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Abstract Carbon dioxide (CO2), whose atmospheric volume has been increasing, is the principal greenhouse gas (GHG) that causes global warming and climate change. Climate change from increasing greenhouse gases (GHGs) has broad health and environmental influences. Human-emitted GHGs constitute a significant cause of global warming. The main objective of this article is to assess the influence of technological innovation, economic growth, fossil energy use, renewable energy consumption, and globalisation on CO2 emissions (CO2Es) in South Africa over the observation period 1990–2020. The Dynamic Ordinary Least Squares (DOLS) estimator was implemented to examine the relationship between the constituents. The study results indicate that economic growth, fossil energy use, technological innovation, and globalisation boost CO2Es, whereas renewable energy consumption reduces CO2Es. The output of the canonical cointegrating regression and fully modified least squares estimators also supports the output of the DOLS estimator. We concluded that, in South Africa, a rise in economic growth, fossil energy use, technological innovation, and globalisation results in an upsurge in CO2Es. In contrast, an increase in renewable energy consumption leads to a reduction in CO2Es. This research work contributes to the discourse on CO2Es in the South African context. The study recommends that policymakers in South Africa should design policies that foster the formation of a low-carbon country, the consumption of renewable energy, and the implementation of environment-friendly technological innovations that reduce the use of fossil energy. Graphical Abstract

Abstract The variability of renewable energy within microgrids (MGs) necessitates the smoothing of power fluctuations through the effective scheduling of internal power equipment. Otherwise, significant power variations on the tie‐line connecting the MG to the main power grid could occur. This study introduces an innovative scheduling strategy that utilizes a data‐driven approach, employing a deep reinforcement learning algorithm to achieve this smoothing effect. The strategy prioritizes the scheduling of MG's internal power devices, taking into account the stochastic charging patterns of electric vehicles. The scheduling optimization model is initially described as a Markov decision process with the goal of minimizing power fluctuations on the interconnection lines and operational costs of the MG. Subsequently, after preprocessing the historical operational data of the MG, an enhanced scheduling strategy is developed through a neural network learning process. Finally, the results from four scheduling scenarios demonstrate the significant impact of the proposed strategy. Comparisons of reward curves before and after data preprocessing underscore its importance. In contrast to optimization results from deep deterministic policy gradient, soft actor‐critic, and particle swarm optimization algorithms, the superiority of the deep deterministic policy gradient algorithm with the addition of a priority experience replay mechanism is highlighted.

Abstract In response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi‐level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power‐to‐gas (P2G) and gas‐to‐power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy‐sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14‐bus power system and a 10‐node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model.

Abstract The high adoption of electric vehicles (EVs) and the rising need for charging power in recent years calls for advancing charging service infrastructures and assessing the readiness of the power system to cope with such infrastructures. This paper proposes a novel model for the integrated operation of dynamic wireless charging (DWC) and power distribution systems offering charging service to in‐motion EVs. The proposed model benefits from a hierarchical design, where DWC controllers capture the traffic flows of in‐motion EVs on different routes and translate them into estimations of charging power requests on power distribution system nodes. The charging power requests are then communicated with a central controller that monitors the distribution system operation by enforcing an optimal power flow model. This controller coordinates the operation of distributed energy resources to leverage charging power delivery to in‐motion EVs and mitigate stress on the distribution system operation. The proposed model is tested on a test distribution system connected to multiple DWC systems in Salt Lake City, and the findings demonstrate its efficiency in quantifying the traffic flow of in‐motion EVs and its translation to charging power requests while highlighting the role of distributed energy resources in alleviating stress on the distribution system operation.

Abstract This research introduces a novel damped alternating current (DAC) testing methodology, which integrates an enhanced DAC generator with a pulse‐based distributed partial discharge (PD) detection technique. The advanced DAC generator is designed without the need for high voltage (HV) solid‐state switches, thereby offering a cost‐effective solution for field‐testing voltage generation through a direct current–alternating current conversion approach. To meet the demand for high instantaneous power, a capacitor bank is employed as the power supply. Furthermore, the implementation of a distributed PD detection technique enhances sensitivity and eliminates limitations associated with cable length. To minimize the construction costs of the distributed PD‐detection system, a pulse synchronization technique has been employed. Efforts to reduce the system's weight were informed by simulations, resulting in the design and development of a prototype weighing 850 kg for a 64/110 kV power cable. The reduction in the number of HV solid‐state switches contributes to significant cost savings, amounting to tens of thousands of dollars, when compared to conventional DAC generators. Laboratory and field tests validated the effectiveness of the cost‐efficient DAC testing methodology.

Abstract Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks.

Abstract To solve the problem of negative‐sequence and reactive power in electrified railway, this paper proposes a comprehensive compensation method based on V/v transformer and electromagnetic single‐phase var compensator (ESVC) for co‐phase power supply. First, based on the principles of static var generator and single‐phase rotary phase shifting transformer, the topology and model of ESVC have been established, and the compensation mechanism has been analysed. Second, the topological structure and mathematical model of co‐phase power comprehensive compensation device (CPCD) are put forward based on the principles of V/v transformer and ESVC, and the operation mode of CPCD in multiple scenarios is designed. Then the strategy of CPCD with double closed loop control is analysed: corresponding compensation mode is selected according to negative‐sequence unbalance degree and expected power factor value. In this strategy, the compensated current output by ESVC is taken as the quantity of external loop control, and the rotation angle of ESVC is taken as the quantity of internal loop control to realize double‐closed loop control of CPCD. Finally, the comprehensive CPCD compensation model is built on the simulation platform and validated the accuracy of the mathematical model and the effectiveness of the control strategy.

Abstract Maintenance of power transmission lines is essential for the safe and reliable operation of the power grid. The use of deep learning‐based networks to improve the performance of power line defect detection faces significant challenges, such as small target sizes, shape similarities, and occlusion issues. In response to these challenges, a transformer‐based end‐to‐end power line detection network called Power‐DETR is introduced. Initially, building upon Deformable DETR, a large pre‐trained model (Swin‐large) is utilized to increase the number of multi‐scale features, and activation checkpoint technology is applied to ensure effective training within limited memory capacity. Subsequently, a contrastive denoising training strategy is integrated to combat ambiguity and instability of the Hungarian matching algorithm during training, aiming to expedite model convergence. Additionally, a hybrid label assignment strategy combining OHEM and cost‐based ATSS is proposed to provide the model with high‐quality queries, ensuring adequate training for the decoder and enhancing encoder supervision. Experimental results substantiate the efficacy of the proposed Power‐DETR model as a novel end‐to‐end detection paradigm, surpassing both one‐stage and two‐stage detection models. Furthermore, the model demonstrates a significant 15.7% enhancement in mAP0.5 compared to the baseline.

Abstract To address the challenge of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and power periodic features in short‐term power prediction for distributed photovoltaic (PV) farms, a TPE‐CBiGRU‐SCA model based on multiscale feature fusion is proposed. First, multiscale feature fusion of meteorological features, temporal features, and hidden periodic features is performed in PV power to construct the model input features. Second, the relationships between PV power and its influencing factors are modelled from spatial and temporal scales using CNN and Bi‐GRU, respectively. The spatiotemporal features are then weighted and fused using the SCA attention mechanism. Finally, TPE‐based hyperparameter optimization is used to refine network parameters, achieving PV power prediction for a single field station. Validation with data from a PV field station shows that this method significantly enhances feature extraction comprehensiveness through multiscale fusion at both data and model layers. This improvement leads to a reduction in MAE and RMSE by 26.03% and 38.15%, respectively, and an increase in R2 to 96.22%, representing a 3.26% improvement over other models.

Abstract For cooperation among distributed generations in a DC microgrid (MG), distributed control is widely applied. However, the delay in distributed communication will result in steady‐state bias and the risk of instability. This paper proposes a novel distributed control for time‐delayed DC MGs to achieve accurate current proportional sharing and weighted average voltage regulation. Firstly, by utilizing an advanced observer based on the PI consensus algorithm, the steady‐state bias problem is addressed. Then, using the passivity theory, stability analysis is conducted to reveal the principle of system instability caused by communication delay. On this basis, to offset the adverse effects of communication delay on the system stability, scattering transformation is introduced in the observer‐based distributed control. Moreover, considering the potential delay from the measurement stage in real‐life scenarios, the sufficient condition of the system stability is concluded by constructing the Lyapunov–Krasovskii functional. Finally, the performance of the proposed control and conclusions of stability analysis are verified by hardware‐in‐loop tests.

Abstract To devise more scientifically rational interruptible load contracts, this paper introduces a novel model for interruptible load contracts within modern electric power systems, grounded in data mining techniques. Initially, user characteristics are clustered using data mining technology to determine the optimal number of clusters. Building on this, the potential for different users to participate in interruptible load programs is analysed based on daily load ratios, yielding various user‐type parameters. Furthermore, the paper develops an interruptible load contract model that incorporates load response capabilities, enhancing the traditional interruptible load contract model based on principal‐agent theory through considerations of user type parameters and maximum interruptible load limits. The objective function, aimed at maximizing the profits of the electric company, is solved, and lastly, through the use of real data, a case study analysis focusing on commercial users with the strongest load response capabilities is conducted. The results affirm the efficacy of the proposed model.

Abstract Biochar application to soil is a potential climate change mitigation strategy. In addition to long-term sequestration of the carbon content of the biochar itself, its application may reduce the emissions of other greenhouse gases (GHGs) from the soil. However, the reported effects of biochar application on soil GHG fluxes exhibit inconsistent results. Prediction of such effects is an important gap that needs to be addressed in biochar research. In this study, rule-based machine learning models were developed based on rough-set theory. Data from the literature were used to generate the rules for predicting the effects of biochar application on soil GHG (CO2, N2O, and CH4) fluxes. Four rule-based models for CO2 fluxes, two rule-based models for N2O fluxes, and three rule-based models for CH4 fluxes were developed. The validity of these models was assessed based on both statistical measures and mechanistic plausibility. The final rule-based models can guide the prediction of changes in soil GHG fluxes due to biochar application, and thus serve as a decision-support tool to maximize the benefits of biochar application as a negative emission technology (NET). In particular, mechanistically plausible rules were identified that predict triggers for GHG fluxes that can offset carbon sequestration gains. This knowledge allows biochar application to be calibrated to local conditions for maximum efficacy. Graphical Abstract

Abstract It is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low‐voltage monitoring and pre‐warning inspection. This study acquired a series of arc‐fault signals according to IEC 62606. The main time‐frequency features were strengthened with high efficiency by applying the generalized S‐transform to them with a bi‐Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high‐frequency harmonic energy reflections, thus increasing the rate of arc‐fault diagnosis and making it suitable for arc‐fault monitoring of non‐linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow‐up arc‐fault monitoring and inspection research.

Abstract Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed.

Abstract As an emerging multi‐energy consumption subject, data centres (DCs) are bound to become crucial energy users for integrated energy systems (IES). Therefore, how to fully tap the potential of the collaborative operation between DCs and IES to improve total energy efficiency and economic performance is becoming a pressing need. In this article, the authors research an optimization coordinated by the energy scheduling and information service provision within the scenario of an integrated energy system with a data centre (IES‐DC). The mathematical model of IES‐DC is first established to reveal the energy conversion process of the electricity‐heat‐gas IES and the DC's energy consumption affected by the scale of active IT equipment. For dynamical providing multi‐energy and computing service by coordinating scheduling energy and information equipment, the formulations of IES‐DC scheduling, which is described as a Markov decision process (MDP), are presented, and it is solved by introducing the twin‐delayed deep deterministic policy gradient (TD3), which is a model‐free deep reinforcement learning (DRL) algorithm. Finally, the numerical studies show that compared with benchmarks, the proposed method based on the TD3 algorithm can effectively control the operation of energy conversion equipment and the number of active servers in IES‐DC.

Abstract A new integration strategy of grid‐forming‐controlled wind farms connected to the bulk power systems through high‐voltage direct current transmission based on the modular multi‐level converter (MMC) is proposed to solve the problem of traditional uncontrollable DC overvoltage during the short circuit faults at the receiving end. However, a new DC overvoltage phenomenon appears after the fault is cleared, and the interaction between the wind farm and sending‐ and receiving‐end MMCs makes the DC overvoltage mechanism more complex; further exploration shows that DC overvoltage during the sending‐end fault recovery stage occurs under the new integration strategy. Therefore, the evolution process and mechanism of the new DC overvoltage are analysed. It is found that affected by the interaction between wind farms and MMCs, the alternate saturation of the integrators in the PI controller of MMCs is the main cause. Based on this understanding, additional controls are proposed to suppress this DC overvoltage during the fault recovery stage. Simulations are carried out on a test with MATLAB/Simulink, and the results verify the efficacy of the proposed methods in suppressing DC overvoltage.

Abstract With the increasing deployment of renewable energy resources, the scale of DC networks and renewable capacity continues to grow. System security is challenged by the decrease in inertia from traditional synchronous generators. In order to accommodate high‐penetration renewable energy, voltage stability should be considered in the renewable energy integration planning. Herein, first, a voltage stability‐constrained minimum startup index and algorithm for conventional thermal power plants are proposed. Then, based on time series production simulation, a renewable energy integration capacity analysis algorithm is designed considering voltage stability and peak shaving constraints. Finally, based on the boundary conditions of the “Fifteen‐Five Plan”, the renewable energy capacity considering voltage stability and peak shaving constraints for Northwest and East China power grids are analysed to verify the effectiveness and engineering practicality of the proposed methodology. The results demonstrate that the proposed method can maintain the renewable energy integration goal outlined in the “Fifteen‐Five Plan” while maintaining the voltage stability in these regions.

Abstract Bird‐related outages greatly threaten the safety of overhead transmission and distribution lines, while electrocution and collisions of birds with power lines, especially endangered species, are significant environmental concerns. Automatic bird recognition can be helpful to mitigate this contradiction. This paper proposes a method for automatic classification of bird species related to power line faults combining deep convolution features with error‐correcting output codes support vector machine (ECOC‐SVM). An image dataset of about 20 high‐risk and 20 low‐risk bird species was constructed, and the feed‐forward denoising convolutional neural network was used for image preprocessing. The deep convolution features of bird images were extracted by DarkNet‐53, and taken as inputs of the ECOC‐SVM for model training and bird species classification. The gradient‐weighted class activation mapping was used for visual explanations of the model decision region. The experimental results indicate that the average accuracy of the proposed method can reach 94.39%, and its performance was better than other models using different feature extraction networks and classification algorithms.

Abstract The increasing penetration of the distributed energy resources (DER) in the power grid, which, while having significant advantages, also pose significant challenges. The behaviors of DERs differ from those of synchronous generators, particularly in abnormal conditions. For this reason, the power grid enforces grid codes to ensure that DERs perform properly in different conditions. For instance, short circuit faults and unbalanced grid voltage are severe transient events that inverters need to be able to pass through without disconnecting from the grid. Furthermore, the inverters are required to support the grid voltage by regulating the active and reactive power injections. This article proposes a voltage support control scheme to support grid voltage during asymmetrical voltage drop by utilizing an optimization problem. In this optimization problem, the active and reactive powers injected into the grid will be obtained optimally by considering constraints such as instantaneous active and reactive power oscillation magnitudes and peak current limitation. To aid in this purpose, the corresponding mathematical formulations such as instantaneous active and reactive power oscillation magnitudes will be obtained by using the currents and voltages in stationary reference frame. The proposed scheme will be verified by simulating it in MATLAB/Simulink under three different scenarios and tested on a real‐time experimental Opal‐RT platform.

Abstract The integration of alternative energy sources, storage systems, and modern loads into the distribution grid is complicating its operation and maintenance. Variability in individual generation and consumption elements dynamically affects voltage profiles, which in turn undermines efficiency and power quality. This study proposes to address this dynamical variability using an online reconfiguration approach that involves opening and closing switches to modify the grid's topology and adjust voltage levels in response to load/generation variations. Other grid optimization techniques, based on reconfiguration, typically focus on static, fully instrumented grids with predictable parameters and homogeneous changes, aiming to minimize power losses but overlooking the dynamics of variable grid elements. This study proposes a testing approach that is dependent on the estimated transient status of the grid only using a limited number of measurement units and considering the individual‐stochastic variations of loads and generators. The proposed approach was tested on the IEEE 33‐bus test feeder with up to five varying distributed generators. The results confirm that the algorithm consistently finds a reconfiguration alternative that could enhance system efficiency and voltage profiles, even in the face of dynamic load/generator behavior, demonstrating its effectiveness and online adaptability for grid operation and management tasks.

Abstract This article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time.

Abstract This study thoroughly investigates the NoisyNet Deep Deterministic Policy Gradient (DDPG) for frequency regulation. Compared with the conventional DDPG method, the suggested method can provide several benefits. First, the parameter noise will explore different strategies more thoroughly and can potentially discover better policies that it might miss if only action noise were used, which helps the actor achieve an optimal control strategy, resulting in enhanced dynamic response. Second, by employing the delayed policy update policy work with the proposed framework, the training process exhibits faster convergence, enabling rapid adaptation to changing disturbances. To substantiate its efficacy, the scheme is subjected to simulation tests on both an IEEE three‐area power system, an IEEE 39 bus power system, and an IEEE 68 bus system. A comprehensive performance comparison was performed against other DDPG‐based methods to validate and evaluate the performance of the proposed LFC scheme.

Abstract China is one of the largest contributors to global greenhouse gas (GHG) emissions, and the livestock sector is a major source of non-CO2 GHG emissions. Mitigation of GHG emissions from the livestock sector is beneficial to the sustainable development of the livestock sector in China. This study investigated the provincial level of GHG emissions from the livestock sector between 2000 and 2020 in China, to determine the driving factors affecting the provincial-level GHG emissions from the livestock sector, based on the logarithmic mean Divisia index (LMDI) model, which took into account of technological progress, livestock structure, economic factor, and agricultural population. Moreover, a gray model GM (1, 1) was used to predict livestock GHG emissions in each province until 2030 in China. The results showed that the GHG of Chinese livestock sector was decreased from 195.1 million tons (MT) CO2e in 2000 to 157.2 MT CO2e in 2020. Henan, Shandong, and Hebei provinces were the main contributors to the reduction in Chinese livestock GHG emissions, with their livestock GHG emissions reduced by 60.1%, 53.5% and 45.5%, respectively, in 2020 as compared to 2000. The reduction in GHG emissions from the Chinese livestock sector can be attributed to two main factors: technological progress and the shrinking of the agricultural laborers. In contrast, the agricultural economic development model with high input and high emissions showed a negative impact on GHG emission reduction in China’s livestock sector. Furthermore, the different livestock structure in each province led to different GHG reduction effects on the livestock sector. Under the gray model GM (1,1), the GHG emissions of the livestock sector will be reduced by 33.7% in 2030 as compared with 2020 in China, and the efficiency factor will account for 76.6% of the positive effect of GHG reduction in 2030. The eastern coastal region will be the main contributor to the reduction of GHG emissions from the Chinese livestock sector in 2030. Moreover, recommendations (such as upgrading livestock management methods and promoting carbon emission mitigation industries) should be proposed for the environmentally sustainable development of the livestock sector in the future.

Australia aims to achieve net zero emissions by 2050, with an interim target of reducing emissions to 43% below 2005 levels by 2030. Electrification of industry processes currently reliant on fossil fuels is a necessary step to achieve these emission reduction goals. This study investigates electrification of steam generation relevant to major industrial operations in the southwest of Western Australia using different renewable energy input levels. The designed system incorporates thermal storage to ensure continuous steam generation. The optimized technology mix, including wind, PV, and concentrated solar thermal (CST) systems for each renewable energy input target, is presented. The optimization process also identifies optimal locations for new renewable energy plants. In summary, the optimization tends towards favouring the development of large CST plants near a demand point. This avoids the use of the transmission network by direct use of the CST system for heating of the storage media, to address the costs and efficiency reductions arising from electrical heating, but the scope of CST use is expected to be limited by site constraints. The levelized cost of heat (LCOH) for the studied renewable energy input targets (i.e., 30–90%) ranges from 15.34 to 36.92 AUD/GJ. This is promising for the 30% renewable energy target, as future natural gas prices in Western Australia are likely to match or exceed the expected LCOH. Cost reductions for renewable generation and storage technologies with further implementation at a large scale in the future may result in more competitive LCOH at higher decarbonization levels, but it is likely that additional technologies will be required for cost competitiveness at very high decarbonization levels.

This paper deals with the problem of determining the optimal capacity of concentrated solar power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimizing the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular, the design factor and the solar multiple factor. This paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for more refined optimization, which allows for the more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown.

Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems.

Fatigue poses significant challenges for the structural integrity of monopiles, the most common type of foundation for offshore wind turbines. These structures are usually manufactured by rolling and welding together large steel plates. Offshore wind turbines are typically designed to operate for 20 years or longer, thus the number of cycles to failure (Nf) that these structures are required to withstand lies in the so called ultrahigh-cycle fatigue (UHCF) regime (Nf>108). Moreover, because, in the past few years, there has been a continuous increase in the size of monopiles, the fatigue life reduction caused by the utilization of thicker steel plates plays an important role (i.e., thickness or size effect). Different regions worldwide apply distinct codes to ensure that offshore structures can withstand fatigue damages, but most of them are tailored for the high-cycle fatigue (HCF) regime. This paper seeks to compare a selection of these codes, highlighting both differences and similarities, while also questioning their suitability in the UHCF regime and for much thicker structures (compared to the reference thickness values reported in the standards). By doing so, it aims to contribute to the ongoing efforts to optimize the efficiency of the fatigue life assessment of offshore wind infrastructures. The focus of this study is on double-V transverse butt welds and their S-N curves in air and seawater (with and without cathodic protection), while the analyzed standards are those provided by the Det Norske Veritas (DNV-RP-C203-2021), the British Standards Institution (BS 7608, including the amendments of 2015), and the European Union (EN 1993-1-9, updated in 2005). The results have been discussed in terms of the level of conservatism that each of these standards offers and in identifying the areas for further research to enable extended lives in the current and future offshore wind monopile foundations.

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices.

Abstract The components and sources of dissolved organic matter (DOM) in urban water bodies are extremely complicated due to multiple environmental and anthropogenic factors. The comprehensive analysis of the DOM molecular composition is crucial for deep understanding their reactivities, environmental behaviors, and fates in aquatic ecosystems, thereby improving our ability to monitor and protect aquatic systems. In this study, ultraviolet–visible spectroscopy (UV–Vis) and excitation-emission matrix fluorescence spectroscopy (EEM) were combined with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to investigate the optical and molecular characteristics of DOM in the Yongding River (YDH) and Beiyun River (BYH), two typical urban rivers with distinct landscapes and anthropogenic influences in Beijing, China. Results showed that DOM in BYH was mainly composed of proteins from microbial and sewage sources with low aromaticity, molecular weight, and abundant saturated labile compounds, along with a significant presence of anthropogenic surfactants. Whereas the YDH was primarily composed of humic substances from plant- and soil-derived terrestrials, with higher aromaticity humification, unsaturation and refractory compounds. In addition, due to the accumulation of refractory compounds, the concentration of DOC and the degree of organic pollution increased from upstream to downstream of the rivers, manifested as an increase in humification and molecular weight at the molecular level. Our results confirmed that landscape urbanization broadly modified the structure of DOM to be more aliphatic and saturated. The findings of this study will provide important insights into the chemdiverse molecular composition of DOM undergoing different influences from human activities and provide significant guidance for water quality management in urban river ecosystems. Graphical Abstract

Abstract The demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non‐renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply‐demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer‐based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink.

Abstract The movement towards electrification is entailing deep changes in power systems. On the demand side, the adoption of electric heating (EH) has grown rapidly in recent years. To eliminate the voltage fluctuations caused by the random switching‐on/‐off behaviors of EHs in some application scenes with special‐power‐line in low voltage distribution networks (LVDNs), a swarm‐intelligence‐based coordinated control approach of EHs for voltage stabilization with zero communication burden while guaranteeing the users’ thermal comforts is researched. A novel bird‐perch‐on‐branch (BPB) ‐based swarm intelligence theory is proposed, which reveals the mapping relation between local observations and global states. On this theoretical basis, a multi‐agent reinforcement learning (MARL) framework is developed for the coordinated dispatch of EHs, where the deep‐Q‐network (DQN) algorithm is adopted to learn and generate the optimal control policy for each EH. The implementation of the proposed MADQN‐BPB approach is described based on the principle of centralized‐training and decentralized‐execution. Comparative control performances of MADQN‐BPB with a commercially available approach, and the global optimal solution are evaluated. Simulation results verify the capability of MADQN‐BPB in voltage stabilization with zero communication burden. Its control performance is close to that of the global optimal solution, and is scalable to the variations of environment.

Abstract This study proposes a frequency coupling suppression strategy for voltage source converters (VSCs) with DC‐link dynamics to address the challenges posed by the asymmetric system structures for system modelling and control design. The strategy introduces a reshaping outer loop into the q‐axis, which eliminates the conjugate voltages and currents induced by the DC‐link dynamics, to achieve decoupling of the DC‐link voltage control (DVC). Therefore, the VSC is modelled as a single‐input single‐output (SISO) admittance model, which provides clear physical insights for the analysis of admittance characteristics. Compared with the admittance matrix, the SISO model reveals the dominance of each control dynamics on VSC admittance in different frequency bands. This insight facilitates the exploration of optimized control strategies to improve the system stability margin. Based on the SISO model, a virtual admittance for compensating the negative resistive effect of the PLL is designed to illustrate the advantages of the proposed method in providing design‐oriented analysis. Experimental results verify the effectiveness of the proposed control strategy.

Abstract By increasing the number of electric vehicles (EVs) to achieve a less carbon environment, not only they consume power from the grid to be charged, but also do they deliver power to the grid, and this can play a significant role in load frequency control. However, EVs take part in frequency regulation based on their state of charge (SoC) which will cause uncertainties. In order to manage these uncertainties, EV aggregator (EVA) concept has been introduced. The EV owner participates in the demand response program provided by the EVA arbitrarily considering her/his requirements. Accordingly, EVA calculates the up and down power reserve for the power system operator. Following any frequency deviation, EVA sends the proper commands to each EV to consume or to inject power to the system. There are also communication delays between different parts, uncertainties, and non‐linearities that existed in the power system. To overcome these issues, this study proposes a new robust load frequency controller based on feedback theory and artificial neural network which is designed through the non‐linear multi‐machine power system. Simulation results on IEEE 39‐bus power system show that the proposed controller regulates frequency more desirably in comparison with other methods.

Abstract Due to intermittent renewable energy and fluctuating load demand, distribution networks with renewable distributed generation (DG) installations are more likely to suffer voltage issues and significant power losses. The performance of conservation voltage regulation (CVR) schemes may be adversely affected by the undesirable voltage profile at specific nodes. This paper aims to reduce power losses in CVR‐implemented networks by optimally planning new renewable DGs without changing the existing ones. A scenario‐based optimal renewable DG planning model is proposed with a novel scenario formation method. The uncertainties of load demand and renewables are captured jointly and formed into a finite number of scenarios based on a multivariate Gaussian mixture model (MultiGMM). The locations and capacities of different types of new renewable DGs are optimally planned for CVR performance improvements on power loss saving by aggregating the operation status and probabilities of the scenarios using mixed‐integer non‐linear programming (MINLP). A time‐series simulation is formulated for accuracy verification. The results of case studies show that the proposed model can significantly reduce power losses, active load demand, and reactive load demand. The accuracy of the planning results can be guaranteed with fewer scenarios compared to a widely used classical scenario‐based planning method.

Abstract Electric vehicles (EVs) and small photovoltaic (PV) installations advance residential power grids by lowering charging costs and fostering eco‐friendly operations. Yet, the variable nature of EV charging presents challenges to grid reliability. This research introduces a Monte Carlo‐based simulation for predicting EV charging loads and a systematic charging method that integrates a ‘green electricity’ pricing scheme with a joint optimization model for PV and EV management. By applying an improved ant lion optimizer (IALO) algorithm enriched with differential evolution features, an optimization strategy that markedly enhances grid performance is devised. In a park scenario, this ‘green electricity’ model reduced the mean square error of EV charging load by 11.82%, smoothed the power load curve, and improved grid stability. When compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO) algorithms, the IALO algorithm boosted overall revenue by 16.8% and 12.8%, increased PV utilization by 162.3% and 37.1%, and significantly cut carbon emissions by 159.6% and 31.6%, respectively. These outcomes affirm the financial, environmental, and functional benefits of our proposed approach.

Abstract Due to the limitations imposed by urban power grid outages for maintenance, on‐line harmonic current detection technology for distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, enhancing the real‐time diagnosis of distribution network cable insulation conditions. This study established a 10 kV distribution network cable test platform and prepared typical defective cables subjected to moisture and long‐term thermal aging. Using COMSOL finite element electromagnetic simulation, the magnetic flux evolution laws of the cable insulation under typical defects were obtained. Experimental tests provided the harmonic current characteristics and statistical features of cables with typical defects. Based on these data, a method for analysing the degradation degree of distribution network cables was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis. Furthermore, a defect‐type identification method based on cluster analysis was proposed. Results indicate that the odd harmonics and the 4th harmonic of the distribution network cable's harmonic current are closely related to the cable's degradation state. A model integrating principal component analysis (PCA) data dimensionality reduction and expectation‐maximization clustering analysis achieved a recognition accuracy of up to 75.64% in distinguishing between moisture‐affected and normal cable states. The proposed on‐line detection and evaluation methods can effectively identify high‐risk cables with latent defects.

Abstract Combined hydrogen, heat, and power (CHHP) is the cogeneration unit simultaneously meeting hydrogen, heat, and power demands. Due to the low efficiency of the electrolyser, the main grid or other generation units supply its input power. Herein, the photovoltaic farm's (PV farm) output power is used as the input power of the electrolyser. Optimal economic and emission scheduling of CHHP‐based microgrid (CHHP_MG) is defined as an objective function. To improve system efficiency, the scheduling problem is done in two cases, reconfigured SP‐PV farm and conventional SP‐PV farm. To optimal management of both demand and generation sides, a time of use (TOU) based demand response program is employed in the proposed short‐term study. To have a comprehensive study, the optimum solar panel tilt angle is calculated, and its impacts on the main function are analysed. Multi‐objective genetic algorithm is used to solve optimization problems. By solving the objective function, the obtained values for profit and pollution amount are 620$ and 69,245.78 gr, respectively. By applying the load response program, the mentioned values are changed to 715.132$ and 6872.5 gr. Since the input power is supplied through solar panels, the placement angle of the panels is very important and with the optimal setting of the placement angle, the values change to 780$ and 6825.3 gr.

Abstract This article introduces a Wasserstein distance‐based distributionally robust optimization model to address the transmission expansion planning considering renewable‐based microgrids (MGs) under the impact of uncertainties. The primary objective of the presented methodology is to devise a robust expansion strategy that accounts for both long‐term uncertainty and short‐term variability over the planning horizon from the perspective of a central planner. In this framework, the central planner fosters the construction of appropriate transmission lines and the deployment of optimal MG‐based generating units among profit‐driven private investors. The Wasserstein distance uncertainty set is used to characterize the long‐term uncertainty associated with future load demand. Short‐term uncertainties, stemming from variations in load demands and production levels of stochastic units, are modeled through operating conditions. To ensure the tractability of the proposed planning model, the authors introduce a decomposition framework embedded with a modified application of Bender's method. To validate the efficiency and highlight the potential benefits of the proposed expansion planning methodology, two case studies based on simplified IEEE 6‐bus and IEEE 118‐bus systems are included. These case studies assess the effectiveness of the presented approach, its ability to navigate uncertainties, and its capacity to effectively optimize expansion decisions.

Abstract In recent years, the “asset management” or “managing assets” technique has been expected to support the rationalization of the maintenance and operation of electric power equipment, especially as a countermeasure for aging equipment. For this purpose, the development of diagnostic methods for aging, remaining life, and faults is necessary. Furthermore, it is important to accumulate case studies applying such diagnostic methods to real equipment and the analysing benefits of the applications of the diagnostic methods. In this report, frequency response analysis and a partial discharge measurement are applied to a shunt reactor suspected of occurrence of partial discharge. Results of the diagnostic methods suggest the breakage of the earth bar connecting the iron‐core blocks and the occurrence of the partial discharge at the iron core. From these diagnostic findings, the shunt‐reactor operator decided to postpone the replacement for 2 years, compared with the case where no diagnostic methods were applied. In this report, the benefits of deciding the postponement is also evaluated.

In this paper, the performance of a 5-level cascaded H-bridge inverter in unmanned aerial vehicle (UAV) applications is analized to identify, at the converter design stage, the better device choice depending on different UAV operation scenarios. Considering that regardless of the specific application there are some typical operations, such as take-off, climb, land, cruise, and potential recurring climbs and descents, the results can support the choice by considering the typical working conditions of the specific application where the UAV would be used. The results have been obtained by simulating the H-bridge inverter considering the circuit models of insulated-gate bipolar transistors (IGBTs), GaN high-electron-mobility transistors (HEMTs), and Si and SiC metal-oxide-semiconductor field-effect transistors (MOSFETs) provided by manufacturers. The study has highlighted that the choice of the device depends on the UAV usage, switching frequency, and load conditions. More specifically, considering the typical devices and systems costs in the case of a selective harmonic elimination procedure operating at the fundamental switching frequency, the Si devices should be used. Moreover, the preference for using IGBTs or Si MOSFETs depends on the typical working conditions of the UAV application. In the case of phase-shift carrier modulation technique, at 4 kHz the MOSFET is the best device and the choice between Si and SiC devices depends on the UAV application's main operation scenarios. At 20 kHz the SiC MOSFET is the best device, while at higher frequencies the GaN HEMT cost should be faced to take advantage of its best performance.

A ring-connected microgrid cluster can be formed by connecting geographically closed microgrids for mutual power sharing to increase the system's reliability. Real-time power balance within individual microgrids and power sharing among the microgrids of an islanded microgrid cluster would be challenging during contingencies if they are not properly sized and controlled. We propose a technique to design a ring-connected microgrid cluster that has several distributed energy resources. The amount of power flow via interconnecting cables was decided considering the size of the energy storage of the neighboring microgrids. A control system was designed to minimize the effect of severe transients in the neighboring microgrids in the network. The performance of the proposed technique was verified using a ring-connected microgrid cluster with four microgrids derived based on a real distribution system. The results illustrated that the proposed ring-connected microgrid cluster could maintain the power balance of the networked microgrid during the contingencies of neighboring microgrids, increasing the resiliency of the system compared to the radial and islanded operations.

This study presents a compact educational photovoltaic/thermal (PV/T) system designed for thorough performance assessment under simulated weather conditions. As an affordable educational tool, the system offers significant pedagogical value. The PV/T system features two photovoltaic modules: a thermally enhanced module and a standard one. The thermally enhanced module uses water as a coolant, which transfers heat from the PV cells to a fan-operated heat exchanger, with the coolant being recirculated to maintain optimal conditions. A halogen lamp, placed between the modules, simulates solar radiation to ensure effective electrical current generation. The system’s remote-control capabilities, managed via the Message Queuing Telemetry Transport (MQTT) protocol, enable real-time adjustments to the coolant flow rate, heat exchanger efficiency, and lamp brightness, as well as monitoring of electrical parameters. Experimental findings indicate that the PV/T module achieves a 7.71% increase in power output compared to the standard PV module and offers a 17.41% improvement in cooling efficiency over scenarios without cooling. Additionally, the numerical methods used in the study show a maximum deviation of 4.29% from the experimental results, which is considered acceptable. This study showcases a best practice model for solar training, applicable from elementary to university levels, and suggests innovative approaches to enhancing solar energy education.

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

The aim of this paper is to apply all the existing pyranometric methods to estimate the sunshine duration from global solar irradiation in order to find the most suitable method for a tropical site in its original form. Then, in a second step, one of these methods will be optimized to effectively fit tropical sites. Five methods in the literature (Step algorithm, Carpentras Algorithm, Slob and Monna Algorithm, Slob and Monna 2 Algorithm, and linear algorithm) were applied with eleven years of global and diffuse solar radiation data. As a result, with regard to its original form, the step algorithm is in the first rank. But in the second step, after improving its main coefficients, the Carpentras Algorithm was found to be the best algorithm for tropical sites in the southern hemisphere.

Failures in hydraulic systems in offshore wind turbines represent an enormous challenge for manufacturers and operators, as the pitch system statistically is one of the subsystems contributing the most to the downtime of the turbines, which is the case for both electrical and hydraulic pitch systems. However, the complex failure mechanisms of the various different hydraulic components mean that, typically, the critical components of hydraulic systems must be tested to better understand the failure mechanisms. Nonetheless, conventional testing procedures are lengthy and costly. Accelerated testing plays a critical role as it can mimic hydraulic system failure mechanisms in a shorter period. However, the lack of standardized test methods and detailed knowledge about the failure-accelerating effects complicates the process. Therefore, this paper offers a comprehensive examination of approaches applicable to conducting accelerated tests on hydraulic systems. It identifies and discusses five primary component types or sub-components related to the acceleration of testing in hydraulic systems: pumps, cylinders, seals, valves, and hoses. Each section references studies that delve into accelerated testing methodologies for these individual components. Furthermore, within each component, a concise overview of the current techniques is provided, followed by a discussion and summary based on the state of the art.

Abstract Packetized energy encapsulates energy into modulated, routable, and trackable energy packets, enhancing the flexibility of managing distributed energy resources and expediting prosumers’ participation in transactive energy markets. In the context of packetized energy trading (PET), energy prosumers are naturally deemed as self‐interested agents seeking to obtain their own benefits. To align with prosumers’ demand, supply, quality of service (QoS), and system‐level social welfare, it is necessary to explore the design of prosumers’ bidding strategies and the market clearing methods, considering prosumers’ utility and the best demand response to markets. This study addresses challenges arising from prosumers’ selfishness and asymmetric preferences by proposing a PET‐oriented iterative double auction (IDA‐PET) design, where prosumers are allowed to iteratively change the bids before the auctioneer clears the market. Moreover, IDA‐PET accommodates system capacity constraints, energy balance, and economic constraints, providing cooperative strategies for both prosumers and the auctioneer. To validate the effectiveness of IDA‐PET, a novel and dedicated co‐simulation platform based on the hierarchical engine for large‐scale infrastructure co‐simulation platform is developed and case studies are conducted within a residential microgrid. The simulation results demonstrate that IDA‐PET can efficiently enhance the revenue of the auction market while meeting prosumers’ QoS requirements.

Abstract Lithium‐ion battery state‐of‐health (SOH) monitoring is essential for maintaining the safety and reliability of electric vehicles and efficiency of energy storage systems. When the SOH of lithium‐ion batteries reaches the end‐of‐life threshold, replacement and maintenance are required to avoid fire and explosion hazards. This paper provides a comprehensive literature review of lithium‐ion battery SOH estimation methods at the cell, module, and pack levels. Analysis and summary of the SOH definition based on the resistance, capacity, and energy indices are presented at each battery hierarchy level. A Comparison of SOH indices in terms of modelling complexity, required measurement time, and accuracy is provided. To the best of knowledge, a comprehensive classification of SOH estimation methods at different battery hierarchy levels is presented for the first time in this review. In addition, SOH estimation methods are further classified based on the applied methodologies, including direct measurement, model‐based methods, data‐driven methods, and hybrid model‐data methods. Advantages and disadvantages of SOH estimation methods are summarized and compared across different battery hierarchy levels. A detailed summary of typical SOH estimation methods is presented along with the battery topology, operating conditions, and performance. The challenges and research prospects of lithium‐ion battery SOH estimation are discussed from the cell to pack levels.

Abstract Renewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Abstract To optimally harness the adjustable capabilities of reactive power sources for voltage control, a dynamic partitioning method that uses reactive power flow tracking for branch cutting through Binary Particle Swarm Optimisation (BPSO) is proposed for Active Distribution Networks (ADNs). Initially, the limitations of existing Voltage/Var Sensitivity (VVS) calculation methods are analysed, leading to the proposition of a novel VVS calculation method capable of capturing variations in source‐load timing characteristics. Subsequently, the fuzzification of the VVS matrix between nodes is used to derive the membership degree matrix. Next, based on the membership relationship between reactive power source nodes, these nodes are pre‐partitioned, and the number of leading nodes and zones alongside are preliminarily determined. Then, the range of the branch to be cut is established, guided by the reactive power flow direction of the branch. Employing the zonal comprehensive coupling degree as the objective function of the BPSO facilitates the identification of optimal branch cutting points, thereby determining the partitioning outcome. Finally, a reactive power reserve check is executed to rectify any non‐compliant zones. In this study, numerical simulations are conducted using the enhanced IEEE 33‐node power system to demonstrate the efficacy of the proposed method.

Abstract Accurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of Value‐at‐Risk and Conditional Value‐at‐Risk compared to original methods.