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Digital twins can provide system operators with a new way to design, operate, and maintain highly-interconnected multiple energy networks (MEN) by integrating interdisciplinary models using the co-simulation technique and implementing faster-than-real-time simulation enabled by powerful modern computing facilities. This series of papers presents digital twins of MEN capable of real-time simulation facilitated by the holomorphic embedding method. As Part I of this series of papers, mechanism-driven modeling is concentrated to guarantee that high-resolution solutions are computationally accessible. A holomorphic embedding-based model (HEM) for MEN is proposed, which uses time-dependent holomorphic functions to depict the time-varying dynamics of gas and heat flows. A convergence radius model (CRM) is proposed to obtain the essential convergence information of HEM, thereby improving its computational performance to match the real-time requirement. Finally, the effectiveness of the proposed method is validated in a medium-sized study case. The results show that the proposed method offers a significant computational benefit with competent precision when compared to standard differential methods, laying the groundwork for the realization of MEN's digital twins. • Real-time simulation of digital twins is enabled via mechanism-driven modeling. • A holomorphic embedding-based model for multiple energy networks is developed. • A convergence radius model is proposed to strengthen computational performance. • Proposed models offer significant computational benefits with competent precision. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers selected for publication in the Special Issue on Climate Change Mitigation and Adaptation in Power and Energy Systems (CMAP). After a rigorous review of 86 submitted manuscripts, 23 papers were accepted for publication. These accepted papers cover various aspects of climate change mitigation and adaptation and are classified as follows: boosting renewable energy efficiency (two papers), climate resilience strategies (four papers), decarbonization strategies (four papers), renewable energy integration (five papers), policies, incentives, and science communications (four papers), and the role of energy markets (four papers). The Guest Editorial Board is optimistic that this Special Issue will serve as a rich resource, offering invaluable insights to propel future research and advancements in climate change mitigation and adaptation.

This paper introduces a framework to yield an electricity rate for vehicle-to-grid (V2G) charging station (CS) to minimize installation capacity of a charging station considering electric vehicle (EV) arrival/departure time distribution. Two different layers are designed to avoid an obstacle encountered when formulating the problem as a convex optimization and to represent an EV aggregator and an electricity rate decision maker – a regulator. The EV aggregator layer focuses on increasing the profit and the regulator minimizes the peak load of the V2G CS. Linear programming was formulated for the former layer, and a modified particle swarm optimization (PSO) method was developed for the latter. Modification of the PSO approach allowed for easier escape of local minima, resulting in a new electricity rate for the V2G CS based on the EV arrival/departure time distribution data. The algorithm employs new matrices devised in this paper to accommodate EV information in the optimization process. In a simulation study, two distinct CSs with V2G operations were evaluated, each with a different EV arrival/departure time distribution. The simulation revealed that the peak load and the profit of the aggregator vary dramatically depending on the arrival/departure time distributions.

Hosting capacity (HC) describes the electricity network’s ability to accommodate distributed generation (DG) without deteriorating electrical performance indicators. Distribution system operators typically express their networks’ HC as a single threshold, called static hosting capacity (SHC). SHC is determined via conservative regulatory criteria, increasing connection costs and time. This paper explores the potential for additional energy injection into the network via dynamic hosting capacity (DHC). A network node’s DHC is derived from the hourly operation of the network, accounting for the time variability of existing distributed generation (DG) output and demand. The methodology considers the network assets’ N-1 contingencies and their probabilities, defining the security-aware DHC (SDHC). The SDHC definition is technologically neutral. Through a case study of a radial medium voltage distribution network, the paper highlights the significant limitations of SHC due to conservative calculation criteria mandated by regulators. Annual injectable energy is increased by 62% to 76% when comparing DHC to SHC. Variations between average DHC and SDHC are below 0.01% due to low N-1 probabilities. This finding points out the potential of dynamic hosting capacity definitions, allowing more efficient use of the existing network and facilitating the integration of new DG capacity with reduced connection costs and time.

User clustering is crucial for tapping the flexibility of the load side and realizing dynamic management of power loads in new power system. K-means method is widely used in clustering analysis due to its simplicity, high efficiency, and scalability, but it needs to specify the number of clusters in advance, and is sensitive to the initial clustering centers. The current initialization method does not take into account the neighborhood distribution of the data points, and the direct use of data that has undergone dimensionality reduction processing leads to inaccurate selection of the initial clustering centers. To address the above problems, a new K-means improvement method that takes into account the initialization problem and the adaptive determination of the number of clusters: K-means clustering method based on nearest-neighbor density matrix is proposed in this paper. The method improves the efficiency of nearest neighbor search by building a K-D tree, and enhances the performance of unsupervised classification by utilizing the adaptive selection strategy of the number of clusters and the initial clustering centers selection algorithm. The proposed method is applied to real datasets, and its effectiveness is assessed by calculating three clustering evaluation metrics of the clustering results in comparison with several existing initialization and clustering methods. The experimental results show that the method proposed in this paper has higher stability and better clustering performance than existing clustering methods.

In recent years, non-characteristic harmonic resonance was observed in several high-voltage direct current (HVDC) systems in China. Intuitively, additional filters can be installed to mitigate the resonance. However, such a solution does not prevent the harmonic penetration from nearby substations. As a result, the harmonic issue in HVDC systems cannot be well addressed. In view of the above, this paper presents a system-level harmonic mitigation method. The basic idea is to address the harmonic pollution by mitigating harmonic sources and harmonic resonance simultaneously. To achieve this goal, candidates of filters to suppress the resonance at the HVDC station are investigated first. Then, an optimal filter design problem is established to determine the location, type and capacity of multiple filters that can achieve the system-level harmonic mitigation with the minimum cost. Particularly, an improved particle swarm algorithm with adaptive mutation operators is tailor-designed to solve the proposed optimization problem. Finally, the proposed method is applied to address the harmonic problem faced by a real-life HVDC system in eastern China. The result demonstrates that compared with the solutions solely mitigating harmonic sources or resonance, the method presented in this paper is more cost-effectiveness.

The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper’s unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach.

This paper analyzes the factors affecting the stability of phase-locked loops (PLLs) in weak power grids. By establishing a PLL model in weak power grids, it is found that line impedance, grid frequency disturbances, and output power all have an impact on PLL stability. The influence of line impedance, grid frequency disturbances, and output power on PLL is analyzed using the phase plane method. Additionally, it is observed that increasing the PLL damping ratio can enhance PLL stability, but when the damping ratio exceeds the critical damping ratio, PLL instability may occur. Therefore, this paper divides the controllable range of PLL into two cases: damping ratio greater than the critical damping ratio and damping ratio less than the critical damping ratio. For the case where the damping ratio is less than the critical damping ratio, a transient virtual inductance control method is proposed to enhance the PLL damping ratio and improve PLL stability without introducing power coupling. For the case where the damping ratio is greater than the critical damping ratio, PLL adaptive parameter adjustment control is proposed to ensure that the PLL trajectory does not diverge by increasing the PLL adjustment time without increasing the damping ratio, thus improving VSC stability. Finally, a comparison with conventional methods is conducted, and the feasibility and correctness are analyzed through time-domain simulations, followed by presenting the result analysis.

With large-scale grid integration of renewable energy sources (RES), power grid operations gradually exhibit the new characteristics of high-order uncertainty, leading to significant challenges for system operational security. Traditional model-driven generation dispatch methods require large computational resources, whereas the widely concerned Reinforcement Learning (RL)-based methods lead to issues such as slow training speed due to the high complexity and dimension of processed grid state information. For this reason, this paper proposes a novel Grid Expert Strategy Imitation Learning (GESIL)-based real-time (5 min intervals in this paper) dispatch method. Firstly, a grid model is established based on the graph theory. Secondly, a pure rule-based grid expert strategy (GES) considering detailed power grid operations is proposed. Then, the GES is combined with the established model to obtain a GESIL agent using imitation learning by offline–online training, which can produce specific grid dispatch decisions for real-time. By designing a graph theory-based grid model, a model-driven purely rule-based GES, and embedding a penalty factor-based loss function into IL offline–online training, GESIL ultimately achieves high training speed, high solution speed, and strong generalization capability. A modified IEEE 118-node system is employed to compare the proposed GESIL to traditional dispatch method and RL method. Results show that GESIL has significantly improved computational efficiency by approximately 17 times and training speed by 14.5 times. GESIL can more stably and efficiently compute real-time dispatch decisions of grid operations, enhancing the optimization effect in terms of transmission overloading mitigation, transmission loading optimization, and power balancing control.

This paper presents a comprehensive small signal analysis of two types of battery energy storage systems (BESSs), including a voltage-controlled BESS (V-BESS) and a current-controlled BESS (C-BESS). This study also introduces dynamic models for integrating these two BESS configurations within a DC microgrid context. Through small signal analysis and participation factor analysis, this study investigates the interplay between the BESS and the DC microgrid and the internal interactions within the BESS. Subsequently, pivotal modeling parameters are discerned, and their impacts on system dynamics due to variations are unveiled through a sensitivity analysis. These results were verified through real-time software-in-the-loop simulations using an OPAL-RT 5707XH. Furthermore, this paper proposes dynamic models for both V-BESS and C-BESS integrated with a DC microgrid that can capture the dominant behavior of BESSs in a DC microgrid with relatively low computation demands. Potential applications of this study not only include providing a reference for modeling BESSs in a DC microgrid but also providing a guideline during the design and operation stages of a BESS in a DC microgrid. Finally, due to their modularity and scalability, the proposed dynamic models can be easily applied to the design and testing of BESS controllers.

By the end of 2023, the installed capacity of renewable energy (RE) in China accounted for 36.0% of the total installed capacity, while the installed capacity of conventional thermal power units had decreased from 64.0% in 2016 to 47.6% in 2023. RE accounts for a higher proportion, leading to a shortage of peak shaving resources (PSR). As a result, the current RE quota system becomes unsustainable. In this paper, RE stations can classify their electricity into high-quality and low-quality parts based on the accuracy of their power curves. High-quality RE will be fully purchased and under stricter assessment, while low-quality RE will compete for clearing in the intraday spot market. Firstly, the article estimates the degree of PSR shortages in different periods based on technical parameters of thermal units, RE forecast information, and load forecast information. Consequently, the PSR supply capacity (PSR supply capacity) can be divided into PSR shortage status and sufficient status. Secondly, a clearing method for low-quality RE in the spot market is designed. The grid can set different PSR supply capacitys for different time periods according to the degree of PSR shortage. The assessment indicator values vary under different PSR supply capacitys. Lastly, based on the classification sales channels, this paper proposes a power curve optimization strategy that considers self-owned battery energy storage (BES). Case studies show that the proposed classification sales channels can effectively decrease the degree of PSR shortage while taking into account the benefits of RE station. The designed PSR supply capacity dividing method can accurately reflect the degree of PSR shortage in real-time, and the proposed power curve optimization strategy can further enhance the economic benefits of RE station.© 2017 Elsevier Inc. All rights reserved.

This paper addresses the issue of enhancing the low voltage ride-through (LVRT) capability of doubly-fed wind farms during asymmetric faults. This research is of critical importance for ensuring the safe and stable operation of the power grid and the sustainable development of wind power technology. Given the stricter LVRT requirements set by the latest national standards in China, existing schemes are insufficient to meet these challenges. This paper first analyzes the control performance of the rotor side converter under asymmetric fault conditions. Based on national standard requirements, the availability of STATCOM, optimized operation targets, and converter voltage and current limitations, an optimized LVRT scheme is proposed. Validation through PSCAD simulations demonstrates that the proposed scheme effectively meets the national LVRT operation requirements. It also shows significant advantages in reducing electromagnetic torque fluctuations, enhancing active power output, protecting converters, and mitigating power fluctuations during faults. The innovation of this research lies in proposing a multi-target coordinated LVRT strategy, providing a new technical approach for improving the LVRT performance of wind farms and the stability of power grid operation.

This paper addresses two complex areas of research: the detection of turn-to-turn faults (TTF) in power transformers and the impact of inverter-based resources on the TTF protection scheme operation. Detecting turn-to-turn faults in power transformers by protection algorithms poses a challenge due to the minimal fault currents observed at transformer terminals. Yet, the demand for dependable TTF protection is very high because of the high fault currents inside the shorted turns and the resulting damage consequences. On the other hand, for such sensitive protection, adverse conditions such as transformer inrush currents or CT errors may lead to protection maloperation. Moreover, the fault current characteristic of the inverter-based source infeed is very different compared to the synchronous machine infeed, particularly concerning the negative-sequence current used in the TTF protection schemes, which calls for thorough research analysis. A simulation model of the converter transformer capable of simulating TTFs, and the Modular Multilevel Converter (MMC) for a High Voltage Direct Current (HVDC) transmission link, has been developed. The test results of turn-to-turn fault protection schemes in inverter-based generation-dominated power systems compared to the synchronous generator infeed are presented. The negative-sequence current protection quantities are analysed in more detail for TTFs with small and large number of shorted turns, i.e. without and with reactive negative-sequence current injection by the MMC control. Finally, the paper assesses the dependability of the transformer differential protection and sensitive TTF protection schemes in detecting faults with different numbers of shorted turns and fault resistance for TTFs occurring in the star and delta winding of the converter transformer.

Effective utilization of spatial–temporal information can improve the accuracy of ultra-short-term prediction of power generation from distributed photovoltaic (PV) stations in the region. This paper introduces an ultra-short-term spatial–temporal prediction model for distributed PV power generation, blending data-driven methodology with deep learning technique. The model integrates a self-attention mechanism (SA), a Bi-directional Convolutional Gated Recurrent Unit (BiConvGRU), and an encoder-decoder structure, called ABCGRU. The spatial–temporal attributes of PV power generation can be effectively utilized to accurately predict the output of PV power stations at different locations. Firstly, this paper proposes a 2D distributed PV measurement frame approach considering the spatial–temporal properties of PV power. The combination of Pearson correlation coefficient, the normalized Euclidean distance, the Shape-based distance (SBD) analysis based on cross-correlation and geographic distance reduces the input dimensionality. Secondly, to better capture the spatial–temporal patterns within the 2D distributed PV measurement frame, this paper proposes the ABCGRU model. Finally, the predictive performance of the model is verified through experiments. On the Birmingham dataset, the relative absolute error (RAE) for single-step (15 min) prediction is 0.13, and the average RAE for multi-step (30–60 min) prediction is about four times higher than ConvGRU. The single-step prediction RAE of Little Rock and New Orleans datasets is about 3–4 times higher than ConvGRU. In the comparison between the same series of models, the 4-layer ABCGRU has the highest accuracy. Moreover, the effectiveness of data dimensionality reduction was verified through experimental comparison. The RAE for single-step prediction on the Datong dataset is 0.0048.

This paper presents a control strategy for voltage source converters connected to weak grids. The proposed approach is based on slight modifications to the conventional vector current control strategy, including the use of two degrees of freedom proportional-integral controllers, with reference weighting factors, in the inner current loop and a proportional controller in the outer loop, resulting in the introduction of only three additional parameters. The paper analyses the effect of these additional design parameters on the robustness improvement and studies the limitations of the proposal, providing design steps to achieve given performance prescriptions such as speed response, noise amplification, delays and phase-locked loop bandwidth, and the ability to face weak grids. Simulations are conducted to evaluate the effectiveness of the proposed approach, which demonstrates that it is possible to achieve a more robust behaviour compared with the conventional vector current control strategy when facing weak grids.

Non invasive load monitoring (NILM) is beneficial for enhancing the monitoring capability of the distribution network and is crucial for improving the safety of smart grid operation. However, household appliances involve a variety of devices and a large amount of data, making it difficult to achieve high-precision load identification. Especially when a large amount of loads are running simultaneously, problems such as feature overlap and reduced distinguish ability may occur, and thus increasing the difficulty of load identification. To solve these problems, this paper proposes a new NILM method based on dynamic time warping (DTW) optimization and event detection. Firstly, a feature extraction algorithm of STFT-SSAE is constructed by using short-time Fourier transform (STFT) to extract time–frequency features from the load, and then by sparse stack autoencoder (SSAE) to extract important features from time–frequency information. Secondly, the above features are input into Bi-LSTM and DTW models respectively, and a new probabilistic model is established. A Bi-LSTM-DTW load recognition architecture is built by combining the two models. Finally, the load identification model of SSAE-Bi-LSTM based on DTW optimization (DOSL) is trained by the preset combined data, which ensures the high confidence of the DOSL model in various complex operating scenarios. This paper introduces the public data set PLAID for experimental verification. The results show that the selected load features has good discrimination, and the proposed algorithm has the best identification effect with the accuracy rate of 0.9412. The proposed algorithm is also validated for generalization ability on the public dataset UK-DALE, with a identification accuracy rate of 0.9306, and the error of different datasets is only about 1%.

In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a testing problem for two-sample covariance matrices, which is related with the so-called Fisher matrix. To save computing resources, the screening step of fault interval based on the test statistic is designed to check the existence of faults. Then two point-by-point methods are proposed to determine the time of the fault in the selected interval. One method detects faults by the limiting spectral distribution of the standard Fisher matrix, which can detect the faults with higher accuracy. The other method tests the faults based on the proposed test statistic , which has a faster detection speed. Compared with existing works, the simulation results illustrate that two methods proposed in this paper cost less computational time and provide a higher degree of accuracy and sensitivity.

The deterministic techniques in use for transmission-grid planning and operation have resulted in a high reliability, but they have certain limitations that could be removed by applying stochastic techniques. This paper presents a detailed review of one such stochastic technique: operational risk assessment. A general procedural model of operational risk assessment is introduced, based on one simple equation, highlighting the important elements. The research trends are presented for each of these elements: contingency definition and calculating the probability of a contingency case; contingency filtration methods; defining and calculating the severity factor. Next to an overview of the state-of-the-art, this paper contains a detailed discussion section, where the most important research gaps are identified. Emphasis is especially on bridging the gap between research and practical applications of operational risk assessment. The paper closes with a future outlook on operational risk assessment and its applications.

The rise of Battery Electric Buses (BEBs) in urban transportation networks, and the adoption of Opportunity Fast-Charging Systems (OFCSs) to support their operation, demand a close examination of the impact of these technologies on protection systems. Thus, this paper analyzes in detail the effects of OFCSs on conventional overcurrent protection schemes and proposes a novel mitigation strategy to the energization and fault plus load effects introduced by this charging strategy on protection systems. The proposed approach includes additional constraints in the overcurrent coordination problem to minimize operation times and uphold coordination delays between main and remote backup protection. Additionally, the resulting optimization problem is solved with the Water Cycle Algorithm (WCA), and is validated on the IEEE 34 and IEEE 123 test systems. In overload situations, the strategy yields a minimal increase of under 200 ms in main protection response times, ensuring non-activation during BEB connections, whereas in non-overload scenarios, optimal settings maintain comparable response times. The paper concludes by recommending coordinated cut-off mechanisms with grid operators to prevent unmet demands during extended BEB connections, addressing critical challenges in the evolving landscape of BEB charging.

Economic nonlinear model predictive control is a great choice to tackle the control issues appearing in the current wind energy industry. In this paper, instead of generator power, aerodynamic power is employed in the economic cost function to establish the required conditions of stability and convergence of the economic performance, such as turnpike and problem convexity. However, since aerodynamic power depends on time-varying wind speed, conventional time-invariant economic nonlinear model predictive controllers cannot guarantee the stability and convergence of economic performance. This paper proposes a new time-varying economic nonlinear model predictive controller for wind turbine control that considers an economic trajectory, instead of a steady-state, in its optimization problem. The proposed time-varying economic cost function directly considers aerodynamic power, the activity of pitch angle and generator torque, and fatigue loads on the shaft and tower. Therefore, this controller can maximize power extraction and reduce fatigue load on the tower, drivetrain, and actuators. Furthermore, a fast-parallel Newton-type method is used to implement the proposed controller in actual wind turbines. An accurate aeroelastic model is used to validate the performance of the proposed control scheme. The proposed controller is also compared with two tracking nonlinear model predictive controllers, the baseline controller, and a newly developed method, under fatigue and extreme load scenarios in the presence and absence of uncertainty. The simulation results show the superior economic performance of the proposed approach. Moreover, the real-time results verify the computational speed that the proposed controller requires to deploy actual wind turbines.

The Energy Internet (EI) has emerged as a promising field within smart grids, addressing capacity limitations and discrepancies in energy resources. It facilitates a peer-to-peer (P2P) energy trading environment for EI prosumers and consumers, reducing reliance on the main grid. To ensure an efficient P2P trading process, it is essential to route energy through the path with minimal power loss optimally. The Energy Router (ER) plays a pivotal role in governing power flow through EI. This paper presents the development of a novel real-time Zbus-based method for directing P2P energy transactions within the EI considering congestion management. The proposed method depends on bus impedance matrix in forming the routing matrix. Firstly, the ER’s structure and function are outlined in relation to the network, and then to design the EI’s topology using an adjacency matrix representation. Secondly, the proposed method methodology is presented in steps and explained on simple 7-bus microgrid. Additionally, a novel congestion management method is integrated into the routing algorithm, considering transmission line loading and available capacity during route selection. Furthermore, the paper applies a power flow technique to the selected routes to optimize power flow performance. The proposed method demonstrates enhanced directness and accuracy compared to existing approaches. Its efficiency is validated by testing a modified IEEE 14-bus system and the standard IEEE 30-bus system. Importantly, the method successfully identifies the minimum loss path, despite the unnecessity of power loss calculations in the routing algorithm. The key contribution of this research lies in providing a comprehensive and practical solution for P2P energy transactions in the EI, showcasing substantial improvements in accuracy, efficiency, and congestion management compared to previous methods.

This paper presents the results, insights and challenges of a small-scale laboratory implementation of a virtual storage plant (VSP) and the distributed control of its portfolio for frequency restoration services. The proposed framework has been experimentally validated using a controller-hardware-in-the-loop setup consisting of 5 battery storage systems, a hydraulic turbine-generator and a real-time digital simulator. The results show that the performance of the VSP whose assets are controlled by the proposed framework is compliant with the grid code requirements regarding manual and automatic frequency restoration service. Furthermore, this paper reveals novel insight into the practical implementation challenges of consensus-based distributed control due to time delays and communication synchronization.

This paper presents a new approach for controlling the bidirectional active power flow using a series voltage source inverter placed in between the microgrid and the main grid in LV distribution network. Implementing the control operation of the series VSI resembles the form of a simulation-based optimization where a novel overall power flow algorithm is implemented to investigate the system behavior. This algorithm considers controlling the active power of the series VSI and the active power between the main grid and the microgrid to zero net energy. The algorithm has three nested levels working together to satisfy an active power reference at PCC. The results revealed in this paper are (1) investigating different settings for the DG droop characteristics to determine the limits of the proposed approach and (2) regulating the magnitude and phase angle of the series VSI according to a desired active power reference at PCC. The obtained results confirm the effectiveness and feasibility of the proposed approach and the capability of the overall power flow algorithm in satisfying the zero-net power transfer target.

Eddy current effects, which include skin and proximity effects, are usually associated only with frequency, but they are also affected by temperature. This paper analyzes the effect of both, temperature and frequency on skin and proximity effects in transformer windings. When analyzing transformer windings, despite the existence of powerful finite element analysis (FEA) modules, they return the total contribution of eddy current effects and do not separate the contributions of skin and proximity effects. This paper proposes an analytical method to determine the contribution of both effects to the AC winding resistance and leakage inductance based on the orthogonality of the skin and proximity effects. This method is based on the exact solution of the skin effect for an isolated round conductor combined with Dowell's solution of the proximity effect. Presented experimental results and FEA simulations based on a single-phase transformer have determined the accuracy of the proposed method, which allows accurate prediction of the temperature and frequency behavior of the winding resistance and leakage inductance.

This paper investigates the optimization design of interior permanent magnet synchronous motors (IPMSM) for electric vehicles (EVs). The optimization process of IPMSM involves numerous design parameters, and the optimization objectives often conflict with each other, resulting in a vast design space and difficulties in establishing an accurate mathematical model. The traditional finite element analysis (FEA) optimization methods are time-consuming and computationally intensive, posing a significant challenge to achieving high-performance IPMSM with high torque, high efficiency, low vibration, and low losses. To address this issue, this paper proposes a multi-objective optimization of IPMSM for electric vehicles based on the combinatorial surrogate model and the hierarchical design method. Firstly, a comprehensive sensitivity coefficient method is employed to categorize design variables into two layers: high-sensitivity design variables (HSDVs) and low-sensitivity design variables (LSDVs). Secondly, using the improved Latin hypercube sampling (LHS) method to extract sample data, a high-precision combined surrogate model (RSM + Kriging) is constructed and combined with the non-dominated sorting genetic algorithm II (NSGA-II) optimization algorithm to optimize HSDVs. Meanwhile, the fuzzy inference Taguchi method (FITM) is utilized to optimize the LSDVs. Finally, the performance of the IPMSM before and after optimization has been analyzed through the FEA method, and different optimization methods were introduced for comparison. The results show that compared to other optimization methods, the optimization approach proposed in this paper can effectively enhance the overall performance of the IPMSM. The average torque of the optimized IPMSM increased by 5.22 %, the torque ripple decreased by 77.64 %, and the total losses were reduced by 6.21 %. Furthermore, compared to the traditional FEA method, this method reduces optimization time and improves optimization efficiency without compromising on optimization accuracy.

In the ongoing transition towards distributed Renewable Energy Sources (RES) and the concurrent transformation of critical energy infrastructures, the efficient coordination of load, storage, and generation flexibilities while avoiding grid congestion is crucial. To orchestrate the growing myriad of distributed devices, digital solutions based on scalable information and communication technologies (ICT) that go far beyond the existing state-of-the-art, are the key enablers.To open a new avenue towards robust and resilient power and energy systems, this paper proposes the concepts of holarchies and holonic structures as underlying design principles for grid automation and coordination of flexibilities in power and energy systems. We argue that the holonic concept and its theoretic underpinning enables designing and building future resilient power systems that can cope with the otherwise overwhelming complexities of the energy transition. Our long-term vision is that the proposed holonic concept encompasses already existing trends in power and energy systems, i.e. decentralization, digitalization as well as observability and controllability improvements, into one holistic framework, whereby holistic integration is likewise pun and serious ambition. Beyond the existing holonic approach in general and partly for limited power system applications so far, our design proposal encompasses ICT infrastructures and the data domain into a consistent novel architectural approach.Holonic structures, or holarchies, extend and build upon the recursiveness and self-similarity of autonomous sub-structures, i.e. holons, of a system. It is a system-of-systems approach and, thus, conceptionally, very different from existing and well-known multi-agent system approaches. In essence, holonic concepts allow for the formalisation of hierarchical system relations regarding physics, information, and data using a part-whole architecture. Hence, they are well-suited for the conceptualisation of automation functionality across all dimensions of the cyber-physical domain of energy infrastructures and potentially also beyond.This paper investigates holonic structures from different novel perspectives, such as control and automation, system modeling and digital twins, as well as the corresponding ICT-infrastructure and data requirements. Three case studies are drawn upon as examples to illustrate how holonic concepts and approaches are already emerging in power and energy systems operation.© 2017 Elsevier Inc. All rights reserved.

The weak characteristics of high-impedance faults and the complex attributes of cross-country faults make faulty feeder detection of cross-country high-impedance faults difficult in medium-voltage networks. This paper deduces the zero-sequence equivalent circuit of cross-country faults through a three-sequence two-port network, analyzes the influence of grounding resistance on cross-country faults, and then explores the limitations of the traditional passive feeder detection methods. Moreover, this paper proposes a novel faulty feeder detection method for cross-country high-impedance faults based on zero-sequence active power regulation. First, based on the zero-sequence current varying characteristics under the regulation of zero-sequence voltage, construct a continuous adjustment region of zero-sequence voltage within the feeder insulation tolerance range. Next, based on the zero-sequence active power varying characteristics in each feeder, propose a discriminant formula for the zero-sequence active power fluctuation coefficient. Finally, adjusting zero-sequence voltage can actively amplify the differences in zero-sequence active power fluctuations between healthy feeders and faulty feeders and accurately select faulty feeders of cross-country high-impedance faults in medium-voltage networks. Various fault conditions are simulated in the PSCAD/EMTDC simulation and field test to verify the effectiveness of the proposed method. The proposed method can accurately identify all faulty feeders of cross-country high-impedance faults in the medium-voltage distribution network.

Distribution network topology identification (DNTI) is an important prerequisite for distribution system operation. Frequent topology changes and limited measurement equipment make identifying the correct topology unavoidably challenging. To this end, this paper proposes a state estimation method for distribution network topology identification using a small amount of phasor measurement unit (PMU) measurement data. Firstly, this paper introduces the application of the structural equation model (SEM) in distribution networks and explains the relationship between branch currents and topology. Then, auxiliary variables are introduced for the feasible domain problem present in the algorithm, which improves the solvability of the algorithm. In addition, this paper reconsiders the operation mode constraints in terms of nodes from the perspective of distribution network operation mode. The Structural Equation Modeling-based Mixed Integer Programming (SEM-MIP) method proposed in this paper can be solved using existing commercial solvers, and its effectiveness has been verified by simulation in IEEE 33-node test system and IEEE 123-node test system.

The paper introduces a novel gradient tracking-based algorithm for solving the power flow problem in a fully distributed manner, using the AB algorithm. The motivation for this work stems from the limitations of centralized approaches, which can be overcome with distributed implementations. Notably, the proposed distributed algorithm eliminates the need for a central monitoring facility, allowing all calculations, input data, and network intelligence to remain within individual buses (agents), thus removing single points of failure and preserving data privacy. The paper presents how this can be achieved by reformulating the power flow study as a purely distributed optimization problem, and then applying the AB algorithm, which can effectively converge even when only partial system information is available. To enhance the performance of the proposed algorithm, two significant modifications—cost function whitening and momentum—are introduced as an additional contribution, which enables faster convergence (in fewer than 20 iterations) while maintaining accuracy comparable to traditional centralized power flow algorithms. The effectiveness of the proposed framework is validated through tests on IEEE 14- and 300-bus systems, demonstrating its practical applicability and robustness. The paper also examines some extreme operating scenarios, such as instances when communication is lost with parts of the network, or when uncertainty exists in grid parameters.

In the context of green grid, it is crucial to enhance the low-carbon transformation of integrated energy systems. To improve the degree of low-carbon in these systems, this paper proposes an optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering the uncertainty of carbon-source-load. Firstly, this paper provides an analysis of uncertainties of carbon-source-load and uses budget uncertainty sets to model the three uncertain parameters. Secondly, in the integrated energy system, the by-product O2 of hydrogen production by electrocution and the oxygen storage device are used to provide oxygen-enriched combustion conditions for the gas turbine to reduce the CO2 generated in the combustion process. At the same time, the carbon capture device is used to collect CO2, and the oxygen enriched combustion-carbon capture integrated energy system is formed. Finally, an improved multi-populations quantum-behaved particle swarm optimization algorithm is proposed to solve the proposed model. This method considers the uncertainty of carbon emission intensity, which makes the optimal allocation of integrated energy system more inclined to low-carbon rather than economic. In addition, in terms of model structure, this method integrates oxygen enriched combustion-carbon capture into the integrated energy system to form a low-carbon model, which directly improves the low-carbon performance of the system. The simulation results show that the carbon emission of the proposed method is reduced by 38.8 % compared with other methods.

This paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested “if-else” statements. The optimization problem is recalled from previous work by modifying the cost function to further enhance the dynamic performance. Additionally, and in contrast to previous works, the online implementation accomplished in this paper allows the system to cover a wider range of operating points. For this, the optimization problem is solved offline for several operating conditions, and the results are gathered into a dataset to train classification and regression trees (CARTs), which are later used online. Due to the generalization capability of the CARTs, a more flexible and less resource-intensive implementation is achieved which is capable of operating at points outside the ones considered in the training dataset. The resulting control strategy is compared in simulation and experiments with several alternative approaches found in the literature. This approach can be extended to other power converter topologies, allowing the implementation of optimized modulations.

The rapid increase of integrated distributed generators results in higher fault currents in the future modern grids. A remedy for the concern is employing series reactors as fault current limiters. This paper elaborates on a ferromagnetic core series reactor, which, when saturated, adversely affects the operation of the series reactor during faults. The main goal of the paper is to calculate grid and series reactor coefficients by applying a simplified power line model during a fault condition. These coefficients are the primary considerations of a series reactor design to avoid its saturation. Moreover, the study of the relationship between the reactor inductance and obtained coefficients will be carried out. The obtained results are validated by simulations performed in MATLAB Simulink.

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

Power grids have undergone a massive transformation due to increasing number of renewable/non renewable distributed generators, storage technologies and electrical vehicles. This paper focuses on the flexible energy management of grid connected microgrid (MG) in order to analyze the effects of the flexibility options such as electric vehicle with demand response (EV-DR), battery storage system (BSS), hydrogen storage system (HSS) and dynamic line rating (DLR) in presence of renewable sources. In addition, microturbine (MT), fuel cell (FC), biomass (BIO), geothermal (GEO), and diesel engine (DIE) whose output is controllable are also considered as flexible sources. In this paper, optimum operation of grid-connected MG is modeled as a mixed-integer linear programming problem considering uncertainties of wind turbine (WT) and photovoltaic (PV) sources. A day-ahead forecasting of irradiance and wind speed are performed with Decision Tree Regression (DTR) and Long Short Term Memory (LSTM) algorithms for sensitive calculation of PV and WT output. The cost function merges operating costs including fuel, operation & maintains (O&M), depletion costs with emission cost of CO2, SO2, and NOx. Obtained numerical results show that total cost of the MG is reduced by 9.40% and emission cost is diminished by 5.59% with inclusion of all considered flexibility options. Further, load factor is improved from 0.7932 to 0.8236 with 100% flexibility condition for MG.

This paper introduces multi-use applications to unlock behind-the-meter flexibility by integrating grid-serving capabilities with user-oriented control strategies. We deploy a hierarchical flexibility coordination process in a co-simulation research environment and propose a control strategy prioritizing self-consumption optimization with a secondary focus on grid-serving applications. This approach enhances the viability of behind-the-meter technologies, particularly benefiting battery energy storage systems with high investment costs. It also facilitates active grid operation amid the growing presence of distributed energy resources. The research’s key contribution lies in designing an information architecture to efficiently utilize behind-the-meter flexibility for grid-serving processes. The paper demonstrates the feasibility of a multi-use operation, combining user-oriented optimization and grid-serving flexibility provision for local grid congestion management. The implemented control algorithm in a home energy management system achieves grid-serving objectives while maintaining behind-the-meter asset performance. Co-simulation results show that in less than 2.5% of simulated time steps, transformer utilization exceeds operational limits in both scenarios, indicating the viability of multi-use operation for leveraging new value streams and managing grid congestion simultaneously.

This research paper delves into the central mechanisms implemented in Spain to foster the adoption of clean energy projects. The study scrutinizes various mechanisms established from 2005 onwards, evaluating their efficacy in achieving the desired outcome of supporting clean energy project implementation. The aim is to comprehensively assess the associated costs, effectiveness, and attainment of renewable energy objectives stemming from implementing diverse support schemes. This paper explores the effectiveness of feed-in tariffs and feed-in premium mechanisms in promoting the integration of new renewable energy generation into the power system. However, the Spanish experience suggests that exhaustive planning control is imperative to prevent excessive support costs that could jeopardize the entire market and cause a significant rise in final energy costs. The research studies the support schemes and associated market distortion and presents the roadmap from regulated tariffs to a “total market” for wind and solar energy. The findings demonstrate that, under proper supervision and planning, a transitional retribution scheme effectively promotes renewable energy plant integration. Finally, the analysis examines the need to regulate the excess benefits that non-emitting power sources receive in a marginal price market, as this can lead to market imbalances and a substantial increase in final costs. These plans serve as a crucial tool in achieving the decarbonization objectives of the European Power System.

Seaports are responsible for consuming a large amount of energy and producing a sizeable amount of environmental emissions. However, optimal coordination and cooperation present an opportunity to transform this challenge into an opportunity by enabling flexibility in their generation and load units. This paper introduces a coordination framework for exploiting flexibility across multiple ports. The proposed method fosters cooperation between ports in achieving lower environmental emissions while leveraging flexibility to increase their revenue. This platform allows ports to participate in providing flexibility for the energy grid through the introduction of a green port-to-grid concept while optimising their cooperation. Furthermore, the proximity to offshore wind farms is considered an opportunity for the ports to investigate their role in harnessing green hydrogen. The proposed method explores the hydrogen storage capability of ports as an opportunity for increasing the techno-economic benefits, particularly through coupling them with offshore wind farms. Compared to existing literature, the proposed method enjoys a comprehensive logistics-electric model for the ports, a novel coordination framework for multi-port flexibility, and the potentials of hydrogen storage for the ports. These unique features position this paper a valuable reference for research and industry by demonstrating realistic cooperation among ports in the energy network. The simulation results confirm the effectiveness of the proposed port flexibility coordination from both environmental and economic perspectives.

This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

This paper presents a model that explores the strategic bidding equilibrium within a microgrid and its interactions with other strategic and non-strategic rivals in a joint energy and balancing market. The model utilizes a tri-level mathematical program with equilibrium constraints (MPEC) to represent the behavior of each strategic producer. Upper level maximizes the profit of each strategic producer, including microgrid and another strategic rival. The first lower-level problem involves maximizing social welfare through the day-ahead market clearing process, while the second lower-level problem deals with the balancing market clearing process. These objectives form the core of the proposed model. To simplify the tri-level problem, duality theory and the Karush-Kuhn-Tucker (KKT) optimally conditions are employed to transform it into a mixed integer linear programming (MILP) problem. By simultaneously solving all MPECs, an equilibrium problem with equilibrium constraints (EPEC) is formulated. The resulting EPEC is then addressed using a diagonalization algorithm and game theory to obtain a market Nash equilibrium, which constitutes the secondary objective of this paper. The effectiveness of the model is evaluated using the 6-bus test system as a case study. The results indicate that, at the equilibrium point, both the microgrid and rivals experience reduced profits compared to the initial state.

Admittance identification is an effective tool to solve the issue of black-box admittance modeling in wind farms. However, due to the large operating range of wind farms, it is difficult to accurately identify the admittance model of wind farms under all operating conditions, which limits the research on wideband oscillation caused by the interaction between wind farms and the power grid. This paper analyses the characteristics of the frequency-coupling admittance for wind farms. It is found that the power-sharing ratio of wind turbine generators (WTGs) affects the admittance characteristic of the wind farm, which leads to a huge number of operating points for the wind farm. The all-operating-point (AOP) admittance model of wind farms considering frequency-coupling characteristics is hard to directly identify. Aiming for this problem, this paper proposes to first identify the AOP-admittance model for WTGs. Then, based on the operating points of each WTG and the network structure, the AOP-admittance model of the wind farm is obtained via the admittance aggregation and model reduction method. This proposed method solves the problem of black-box admittance modeling and addresses the problem of dimension disaster in admittance modeling for wind farms. The identified model of the wind farm can be used to accurately assess the stability of the wind power integration system under various operating conditions. Finally, the effectiveness of the proposed method is verified through the admittance measurement and stability analysis of the wind farm.

Active front-end converters are part of high power installations for regenerative energy systems, feeding the regenerated energy into the utility. Under the island mode operation of the regenerative energy system, the load characteristics are difficult to detect, and the change of the load characteristics will cause the corresponding resonance. V/f control is usually used to ensure the harmonic quality. At high switching frequency, it has a good control effect. However, high switching frequency causes high switching loss. In order to reduce the switching loss, it will produce more harmonic distortion. Furthermore, the commonly used modulation strategies in this case face the problem of low real-time performance. Therefore, in order to avoid the harm caused by circuit resonance under the condition of reducing the switching frequency, this paper proposes a specific harmonic suppression algorithm with high real-time performance, which can be used as an optional control algorithm in the island operation state of power system. At the same time, aiming at the difficulty of adjusting the weight factors, this paper proposes a weight factor automatic tuner, which automatically adjusts the weight factors according to the harmonic component.

With the rapid development of distributed generation, represented by photovoltaic power, the access of a large number of distributed generation poses threats to the security and reliable operation of islanded distribution networks. However, relying on the distributed energy storage system can stabilize the island power supply, which can effectively improve the reliability of the island distribution network. To this end, under the premise of knowing photovoltaic output and load forecast curve, this paper proposes a distributed energy storage optimization configuration method in the active islanding operation mode of multi-source distribution network, which satisfies the “N-1″ safety criterion. First, this paper establishes an optimization configuration model for distributed energy storage with multiple objectives, including minimizing the load shedding in the non-fault loss of power zone, the initial investment cost of distributed energy storage, the node voltage deviation and the system frequency offset. Then, aiming at the islanded power flow calculation process mentioned above, an improved trust region algorithm is proposed, and integrating this method with the harmony search algorithm and ”N-1″ safety criterion solves the model. Finally, the IEEE 33-node distribution system is used to test the performance of the proposed algorithm. The results show that the proposed improved trust region algorithm has the advantages of fast convergence speed and short time-consumption compared with the traditional trust region algorithm. And the proposed optimization configuration scheme for distributed energy storage can satisfy the “N-1″ safety criterion of the distribution network under different fault scenarios.

Self-synchronizing voltage source inverter (SSVSI) can effectively improve grid frequency stability. However, the synchronous frequency resonance (SFR) inevitably exists in SSVSI. In this paper, a small-signal model considering power coupling is established to analyze the system characteristics. It is found that the control parameter adjustment and the virtual resistance method can suppress the SFR. However, the control parameter adjustment will change the dynamic and steady-state characteristics of the system, and the virtual resistance method will exacerbate the power coupling of the system. For this reason, based on the idea of eliminating the resonance peaks, this paper proposes an SFR suppression method based on band-stop filters. By connecting band-stop filters in series in the active power control loop (APCL) and the reactive power control loop (RPCL), the proposed method effectively suppresses the resonance peaks without changing the original control parameters of the system, and the control bandwidth is wider, which improves the power response speed of the system. In addition, the proposed method avoids exacerbating the power coupling of the system compared with the virtual resistance method. Finally, the simulation and experimental results verify the correctness of the theoretical analysis and the effectiveness of the control method.

[Display omitted] • We assess a relaxation of the calculation criteria for nodal hosting capacity. • Dynamic HC (DHC) accounts for RES output stochasticity, enhancing grid flexibility. • Security-aware DHC (SDHC) contemplates the probability N-1 contingencies. • Security-aware DHC minimises risks, ensuring reliable energy integration. • DHC and SDHC allow to inject significantly more energy compared to static HC. Hosting capacity (HC) describes the electricity network's ability to accommodate distributed generation (DG) without deteriorating electrical performance indicators. Distribution system operators typically express their networks' HC as a single threshold, called static hosting capacity (SHC). SHC is determined via conservative regulatory criteria, increasing connection costs and time. This paper explores the potential for additional energy injection into the network via dynamic hosting capacity (DHC). A network node's DHC is derived from the hourly operation of the network, accounting for the time variability of existing distributed generation (DG) output and demand. The methodology considers the network assets' N-1 contingencies and their probabilities, defining the security-aware DHC (SDHC). The SDHC definition is technologically neutral. Through a case study of a radial medium voltage distribution network, the paper highlights the significant limitations of SHC due to conservative calculation criteria mandated by regulators. Annual injectable energy is increased by 62% to 76% when comparing DHC to SHC. Variations between average DHC and SDHC are below 0.01% due to low N-1 probabilities. This finding points out the potential of dynamic hosting capacity definitions, allowing more efficient use of the existing network and facilitating the integration of new DG capacity with reduced connection costs and time. [ABSTRACT FROM AUTHOR]

• An optimization framework is introduced to determine the new electricity rate, considering demand-side power capacity limitations. • An EV connection matrix was developed to make charging/discharging scheduling algorithm implementation much easier. • The modified particle swarm optimization (PSO) algorithm increases the convergence rate of the algorithm. • Simulation results revealed that the TOU rates should be applied according to a specific pattern, depending on the location. This paper introduces a framework to yield an electricity rate for vehicle-to-grid (V2G) charging station (CS) to minimize installation capacity of a charging station considering electric vehicle (EV) arrival/departure time distribution. Two different layers are designed to avoid an obstacle encountered when formulating the problem as a convex optimization and to represent an EV aggregator and an electricity rate decision maker – a regulator. The EV aggregator layer focuses on increasing the profit and the regulator minimizes the peak load of the V2G CS. Linear programming was formulated for the former layer, and a modified particle swarm optimization (PSO) method was developed for the latter. Modification of the PSO approach allowed for easier escape of local minima, resulting in a new electricity rate for the V2G CS based on the EV arrival/departure time distribution data. The algorithm employs new matrices devised in this paper to accommodate EV information in the optimization process. In a simulation study, two distinct CSs with V2G operations were evaluated, each with a different EV arrival/departure time distribution. The simulation revealed that the peak load and the profit of the aggregator vary dramatically depending on the arrival/departure time distributions. [ABSTRACT FROM AUTHOR]

• A system-level distribution power system integrating energy source, EV consumers and smart meters is modeled for the EVs charging and discharging scenario, leading a new research perspective for studying EVs charging and discharging with consideration of the vehicle owners' information exchange and interactions in the power system framework. • A two-stage optimization problem with priority for EVs charging and discharging is solved with a hierarchical optimal method, which decompose the problem as high-priority load shifting problem and low-priority cost minimization problem. • A penalty-based consensus algorithm is proposed to solve the cost minimization problem, in which two equational constraints are split and two similar optimization sub-problems which can be solved by a consensus distributed algorithm, and the convergence analysis of the algorithm is given mathematically. In addition, this paper combines the hierarchical algorithm with receding horizon method to deal with a more practical situation, where the EVs arrive and leave randomly. In preparing for the widespread adoption of electric vehicles (EVs), this paper investigates a two-stage optimization problem with priority for EV charging and discharging scheduling, while taking into account power load stability, economic objectives, and technical constraints of the distribution network. To this end, a distribution power system integrating energy source and EV owners is modeled. Considering the disturbance or even loss of the communication link, a periodical connected topology is adopted for information exchange among EV owners with the deployed smart meters, which is substantially different from the existing works. The main challenges of the problem are that the information flow among EV owners is time-varying and asymmetric, and each EV owner's demand should be satisfied throughout the whole scheduling horizon. A novelty distributed hierarchical optimal algorithm, combined with iterative water-filling-based method and penalty-based consensus method, is proposed for the two-stage optimization problem with priority. The convergence and optimality of the algorithm are presented mathematically. In addition, the proposed method combined with receding horizon optimization is performed for EVs random arrival and departure scenarios in the system. Finally, we demonstrate advantages of the proposed algorithm in both theory and simulation. [ABSTRACT FROM AUTHOR]

• Introducing a novel density measurement utilizing shared nearest neighbors, enhancing the assessment of data point density. • Developing an advanced K-means enhancement technique to dynamically determine cluster quantity and initial cluster center. • Integration of density-based and partition-based clustering methodologies, refining the K-means method's performance. • Achievement of peak performance in clustering outcomes via the proposed K-means modification. • Synergizing with various feature selection strategies for optimal clustering results proves its superior adaptability. User clustering is crucial for tapping the flexibility of the load side and realizing dynamic management of power loads in new power system. K-means method is widely used in clustering analysis due to its simplicity, high efficiency, and scalability, but it needs to specify the number of clusters in advance, and is sensitive to the initial clustering centers. The current initialization method does not take into account the neighborhood distribution of the data points, and the direct use of data that has undergone dimensionality reduction processing leads to inaccurate selection of the initial clustering centers. To address the above problems, a new K-means improvement method that takes into account the initialization problem and the adaptive determination of the number of clusters: K-means clustering method based on nearest-neighbor density matrix is proposed in this paper. The method improves the efficiency of nearest neighbor search by building a K-D tree, and enhances the performance of unsupervised classification by utilizing the adaptive selection strategy of the number of clusters and the initial clustering centers selection algorithm. The proposed method is applied to real datasets, and its effectiveness is assessed by calculating three clustering evaluation metrics of the clustering results in comparison with several existing initialization and clustering methods. The experimental results show that the method proposed in this paper has higher stability and better clustering performance than existing clustering methods. [ABSTRACT FROM AUTHOR]

The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper's unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach. • A stochastic simulation-based approach is proposed to determine the maximum allowable DRES penetration level. • Method for DSOs is provided to select the most appropriate voltage control method. • Method for DSOs is provided to select the most appropriate power smoothing method. • Impacts of different voltage control and power smoothing methods are evaluated. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers selected for publication in the Special Issue on Climate Change Mitigation and Adaptation in Power and Energy Systems (CMAP). After a rigorous review of 86 submitted manuscripts, 23 papers were accepted for publication. These accepted papers cover various aspects of climate change mitigation and adaptation and are classified as follows: boosting renewable energy efficiency (two papers), climate resilience strategies (four papers), decarbonization strategies (four papers), renewable energy integration (five papers), policies, incentives, and science communications (four papers), and the role of energy markets (four papers). The Guest Editorial Board is optimistic that this Special Issue will serve as a rich resource, offering invaluable insights to propel future research and advancements in climate change mitigation and adaptation. [ABSTRACT FROM AUTHOR]

With large-scale grid integration of renewable energy sources (RES), power grid operations gradually exhibit the new characteristics of high-order uncertainty, leading to significant challenges for system operational security. Traditional model-driven generation dispatch methods require large computational resources, whereas the widely concerned Reinforcement Learning (RL)-based methods lead to issues such as slow training speed due to the high complexity and dimension of processed grid state information. For this reason, this paper proposes a novel Grid Expert Strategy Imitation Learning (GESIL)-based real-time (5 min intervals in this paper) dispatch method. Firstly, a grid model is established based on the graph theory. Secondly, a pure rule-based grid expert strategy (GES) considering detailed power grid operations is proposed. Then, the GES is combined with the established model to obtain a GESIL agent using imitation learning by offline–online training, which can produce specific grid dispatch decisions for real-time. By designing a graph theory-based grid model, a model-driven purely rule-based GES, and embedding a penalty factor-based loss function into IL offline–online training, GESIL ultimately achieves high training speed, high solution speed, and strong generalization capability. A modified IEEE 118-node system is employed to compare the proposed GESIL to traditional dispatch method and RL method. Results show that GESIL has significantly improved computational efficiency by approximately 17 times and training speed by 14.5 times. GESIL can more stably and efficiently compute real-time dispatch decisions of grid operations, enhancing the optimization effect in terms of transmission overloading mitigation, transmission loading optimization, and power balancing control. • The graph theory is used to establish a grid model. • The Grid Expert Strategy (GES) is designed to guide Imitation Learning (IL) training. • Combining IL and GES to enhance computational efficiency and generalization of GESIL. • The BC and DAgger algorithms is used for GESIL faster training. [ABSTRACT FROM AUTHOR]