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In order to optimize the economic operation level of the active distribution network and improve the energy utilization rate, a layered coordinated intelligent control method of source network load–storage for the active distribution network is studied. In this method, a layered coordinated intelligent control model of source network load and storage is established. The upper model takes the minimum annual comprehensive cost as the coordination control goal and optimizes the installation capacity of distributed generation and energy storage systems in the candidate installation locations; the lower level model proposes a peak-shaving and valley-filling operation strategy for the energy storage system, which aims at minimizing the network loss to control the stable operation of the power system; using a dual-objective Pareto solution method based on a modified ε-constraint method, solve the upper and lower control models, obtain the Pareto optimal solution set of available schemes, and then use the fuzzy decision method to extract the optimal location and capacity scheme of distributed generation and energy storage systems that can ensure the stable operation of power systems. After testing, the annual investment cost and maintenance cost of DG and ESS decreased by 5,139,500 yuan and 0.024 yuan, respectively, after using this method; The fluctuation of load power in energy storage systems is significantly reduced. At the same time, the consumption of wind/photovoltaic power has increased from around 1800 MW h to around 3200 MW h, indicating a significant improvement in the consumption of new energy. It has higher economic efficiency and good peak-shaving and valley-filling effects. [ABSTRACT FROM AUTHOR]

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.)

This study aims to advance the development of the active distribution network (ADN) by optimizing resource allocation across different stages to enhance overall system performance and economic benefits. First, an ADN optimization model is constructed based on a two-stage robust optimization approach. The first stage focuses on determining optimal decision variables within the uncertainty set, while the second stage adjusts control variables based on the initial stage decisions. This model effectively addresses source-load uncertainties while preserving the flexibility and adaptability of decision-making solutions. Additionally, this study explores uncertainty models that incorporate correlation factors. The IEEE33-node model is employed to validate the effectiveness and superiority of the proposed optimization strategy through numerical simulations. Simulation results demonstrate that Model 3 comprehensively accounts for photovoltaic and wind turbine generator planning by optimizing their capacity configurations, leading to a 23% increase in distributed generation (DG) penetration. During high-load periods (e.g., 13:00 and 16:00), DG output reaches 47% and 50% of the demand load, underscoring the critical role of DG in supporting the power grid during peak hours. Overall, the proposed two-stage optimization strategy considers source-load uncertainties, significantly reducing economic costs, enhancing DG output, and improving overall system performance. In scenarios with correlated uncertainties, the optimized results exhibit greater accuracy and reliability, providing robust support for the planning and operation of practical distribution networks. [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]

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]

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]

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Copyright of International Journal of Engineering Transactions B: Applications is the property of International Journal of Engineering (IJE) 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.)

The unprecedented growth of distributed renewable generation is changing the distribution network from passive to active, resulting in issues like reverse power flow, voltage violations, malfunction of protection relays, etc. To ensure the reliable and flawless operation of active distribution networks, an electrical device enabling active network management is necessary, and a hybrid distribution transformer offers a promising solution. This study introduces a novel hybrid transformer topology and multi-mode control strategy to achieve coordinated voltage and reverse power regulation in active distribution networks. The proposed hybrid transformer combines conventional transformer windings with a partially rated SiC-MOSFET-based back-to-back converter, reducing additional investment costs and enhancing system reliability. A multi-mode control strategy is proposed to facilitate the concurrent reverse power control and voltage violation mitigation of the presented hybrid transformer, allowing a smooth transition between the P–Q control mode and the V–f control mode. The control mode switching can be activated manually or autonomously in response to voltage violations or reverse power overloading. The effectiveness of the proposed hybrid transformer configuration and its control mode transition mechanism are examined through comprehensive case studies conducted in the PSCAD/EMTDC environment. The proposed HT design has been confirmed to achieve a voltage regulation range of ±20% of the nominal voltage and effectively regulate bidirectional active power flow within a range of −25% to 25% of the rated power. [ABSTRACT FROM AUTHOR]

With the penetration of distributed resources into power distribution networks, power distribution networks are transforming into active distribution networks with a high proportion of distributed generations and power electronic equipment. Efficient modeling and simulation methods are essential to perform dynamic response analysis. In order to satisfy the fast/steady/slow multiple time-scale simulation requirements of active distribution networks, a fast/medium/slow time partition model and a network decoupling method for short line characteristic lines is proposed in this paper. Through the decomposition coordination simulation method, the network is decomposed into multiple regions that can be simulated in parallel. Based on the interconnection of fiber optic network cards, a multi-rate parallel simulation and synchronization strategy is proposed, which significantly improves the simulation speed of active distribution networks while ensuring simulation accuracy. The numerical experiments have been conducted based on a modified IEEE 33-bus and a PG&E 69-bus, and simulation results show the feasibility of the proposed method. The verification results of the example show that using adaptive variable-step-size multi-rate parallel simulation technology can increase the subnet computation-time balance rate and simulation acceleration ratio to 119.90% and 121.31% in the same rate-parallel mode. [ABSTRACT FROM AUTHOR]

This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method is validated by an IEEE 30-node simulation distributed system. [ABSTRACT FROM AUTHOR]

The integration of distributed power generation mainly consisting of photovoltaic and wind power into active distribution networks can lead to safety accidents in grid operation. At the same time, climate change can also cause voltage fluctuations, direct current injection, harmonic pollution, frequency fluctuations, and other issues. To achieve economic and safe operation of the distribution network, an active distribution network-network planning model considering the dynamic configuration of energy storage system energy storage is constructed. This model focuses on energy storage batteries with high ease of use, high modularity, and strong mobility. The route location planning involving different load operating scenarios in spring, summer, autumn, and winter is constructed. The objective function includes the revenue from selling electricity in the distribution network, the expenditure on purchasing electricity in the distribution network, and the cost during the planned construction period. The constraints include three major constraints: distribution network operation, network topology, and energy storage system operation. Three numerical examples are set up to analyze the impact of energy storage system dynamic configuration on grid planning. The results confirmed the active distribution network-grid planning model for dynamic configuration of energy storage systems. Both Example 2 and Example 3 had 3 ESS configurations. Case 3 showed different access methods for ESS in different seasons. The access nodes for ESS in spring and winter were 4, 5, and 6, while the access nodes for summer and autumn were 3, 5, and 6. After applying the proposed method, the daily energy storage investment of commercial, residential, and industrial users gradually stabilized. The reliability of electricity consumption was improved, with an improvement rate of about 40%. The research has brought considerable economic benefits to distribution network operators. It has forward-looking academic value in the joint planning of grid structures and energy storage. [ABSTRACT FROM AUTHOR]

The high expansion of a variable and intermittent nature of distributed generation, such as photovoltaics (PV), can cause technical issues in existing distribution networks (DN). In addition to producing electrical energy, PVs are inverter-based sources, and can help conventional control mechanisms in mitigating technical issues. This paper proposes a multi-stage optimal power flow (OPF)-based mixed-integer non-linear programming (MINLP) model for improving an operation state in LV PV-rich DN. A conventional control mechanism such as on load tap changer (OLTC) is used in the first stage to mitigate overvoltage caused by PVs. The second stage is related to reducing losses in DN using reactive power capabilities from PVs, which defines the optimization problem as a fully centralized observed from the distribution system operator's (DSO) point of view. The optimization problem is realized under the co-simulation approach in which the power system analyzer and computational intelligence (CI) optimization method interact through an interface. This approach allows keeping the original MINLP model without approximations and using any computational intelligence method. OpenDSS is used as a power system analyzer, while particle swarm optimization (PSO) is used as a CI optimization method in this paper. Detailed case studies are performed and analyzed over a single-day period. To study validation and feasibility, the proposed model is evaluated on the IEEE LV European distribution feeder. The obtained results suggest that a combination of conventional control mechanisms (OLTC) and inverter-based sources (PVs) represent a promising solution for DSO and can serve as an alternative control method in active distribution networks. [ABSTRACT FROM AUTHOR]

The integration of distributed generation (DG) into the decentralized access of the distribution network transforms the existing structure into an active distribution network. The alteration in fault characteristics poses significant challenges to the coordinated operation of relay protection. Fault location within the distribution network plays a vital role in facilitating fault recovery and enhancing the resilience of the power system. It proves instrumental in improving the network's ability to withstand extreme disasters, thereby enhancing the reliability of power distribution. Therefore, this paper provides a detailed analysis of the voltage fault components occurring during various fault types within an active distribution network. Building upon the identified characteristics of voltage fault components, a novel approach for the longitudinal protection of active distribution networks is proposed. This method involves comparing the calculated values of voltage fault components with their actual values. The proposed approach is applicable to various fault scenarios, including short-circuit faults, line break faults, and recurring faults. It exhibits advantages such as insensitivity to the penetration of distributed power supplies and robustness in withstanding transition resistance. The simulation results validate the effectiveness of the proposed method, affirming its applicability to diverse protection requirements within active distribution networks. [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.)

The energy crisis and environmental concerns have accelerated the development of the active distribution network (ADN) with a high proportion of renewable energy, which poses a challenge to the operation of the power system. Moreover, using active management means to promote the consumption of renewable energy is an important task of ADN. Therefore, as an important operation means, the network reconfiguration is used to enhance the adjustable capacity of the power system at the planning stage. Firstly, a "wind–light–load" uncertain scenario set is constructed to address the uncertainty of wind speed, lighting, and load. On this basis, a robust optimization model for distributed power generation taking into account network reconstruction and in ADN is proposed. In addition, the distributed generator (DG) permeability indicator is introduced in the planning model to improve the ADN ability of absorbing renewable energy. A linearized AC power flow model is utilized to calculate the power flow. Finally, via simulation in an IEEE 33-bus system and IEEE 69-bus system, the influence of network reconfiguration and robustness on distributed generator planning, economy and reliability of ADN is analyzed, and the validity of the model is verified. [ABSTRACT FROM AUTHOR]

An active distribution network is an important development trend of the power grid with widespread use of the distributed generation. The reliability of the active distribution network is not negligible due to the uninterruptible power supply. In the paper, the reliability evaluation method of the active distribution network is proposed in detail, based on combining the roulette wheel selection and the sequential Monte Carlo algorithm. The uncertainty of both the distribution generation and the load is taken into consideration based on the power probability distribution and the working state in the presented model. Furthermore, the IEEE-RBTS Bus 6 is used to verify the validity of the proposed method. The result shows that the new energy access improves the availability and the reliability of the active distribution network. [ABSTRACT FROM AUTHOR]

Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data. [ABSTRACT FROM AUTHOR]

The present and future conditions in the energy market impose extremely high standards to the operation of building energy systems. Moreover, distribution networks face new operational and technical challenges as a result of the rapid penetration of renewable energy sources (RES) and other forms of distributed generation. Consequently, active distribution networks (ADNs) will play a crucial role in the exploitation of smart building prosumers, smart grids, and RES. In this paper, an optimization method for the sustainable operation of active distribution networks hosting smart residential building prosumers, plug-in electric vehicle (PEV) aggregators, and RES was developed. The thermal and electrical loads of the residential buildings were modeled in detail and an aggregation method was implemented to the hosted PEVs. Moreover, smart power dispatch techniques were applied at each building prosumer and PEV aggregator hosted by the active distribution network. Simultaneously, all the operational limitations of the active distribution network, building energy systems, and the hosted PEVs were satisfied. The constrained optimal power flow (OPF) algorithm was exploited to keep the voltages of the hosting distribution network between the permissible bounds. A significant operation cost reduction of 17% was achieved. The developed models were verified through detailed simulation results. [ABSTRACT FROM AUTHOR]

• For the path movement of mobile energy storage system, traffic condition is taken into account which includes various types of routes and congestion levels on different routes. It is more in line with the actual situation. • The concept of virtual switch is introduced to achieve the decoupling of transportation network and active distribution network. The path movement of mobile energy storage system in transportation network is converted to the switching of virtual switch in active distribution network. A coordinated optimal model considering mobile energy storage system and dynamic network reconfiguration can be solved in active distribution network. • To improve the computational efficiency, penalty alternating direction method is utilized to handle the binary variables in the optimization model. The model can be solved in a relatively short time. • A coordinated optimal model including mobile energy storage system and dynamic network reconfiguration is proposed and linearized, and the effectiveness of the optimization strategy can be verified by simulation in the IEEE 33-bus system coupled with the 15-node transportation systems, and the stability of the proposed strategy was validated in a larger scale expansion system. The active distribution network (ADN) can face with challenges due to the increasing renewable distributed generation (RDG), which may result in elevated network losses and voltage fluctuations. To address these issues, a novel operation strategy is proposed which integrates the mobile energy storage system (MESS) and dynamic network reconfiguration (DNR) to adjust the active and reactive power of the ADN. The transportation network (TN) is modeled considering the traffic congestion, and the path movement of MESS in TN is converted to the switching of virtual switch (VS) in ADN. A coordinated optimal model is formulated for DNR and MESS, furthermore, which can be transformed into a mixed-integer second-order cone programming (MISOCP) model. The penalty alternating direction method (PADM) is employed to enhance the computational efficiency. Then the proposed strategy is tested by the IEEE 33-bus system coupled with the 15-node transportation systems, and the stability of the proposed strategy was validated in a larger scale expansion system. The simulation results demonstrate that the coordinated optimal strategy considering MESS and DNR can effectively reduce network loss and transportation cost, enhance the voltage quality of the ADN and promote the consumption of renewable energy. [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.)

The present research proposes a comprehensive model to determine the retailer strategy for purchasing electrical power from the wholesale and/or local market in an active distribution network. The uncertainties associated with the load and distributed generation resources in the active distribution network, wholesale market price, and behavior of the local market players were all incorporated into the presented model. The demand response program benefits the retailers in governing the risks. A risk-based decision-making scheme was established in this paper that considered every instrument accessible to retailers and the uncertainties involved. The major objective of this paper is to maximize the retailer benefit concerning a tolerable risk. In order to model risks, scenario theories were exploited and Particle Swarm Optimization (PSO) was employed to solve the optimization problem. The proposed scheme was simulated in an actual network, and the obtained results confirmed the effectiveness and computability of this method. [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.)

With a proliferation of diverse types of renewable distributed generation (DG) into the distribution network, an equivalent model of an active distribution network (ADN) is extremely important, since the detailed modeling of the whole ADN is much more complex and time consuming. However, different studies developed different model structures of ADNs, which are difficult to be applied in a power system simulation. At the same time, the DG's low voltage ride through the (LVRT) control was not considered in the existing ADN model, which may lead to a large modelling error. In this paper, a general equivalent model is developed for the ADN with a significant amount of DGs, based on a two-step modelling method. Step one, motivated by the dynamic similarities between the doubly-fed induction generator (DFIG)-based wind turbines, direct drive permanent magnet synchronous generator (DDPMSG)-based wind turbines, and photovoltaic (PV) generation, a general model structure of a renewable DG is initially developed. Then, an aggregation method for the DG's nonlinear subsystems of the low voltage ride through (LVRT) control and the converter's current limits are presented. Step two, the ADN model is represented by a general renewable DG model paralleled with a composite load model, and the model is validated, based on an actual distribution network with different renewable DG penetrations and different disturbance degrees. The simulation results show that our model outperforms others with acceptable errors. [ABSTRACT FROM AUTHOR]

• This paper proposes to locate faulty zones based on the consistency of waveform change directions of PSCFCs at both ends of each zone. • This paper employs the KCC algorithm to qualitatively analyze the similarity between the waveforms of each zone in the event of faults. • This paper proposes an auxiliary criterion based on the amplitude ratio ε of PSCFCs. • An improved correlation coefficient τ '' is presented by multiplying the correlation coefficient τ ' and an amplification factor. When a fault occurs in distribution networks (DNs) incorporating distributed generation (DG), the fault characteristics markedly differ from those of conventional DNs. As the penetration rate of DG gradually increases, the reliability and sensitivity of traditional three-zone current protection cannot be ensured. In response to the declining performance of traditional protection schemes in active distribution networks (ADNs), a novel pilot protection scheme for ADNs is proposed, based on the waveform similarity of positive-sequence current fault components (PSCFCs). Initially, the amplitude and phase characteristics of PSCFCs at both ends of faulty and non-faulty zones are analyzed when a Motor Type Distributed Generator (MTDG) or an Inverter-Interfaced Distributed Generator (IIDG) is connected to the DN. It is concluded that the phase difference between PSCFCs at both ends of a faulty zone is relatively small, while that of a non-faulty zone approaches 180°, serving as the foundation for constructing the protection action criterion. Subsequently, Kendall's correlation coefficient (KCC) algorithm is employed to assess the waveform similarity of PSCFCs at both ends of each zone. If the KCC is greater than zero, it indicates a high similarity in the PSCFC waveforms at both ends of the zone; conversely, a negative KCC suggests low similarity. To address the issue of reduced waveform similarity at both ends of the faulty zone due to increased transition resistance, an auxiliary criterion is established using the amplitude ratio of PSCFCs at both ends of each zone. When the amplitude ratio exceeds a predetermined threshold, the original KCC is improved using the sgn function and multiplied by an amplification factor to enhance the discrimination in improved KCC values between faulty and non-faulty zones. Utilizing the improved KCC for fault location, if the value exceeds zero, the zone is judged to be faulty; otherwise, it is deemed non-faulty. Finally, simulations validate that the proposed protection scheme is robust against various factors such as fault location, fault type, transition resistance, and white noise, demonstrating high protection reliability. [ABSTRACT FROM AUTHOR]

Topology identification (TI) is critical in electric grids, especially in distribution networks, for detecting unintended changes due to fault events and for some control and optimization functions in normal operation. Topology identification in distribution networks has been widely investigated in the literature. This paper reviews the literature on topology identification in passive and active distribution networks (PDNs, ADNs). The paper discusses model-based and model-free methods of TI, including the methods that use time-series and multi-snapshot data. This paper also reviews special problems such as joint parameters estimation/topology identification and simultaneous multiple switching actions. The problems in TI are due to the presence of distributed energy resources (DERs) in the ADNs, data availability problems including optimal micro phasor measurement units (μ PMUs) placement and data quality, and application of TI including normal operation switching detection, and outage detection have been addressed in this paper. • Frequent topology changes due to the intermittency of distributed energy resources. • Strong correlation of adjacent buses in the presence of distributed generations. • Significant effect of meter places and data quality on topology identification result. • Renewable inverters provide intentional probing topology identification methods. • Cyberattack detection comes from data availability of customers and power utilities. [ABSTRACT FROM AUTHOR]

To improve the economy and stability of distribution networks with a high penetration of distributed generation (DG), active management strategies should be considered during the planning stages and operating stage. This article presents a bi‐level model of DG placement and an optimal reactive power dispatch, where the total investment and operation cost are minimized through an optimal allocation of the DG at the upper planning level, and the active power loss and voltage deviation are optimized based on the optimal reactive power output of the DG, static var compensator (SVC), and shunt capacitor bank (SCB). In this study, a period division strategy based on the Grey relation analysis was determined to reduce the switching time of the SCB. The co‐evolution algorithm based on the improved sparrow algorithm was used to solve the adopted bi‐level model. The validation results for the IEEE‐33 node system show that the adopted bi‐level model and strategy applied in this study can reduce the comprehensive cost and effectively improve the voltage profile while reducing the active power loss of the distribution network. In addition, the results obtained by the algorithm adopted in this research are compared to those of other previously published studies. The proposed algorithm yields much better results in terms of the improvement in the voltage profile and active power loss minimization. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to advance the recently progressing research on solid‐state transformers (SST) and examines the impact of uncertainty on load demand and distributed generation (DG) units while integrating SST into an active distribution network (ADN) in the presence of battery backed solar photovoltaic (BBSPV) and wind generation. The uncertainty related to solar irradiance, wind speeds, and load demands is modeled through kernel density estimation (KDE), generalized extreme value (GEV) distribution, and Gaussian distribution summed with the maximum likelihood estimation (MLE) approach respectively. The K‐medoid clustering procedure is utilized to group the load demand into multiple load levels, which helps in the intelligent scheduling of charging and discharging of battery energy storage systems (BESS). The distribution network planning process manages two conflicting criterion functions of real power loss (RPL) reduction and sum of square deviations of the expected voltage values, which are optimized utilizing the non‐dominated sorting genetic algorithm (NSGA‐II). The probabilistic power flow uses the direct load flow procedure utilizing bus injection to branch current (BIBC) matrix along with the forward sweep method to investigate each run of Monte Carlo simulation (MCS). Multiple case studies have been conducted on the IEEE 33 bus radial distribution network (RDN). The outcomes demonstrate the potential benefits of RPL reduction and voltage profile improvement (VPI) with increasing penetration levels of SST. [ABSTRACT FROM AUTHOR]

• A two-stage mixed integer second-order cone programming (MISOCP) method based on radial basis function stochastic response surface method (RBF-SRSM) is proposed, which takes into account the impact of the randomness of source and loads on the security of the distribution network. • A RBF-SRSM based stochastic optimization method for high dimensional configuration points is proposed. • The proposed model can ensure economic and environmental protection of dispatch strategy while preventing voltage overruns. • The RBF-SRSM based scheme is more efficient than SRSM and can be used to solve the larger uncertainty problem of future distribution network. As the high proportion of distributed generation (DG) and diversification of loads increase, the distribution network faces increasingly greater impact. To address the high-dimensional stochastic problems arising from the multi-dimensional uncertainty of power source and load, this paper proposes a two-stage optimization scheduling scheme for AC distribution networks based on RBF-stochastic response surface method (RBF-SRSM). This scheme takes into account economy, environmental protection and safety, and consists of day-ahead and intra-day stages. The day-ahead decision scheduling is based on an optimal low-carbon economy operation plan, while the intra-day reactive power optimization is achieved by converting nonlinear integral indicators into discrete linear indicators through probabilistic power flow calculation and the first moment derivation principle inspired by SRSM. The mixed integer second-order cone programming (MISOCP) method is used to solve the dispatching plan, and the results suggest that the proposed method is more efficient than the SRSM. [ABSTRACT FROM AUTHOR]

• Electrical Power Systems Research. • This paper proposes a joint planning scheme for soft open points and energy storage to address the issue of unbalanced supply and demand in distribution networks, aiming to enhance the system's economic efficiency and stability. • This paper evaluates the proposed scheme in comparison with other literature under different electricity price conditions. The results indicate that as the electricity price increases, the economic growth rate of the proposed scheme in this paper is lower than that of the schemes in other literature, further confirming the advantages of this scheme. • This paper takes full account of the uncertainty of distributed power sources, adopts a two-stage robust model, and provides reasonable values for robust control parameters based on the planning results. With the increasing penetration rate of renewable energy, the uncertainty of its output has exacerbated power imbalances among different distribution networks. In this paper, we propose collaborative planning of soft open points and energy storage systems to balance a distribution network with source-load imbalance, aiming to improve the economy and reliability of the distribution network. Firstly, considering that distributed generation has uncertainty, the K-means is used to construct a typical day scenario, on the basis of which a collaborative planning model is established with the objective function of minimizing the annual integrated cost. Then, the original nonlinear nonconvex model is transformed into a mixed-integer second-order conical planning model by Big-M and second-order conical relaxation, and a two-stage robust optimization is used to seek the planning solution with the lowest operational cost under the worst-case scenarios, aiming to enhance the robustness of the planning model. Finally, the feasibility of the proposed planning model is verified on two source-load unbalanced IEEE 33-node active distribution network arithmetic cases. [ABSTRACT FROM AUTHOR]

With the large-scale configuration of distribution automation and the widespread implementation of distributed generation (DG) and flexible load (FL), the distribution network can achieve active regulation, which makes the traditional fault restoration strategy and distribution network reliability evaluation more complicated. Given this background, a method of reliability evaluation for the active distribution network considering ‘supply-grid-load’ is proposed. On the supply side, a distribution network islanding model considering DG is established, with the objective of supplying power to the maximised amount of equivalent power load. On the grid side, the influence of distribution network automation on the distribution network reliability is elaborately analysed and the time-varying failure rate model of equipment which considers the life cycle characteristics is presented. On the load side, an incentive-based FL model is proposed based on the elastic coefficient matrix. Finally, simulation results of RBTS-BUS6 system demonstrate the effectiveness of the proposed model and method.

With the integration of more and more renewable energy generations (REGs), the structure of traditional distribution networks is hard to accommodate the volatile power injections of REGs. As a new power electronic device, soft open point (SOP) can be installed to control both active and reactive power flow among active distribution networks (ADNs). This paper presents a comprehensive optimization method for allocating SOPs within an ADN with high penetration of REGs. In order to find proper SOP candidate locations, a selection strategy based on two technical indices is proposed. To mitigate the risk of voltage violation caused by REG forecast errors and improve the adaptiveness of allocation results, a two-stage robust optimization model for SOP allocation is formulated to minimize the total cost of SOP investment and network operation. The proposed model is converted into a mixed-integer second-order cone programming (MISCOP) problem, which is then decoupled into a master problem of planning and a subproblem of operation and solved by column and constraint generation (CCG) algorithm. Simulation results show that the proposed method can effectively find the optimal SOP allocation schemes. Comparisons with different mathematical formulation and solution methods show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This study presents a model predictive control (MPC)-based centralised control approach for maintaining the voltages of the nodes within permissible limits in the presence of high photovoltaic (PV) penetration. The proposed control scheme coordinates the actions of on-load tap changer (OLTC) and PV inverters optimally to fulfil the desired objectives. The objectives are minimisation of change in control variables, slack variables, energy loss, and voltage error. These objectives are weighted to form the overall objective function. Three rules are formulated based on the severity of voltage magnitudes. The weights of the objectives are adjusted according to these pre-defined rules. Simulations are performed in an active distribution network (ADN) integrated with and without microgrids, where both demands and generations vary hourly over the day. As power is injected by the microgrids during most of the time of the day, the excursions of node voltages are slightly higher in the microgrids integrated ADN. Moreover, the incorporation of the proposed rule-based MPC drastically reduces the energy loss due to active power loss in distribution networks, as is evident from the simulation results obtained by comparing the proposed approach with an existing MPC-based approach. [ABSTRACT FROM AUTHOR]

With the large-scale configuration of distribution automation and the widespread implementation of distributed generation (DG) and flexible load (FL), the distribution network can achieve active regulation, which makes the traditional fault restoration strategy and distribution network reliability evaluation more complicated. Given this background, a method of reliability evaluation for the active distribution network considering 'supply-grid-load' is proposed. On the supply side, a distribution network islanding model considering DG is established, with the objective of supplying power to the maximised amount of equivalent power load. On the grid side, the influence of distribution network automation on the distribution network reliability is elaborately analysed and the time-varying failure rate model of equipment which considers the life cycle characteristics is presented. On the load side, an incentive-based FL model is proposed based on the elastic coefficient matrix. Finally, simulation results of RBTS-BUS6 system demonstrate the effectiveness of the proposed model and method. [ABSTRACT FROM AUTHOR]

As the scale of distributed generations connecting to the power system increases continuously, communication system considering the requirements of real‐time control is becoming much more important in active distribution system. Therefore, in order to improve the responsiveness of the distribution network during the communication interruption, an offline backup strategy is proposed considering the combined output of distributed generations and energy storage systems involving the characteristics of the active distribution system. Based on a typical set of clusters which describe the features of wind turbines and photovoltaics, the strategy analyses operational vulnerability combining structural characteristics of the active distribution system and solves the output level of each distributed generation according to the improved algorithm. A numerical case study is presented to demonstrate the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Accurately identifying the key links in active distribution network (ADN) that affect the system's safety and stability is of great significance to improve the efficiency of operation and maintenance and risk early warning for distribution network (DN). In this study, the node resilience (NR) is first proposed as an indicator for structural stability identification of DN. Then, a critical node identification method considering the security, economy and structure stability in ADN is proposed, which uses the NR, the utility risks of branch load change, node voltage deviation and line loss variation as the identification indicators. Also, combining with subjective experience and objective data, the comprehensive evaluation method of critical nodes is improved, and the multilevel and multidimensional evaluation model of critical nodes is constructed. Furthermore, the time sequence of load change and photovoltaic output is considered. Finally, the rationality and effectiveness of the proposed method are proved by taking IEEE 33-bus system and an actual ADN as examples. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed energy resources (DERs) in the active distribution network (ADN), how to enable joint planning of DERs under the uncertainty of distributed generations (DGs) has become a challenging problem. This study establishes a two‐stage joint planning model considering doubly‐fed induction generator, photovoltaics (PVs) with the ancillary services of PV inverter, distributed energy storage systems and different types of controllable loads in the ADN. To address the uncertainties of DGs, a two‐stage data‐driven distributionally robust planning model is constructed. The proposed model is solved in a 'master and sub‐problem' framework by column‐and‐constraint generation algorithm, where the master problem is to minimise the total cost and find the optimal planning decision under the worst probability distributions, and the sub‐problem is to find the worst probability distribution of given uncertain scenarios. Besides, the original mixed‐integer non‐linear planning problem is converted into a mixed‐integer second‐order cone programming problem through second‐order cone relaxation, Big‐M and piecewise linearisation method. The numerical results based on 33‐bus system verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Massive integration of distributed generation originates the active distribution networks, introducing new control, protection and operation challenges. This paper proposes a strategy for adaptively protecting active distribution networks that undergo frequent changes in operating conditions. Numerical overcurrent relays capable of storing multiple setting groups, each with different settings designed to address various operating conditions, are used to overcome this issue. The proposed strategy uses locally acquired voltage and current measurements to accurately identify the operating conditions during fault occurrence. The identified condition is then used to select the appropriate overcurrent relay settings using a strategy which includes signal characterisation, data recognition, and optimisation to minimise the operating time for both primary and backup relays. Validation of the protection strategy is conducted on the IEEE 34-Bus test system, and the results indicate its ability to accurately detect different operating conditions in both connected and islanded operating modes, even in the presence of high-impedance faults and load variation. The performance rate exceeds 97.27% for all analysed cases, and the proposal identifies the operating condition in less than 20 ms after fault inception, demonstrating its low computational burden and suitability for implementation in field applications. • A fast method for recognising system operating conditions (OCs) is presented. • Relays are adaptively adjusted using the recognised OC. • The proposed adaptive relay uses locally acquired voltage and current measurements. • The optimised relay coordination is based on nonlinear objective functions. [ABSTRACT FROM AUTHOR]

The rise in distributed renewable generation and active prosumers has heightened power exchanges in Distribution Systems (DS), causing congestion issues. This paper presents a four-layer optimization model for Distribution System Operators (DSOs) to efficiently manage network congestion through smart prosumers. The first two layers involve an iterative bidding process in the energy market to maximize social welfare during settlement. The third and fourth layers address congestion in the market, employing an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm for decentralized coordination between DSOs and smart prosumers, including Microgrids (MGs), Energy Hubs (EHs), and Electric Vehicle Parking lots (EVPLs). Layer 1 optimizes market player bids, layer 2 maximizes social welfare, layer 3 handles congestion using various strategies, and layer 4 reschedules smart prosumers based on DSO's requests. Implemented on a 118-bus DS in GAMS, the model demonstrates the efficacy of the proposed adaptive ADMM in ensuring player privacy and market efficiency. Additionally, integrating the market-based mechanism with Distribution Feeder Reconfiguration (DFR) optimally utilizes smart prosumers, resulting in a 46.85% reduction in net DSO costs. This practical implication underscores the model's effectiveness in addressing network congestion challenges and providing economic benefits for DSOs. [ABSTRACT FROM AUTHOR]

The widespread adoption of distributed generation (DG) is changing the operation of distribution networks. To increase the control of power flows and avoid the detriments of DG intermittency, active distribution networks (ADN) emerge as a feasible solution. In this paper, we propose a convex model for ADN expansion planning. Four active network management (ANM) schemes are considered: DG active power curtailment, DG reactive power control, on-load tap changer (OLTC) tap adjustment, and shunt compensation device control. The problem is formulated as a mixed integer second-order cone programming problem (MISOCP) using the distflow equations and second-order cone relaxations (SOCR). By scenario analysis, we address the uncertainty associated with power demand, renewable generation, and electricity prices. The proposed model is used to study the benefits of different combinations of ANM schemes to a modified IEEE 33-bus system and compare them with the individual benefits of each scheme. The results indicate that ANM schemes can reduce total network costs, reduce losses, delay network reinforcement, and improve the voltage profile. In addition, some combinations of schemes develop synergies in which the combined economic benefits are greater than the sum of the individual benefits. [Display omitted] [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.)

With the access of renewable distributed generations and the uncertainty of demand‐side response, the operation states of the active distribution network (ADN) are uncertain. This study proposes a novel optimal strategy taking active management costs of the source–network–load as the goal and considering the site selection and the incentive price for the responsive load to optimise the ADN. To deal with uncertainties, a chance‐constrained optimisation model is established and the Monte Carlo sampling method is used. Then, the uncertain model is settled by the improved backtracking search algorithm, which uses an adaptive mutation scale factor. Finally, the practicability of the proposed strategy and the performance of the algorithm are verified by the modified IEEE 33‐bus distribution system and an actual distribution system in China. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter 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 a hierarchical coordination control strategy for solving the power management problem of an active distribution network with high penetration of distributed generation. The proposed strategy is intended to achieve four aims: 1) regulate the common coupling point demand at given references; 2) share the active power among power generation sources; 3) balance the voltages among controllable energy sources; and 4) recover the battery energy storage system’s state-of-charge. The main contribution of the proposed strategy is that it can regulate both the active and reactive power injection at the common coupling point by coordinating the controllable energy sources such that the distribution network can be scheduled as an adjustable PQ node. The strategy considers the interests of three groups including the operators of power system, independent investors of distributed generators and the users in the distribution network. Sufficient conditions for the strategy are presented. Theoretical analysis and simulation studies are provided to illustrate and validate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Highlights • A protection which isolates the SLG fault in neutral non-effectively grounded distribution with DG. • The time setting based on the grading coordination is added to the traveling-wave-protection. • Based on the coordination of relays the fault can be removed from both sides without communication. Abstract Electricity networks are in the era of major transition from stable passive distribution networks with unidirectional electricity transportation to active distribution, and the fault on the distribution lines should be cleared from both sides accordingly. This paper proposes a traveling-wave-based line protection scheme which can isolate the Single-Line-to-Ground (SLG) fault from both sides of the faulted line in neutral non-effectively grounded distribution network including Distributed Generation (DG) sources. In the proposed scheme, when a SLG fault occurs, first, relays distinguish the fault direction by comparing the polarities of the initial voltage and current traveling waves; and then the relays that detect the fault on the downstream and upstream sides operate based on the conventional time grading coordination, and the electrical changes of three-phase in a specified time window, respectively; finally, after operation of the relay upstream of the fault on the faulted line with the fastest time setting, the relay downstream of the fault operates by detecting the tripping of the opposite circuit breaker. In order to validate the efficiency of the proposed scheme, extensive simulations have been performed on a typical active distribution system using ATP-EMTP software. The simulation results indicate that the proposed line protection scheme can accurately and quickly isolate the SLG fault from both sides. [ABSTRACT FROM AUTHOR]

Highlights • A risk aversion active distribution network (ADN) model is developed. • The uncertainties of electricity price and wind power are considered in AND planning. • The proposed model can result in a trade-off planning between the profit and risk. • The reliability and effectiveness of the proposed model are discussed. Abstract This paper introduces a new computational framework to account for uncertainties in active distribution network (ADN) planning in the presence of wind power, energy storage and electricity price. A key challenge when dealing with wind power and electricity price is the inaccuracy of forecasting methods because of the uncertainty of distributed power such as wind power, and the different demand response providers. We propose a risk aversion planning model for ADN with respect to uncertainties of wind power and electricity price. The proposed model needs neither sampling different scenario nor to calculate multi-objective while guaranteeing a quantifiable risk level by a conditional expectation. Then, an electricity price driven ADN planning has been discussed, and the problem of selecting custom electricity contracts and finding the optimal procurement strategy of meeting contract obligations under spot price uncertainty is considered. Finally, simulation studies are carried out for several IEEE test systems to validate the effectiveness of the proposed model in obtaining the optimal trade-off solution between profit and risk. [ABSTRACT FROM AUTHOR]

Considering the problem of cooperative operation of highly intermittent renewable generations (photovoltaic and wind power) and flexible load in active distribution network, a distributed cooperative optimisation operation strategy based on combine-then-adapt diffusion algorithm is proposed in this study. Distributed agents of distributed generators are connected by the distributed sparse network through distributed hierarchical control method. As an agent node of networked system, it interchanges information with its neighbours. In order to make the distribution generator meet the equal incremental cost principle, the adaptive adjacency matrix and fusion matrix with the weighted factor are introduced to update the state value of nodes to obtain the same convergence value. In this strategy, the coordinated source and load is applied by the active power optimisation dispatching for active distribution network. Stimulate analysis and experimental results show that the proposed combine-then-adapt diffusion algorithm can effectively solve the problem of real-time detection signal, can quickly learn and adapt to environmental changes. Finally, the simulation analysis and experimental results of IEEE14-bus distribution test system is carried out to verify the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

• Traditionally, assessment of static voltage stability for distribution power networks with distributed generation (DG) utilizes the continuous power flow (CPF) method by calculating load margin index (LMI) at various load levels, which suffers from slow calculation speed and low accuracy when power flow calculation becomes ill-conditioned. • To overcome this issue, this paper presents a novel holomorphic embedding load flow method (HELM)-based approach that calculates voltage stability indices for various types of nodes in distribution network. • Different orders of sensitivity information are calculated using the HELM method, and the criteria for assessing voltage stability are derived, which are used as constraints to optimize the location and capacity of DG. • Such formulation of the optimization problem does not need to calculate the maximum load margin using CPF that can be effectively solved using the Genetic Algorithm and easily adaptable to various operating conditions of DG. • The effectiveness of the proposed approach is verified on IEEE 33-node and 69-node distribution power network model. • Results show that considering the HELM VSI, the location and capacity of DG in the distribution network can be better optimized. Traditionally, assessment of static voltage stability for distribution power networks with distributed generation (DG) utilizes the continuous power flow (CPF) method by calculating load margin index (LMI) at various load levels, which suffers from slow calculation speed and low accuracy when power flow calculation becomes ill-conditioned. To overcome this issue, this paper presents a novel holomorphic embedding load flow method (HELM)-based approach that calculates voltage stability indices for various types of nodes in a distribution network. Different orders of sensitivity information are calculated using the HELM method, and the criteria for assessing voltage stability are derived, which are used as constraints to optimize the location and capacity of DG. Such formulation of the optimization problem does not need to calculate the maximum load margin using CPF. Moreover, it can be effectively solved using the Genetic Algorithm and easily adaptable to various operating conditions of DG, which improve the calculation speed of the stability index. The effectiveness of the proposed approach is verified on IEEE 33-node and IEEE 69-node distribution power network models. Results show that considering the HELM VSI, the location, and capacity of DG in the distribution network can be better optimized. For the IEEE 33-node model, the total cost is reduced by approximately 2.33%, the network loss is reduced by 1.2%, and the HELM voltage stability criteria index is increased by about 4.23%; while for the IEEE 69-node system, the total cost is reduced by about 2.98%. [ABSTRACT FROM AUTHOR]