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The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network. Electrical and hydrogen energy are administered in an integrated energy system. The modeling of the uncertainties regarding the quantity of load and renewable resources is achieved through stochastic optimization using the Unscented Transformation method. The novelties of the scheme include the sizing and placement of a combined hydrogen and power-based renewable integrated energy system, the consideration of the impacts of bio-waste units, P2H, and H2P systems on the planning of the integrated energy system and the operation of the active distribution network, and the modeling of uncertainties using the Unscented Transformation method to reduce the calculation time. The study's results demonstrate the scheme's ability to improve the technical conditions of the distribution network by considering the optimal planning of integrated energy systems. In comparison to the network power flow, the operation status of the network has been improved by approximately 23-45% through the optimal siting, sizing, and energy management of hydrogen storage equipment, as well as renewable resources in the form of integrated energy systems. In other words, optimal energy management and planning of the integrated energy systems in the distribution network has been able to reduce energy losses and voltage drop by 44.5% and 42.4% compared to the load flow studies. In this situation, peak load carrying capability has increased by about 23.7%. In addition, compared to the case of the network with renewable resources, the overvoltage has decreased by about 43.5%. Also, Unscented Transformation method has a lower calculation time than scenario-based stochastic optimization. [ABSTRACT FROM AUTHOR]

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research. [ABSTRACT FROM AUTHOR]

To address the issues of low efficiency and poor noise immunity in traditional active distribution network (ADN) fault location methods based on swarm intelligent optimization algorithms, this paper proposes a two-stage fault location method utilizing the COA-SVM model and cosine similarity. First, this paper constructs the fault signature database for the target distribution network by randomly simulating single- and multi-point faults using the fault current state equation. Next, this paper introduces the COA-SVM classification model, establishing the high-dimensional mapping relationship between the fault current direction matrix and the fault zones through model training. The well-trained COA-SVM classification model is used to identify the fault zones, which include the fault line segments. Finally, for each identified fault zone, this paper calculates the cosine similarity of the fault current direction information of adjacent line segments, accurately pinpointing the fault line segments by identifying mutation points of the cosine similarity. Using the modified IEEE 33 node test distribution network as an example, simulation results demonstrate that the proposed two-stage fault location method offers higher accuracy and resistance to signal interference compared to fault location methods based on swarm intelligence optimization algorithms. The COA-SVM classification model surpasses conventional models, achieving high accuracy and excellent noise resilience. It accurately identifies fault segments within the test distribution network with a remarkable 100% precision. Moreover, the accuracy of fault localization remains above 83% when the FTU encounters fewer than three abnormal signals. [ABSTRACT FROM AUTHOR]

Due to the radial network structures, small cross-sectional lines, and light loads characteristic of existing AC distribution networks in mountainous areas, the development of active distribution networks (ADNs) in these regions has revealed significant issues with integrating distributed generation (DGs) and consuming renewable energy. Focusing on this issue, this paper proposes a wide-range thyristor-controlled series compensation (TCSC)-based ADN and presents a deep reinforcement learning (DRL)-based optimal operation strategy. This strategy takes into account the complementarity of hydropower, photovoltaic (PV) systems, and energy storage systems (ESSs) to enhance the capacity for consuming renewable energy. In the proposed ADN, a wide-range TCSC connects the sub-networks where PV and hydropower systems are located, with ESSs configured for each renewable energy generation. The designed wide-range TCSC allows for power reversal and improves power delivery efficiency, providing conditions for the optimization operation. The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

When multiple CCHP microgrids are integrated into an active distribution network (ADN), the microgrids and the distribution network serve as distinct stakeholders, making the economic optimal dispatch of the system more complex. This paper proposes a distributed dispatch model of ADN with CCHP multi-microgrid, and refines the objective functions of each region. The analytical target cascading approach (ATC) is employed to model the power transaction as virtual sources/loads, and solve the optimal dispatch in parallel. Case studies demonstrate the proposed distributed model is capable of achieving economic optimization for both stakeholders. [ABSTRACT FROM AUTHOR]

To address issues such as inadequate fault tolerance, long computation time, and limited universality in fault location for active distribution networks, this paper proposes a fault location method that combines Petri nets with an improved particle swarm optimization (PSO) algorithm. This method enhances the efficiency of fault location in distribution networks with distributed power sources, demonstrating good applicability and convergence, especially in complex network scenarios. The results from two test functions and simulation analyses of two types of node distribution networks show that (1) in the single-point fault simulation, the improved algorithm successfully located the fault in the ninth iteration, outperforming the 14th iteration result of the standard PSO algorithm. (2) In networks with randomly interconnected distributed power sources, the algorithm accurately located both single and multiple faults. (3) Experimental verification further supports the simulation results, proving the effectiveness of this method in practical applications. [ABSTRACT FROM AUTHOR]

With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples. [ABSTRACT FROM AUTHOR]

The electrical protection of active distribution networks is crucial for ensuring reliable, safe, and flexible operations. However, protecting these networks presents several challenges due to the emergence of bi-directional power flows, network reconfiguration capabilities, and changes in fault current levels resulting from the integration of inverter-based resources. This paper introduces an innovative protection strategy for active distribution networks, leveraging the principles of distributed coordination and multi-agent systems. The proposed strategy consists of two stages. The first stage involves a fault detection algorithm that relies solely on local measurements, while the second stage uses agent classification to compute the optimal operating time based on a dynamic matrix representation of the fault path, combined with a simplified distributed optimization problem. The coordination process is formulated as a set of linear optimization problems, simplifying the solution. The proposed protection strategy is validated in a real-time simulation environment using a modified CIGRE MV European grid as a case study, considering low-impedance symmetric fault scenarios and topological changes. The results demonstrate that the protection scheme exhibits robust performance, enhancing the adaptability of the protection equipment while ensuring suitable sensitivity and operational speed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes an impedance-based approach for locating short-circuit faults in active distribution networks (DNs). This topic is a crucial task for operators, especially in grids with inverter-based distributed generators (IBDGs). Various methods have been proposed in this research area, including traveling waves, impedance-based methods, and artificial intelligence (AI) techniques. Among them, the impedance-based scheme offers a straightforward and efficient feature suitable for integration with AI-based techniques. This paper introduces an enhanced fault localization method based on impedance estimation, consisting of two main components: (i) fault distance determination and (ii) faulty section identification. This method accounts for the modeling of inverter-based resources under both symmetrical and asymmetrical faults, incorporating the impact and behavior of such sources. Unlike conventional impedance-based methods, our approach does not require network information such as structure, lines, load data, or voltage and current measurements along the feeder at multiple points. It can serve as a feature in AIbased techniques, significantly enhancing accuracy and reducing the complexity of such techniques. To validate the efficacy of the proposed approach, we conducted a series of time-domain case studies and provided mathematical proofs. The results demonstrate the effectiveness of our scheme in accurately locating faults with varying resistances at different positions in the presence of IBDGs. [ABSTRACT FROM AUTHOR]

Abstract The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network. Electrical and hydrogen energy are administered in an integrated energy system. The modeling of the uncertainties regarding the quantity of load and renewable resources is achieved through stochastic optimization using the Unscented Transformation method. The novelties of the scheme include the sizing and placement of a combined hydrogen and power-based renewable integrated energy system, the consideration of the impacts of bio-waste units, P2H, and H2P systems on the planning of the integrated energy system and the operation of the active distribution network, and the modeling of uncertainties using the Unscented Transformation method to reduce the calculation time. The study’s results demonstrate the scheme’s ability to improve the technical conditions of the distribution network by considering the optimal planning of integrated energy systems. In comparison to the network power flow, the operation status of the network has been improved by approximately 23-45% through the optimal siting, sizing, and energy management of hydrogen storage equipment, as well as renewable resources in the form of integrated energy systems. In other words, optimal energy management and planning of the integrated energy systems in the distribution network has been able to reduce energy losses and voltage drop by 44.5% and 42.4% compared to the load flow studies. In this situation, peak load carrying capability has increased by about 23.7%. In addition, compared to the case of the network with renewable resources, the overvoltage has decreased by about 43.5%. Also, Unscented Transformation method has a lower calculation time than scenario-based stochastic optimization.

In order to give full play to the advantages of shared energy storage in improving economy and energy utilization, while considering the role of multi-energy complementation and coupling of integrated energy microgrids in active distribution networks, a multi-objective optimal allocation strategy of shared energy storage is proposed for the active distribution network connected with integrated energy microgrid. First, the optimization objectives of the economy and voltage stability of the distribution network and the configuration capacity of the shared energy storage are analyzed, the coordinated operation of the source-net-load side multi-flexible resources of the active distribution network is considered, and the active distribution network and the integrated energy microgrid are modeled. Then, the model is solved based on the Pareto optimal multi-objective particle swarm algorithm. Finally, the optimization algorithm of shared energy storage configuration is established in conjunction with the IEEE 33-node distribution system to verify the effectiveness of the proposed configuration strategy.

This paper proposes an impedance-based approach for locating short-circuit faults in active distribution networks (DNs). This topic is a crucial task for operators, especially in grids with inverter-based distributed generators (IBDGs). Various methods have been proposed in this research area, including traveling waves, impedance-based methods, and artificial intelligence (AI) techniques. Among them, the impedance-based scheme provides a simple and efficient feature that could be used in AI-based techniques. In this paper, an enhanced fault localization method based on impedance estimation is introduced. This method comprises two main components: (i) fault distance determination and (ii) faulty section identification. When developing the proposed method, the modeling of inverter-based resources under symmetrical and asymmetrical faults is considered, which includes the impact and behavior of such sources in the proposed approach. Unlike conventional impedance-based methods, the proposed approach does not require network information such as structure, lines, load data, or voltage and current measurements along the feeder at multiple points. The proposed method can be utilized as a feature in AI-based techniques, significantly enhancing accuracy and reducing the complexity of such techniques. To validate the efficacy of the proposed approach, various series of time-domain case studies are performed, in addition to mathematical proofs. The results demonstrate the effectiveness of the proposed scheme in accurately locating faults with varying resistances at different positions in the presence of inverter-based distributed generators.

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

In order to coordinate the interests between electric vehicle owners and the active distribution network, a multi-objective hierarchical optimization method for the optimal scheduling of the active distribution network with electric vehicles is proposed. This method takes into account both the comprehensive satisfaction of electric vehicle charging and the operational benefits of the active distribution network. The upper layer model focuses on maximizing the charging benefits of electric vehicle owners, and the normalized normal constraint method is used to solve the optimal charging and discharging plan of electric vehicle, which is input to the lower layer optimization model. The lower layer model aims to maximize the operational efficiency of the active distribution network by adjusting the output power of the controllable distributed generation according to the charging and discharging plan of electric vehicle. The second-order cone program and weighted minimal modular ideal point method are used to solve the nonlinear multi-objective problem. Simulation results show that the proposed multi-objective hierarchical optimization method can promote the comprehensive satisfaction of electric vehicle charging exceeding 0.9 while reducing active power loss by 94.12% and operating costs by 30.90%, which achieves a win-win situation for both electric vehicle owners and the active distribution network.

The varying carbon emission characteristics of distributed resources result in spatial-temporal differences in emission intensity at various nodes in the distribution networks. To effectively support low-carbon analysis and optimization in distribution networks as well as low-carbon accounting and trading at the user side， a model for electricity-carbon coupling in active distribution networks is built. Using load flow tracking， quantitative analysis and research on the carbon emission flow in active distribution networks is conducted， incorporating carbon emission costs into the economic costs of the distribution system. Aiming for the minimum total economic cost of the power system， simulation analysis is carried out using the IEEE 33-node test case， validating the feasibility and efficacy of the proposed method.

Under the “ dual carbon” goals, the rapid development of new energy sources poses challenges to the secure and stable operation of the power grid due to the uncertainty in their output. Active distribution networks have the capability to control various combinations of distributed energy sources, providing an effective solution to the current challenges of integrating new energy sources into the grid. Simultaneously, demand response, as an effective means of controlling load-side resources, when combined with active distribution networks, can effectively mitigate the impact of uncertainties on both the supply and demand sides of the grid. A proactive distribution network day-ahead optimization scheduling model was constructed based on the “ source-grid-load-storage” coordinated optimization. The objective was to maximize the revenue of the active distribution network, taking into account the participation of electric vehicles in demand response, grid security, energy storage battery configuration, as well as the costs and constraints of each component unit. Simulation results demonstrate that the constructed proactive distribution network optimization scheduling model is reasonable, capable of reducing active power grid losses, decreasing peak loads, narrowing load peak-valley differentials, and ultimately enhancing the revenue of the distribution network. [ABSTRACT FROM AUTHOR]

As distributed energy resources, flexible loads, and energy storage devices continue to grow, the distribution grid is gradually transforming into an active distribution network that actively participates in grid operation, regulation, and control. To address the inefficiency in utilizing energy storage devices and flexible loads, a coordinated optimization scheduling method for source, load and storage was proposed. Firstly, a multi-timescale scheduling model for active distribution networks was established. The objective functions were set to optimize the power quality and minimize the operating cost of the distribution grid, while considering constraints such as energy storage, network balance, and flexible load utilization. Finally, an active distribution network was constructed using the IEEE-33 system, and a multi-timescale scheduling strategy for day-ahead and intra-day operations was developed. A stochastic weighted particle swarm optimization algorithm was employed to solve the multi-objective optimization problem. The feasibility of the proposed method was validated. [ABSTRACT FROM AUTHOR]

As the demand for power supply stability and reliability in the active distribution network (ADN) increases, the primary and secondary integrated intelligent switch (PSIIS) has become the primary choice for smart grid transformation in many weak infrastructure areas. However, the number of PSIISs that can be configured is often limited. It is necessary to comprehensively utilize the measurement data and sectional capabilities of PSIISs through the optimal configuration method to find the optimal configuration scheme. Therefore, fault observability-related indexes (FORIs) are proposed based on the functional characteristics of PSIISs. These indexes include fault type observability, fault location observability, fault current distribution characteristics observability, transition resistance observability, and weak infrastructure area observability. An optimization configuration model of PSIISs considering the comprehensive fault observability (CFO) is constructed. The adaptive genetic algorithm (AGA) is selected as the model-solving method. Subsequently, an optimal configuration method of PSIISs considering CFO is proposed. Finally, an example analysis is conducted in MATLAB to verify the effectiveness and feasibility of the proposed method. This optimal configuration method aims to maximize the use of a limited number of PSIISs by considering CFO, and the AGA proves to be an effective tool in solving the optimal configuration scheme. [ABSTRACT FROM AUTHOR]

Variable-speed pumped storage has the advantages of flexible adjustment and low economic cost, which can reduce the adverse effects caused by a high proportion of new energy access. In order to reduce the system operation risk caused by the randomness of new energy output, a distribution robust optimization method for an active distribution network with variable-speed pumped storage is proposed. Firstly, considering the probability distribution uncertainty of wind–solar prediction error, a two-stage distributed robust optimization model of an active distribution network is constructed with the sum of day-ahead operating cost, intra-day adjustment cost expectation and conditional value-at-risk as the objective function. Then, the probability distribution fuzzy set is constructed based on the norm distance, and the fuzzy set boundary is determined by a data-driven method. Finally, the model is transformed into a mixed integer second-order cone optimization model by a linearization method and duality theory, and verified by an example. The results show that the proposed model can effectively reduce the operation risk caused by the uncertainty of operating cost and wind–solar output and reduce the operational costs and the risks associated with uncertainty in wind and solar power output. [ABSTRACT FROM AUTHOR]

High-penetration photovoltaic (PV) integration into a distribution network can cause serious voltage overruns. This study proposes a voltage hierarchical control method based on active and reactive power coordination to enhance the regional voltage autonomy of an active distribution network and improve the sustainability of new energy consumption. First, considering the reactive power margin and spatiotemporal characteristics of distributed photovoltaics, a reactive voltage modularity function is proposed to divide a distribution grid into voltage regions. Voltage region types and their weak points are then defined, and the voltage characteristics and governance needs of different regions are obtained through photovoltaic voltage regulation. Subsequently, a dual-layer optimal configuration model of energy storage that accounts for regional voltage regulation is established. The upper-layer model focuses on planned configurations to minimize the annual comprehensive operating cost of the energy storage system (ESS), while the lower-layer model focuses on optimal dispatch to achieve the best regional voltage quality. KKT conditions and the Big-M method are employed to convert the dual-layer model into a single-layer linear model for optimization and solution. Finally, an IEEE 33-node system with high-penetration photovoltaics is modeled using MATLAB (2022a). A comparative analysis of four scenarios shows that the comprehensive cost of an ESS decreased by 8.49%, total revenue increased by 19.36%, and the overall voltage deviation in the distribution network was reduced to 0.217%. [ABSTRACT FROM AUTHOR]

In order to enhance the effectiveness of power supply recovery and better address the deepening information-physical coupling in active distribution networks, an island partitioning algorithm is proposed. This algorithm takes into account the multi-flow fusion of information flow, energy flow, service flow, and the function of SOP(soft open point). Firstly, to ensure reliable load recovery, the strategy thoroughly examines information-physical coupling and models it by ranking the importance of node information and discussing its impact on load recovery. Secondly, a steady-state model of SOP was constructed, analyzing its working principle and control mode. Then, considering the role of SOP for restoring load to a greater degree and more flexibly, an active distribution network island division model was established that considers PV (photovoltaic) time variability and load operation characteristics. Finally, a simulation example was provided to verify the effectiveness of this proposed strategy. [ABSTRACT FROM AUTHOR]

The fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing penetration of distributed energy in power systems, the optimal power flow (OPF) analysis-based distribution network energy management system faces challenges such as high data dimensionality, long solution time, and large computational load. In response to these challenges, this paper aims to use the alternating direction method of multipliers (ADMM) to solve the OPF problem of active distribution systems in a fully distributed fashion. Considering the impact of distribution system partitioning on the performance of distributed algorithms, a data-driven network partitioning method based on measurement data is proposed to accelerate the convergence rate of the optimization algorithm. Moreover, different from traditional ADMM algorithms that rely more or less on certain global information, this paper integrates a consensus-based method to coordinate the balance problem at the interface between regions, thereby achieving a fully distributed solution to the OPF problem. In addition, this paper employs the LinDistFlow AC power flow approximation model to resolve the non-convexity challenges of the OPF problem. Finally, the effectiveness of the proposed method is verified through tests on IEEE distribution network cases of different sizes, and its performance in terms of error accuracy and computation time of the optimization algorithm is superior in comparison to other partitioning methods. [ABSTRACT FROM AUTHOR]

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This article proposes a hierarchical distributed voltage control (HDVC) scheme for active distribution networks (ADNs) with high penetration of photovoltaics based on distributed model predictive control (DMPC) and alternating direction method of multipliers (ADMM). The reactive power outputs of several photovoltaic clusters (PVCs) and photovoltaic (PV) units within each PVC are optimally coordinated to keep PV terminal voltages and the voltages of all critical buses of ADNs within the feasible range and mitigate voltage fluctuations. In the ADN layer, a distributed reactive power control scheme based on DMPC is designed for the PVC, which regulates the voltages of all critical buses to be closed to the rated value and mitigates the reactive power variations. In the PVC layer, the reactive power outputs of PV units are optimized based on ADMM to minimize the voltage deviation of each PV terminal and track the reactive power reference from the PVC control. The proposed HDVC scheme requires communication only between neighboring PVC controller, while each PV controller only communicates with the corresponding PVC controller. This regulates the voltages in a completely decentralized manner and effectively reduces the computation burden of the PVC and PV controllers. A modified Finnish distribution network with 10 PVCs was used to validate the control performance of the proposed HDVC scheme. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the future, a high proportion of distributed generations (DG) will be integrated into the distribution network. The existing active distribution network (ADN) planning methods have not fully considered multiple uncertainties, differentiated regulation modes or the cost of multiple types of interconnection switches. Meanwhile, it is difficult to solve large-scale problems at small granularity. Therefore, an expansion planning method of ADN considering the selection of multiple types of interconnection switches is proposed. Firstly, a probability distribution ambiguity set of DG output and electrical-load consumption based on the Wasserstein distance is established for dealing with the issue of source-load uncertainty. Secondly, a distributionally robust optimization model for collaborative planning of distribution network lines and multiple types of switches based on the previously mentioned ambiguity set is established. Then, the original model is transformed into a mixed integer second-order cone programming (SOCP) model by using the convex relaxation method, the Lagrangian duality method and the McCormick relaxation method. Finally, the effectiveness of the proposed method is systematically verified using the example of Portugal 54. The results indicate that the proposed method raises the annual net profit by nearly 5% compared with the traditional planning scheme and improves the reliability and low-carbon nature of the planning scheme. [ABSTRACT FROM AUTHOR]

The reactive power optimization of an active distribution network can effectively deal with the problem of voltage overflows at some nodes caused by the integration of a high proportion of distributed sources into the distribution network. Aiming to address the limitations in previous studies of dynamic reactive power optimization using the cluster partitioning method, a three-stage dynamic reactive power optimization decoupling strategy for active distribution networks considering carbon emissions is proposed in this paper. First, a carbon emission index is proposed based on the carbon emission intensity, and a dynamic reactive power optimization mathematical model of an active distribution network is established with the minimum active power network loss, voltage deviation, and carbon emissions as the satisfaction objective functions. Second, in order to satisfy the requirement for the all-day motion times of discrete devices, a three-stage dynamic reactive power optimization decoupling strategy based on the partitioning around a medoids clustering algorithm is proposed. Finally, taking the improved IEEE33 and PG&E69-node distribution network systems as examples, the proposed linear decreasing mutation particle swarm optimization algorithm was used to solve the mathematical model. The results show that all the indicators of the proposed strategy and algorithm throughout the day are lower than those of other methods, which verifies the effectiveness of the proposed strategy and algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Nanchang University (Engineering & Technology) is the property of Nanchang University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the realm of active distribution networks, the Inverter Interfaced Distributed Generator (IIDG) poses a challenge with its diverse non-linear fault outputs stemming from varied control strategies and objectives. This presents a dilemma, balancing computational precision and speed in short-circuit current calculation. To address this issue, a novel methodology is proposed, utilizing a Graph Attention Network (GAT)-based model. This model is designed for rapid and precise computation of IIDG fault outputs, thereby enhancing the efficiency of the iterative calculation process. Integration of the superposition theorem significantly boosts the efficiency of short-circuit current calculation in active distribution networks. Moreover, the proposed approach successfully overcomes common problems, such as reduced accuracy and non-convergence, often encountered due to the dynamic nature of network structures. The utility and adaptability of this method are illustrated through various examples, showcasing its ability to accommodate networks with unknown structures and increased branch circuits, while ensuring consistent and reliable computational results.

Proliferation of distributed energy resources (DERs) and proactive consumers has mooted over the pre-existing pricing mechanisms for active distribution networks (ADNs). This entails the need for a pricing mechanism in conjunction with cost-effective ADN operations. Locational marginal price (LMP) is a well-established pricing mechanism of the day-ahead wholesale market in most countries and provides economic signals and incentives to market participants. However, the alternating current optimal power flow (ACOPF) model (being non-deterministic polynomial-time hard) has inherent complexities and convergence issues. Besides, the approximations involved in its implementation for transmission networks may not be applicable to ADN due to their technical and structural differences. Hence, a distribution LMP (DLMP) model is indispensable for the evolving ADN. This paper proposes a network-dependent sensitivity-based branch-flow quadratic OPF model for evaluating active and reactive power DLMPs of ADNs. The DLMPs are calculated using the sensitivities and dual variables of the OPF model, which consist of incremental costs for energy, loss, congestion, and voltage components. These signals would offer an equitable price for each DER, accounting for their contribution to network conditions. The efficacy of the proposed model has been elucidated on the 33, 69, 118, and 141-node ADNs, and the results are compared with five state-of-the-art models.

Cluster division can effectively solve the problem of massive data analysis and a large number of equipment regulation caused by large-scale access of new energy to the distribution network. However, existing research on cluster partitioning algorithms exhibits low accuracy and may yield unreasonable outcomes. In order to solve the above problems, factors that should be considered in the cluster division strategy of distribution network are described when a large number of distributed power sources are connected, and the scale limit index is designed accordingly. By studying the process of genetic algorithm, the reason why genetic algorithm shows poor global optimization ability is found out, and then the algorithm is enhanced. Simulation results demonstrate that the proposed scale limit index successfully avoids unreasonable partitioning outcomes. The proposed improved genetic algorithm greatly improves the accuracy of the algorithm. Because the genetic algorithm has no convergence criterion, the reduction of the number of iterations can not directly reduce the experiment time. In summary, the research effectively improve the accuracy of genetic algorithm and enhance the efficiency of cluster division in distributed network.

Abstract High penetration of uncertain wind power generation brings challenges to power system operational security and economy. Here, an adjustable box uncertainty set is first presented to characterize the spatiotemporal correlation of multiple wind power generations. Afterward, a two‐stage risk‐adjusted robust energy dispatching model is established as a min‐max‐min optimization problem for active distribution networks operating with high penetration wind power generation. Then a three‐layer Nest Column and Constraint Generation (NCCG) decomposition approach is designed to efficiently solve the proposed model. Finally, the proposed two‐stage adaptive robust optimization model and NCCG approach are validated by a distribution network with uncertain wind power generations, and the simulation results indicate the distribution network operation economy could be compromised with robustness by adjusting the conservative regulation parameter.

The optimal scheduling of the active distribution network (ADN) is increasingly challenging due to the rising risk of ADN operation and the growth of user-side flexible resources, driven by clean energy and home device popularization. The method proposed in this paper utilizes network-side and user-side flexible resources to lower operating costs and raise system performance levels under the consideration of grid demand and user demand. In this study, mathematical models of the soft open point (SOP) and electric water heater were first constructed, and the optimal power flow model of the ADN with dynamic network reconfiguration (DNR) was established. By utilizing second-order cone relaxation (SOCR), the original mixed integer nonlinear problem (MINLP) is converted into mixed-integer second-order cone problem (MISOCP). Finally, considering the privacy of user information, a distributed optimization method for ADN integrated with SOPs and user side flexible resources is proposed based on the alternating direction method of multiplier (ADMM). An IEEE 33 bus system is used to confirm the viability of the proposed approach. The results show that the proposed method can lower the operating cost of the ADN, improve the system voltage quality, and improve system performance.

With the advancement of power systems, the integration of a substantial portion of renewable energy often leads to frequent voltage surges and increased fluctuations in distribution networks (DNs), significantly affecting the safety of DNs. Active distribution networks (ADNs) can address voltage issues arising from a high proportion of renewable energy by regulating distributed controllable resources. However, the conventional mathematical optimization-based approach to voltage reactive power control has certain limitations. It heavily depends on precise DN parameters, and its online implementation requires iterative solutions, resulting in prolonged computation time. In this study, we propose a Volt-VAR control (VVC) framework in ADNs based on multi-agent reinforcement learning (MARL). To simplify the control of photovoltaic (PV) inverters, the ADNs are initially divided into several distributed autonomous sub-networks based on the electrical distance of reactive voltage sensitivity. Subsequently, the Multi-Agent Soft Actor-Critic (MASAC) algorithm is employed to address the partitioned cooperative voltage control problem. During online deployment, the agents execute distributed cooperative control based on local observations. Comparative tests involving various methods are conducted on IEEE 33-bus and IEEE 141-bus medium-voltage DNs. The results demonstrate the effectiveness and versatility of this method in managing voltage fluctuations and mitigating reactive power loss. [ABSTRACT FROM AUTHOR]

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC-NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid's resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33- bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level. [ABSTRACT FROM AUTHOR]

Active distribution network is integrated with a large number of renewable energy sources and flexible loads such as electric vehicles(EVs). The unstable output of distributed generation and the random access to different phases cause three phase unbalance in the distribution network, which increase the energy loss and carbon emissions. Three phase unbalance reduction requires high real‐time dispatch and optimization of the power flow. This paper attempts to reduce the energy loss and carbon emission by using flexible multi‐state switch (FMSS) with optimal energy dispatch strategy. A multi‐objective optimization model is established based on second‐order cone relaxation to reduce the three‐phase unbalance and energy loss. EVs regulation strategy is proposed by analyzing their charging flexibility. A fast solution method is brought up based on second‐order cone model transformation. The effectiveness and rapidity of the method is verified by multiple cases. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High penetration of uncertain wind power generation brings challenges to power system operational security and economy. Here, an adjustable box uncertainty set is first presented to characterize the spatiotemporal correlation of multiple wind power generations. Afterward, a two‐stage risk‐adjusted robust energy dispatching model is established as a min‐max‐min optimization problem for active distribution networks operating with high penetration wind power generation. Then a three‐layer Nest Column and Constraint Generation (NCCG) decomposition approach is designed to efficiently solve the proposed model. Finally, the proposed two‐stage adaptive robust optimization model and NCCG approach are validated by a distribution network with uncertain wind power generations, and the simulation results indicate the distribution network operation economy could be compromised with robustness by adjusting the conservative regulation parameter. [ABSTRACT FROM AUTHOR]