clear gap in the literature that focuses on the possibility of using the wealth generated through cryptocurrency trading in financing environmentally friendly projects and attaining the UN's SDG 7 and SDG 13. The findings and the future research direction of this study aim to firstly expand the academic literature related to SDG 7 and SDG 13 and secondly to examine the relationship between cryptocurrency and sustainability even during an uncertain period. This study provides evidence pertaining to the theoretical models that can be applied within discussion of the complex relationship between cryptocurrency, clean energy and climate action. Our findings will provide policymakers with information regarding actions that need to be taken in order to convert cryptocurrency generated wealth and consequently attaining sustainable socio‐economic goals in the future. [ABSTRACT FROM AUTHOR]

In this paper, a proper noun "Region" is used for nm‐scale n‐type dopant‐rich region in p‐type Si crystal. Using this Region, certain solar cells have been assumed. By resonance absorption between photon energy and potential barrier of the Region, the cell can absorb most photons for visible light frequency without passing loss or thermal loss. This light absorption mechanism is different from conventional band gap absorption. Despite this benefit, output voltage is anticipated to decline according to the principle of detail balance. To control the decline, two methods are proposed in this paper. Theoretical energy conversion efficiencies for several cases are calculated with an ideal condition. The calculation result is over 70% as a theoretical value. [ABSTRACT FROM AUTHOR]

In recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%. [ABSTRACT FROM AUTHOR]

The research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field. [ABSTRACT FROM AUTHOR]

With the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the on‐board passengers' weight. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies. The results show that the optimized timetable increases the regenerative energy utilization rate by 21.37% and reduces the total energy consumption by 5.02% compared to the operational timetable. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. [ABSTRACT FROM AUTHOR]

The consumption of coal in winter heating period in northern China is large, and the combustion generates greenhouse gases that pollute the environment. At the same time, wind abandonment is widespread in northern China, causing waste of energy. In order to solve these problems, this paper proposes to apply clean energy heating and waste wind power generation for heating, and build a multi‐objective optimal dispatching model under the goal of considering customer satisfaction and operating costs. Finally, taking a region in the north of China as an example, the improved genetic algorithm is used to solve the model, the improved genetic algorithm ensures the survival rate of excellent genes, which is more efficient than the traditional genetic algorithm. The example results verify that the use of clean energy heating can increase the wind power consumption space and reduce the heating cost in winter. [ABSTRACT FROM AUTHOR]

Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

Fast and accurate fault location in DC power systems is of particular importance to ensure their reliable operation. One of the approaches for implementing a fast‐tripping protection scheme is to use Traveling waves (TW) initiated by a fault scenario. This paper proposes a physics‐informed machine learning approach that utilizes TWs for fault location in DC microgrids. TWs are extracted by the so‐called multiresolution analysis which identifies the TW's wavelet coefficients for multiple frequency ranges. This paper deploys Parseval's theorem to find the energy of wavelet coefficients as a quantitative metric for describing TWs. The hypothesis of this paper is that once the Parseval energy curves for a specific cable are extracted, they can be utilized to locate faults along with that cable regardless of the DC system in which the cable is deployed. The fault location algorithm uses Parseval energy curves to train a Gaussian Process (GP) estimator. With the Parseval energy values of measured current at the protection device location, the GP estimator is able to estimate fault locations with high accuracy. The effectiveness of the proposed algorithm is verified by simulating a DC microgrid system in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Globally, the encouragement of using renewable fuels like biodiesel for diesel engines is driven by concerns over the fossil fuel depletion and harmful emissions. Additionally, the utilization of renewable fuel additives like diethyl ether has the potential to enhance fuel properties and boost engine performance. The aim of this paper was to construct a computer simulation using Ricardo Wave program in order to predict the performance and nitrogen oxides (NOx) emission of a diesel engine fuelled by a diesel‐biodiesel blend and a diethyl ether (DEE) as a fuel additive. The computer model was validated by comparing the simulation engine performance and NOx emission results against the corresponding experimental data for diesel, diesel‐biodiesel blend with 30% biodiesel proportion (B30), and two blends of diesel‐biodiesel‐DEE with DEE proportions of 5% and 10% on a volume basis. Also, the effect of varying the inlet air pressure on engine performance and NOx emission was compared for all investigated fuels. It was numerically demonstrated that using the DEE with an optimum proportion of 5% enhanced engine performance as it decreased engine fuel consumption by 5.9% and increased engine thermal efficiency by 9.6% compared to diesel fuel at engine full load condition. Also, a significant reduction of 20.5% in NOx emission resulted from the addition of DEE. Increasing the inlet air pressure increased engine power and decreased engine fuel consumption for all investigated fuels. Increasing the inlet air pressure from 1 to 3 bar increased engine brake thermal efficiency by almost 20% for all tested fuels. However, NOx emission increased slightly within a range from 1.7% to 7% for the different investigated fuels. [ABSTRACT FROM AUTHOR]

Electric power provision for all the customers is not always possible for distribution companies. Some customers are interested in serving their load through renewable resources regarding the climate situation. Electrically off‐grid zero‐energy building is an applicable concept, in which the electrical energy provision of the buildings is isolated from the power supply infrastructures. This paper introduces a robust bi‐level programming model to create a cost‐effective off‐grid zero energy complex in Kish Island under risk management. The upper level of the planning is composed of two components, the passive design of buildings within the complex and the design of a stand‐alone energy system. The passive design as an energy‐saving tool includes the selection of insulation material for building external walls and finding the optimum thickness. Also, the stand‐alone energy system design denotes the sizing of diesel generator, photovoltaic, and battery energy storage as the distributed energy resources. The lower‐level problem optimally handles the annual scheduling of these resources to meet the complex demand under the impact of passive cooling. The Karush–Kuhn–Tucker condition method is used to solve this bi‐level planning problem. Furthermore, the battery degradation is concerned via the throughput model to consider the replacement cost of the problem. [ABSTRACT FROM AUTHOR]

Reversible substations (RSs) permitting bidirectional power flows can recover the regenerative braking energy of trains in DC traction power supply systems (TPSSs), increasing the energy efficiency of railway systems. To predict their effects on system dynamics and energy savings, the paper develops multiresolution models (MRMs) to simulate the RS roles with different fidelities. A high‐resolution model for the transient simulation replicates a particular topology where a three‐level voltage source inverter is connected to the secondary winding of an existing 12‐pulse rectifier transformer and regulated to keep a constant DC voltage in the inverting mode. Furthermore, it can model the transient effects of pantograph‐to‐line arcing by inserting arc voltage profiles at the train's input stage. To increase the computation speed in the long‐term energy flow simulation, a low‐resolution model simplifies the rectifiers into a series connection of a diode and a controlled voltage source depicting their nonlinear output characteristics and then places a DC voltage source in parallel to form a reverse path for braking power recovery. In addition, nonlinear conversion efficiencies are introduced to calculate energy flows across substations. The MRMs are tested based on a 1.5 kV DC TPSS and discussed alongside system dynamics under normal operation or pantograph arcing and the consistencies between different models. The RS using bidirectional voltage source converters only is additionally modelled to compare the technical performance of the two topologies in terms of system dynamics and energy efficiencies. [ABSTRACT FROM AUTHOR]

Improving the utilization of thermal energy is crucial in the world nowadays due to the high levels of energy consumption. One way to achieve this is to use phase change materials (PCMs) as thermal energy storage media, which can be used to regulate temperature or provide heating/cooling in various applications. However, PCMs have limitations like low thermal conductivity, leakage, and corrosion. To overcome these challenges, PCMs are encapsulated into microencapsulated phase change materials (MEPCMs) capsules/fibers. This encapsulation prevents PCMs from leakage and corrosion issues, and the microcapsules/fibers act as conduits for heat transfer, enabling efficient exchange between the PCM and its surroundings. Microfluidics‐based MEPCMs have attracted intensive attention over the past decade due to the exquisite control over flow conditions and size of microcapsules. This review paper aims to provide an overview of the state‐of‐art progress in microfluidics‐based encapsulation of PCMs. The principle and method of preparing MEPCM capsules/fibers using microfluidic technology are elaborated, followed by the analysis of their thermal and microstructure characteristics. Meanwhile, the applications of MEPCM in the fields of building energy conservation, textiles, military aviation, solar energy utilization, and bioengineering are summarized. Finally, the perspectives on MEPCM capsules/fibers are discussed. [ABSTRACT FROM AUTHOR]

The time‐varying external environment is one of the main variables influencing heating energy consumptions, so that its influence should be rectified when energy savings of different heating modes are calculated. This paper proposes an energy consumption rectification method based on Bayesian linear regression and heating degree‐days, to obtain heating energy consumptions without the influence of different outdoor temperatures. The proposed method consists of three main steps. First, a physical model of heating houses is used to prove a relationship between energy consumptions and heating degree‐days. Second, Bayesian linear regression is exploited to estimate uncertainty ranges of heating energy consumptions. Finally, heating energy consumptions are rectified, and energy savings with their uncertainty ranges for different heating modes under the same outdoor temperature are obtained. The proposed method does not require the physical parameters of heating houses to facilitate practical implementation. Additionally, it provides uncertainty ranges of heating energy consumptions to measure the estimation accuracy. Numerical and experimental examples show that the proposed method provides more accurate estimates of heating energy consumptions than existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a rule‐based energy management system (EMS) designed for a standalone DC microgrid incorporating solar photovoltaic (PV), fuel cell, battery energy storage system (BESS), and electric vehicle. The unpredictable nature of renewable energy sources and the instability of loads pose challenges for maintaining DC bus voltages and power‐sharing arrangements, impacting the microgrid's smooth operation. The proposed EMS aims to ensure power balance between generation and demand, mitigating vulnerabilities of the DC bus to voltage instability caused by fluctuations from both the load and source sides. This is achieved through an autonomous DC bus voltage stabilization strategy, involving the maintenance of a nominal state of energy (SoE) for the BESS and hydrogen fuel consumption for the fuel cell within predefined lower and upper limits. By regulating these two factors, the EMS algorithm facilitates optimal performance of the PV, battery, and fuel cell components. Consequently, the EMS provides decision‐making instructions to each individual energy source, ensuring efficient operation under various conditions. The effectiveness of the proposed EMS is evaluated through hardware‐based testing on a DC microgrid and simulations in the MATLAB Simulink environment across multiple operating scenarios. [ABSTRACT FROM AUTHOR]

Integrated demand response (IDR) is deemed as an effective tool to balance energy supply and demand. User's uncertain information containing prior uncertain information and posterior uncertain information is a key factor affecting the implementation effectiveness of IDR, but existing studies fail to consider the two types of uncertain information, response risk caused by the uncertain information, and risk appetite comprehensively. Based on the principal‐agent theory of optimal incentive contract under uncertain information and Markowitz's mean‐variance portfolio theory, a new IDR model is established in this paper, and an IDR optimization strategy considering risk appetite under uncertain information is proposed. By proposing the user model considering multi‐dimensional uncertain information and the risk appetite‐based integrated energy service providers (IESP) model based on the principal‐agent theory and Markowitz's mean‐variance portfolio theory, we have achieved effective modelling of the user's uncertain information and the risk borne by IESP. The arithmetic examples have verified advantages of the model in enhancing the accuracy of user's actual response prediction and the superiority of incentive strategies, which is beneficial to reduce the cost of IESPs and enhance the benefit of users participating in IDR. [ABSTRACT FROM AUTHOR]

The increasing energy consumption and changing load variations place a significant burden on the sophisticated utility grid, which affects the source's dependability and quality. Researchers examining modern power networks must focus on those factors to prevent grid breakdowns. Superior power quality (PQ) is still intended to make sure everything runs smoothly under diverse organizations. The main cause of PQ problems, however, is now renewable energy used in power electronic converters that are integrated into the electrical grid. Even though new, better solutions are still being developed, adhering to international standards have been strongly advised. Consequently, microgrid clusters powered through renewable energy and incorporating multiple structures in an urban area has been proposed in this paper. This increases the dependability of the power sources by managing the energy that is available inside the cluster instead of having it focused around the utility grid. Additionally, a deep belief network model based on Improved Dwarf Mongoose Optimization is recommended for regulating the inverter by generating optimal pulse‐width modulated signals that increases the quality of the power supplies. When compared to other conventional techniques, the suggested technique possesses less real power and reactive power setting duration of 0.8 ms and 0.75 ms, respectively. [ABSTRACT FROM AUTHOR]

Distributed energy resources are being progressively deployed by industry. The penetration of distributed generation in low voltage (LV) networks can position traditional consumers as market participants. With the improvements in communication networks and the introduction of new energy markets, these prosumers are incentivized to sell their excess production to other industries by participating in peer‐to‐peer (P2P) energy markets. This market paves the way for developing new technologies such as a shared battery energy storage system (SBESS). In this paper, a central storage unit rents its capacity for the prosumers to reduce the overall peak‐load of the microgrid. Each user requests the required SBESS capacity and calculates the best charging and discharging times to reduce their cost. To this end, this paper organized a P2P energy trading paradigm with the presence of SBESS. The optimization problem was simulated and solved using the alternating direction method of multipliers (ADMM) algorithm. Results demonstrate how combining features of P2P energy trading and SBESS can save up to 29% for the industrial town. [ABSTRACT FROM AUTHOR]

Using modularized power converters with scalable energy storage in particle accelerators can be further enhanced with controls that can be adapted to optimise different design targets, such as to minimise the front‐end stage peak current, the depth of discharge of electrolytic capacitors or the thermal cycling of semiconductors. This paper proposes a control system that is able to individually adjust the power flow of the converter modules under pre‐defined load profiles, and hence separates the use of energy storage and grid connection even under rapid cycling. The paper briefly summarises the characteristics of the modular converter and proposes four energy control strategies that have been validated by simulation as well as experimentally on a 800 kW full scale prototype. It is shown that by applying each strategy, the corresponding design objectives can be achieved, for example, optimal utilisation of the energy storage systems or minimised current stress in the semiconductors. [ABSTRACT FROM AUTHOR]

The multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed. [ABSTRACT FROM AUTHOR]

In the context of automated driving, the connected and automated vehicles (CAVs) technology unlock the energy saving potential. This paper develops an LSTM‐based deep learning framework for eco‐driving adaptive identification on Intelligent vehicle multivariate time series data. The framework can be adapted for Driver Assistance Systems (DAS) to reduce fuel consumption. Specifically, considering overtaking on rural road is a critical maneuver for operation and has potential to reduce consumption, a simulated driving experiment with 30 participants was conducted to collect the multivariate time series data of the overtaking operation behaviors in conditional automation driving. Driving behaviors were classified into eco‐driving operation behaviors and high fuel consumption operation behaviors based on fuel consumption calculated by using vehicle specific power (VSP). Significance analysis based on linear regression was adopted to identify operation behaviors, and an eco‐driving behavior identification model was established with the use of long short‐term memory (LSTM) for multivariate classification theory. Meanwhile, the other four classification algorithms were used to establish identification models for comparison. The results indicated that the gear position, lane position, the acceleration pedal depth, the clutch pedal depth, and the brake pedal depth had a significant influence on fuel consumption. The eco‐driving behavior identification model of overtaking demonstrated a high classification power and robustness with a classification accuracy of 89.16%. According to the simulation results, the developed adaptive identification model is with promising performance. The conclusions provide theoretical support for developing an adaptive strategy for connected eco‐driving. [ABSTRACT FROM AUTHOR]

In order to solve the collaborative optimization scheduling of multi‐microgrid under the high penetration rate of new energy, this paper considered the energy interaction between micro‐grids in multi‐microgrid and the relationship between new energy consumption and electricity cost, constructed a collaborative scheduling model considering both micro‐grid load and main grid wind and optical energy storage, proposed objective function based on economic cost, and improved Genetic Algorithm (GA). The elite thought and catastrophe thought are used to optimize the selection operation, and the particle swarm optimization algorithm is used to optimize the mutation operation. Furthermore, three scenarios were selected to verify the effectiveness of the proposed model and the improved GA. The results show that the proposed model can ensure the stable operation of the multi‐microgrid system with a high proportion of new energy access and reduce the operation cost. In addition. the improved algorithm solves the problems of easy to fall into local optimum and slow iteration speed, and has better performance in solving the model. [ABSTRACT FROM AUTHOR]

Residential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers' costs in the proposed model. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time‐outs and its back‐off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light‐weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next‐generation IoT networks. [ABSTRACT FROM AUTHOR]

This paper evaluates various modes of transport against the dual requirements of Net‐Zero carbon emissions and user convenience, in particular, speed of travel and cost of transportation. Results show that when operated across a whole country, battery‐powered ePlanes have the lowest energy use as measured by well‐to‐wing efficiency of other high‐speed transport systems such as the UKs HS2 and conventional diesel rail systems. This condition may not hold for extremely high passenger numbers per hour as seen in metropolitan areas with high density populations. Various proposed disruptive technologies lower cost of ownership when combined with changes in the transport paradigm that has rarely been explored in other papers. [ABSTRACT FROM AUTHOR]

This paper investigates a backscatter communication (BackCom) based non‐orthogonal multiple access (NOMA) system in a multiple‐input and single‐output (MISO) scenario, where two decoding methods are deployed, including the sum‐capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high‐performance and low‐complexity solutions, respectively. Simulation results indicate that the combination of the sum‐capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity. [ABSTRACT FROM AUTHOR]

During the operation of the ITER machine, hundreds of MW/Var of active and reactive power will be exchanged with the grid. The E‐STATCOM scheme composed of the Modular Multilevel Converter (MMC) and split supercapacitor energy storage has been proposed to improve the power compensation performance of the existing reactive power compensation system in the previous study. However, one of the main technical challenges which is lack of research is to precharge all submodule capacitors and supercapacitors from zero to their nominal voltage values efficiently during a startup process. As the capacitance and operating voltage of supercapacitors are much different from capacitors in each submodule, the startup of E‐STATCOM is a more complicated process. To coordinate the energy exchange between submodule capacitors and supercapacitors, submodule capacitors and the grid, this paper presents an optimized four‐stage AC side startup strategy for the E‐STATCOM. The proposed method minimizes the use of current‐limiting resistors while suppressing the surge current in the zero‐voltage startup process of supercapacitors, in addition to optimizing the energy consumption. The pulse current charging, constant current charging and constant power charging strategies of supercapacitors are adopted in different charging stages, and the detailed coordinated control scheme between submodule capacitors and supercapacitors are described and analyzed. The effectiveness and performance of the proposed method are verified by simulation results and hardware‐in‐the‐loop (HIL) experiments. [ABSTRACT FROM AUTHOR]

Connected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic. [ABSTRACT FROM AUTHOR]

In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles' tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions. [ABSTRACT FROM AUTHOR]

New‐type professional farmers are new‐type rural talents with scientific and cultural literacy and professional knowledge of agriculture under the background of rural revitalization. The new type of professional farmers is of great significance for accelerating agricultural modernization and promoting the construction of a powerful modern socialist country. In the process of cultivating new types of professional farmers in rural areas, it is necessary to break through the previous informatization constraints of poor information flow, and give full play to the role of advanced information technology to promote the sustainable economic development. This paper proposes a new type of professional farmer cultivation platform based on edge computing architecture to improve the cultivation efficiency of new type of professional farmers, so as to better promote the development of rural informatization. Aiming at the challenges of energy consumption under the edge platform architecture, this paper proposes a dynamic scheduling method for semi‐online tasks for edge computing platforms. In order to evaluate the effectiveness of the algorithm, the method in this paper is compared with the classic scheduling algorithm, and simulated and verified on the CloudSim platform. Experimental results show that the proposed method outperforms other algorithms in the task completion time metric. With the expansion of task scale, more energy consumption can be saved by using the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better. [ABSTRACT FROM AUTHOR]

A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network's regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML‐Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision‐making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end‐to‐end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods. [ABSTRACT FROM AUTHOR]

This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim. [ABSTRACT FROM AUTHOR]

This paper demonstrates improved throughput and energy efficiency of wireless communications by exploiting nonorthogonal multiple access (NOMA), multiple input–multiple output (MIMO), and radio frequency energy harvesting (EH) technologies. To assess the performance of NOMA MIMO communications with EH (MMe), we consider the nonlinear characteristics of EH devices and propose explicit expressions for throughput and outage probability. Based on our results, the system performance is significantly mitigated by EH nonlinearity and is considerably improved by increasing the number of antennas. Additionally, by appropriately adjusting the system parameters, our NOMA MMe innovation can avert complete outages while optimizing system performance. Moreover, the results demonstrate the superiority of the NOMA MMe over its orthogonal multiple access MMe counterparts. [ABSTRACT FROM AUTHOR]

When the traditional two‐stage boost inverter is used in photovoltaic (PV) and energy storage systems, it is necessary to connect additional bidirectional conversion devices, which will increase the loss of the system and increase the complexity of system control. Therefore, an improved energy storage switched boost (ESSB) grid‐connected inverter is proposed in this paper. The system has the advantages of high integration, high gain and dead time immunity. By controlling the duty cycle of the system, the energy management of the battery can be realized. The system consists of three parts: PV cells, ESSB network and grid‐connected inverter. In order to maximize the energy utilization, this paper uses the disturbance observation method to track the maximum power point of PV cells, and formulates a set of energy management strategy to control the energy flow between the three energy units. Simulation and experiments verify the superiority of the topology and the feasibility of the energy storage strategy. [ABSTRACT FROM AUTHOR]

The transition to Smart Grids increases the complexity of power grids by involving many more interdependent actors and integrating additional information and communications technology. To provide a common basis for Smart Grid data representation and exchange, the standardized Common Information Model (CIM) has been introduced and extended, i. a., by the Common Grid Model Exchange Specification (CGMES). An increasing acceptance by power grid operators and other actors has made CIM and CGMES more and more relevant. However, the implementation of CIM / CGMES support in software projects appears to be challenging due to the complexity of CIM / CGMES and the ongoing standardisation process with iterative adaptations. Thus, the main contribution of this paper is the presentation of a methodology for an automated generation of programming language specific code from CIM / CGMES specifications. The approach is based on the use of a template language and enables to keep software projects fully compliant with CIM / CGMES specifications. The paper outlines the process of code generation and the consecutive codebase integration for a JavaScript based CIM / CGMES web editor and for two CIM / CGMES de‐/serialiser libraries in C++ and Python. The approach is evaluated in use cases involving the visualisation and simulation of a benchmark grid. [ABSTRACT FROM AUTHOR]

Technically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes. [ABSTRACT FROM AUTHOR]

The presence of smart buildings in a smart grid as prosumers with the ability to both consume and produce clean energy can help to decentralize and decarbonize the electricity industry. An energy community (EC) refers to a collection of energy users and energy sources that participate in peer‐to‐peer (P2P) energy trading and contribute to the energy supply of the community through appropriate communication infrastructure. This paper examines the management of smart grid energy, including ECs, in order to promote energy democracy by incorporating smart buildings into P2P energy trading and demand response programs (DRPs). A model for energy democracy has been developed for a group of energy users consisting of prosumers and managers within ECs, based on user preferences. The paper classifies prosumers into three categories based on their preferences: flexible, economic, and social. Additionally, EC managers have been classified into two categories based on their preferences: interactive and non‐interactive managers. The CPLEX method in the GAMS software was utilized to solve the problem. The results show that the proposed approach improves access to energy within the smart grid system. [ABSTRACT FROM AUTHOR]

Due to the rapid development of today's industry, it not only greatly accelerates the consumption of energy, but also causes serious pollution to the environment. Therefore, reducing energy waste and recovering energy has become an important research direction today. This paper introduces the future development trend of renewable energy, discusses the progress of recovery of nonpolluting energy such as marine, bioenergy and thermal energy, and analyzes the efficiency and social benefits of various types of energy recovery. Energy recovery methods for natural gas pipelines are briefly analyzed, and current research advances in energy pressure recovery devices are provided, leading to a summary of methods to improve the efficiency of fluid energy recovery. Finally, it is concluded that although the current energy sources are diversified, the most valued energy sources at present and in the future should be solar energy and ocean, which are infinitely recyclable. This paper will benefit researchers to further understand and study the various aspects of energy recovery. [ABSTRACT FROM AUTHOR]

Blockchain is a distributed digital ledger system that establishes transparent contract processes and facilitates secure but trusted business transactions. Policy‐makers around the world are intrigued by the potential of this emerging technology to solve policy problems, including the challenges of the transition away from centralised, linear models of energy generation and consumption towards decentralised and distributed energy systems. Blockchain has also been promoted as a mechanism to transform carbon markets, yet the focus in this area to date has been on using blockchain to create new carbon market schemes. This paper addresses an important research gap by asking how blockchain could be applied to an existing carbon market. To answer this question, the study uses an established design process to develop an Australian carbon market blockchain design. The paper finds that this design could improve the efficiency, equity and effectiveness of the Australian carbon market. This paper makes an important research contribution to carbon market policy development by developing a blockchain design that could improve how an existing carbon market functions, and the findings presented here are relevant to government and industry stakeholders globally. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

With the increasing concerns about railway energy efficiency, two typical driving strategies have been used in actual train operation. One includes a sequence of full power traction, cruising, coasting, and full braking (CC). The other uses coasting–remotoring (CR) to replace cruising in CC. However, energy‐saving performance by CC and CR, which can be affected by route parameters of gradients and speed limits, has not been fully compared and studied. This paper analyses the energy distribution of CC and CR considering various route parameters and proposes an improved strategy for different gradients and speed limits. The detailed energy flow of CC and CR is analysed by Cauchy–Bunyakovsky–Schwarz inequality and the generalised Hölder's inequality, and then, a novel driving strategy CC_CR is designed. To verify the theoretical results and the effectiveness of the proposed strategy, three simulators with CC, CR, and CC_CR driving modes have been developed and implemented into case studies of four scenarios as well as a real‐world metro line. Simulation results demonstrate that CR can only outperform CC on routes with steep downhill and CC_CR is always the best strategy. The energy savings of CC_CR can be as much as 15% more than CR and 42% greater than CC. [ABSTRACT FROM AUTHOR]

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]