Advanced Operation, Control, and Planning of Intelligent Energy Systems

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids. Full article

In recent years, the global power industry has experienced rapid development, with significant advancements in the source, network, load sectors, and energy storage technologies. The secure, reliable, and economical operation of power systems is a critical challenge. Due to the stochastic nature of intermittent renewable energy generation and the coupled time-series characteristics of energy storage systems, it is essential to simulate uncertain variables accurately and develop optimization algorithms that can effectively tackle multi-objective problems in economic dispatch models for microgrids. This paper proposes a pelican algorithm enhanced by multi-strategy improvements for optimal generation scheduling. We establish eight scenarios with and without pumped storage across four typical seasons—spring, summer, autumn, and winter—and conduct simulation analyses on a real-world case. The objective is to minimize the total system cost. The improved pelican optimization algorithm (IPOA) is compared with other leading algorithms, demonstrating the validity of our model and the superiority of IPOA in reducing costs and managing complex constraints in optimization. Full article

The resilient control issue for the generation unit (GU) in a local power plant with unreliable communication is addressed in this article, where the communication may be jammed by denial-of-service (DoS) attacks. Based on the GU model of voltage and current at the point of common coupling, a demand-driven network communication protocol is proposed to decrease the number of scheduling signal transmissions, and an observer-based prediction method is provided to replenish the lack of dispatching data during transmission intervals when the demand has not changed. The closed-loop performance is analyzed for the GU system in the input-to-state stable framework with or without attack. According to the DoS attack model, which is described by the assumptions of frequency and duration, the conservativeness of the tolerable DoS attack index is reduced by using the thought of robustness to the maximum disturbance-induced error. Simulation examples are provided to verify the effectiveness of the approach proposed in this article. Full article

This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties in traffic flow and renewable energy generation, optimizing system performance under worst-case conditions to mitigate risks of grid instability. Applied to a highway with eight dynamic charging lanes (500 kW per lane), serving up to 50 EVs simultaneously, the framework balances energy contributions from 15 renewable generators (60% of the mix) and 10 non-renewable generators. Simulation results highlight its effectiveness, maintaining grid stability with voltage deviations within 0.02 p.u., reducing energy losses to under 0.8 MW during peak traffic (1500 vehicles per hour), and achieving 95% lane utilization. Dynamic charging enabled EV users to save USD 0.08 per kilometer through reduced stationary charging downtime, optimized travel efficiency, and lower energy costs. Additionally, the system minimizes maintenance costs by optimizing lane and grid reliability. This study underscores the potential of GAN-based DRO methodologies to enhance the efficiency of power grids supporting dynamic EV charging, offering scalable solutions for diverse regions and traffic scenarios. Full article

In recent years, the share of renewable energy in the distribution network has been increasing. To deal with high renewable energy penetration, it is important to improve the energy efficiency and stability of the distribution network. In this paper, the optimal configuration of a distribution network with a high proportion of new energy and electric vehicles is investigated. Firstly, based on the copula theory, the clustered new energy data are obtained by optimizing the wind and solar output scenarios. Secondly, the uncertainty of renewable energy output is fully considered in the planning stage of the distribution network. Subsequently, an improved multi-objective particle swarm optimization algorithm is adopted to determine the optimal capacity and location of charging stations. Finally, the IEEE 33-node distribution network is used for case analysis. Through the comparison of network loss, voltage change, and other related parameters, the advantages of shared energy storage characteristics of electric vehicles in smoothing the uncertainty of the high proportion of new energy are verified. Full article

As an important means to ensure the safety of power transmission, the inspection of overhead transmission lines requires high accuracy for detecting small objects on the transmission lines and relies heavily on the construction of large-scale datasets by using deep learning instead of manual inspection. However, transmission inspection data often involve some sensitive information and need to be labeled by professionals, so it is difficult to construct a large transmission inspection dataset. In order to solve the problem of how to effectively train only on a small amount of transmission line data and achieve high object detection accuracy considering the large-scale variation in transmission objects, we propose an enhanced self-supervised pre-training model for DETR-like models, which are innovative object detectors eliminating hand-crafted non-maximum suppression and manual anchor design compared to previous CNN-based detectors. This paper mainly covers the following two points: (i) We compare UP-DETR and DETReg, noting that UP-DETR’s random cropping method performs poorly on small datasets and affects DETR’s localization ability. To address this, we adopt DETReg’s approach, replacing Selective Search with Edge Boxes for better results. (ii) To tackle large-scale variations in transmission inspection datasets, we propose a multi-scale feature reconstruction task, aligning feature embeddings with multi-scale encoder embeddings, and enhancing multi-scale object detection. Our method surpasses UP-DETR DETReg with DETR variants when fine-tuning PASCAL VOC and PTL-AI Furnas for object detection. Full article

In active distribution networks (DNs), distributed energy resources (DERs) must be disconnected from the grid prior to automatic reclosing actions. Many scholars have proposed non-voltage checking reclosing methods, but a significant challenge arises; many substations lack line-side voltage transformers (LSVTs), making these schemes impractical. To address this, we introduce a time-limited adaptive automatic reclosing (TLAR) method that integrates DERs’ anti-islanding protection (AIP) with automatic reclosing. This method estimates the AIP action time using bus-side voltage measurements before the system-side protection (SSP) is tripped and adjusts the reclosing time accordingly to enhance power supply reliability. Simulations using PSCAD validate the method’s effectiveness. The TLAR method is well-suited for distribution lines without conditions for non-voltage checking, is cost-effective, easy to implement, and contributes to power system stability. Full article

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = $94.2$%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%. Full article

A virtual synchronous generator (VSG) is prone to transient instability under a grid fault, which leads to the loss of synchronization between the new energy converter and grid, and threatens the operation safety of high-proportion new energy grids. There are a variety of control models in the existing VSG control, including active and reactive power models, which lead to their different transient stabilities. However, the evolution characteristics, correlation between different models of VSG, and the internal mechanism affecting transient stability have not been fully studied. To this effect, this paper analyzes their evolution characteristics based on the existing mainstream VSG control models and establishes a unified VSG model and its equivalent correspondence with other models. Then, the phase plane method is used to comprehensively analyze and compare the transient stability of the VSG unified model with other models. It is pointed out that the key factors affecting the transient stability of different models are three links of primary frequency regulation, reactive power regulation and reactive power tracking. Finally, the correctness of the established VSG unified model and the conclusion of transient stability analysis is verified by experiments. Full article

This study is aimed at addressing the problem of optimizing microgrid operations to improve local renewable energy consumption and ensure the stability of multi-energy systems. Microgrids are localized power systems that integrate distributed energy sources, storage, and controllable loads to enhance energy efficiency and reliability. The proposed approach introduces a novel microgrid optimization method that leverages the parameterized Dueling Deep Q-Network (Dueling DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. The method employs a parametric hybrid action-space reinforcement learning technique, where the DDPG is utilized to convert discrete actions into continuous action values corresponding to each discrete action, while the Dueling DQN uses the current observation states and these continuous action values to predict the discrete actions that maximize Q-values. This integrated strategy is designed to tackle the co-scheduling challenge in microgrids, enabling them to dynamically select the most favorable control strategies based on their specific states and the actions of other intelligent entities. The ultimate objective is to minimize the overall operational costs of microgrids while ensuring the efficient local consumption of renewable energy and maintaining the stability of multi-energy systems. Simulation experiments were conducted to validate the efficacy and superiority of the proposed method in achieving the optimal microgrid operation, showcasing its potential to improve service quality and reduce operational expenses. Average rewards increased by 30% and 15% compared to the use of the Dueling DQN or DDPG only. Full article

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability. Full article

Cogeneration is an important means for heat supply enterprises to obtain heat, and accurate load prediction is particularly crucial. The heat load of a centralized heat supply system is influenced by various factors such as outdoor meteorological parameters, the building envelope structure, and regulation control, which exhibit a strong coupling and nonlinearity. It is essential to identify the key variables affecting the heat load at different heating stages through data mining techniques and to use deep learning algorithms to precisely regulate the heating system based on load predictions. In this study, a heat station in a northern Chinese city is taken as the subject of research. We apply the Fuzzy Clustering based on Fourier distance (FCBD-FCM) algorithm to transform the factors influencing the long and short-term load prediction of heat supply from the time domain to the frequency domain. This transformation is used to analyze the degree of their impact on load changes and to extract factors with significant influence as the multifeatured input variables for the prediction model. Five neural network models for load prediction are established, namely, Backpropagation (BP), convolutional neural network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and CNN-BiLSTM. These models are compared and analyzed for their performance in long-term, short-term, and ultrashort-term heating load prediction. The findings indicate that the load prediction accuracy is high when multifeatured input variables are based on fuzzy clustering. Furthermore, the CNN-BiLSTM model notably enhances the prediction accuracy and generalization ability compared to other models, with the Mean Absolute Percentage Error (MAPE) averaging within 3%. Full article

With the large-scale integration of new energy, the obstruction of new energy consumption is prone to occur often during peak-down periods with a low load and high output of new energy. It is urgent to mobilize controllable load shifting through compensation mechanisms to achieve the goals of peak shaving, valley filling, and promotion of new energy consumption. This study constructs a framework of auxiliary service market and compensation mechanism for power shift between new energy power generation enterprises and controllable load enterprises. Secondly, aiming to achieve the principle of revenue balance between new energy power generation enterprises and controllable load enterprises, a quantity and price compensation model based on the particle swarm optimization algorithm is proposed. Then, under the principle of determining the compensation order of different controllable load enterprises through comprehensive evaluation and formulating differentiated compensation prices one by one, a compensation method and process for controllable load enterprises to shift have been established. Finally, through a case analysis, compensation prices for five types of controllable loads were formulated, with values ranging from 99.36 to 197.41 CNY/MWh. This increased the compensation for controllable loads on the basis of the original peak-valley price, verifying the feasibility of the method described in this study. Full article

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS^®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability. Full article

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments. Full article

In response to the poor reliability in identifying fault direction in distribution networks with Inverter Interfaced Distributed Generators (IIDGs), considering the control strategy of low-voltage ride-through, a fault direction criterion based on post-fault positive-sequence steady-state components is proposed. Firstly, the output steady-state characteristics of IIDGs considering the low-voltage ride-through capability are analyzed during grid failure, and the applicability of existing directional elements in a distribution network with IIDGs connected dispersively is demonstrated. Subsequently, for the typical structure of an active distribution grid operating under flexible modes, the positive-sequence voltage and current are examined in various fault scenarios, and a reliable direction criterion is suggested based on the difference in post-fault positive-sequence impedance angles on different sides of the lines that are suitable whether on the grid side or the IIDG side. Lastly, the reliability of the proposed direction criterion is verified by simulation and the results indicate that the fault direction can be correctly determined, whereas phase-to-phase and three-phase short circuit faults occur in different scenarios, independent of the penetration and grid-connected positions of IIDGs, fault location, and transition resistance. It is suitable for fault direction discrimination of an IIDGs multi-point grid-connected system under a flexible operation mode. Full article

As global climate change intensifies, extreme weather events are becoming more frequent, with ice disasters posing an increasingly significant threat to the stable operation of power distribution networks. Particularly during power outages for de-icing, multiple power islands may form within a distribution area, increasing the complexity of grid operations. Existing research has not fully considered the comprehensive coordination of stable operation of these power islands and de-icing maintenance schedules. Therefore, for the potential multi-island operation of distribution networks caused by freezing disasters, this paper first establishes a dynamic island partitioning model based on distribution network reconfiguration technology. Secondly, based on the characteristics of the de-icing phase, a de-icing maintenance schedule model is established. Finally, dispatch optimization of the distribution network is coordinated with the line de-icing maintenance schedule. By adjusting the de-icing strategies and network structure, the aim is to minimize the risk of load loss. The relevant case analysis indicates that the collaborative optimization model established in this paper helps power distribution networks to reduce their economic losses when facing adverse weather conditions. Full article

An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very complicated, and most of the current literature focuses on optimization of a specific system. In this work, a simulation-based two-step decoupling method is proposed to simplify the optimization of an IES. The generalized IES is split into four subsystems, and a two-layer optimization method is applied for optimization of the capacity of each piece of equipment. The proposed method enables fast comparison among abundant optional configurations of an IES, and it is applied to a hospital in Beijing, China. The optimized coupling system includes the gas-fired trigeneration system, the GSHP, and the electric chiller. Compared with the traditional distributed systems, the emission reduction rate of CO₂ and NO_X for the coupling system reaches 153.8% and 314.5%, respectively. Moreover, the primary energy consumption of the coupling system is 82.67% less than that of the traditional distributed energy system, while the annual cost is almost at the same level. Full article

In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme. Full article

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively. Full article

Achieving precise stopping of electric locomotives is crucial for the realization of intelligent and unmanned auxiliary transportation systems. Presently, human drivers play a central role in ensuring accurate stopping, presenting obstacles to automation and cargo location precision, especially within the coal mining sector. This article centers on achieving the precise stopping of electric locomotives under various conditions through the utilization of permanent magnet synchronous motor-driven locomotives. This approach introduces a novel stopping control method that integrates a fuzzy proportional–integral–derivative (F-PID) controller with a vector control model for permanent magnet synchronous motors (PMSM). Subsequently, we develop the F-PID controller using the PMSM technique, incorporating new fuzzy rules for each subsystem to enhance control accuracy and efficiency. Finally, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed to validate the effectiveness of the proposed control method. The results show that the method consistently achieves precise stopping under diverse working conditions, with an error of less than 0.3 m, confirming its robustness and reliability. Full article

The modular multilevel converter (MMC) has many advantages of low switching losses, good harmonic performance and high modularity structure in state-of-the-art HVDC applications. The full-bridge submodules (FBSMs) of the hybrid MMC can inherently output negative voltage to absorb fault currents, and consequently the hybrid MMC can ride through severe DC faults without blocking. During the DC fault-ride-through process, the submodule capacitor voltage and arm current of the MMC will be temporarily increased. These characteristics limit the proportion of the FBSMs, which should not be too low and thus increase the cost and operating losses of the hybrid MMC. In this paper, an improved sorting algorithm of SM capacitor voltage is established, and a novel virtual damping control strategy is proposed that can effectively suppress the increase in submodule capacitor voltage and arm current of the hybrid MMC during the DC fault-ride-through process. By adopting this optimization control, the proportion of FBSMs can be reduced significantly without deteriorating the fault-ride-through capability or safety of the MMC. The effectiveness of the proposed control is verified by careful theoretical analysis and detailed simulation results. Full article

In the case of strong non-Gaussian noise in the measurement information of the distribution network, the strong non-Gaussian noise significantly interferes with the filtering accuracy of the state estimation model based on deep learning. To address this issue, this paper proposes an enhanced kernel ridge regression state estimation method based on ensemble empirical mode decomposition. Initially, ensemble empirical mode decomposition is employed to eliminate most of the noise data in the measurement information, ensuring the reliability of the data for subsequent filtering. Subsequently, the enhanced kernel ridge regression state estimation model is constructed to establish the mapping relationship between the measured data and the estimation residuals. By inputting the measured data, both estimation results and estimation residuals can be obtained. Finally, numerical simulations conducted on the standard IEEE-33 node system and a 78-node system in a specific city demonstrate that the proposed method exhibits high accuracy and robustness in the presence of strong non-Gaussian noise interference. Full article

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM. Full article

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated with the unpredictability of wind power. The EDP is transformed into an equivalent optimization problem with only an equality constraint and thus can be solved by an alternating-direction method of multipliers (ADMM). Then, to tackle this problem in a distributed manner, the outer-layer framework of the proposed strategy adopts a parallel ADMM, where different variables can be calculated simultaneously. And the inner-layer framework adopts a finite-step consensus algorithm. Convergence to the optimal solution is achieved within a finite number of communication iterations, which depends on the scale of the communication network. In addition, leveraging local and neighbor information, a distributed algorithm is designed to compute the eigenvalues of the Laplacian matrix essential for the finite-step algorithm. Finally, several numerical examples are presented to verify the correctness and effectiveness of the proposed strategy. Full article

Combining various sources to create a complementary system plays a key role in utilizing clean energy sources economically and mitigating air pollution during the heating season in Northern China. However, there is a lack of unified and reasonable design methods for such systems, resulting in the excessive capacity of equipment and the waste of energy. In this work, a unified design method is proposed to solve this problem. A generalized structure and its mathematical model are firstly established, enabling transient simulations on the TRNSYS platform. Then, a preliminary screening criterion for the system composition a general operation strategy is proposed. Finally, the system configuration is optimized by using the genetic algorithm. The method is successfully applied in a demonstration project in China. The results show that the coupling system consisting of a biomass boiler (384 kW), an air-source heat pump (430 kW) and a ground-source heat pump (369 kW) is the most economical, and the annual cost is 26.7% lower than that of a single-equipment system. Additionally, the sensitive factors that strongly affect the optimization results are explored. The establishment of the generalized structure and its mathematical model enables the quick calculation and convenient comparison of various schemes, and simplifies the complicated optimization problem of the capacity optimization of each piece of equipment. The proposed design method can reduce the annual cost to a minimum value, and thus it provides a theoretical basis for the large-scale application of clean energy sources for heating. Full article

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric–thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of the proposed capacity optimization method in terms of economy and environmental friendliness through a comparative analysis of different planning cases. Simulation results show that, compared with the scenario of completing planning at the beginning of the life cycle at one time, the proposed low-carbon-oriented capacity optimization method that considers construction time sequence and uncertainty can not only reduce the cost of the integrated energy system, but also help to enhance renewable energy utilization and reduce the system’s carbon emissions; the total cost of phased planning is reduced by 11.91% compared to the total cost of one-time planning at the beginning of the year. Full article

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm. Full article

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. Full article

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately. Full article

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the network operator following disruptive events. The analysis is divided into two fundamental aspects. Firstly, it addresses the topological reconfiguration of the network, involving the addition of lines and distributed energy resources such as distributed generation. To determine the optimal topological reconfiguration, a genetic algorithm was developed and implemented. This approach aims to restore electrical service to the maximum load within the system. Secondly, an optimal energy dispatch was performed for each generator, considering the variation in load throughout the day. The system’s load curve is taken into account to determine the optimal energy distribution. Thus, the problem of economic losses is approached from two perspectives: the minimization of costs due to nonsupplied electrical energy and the determination of efficient energy dispatch for each generator after network reconfiguration. For the analysis and case studies, simulations were conducted on the IEEE 9- and 30-node test systems. The results demonstrate the effectiveness of the proposed solution, evaluated in terms of reduced load shedding and economic losses. Full article

In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on the optimal node selection strategy that does not depend on the network coupling strength is improved, and a finite-time controller with a simpler structure is constructed. Secondly, based on the event-triggered control strategy an effective trigger event is designed to achieve the low energy cost synchronization of Markovian switching complex networks on the basis of reducing the information transmission and interaction between networks. Finally, the two control strategies mentioned in this paper are compared and analyzed from multiple perspectives through numerical simulations to better guide practical engineering. Full article

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia support under maximum power point tracking control. To make the wind turbine improve the inertia support ability of the system based on virtual inertia control, a method for active power allocation based on the rotor speed of DFIG is proposed. Firstly, the minimum system inertia requirement based on the frequency change rate of the system is established. Active power allocation assumes that the wind farm inertia meets the minimum system inertia requirement. Secondly, the objective is to enhance the inertia support capability and overall active power output of the wind farm, considering the constraint of the minimum system inertia requirement. Based on the rotor speed to establish the inertia allocation weight factor, the weight of the power command is assigned to a single machine to achieve the wind farm active power allocation. Finally, it is verified that the system’s equivalent inertia meets the minimum inertia requirement of the system. Simulations show that the proposed allocation method can adequately elevate the inertia support capability of DFIGs to the system and the rotor kinetic energy utilization. Full article

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified. Full article

With the rapid development of power electronic equipment, the automation and intelligence level of active distribution networks (ADNs) continues to improve. Against this background, soft open points (SOPs) are gradually replacing traditional segmented switches and interconnection switches. The voltage support capability and fast response characteristics of SOPs can shorten power outage time and expand load recovery range. However, the widespread integration of distributed renewable energy and new power electronic devices has made the fault characteristics of ADNs more complex, significantly increasing the computational complexity of ADN reliability assessment. At present, there are few studies that comprehensively consider ADNs with multiple devices. Therefore, this paper proposes a reliability evaluation method for ADNs that considers multiple devices. Firstly, the impact of circuit breakers, SOPs, and segmented switches on the load recovery process is analyzed. Secondly, an improved virtual fault flow model based on the action mechanisms of circuit breakers, SOPs, and segmented switches is established. The virtual fault flow is represented by logical variables to simulate post fault network reconstruction strategies that include circuit breaker tripping, SOP power supply recovery, and segmented switch isolation actions. Then, with the goal of minimizing the system average outage time after network reconstruction, a generalized mixed integer linear programming (MILP) model is given. Finally, taking the IEEE 33-node testing system as a case, the effectiveness and feasibility of the proposed method are demonstrated. Full article

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme. Full article

The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the increasing integration of renewable energy sources introduces heightened fluctuations, rendering the prediction and simulation of working modes more challenging. This paper presents an improved algorithm for calculating power transformer losses attributed to harmonics, with a comprehensive validation against simulation results obtained from the Power Factory application and real-world measurements. The advantages of the algorithm are that all evaluations are performed in real-time based on single-point measurements, and the algorithm was easy to implement in a Programmable Logic Controller (PLC). This allows us to receive the exchange of information to energy monitoring systems (EMSs) or with Power factor Correction Units (PFCUs) and control it. To facilitate a more intuitive understanding and visualization of potential hazardous scenarios related to resonance, an extra Dijkstra algorithm was implemented. This augmentation enables the identification of conditions, wherein certain branches exhibit lower resistance than the grid connection point, indicating a heightened risk of resonance and the presence of highly distorted currents. Recognizing that monitoring alone does not inherently contribute to increased energy efficiency, the algorithm was further expanded to assess transformer losses across a spectrum of Power Factory Correction Units power levels. Additionally, a command from a PLC to a PFCU can now be initiated to change the capacitance level and near-resonance working mode. These advancements collectively contribute to a more robust and versatile methodology for evaluating power transformer losses, offering enhanced accuracy and the ability to visualize potentially critical resonance scenarios. Full article

The implementation of renewable portfolio standards (RPS) and tradable green certificate schemes will exert significant influences on the market equilibrium outcomes and generation firms’ strategic behaviors. To quantitatively investigate these influences, firstly, considering the difference in power generation cost and the uncertainty of renewable energy power generation, the equilibrium model for various trade subjects in the electricity market is established. Secondly, the nondominated sorting genetic algorithm II is adopted for solving the equilibrium model to find well-distributed Pareto-optimal solutions. Finally, the grey relational projection method is used to calculate the priority membership of each decision-making scheme so as to determine the optimal compromise solution. The simulation focuses on analyzing the impact of RPS on the equilibrium results and market behavior of power generators and introduces the Lerner index to quantify the market power of generators. The results show that: (1) An increase in the quota ratio can effectively increase power generation in renewable energy generators. The game between thermal power generators and renewable energy generators raises the prices of both markets at the same time. (2) Improving the forecasting accuracy is conducive to alleviating the market power behavior of various power generators, thereby ensuring the healthy operation of the power market. Full article

With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading model for microgrid cluster based on price trading mechanism and Conditional value-at-risk (CVaR) theory. Firstly, the upper-layer establishes an objective to minimize the overall power fluctuation of the microgrid cluster using Demand response (DR) with a penalty mechanism. The microgrid cluster adopts an internal pricing mechanism and adjusts transaction prices based on internal supply-demand conditions to guide microgrids’ participation in intracluster trading, thereby encouraging the microgrid to use the flexible resources to reduce power fluctuation. Secondly, the lower-layer optimization establishes an optimization model with the objective of minimizing the comprehensive operating cost of the microgrid cluster. The model employs backward scenario reduction techniques to obtain multiple sets of typical scenarios for renewable energy generation, and the CVaR theory is introduced to quantify the potential risk of transaction default. Finally, the effectiveness of the proposed models is verified through case studies considering various application scenarios. Full article

To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the time-consuming problems of boiler online combustion optimization systems. Firstly, A whole year’s worth of the historical operating data preprocessing procedure of a coal-fired boiler in a power station including data resampling, data cleaning, steady-state selection, and cluster analysis is performed. In order to meet the applicable conditions of the linear model, the historical operating data are divided into different sub-datasets (combination mode of coal mills, main steam flow, ambient temperature, lower heating value of coal). Secondly, the multi-objective optimization strategy of economical, carbon, and NOx emissions indexes is employed to select operating optimum data packets, and a new dataset is established that is better than the average value of the optimization target in each sub-dataset. On this basis, a stepwise regression algorithm (SRA) is used to select the specific manipulated variables (MVs) that are significant to the multiple optimization targets from 47 candidate MVs in each sub-dataset (different partitions have different types of MVs), and an MLR prediction model is developed. In order to further realize combustion optimization control, the MVs are optimized by employing the MLR model. According to the deviation between the optimal value and the real-time value of the MVs, a boiler combustion closed-loop control system is developed, which is connected with the DCS using the sum of the deviation signal and the corresponding original one. Then, a boiler combustion application test was carried out under some working conditions to verify the feasibility and effectiveness of the approach. The update time of the system signals running on industrial computers is less than 1 s and suitable for online applications. Finally, a full-scale test of the combustion optimization online control system (OCS) is executed. The results show that the boiler thermal efficiency increased by 0.39% based on standard coal, the NOx emissions reduced by 2.85% and the decarbonization effect is significant. Full article

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability. Full article

Advance in renewable penetration has promoted the interaction between the AC and DC power system and multi-energy sources. However, the disparate nature of different energy utilization technologies presents strong challenges for the economic dispatch of such a complex system. This paper proposes a comprehensive characterization of the AC/DC power system considering multi-energy and renewable integration. Detailed models are elaborated for the AC/DC power grid, the district heating system (DHS), coupling units, and renewables to describe their inner interactions accurately. On this basis, an economic dispatch method is developed to minimize the operation cost and renewable’s abandonment. Simulations indicate that the interaction between the AC/DC power systems and multi-energy sources can enhance voltage levels and improve operational economy. Full article

In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras used to detect abalones. Considering the computational effectiveness and improved detection algorithms, this proposal is promising compared to the previously proposed methods. Some of these methods have low effectiveness and accuracy, and they provide an incorrect counting rate because some of the abalones tend to entangle, resulting in counting two or more abalones as one. Conducting detection and tracking research is crucial to achieve modern solutions for small- and large-scale fishing industries that enable them to accomplish higher automation, non-invasiveness, and low cost. This study is based on the development and improvement of counting analysis tools for automation in the fishing industry. This enhances agility and generates more income without the cost created by inaccuracy. Full article

Multi-terminal DC transmission (MTDC) systems have attracted much attention due to their significant advantages in long-distance and high-capacity transmission. To improve their reliability and operation performance, a distributed fixed-time secondary control of frequency restoration and active power sharing is proposed under event-triggered communication, which only depends on the states of each AC grid and its neighbors. By utilizing Lyapunov theory, we prove that the MTDC system with the fixed-time secondary control can be stable in a settling time, and the conditions of the settling time are established for fixed-time algorithms. In addition, we simulate a five-terminal MTDC system in Matlab/Simulink. Several cases of MTDC systems are exhibited to showcase how well the suggested controller works when dealing with load changes and attacks. The comparison of the number of event-triggered instants shows that the proposed control method can effectively reduce communication resources. Full article

To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives. Full article

In order to solve the current fluctuation problem in microgrids, a suppression method called the Direct-driven Permanent Magnet Synchronous Generator (DPMSG)-based Wind Power System (WPS) based on an adaptive enhanced moving average filter algorithm is proposed. Firstly, the mathematical model of the WPS is established. On this basis, the suppression method under unbalanced conditions is derived by the instantaneous power equation to ensure the stable operation of the microgrid. In order to improve the dynamic compensation capability of the DPMSG-based WPS, an enhanced moving average filtering algorithm with frequency adaptability is proposed. The positive and negative sequence components are obtained in the dq frame by this filtering algorithm. Subsequently, the angular frequency of the microgrid is obtained according to the changing phase, which realizes the high-performance control of the WPS and avoids the complicated parameter adjustment of traditional methods. The correctness of this method is verified by the simulation results. The DPMSG-based WPS with the proposed method can improve the stability of the microgrid. Full article

The adoption of Industry 4.0 is attracting manufacturing companies, but the implementation barriers they expect to face, such as huge investment costs, and lack of skilled workers and infrastructure, make many of them hesitate to go through with implementation. The lack of a standardization approach also adds more difficulties in the implementation of advanced key technologies. Based on the design for the six sigma (DFSS) method, a new decision-making and implementation approach (DM&I) is proposed to address some implementation barriers and provide strategic guidance to implement the Industry 4.0 advanced key technologies. In this study, a systematic literature review was conducted to determine the impact of the implementation barriers for Industry 4.0 adoption. The DM&I approach has been applied in a bearing ring production line as a real case. The proposed method consists of two steps: decision-making, which consists of the define phase in determining the exact system weak point in order to reduce the improvement risk and system operation disturbance; the evaluate phase, which is the determining of the level of use of key technologies; the visualize phase, which is the stage of designing and modeling the proposed system and creating the virtual environment to simulate the system in real-time in order to support the improved decision-making process and avoid the fear of high costs; and then the optimize phase, where the optimal level of use of key technologies is determined. Then, the implementation phase consists of the develop phase, which encompasses the stage of physical system construction and hardware software development, followed by the validate phase and the integrate phase. These phases support infrastructure improvement. However, the proposed approach can be used by manufacturing companies to improve production efficacy and competitiveness. Full article

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of $1.1\times {10}^{-5}$. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled. Full article

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology is a key means through which to deal with the instability of modern energy sources. One of the key development paths in the electricity market is the development by energy merchants of energy storage power plants in the distribution network to engage in a grid demand response. This research proposes a two-stage energy storage configuration approach for a cold-heat-power multi-energy complementary multi-microgrid system. Considering the future bulk connections of distributed power generation, the two most critical points of energy storage station construction are the power generation equipment and specific scenarios for serving the community, as well as the purchase and sale price of electricity for serving the community microgrid (which directly affects the investment revenue). Therefore, this paper focuses on analyzing the different impacts caused by these two issues; namely, the two most important concerns for the construction of energy storage configurations. First, the basic model enabling wholesale electricity traders to construct energy storage power plants is presented. Second, for a multi-microgrid system with a complementary cold-heat-power multi-energy scenario, a two-stage optimum allocation model is constructed, whereby the upper model calculates the energy storage allocation problem and the lower model calculates the optimal dispatch problem. The lower model’s dispatch computation validates the upper configuration model’s reasonableness. Finally, the two-layer model is converted to a single-layer model by the KKT condition, and the nonlinear problem is converted to a linear problem with the big-M method. The validity is proved via mathematical examples, and it is demonstrated that the planned energy storage plants by merchants may accomplish resource savings and mutual advantages for both users and wholesale power traders. Full article

Research on energy storage plants has gained significant interest due to the coupled dispatch of new energy generation, energy storage plants, and demand-side response. While virtual power plant research is prevalent, there is comparatively less focus on integrated energy virtual plant station research. This study aims to contribute to the integrated energy virtual plant station research by exploring the relationship between the integrated energy electro-thermal coupling capacity, various forms of electro-thermal integrated energy response, and electro-thermal integrated energy storage. Analyzing the attributes of an integrated energy microgrid, including energy storage characteristics, time-of-use tariffs, and electric and thermal loads, is crucial. A grid-connected microgrid with cogeneration systems, electric boilers, fuel cells, and energy storage systems is used as an illustrative example. The dispatching method prioritizes multiple complementary energy sources while considering the integrated energy demand response. The study presents different models for the electricity demand and thermal energy demand response and introduces the design of a wholesale power trader involved in building energy storage facilities and participating in the demand response. To verify the feasibility and rationality of the integrated energy demand response scenario, three different schemes are compared, and an economic analysis is conducted. Full article

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to compute the net pairwise dynamic connectedness (NPDC), which originated as a volatility spillover index for financial markets studies in the field of econometrics. This work used the anomaly-detection benchmark Tennessee-Eastman chemical process (TEP) dataset, which consists of 41 measured variables and 11 manipulated variables subjected to various faulty operating conditions. The data analytics was performed using key functions from the R-package ‘ConnectednessApproach’ that implements connectedness computations based on time and frequency. The NPDC coefficient matrices were then transformed into network adjacency matrices for the rendering of the network topology of connectedness for TEP. The resulting network topologies allow a comprehensive analysis of oscillation effects across all plant-measured and manipulated variables. Analyzing the directed connectedness of the system dynamics at short-range, mid-range, and long-range frequencies showed how the oscillation effects of disturbances and faults propagate and dissipate in the short-term, mid-term, and long-term periods. Full article

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can effectively reduce the electricity purchasing cost of charging stations and improve the operational performance of FDNs. Full article

Interval forecasting has become a research hotspot in recent years because it provides richer uncertainty information on wind power output than spot forecasting. However, compared with studies on single wind farms, fewer studies exist for multiple wind farms. To determine the aggregate output of multiple wind farms, this paper proposes an interval forecasting method based on long short-term memory (LSTM) networks and copula theory. The method uses LSTM networks for spot forecasting firstly and then uses the forecasting error data generated by LSTM networks to model the conditional joint probability distribution of the forecasting errors for multiple wind farms through the time-varying regular vine copula (TVRVC) model, so as to obtain the probability interval of aggregate output for multiple wind farms under different confidence levels. The proposed method is applied to three adjacent wind farms in Northwest China and the results show that the forecasting intervals generated by the proposed method have high reliability with narrow widths. Moreover, comparing the proposed method with other four methods, the results show that the proposed method has better forecasting performance due to the consideration of the time-varying correlations among multiple wind farms and the use of a spot forecasting model with smaller errors. Full article

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, retailers’ risk preferences will impact their price bidding strategies. To examine the effects of retailers’ risk preferences on their strategies and equilibrium outcomes in the retail market, an equilibrium model for price-making retailers is proposed by employing the mean–variance utility theory to model the risk preferences of retailers. The market share function is used to characterize consumers’ price-elasticity and switching behavior in the retail market. Few works in the literature address the issue of bidding strategies of retailers with different risk preferences in the electricity market with switchable consumers. Moreover, the existence and uniqueness of the Nash equilibrium are theoretically proved. A theoretical analysis is presented to investigate the impacts of wholesale price uncertainty and retailer’s risk preference on the bidding strategy. By adopting the nonlinear complementarity approach, the proposed game model is transformed into a set of nonlinear equations, which is further solved by the Levenberg–Marquardt algorithm. Finally, examples are included to verify the effectiveness of the proposed theory, and the results show that the bidding price of a retailer will increase with the increasing uncertainty of the wholesale price and the increasing risk-averse levels of itself and its rivals. Full article

This study examines the extent to which electricity shortage influences technical efficiency using data of 805 listed manufacturing companies in China from 2009 to 2020 collected from the CSMAR database. To achieve the objectives of this paper, first, a stochastic frontier analysis (SFA) is used to estimate the technical efficiency (TE) score of manufacturing companies. Subsequently, the TE score is used to evaluate the electricity shortage index and other factors that are postulated to affect enterprise productivity. Two estimation methods have been adopted including ordinary least squares (OLS), which is less robust to endogeneity and instrumental variable (IV) estimation, which turns out to be more robust to endogeneity in the data. The empirical results show that, under OLS estimation, electricity shortage has a significantly negative impact on the technical efficiency of the listed manufacturing companies. However, when IV regression is implemented to address endogeneity issues in the data, electricity shortages tend to have a significantly positive impact on the technical efficiency, underscoring the importance of capturing endogeneity in the data. Extending the baseline results, this study also finds that, while the size of an enterprise may have no bearing, state-owned companies are more likely to be negatively affected by electricity shortages compared to privately owned companies. These results have significant implications for industrial policy design in China in particular, and developing countries in general. Most importantly, the results of this study underscore the importance of policies and measures to promote a shift in the ownership structure towards the private sector. Full article

Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The model enables the planner to make a trade-off between the market power indices and the investment cost. Using comparable results of the IEEE 118-bus system, we show that the proposed TEP outperforms the existing models in terms of market power indices and facilitates open access to the transmission network for all market participants. Full article

Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms of power supply and load in distribution networks are analyzed, the dynamic model of distribution networks based on complex network theory is established, and the risk index of voltage exceeding limits under the conditions of high permeability distributed power supply access is put forward. Secondly, the random power flow of distribution networks based on the Monte Carlo method is studied by sampling and analyzing the dynamic model of distribution networks. Then, the risk calculation and safety assessment of voltage exceeding limits are carried out on the currently extracted model, and the risk control strategy of distribution network operation is put forward. Finally, an improved IEEE30-node distribution network topology is proposed. Through simulation analysis, it is proven that the load situation awareness early warning method of distribution networks can effectively predict, improve the security of distribution networks, and provide timely early warning information for maintenance personnel. Full article

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground coal mines to carry out large-scale calculations to diagnose the grounding fault of the cable. Additionally, there are many types of cable grounding faults, such as single grounding fault and two-phase ground fault. Therefore, how to determine the type of grounding fault quickly through effective calculations and alarms to select the grounding cable is always a difficult task. In this study, to reduce the complexity of cable insulation state classification, we develop a novel classification method based on the decision tree algorithm. Concerning the zero-sequence network under different insulation conditions, the first calculation’s positive and negative values were generated to identify whether the cable insulation was symmetrical. Then, the insulation degradation phase is identified by the relationship between the three-phase voltage phase angle and the current difference variation between the beginning and end of the line. By substituting the correlation quantities collected by a wide-area synchronous measurement system into different equations, the whole grid’s decision tree was constructed in different insulation states. Then, the insulation state of each line was evaluated according to the conductance value. The effectiveness of the proposed method was verified using a 35/6 kV mine power distribution system model based on the MATLAB/Simulink platform. The test results illustrate that the method can accurately diagnose the power cable insulation state based on the decision tree of whether the grid three-phase loads operate in an ungrounded mode or not. Full article

This paper proposes an innovative inertial response imitation (IRI) and rotor speed recovery (RSR) control scheme of a doubly-fed induction generator (DFIG, Type 3 wind turbine generator) to provide better frequency support response and RSR services for a high wind power penetrated electric power grid. To achieve the first benefit, the coupling relationship between the control coefficient of DFIGs and the frequency deviation was established by using the exponential function so that the control coefficient becomes large with the increasing frequency deviations and sizes of disturbance. After supporting the system frequency, the exponential function was employed to schedule the dynamic control coefficient to alleviate the negative effects of RSR on the instantaneous system frequency. The benefits of the proposed IIR and RSR strategy were investigated in a test system under various scenarios of sizes of disturbance and wind speed conditions. Test results clearly demonstrate that the proposed IIR and RSR strategy is capable of boosting the maximum system frequency excursion and reducing the negative influences on the system frequency during the speed recovery period. Full article