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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.

Renewable energy systems (RESs) and electric vehicles (EVs) have become global necessities to meet the sustainable development goals (SDGs). Because they are intermittent and stochastic, these two technologies have caused power grid operation and regulation challenges despite their environmental benefits. A new combined dynamic optimal network reconfiguration (DONR) and capacitor bank switching (CBS) optimization technique reduces solar and multi-type load effects on the distribution network performance. Considering substantial EV load penetrations, ONR and CBS should coordinate to reduce energy loss, improve the voltage profile, and save money during 24 h operation. The artificial hummingbird algorithm (AHA) solves this difficult optimization problem for the first time. An enhanced IEEE 33-bus benchmark test system was used to simulate various instances to evaluate the computational characteristics of the AHA and compare it with other similar algorithms. The obtained results demonstrate the superiority of the combined DONR and CBS for real-time scenarios over any individual approach.

This paper proposes an analytic hierarchy process (AHP) based optimal load scheduling (AOLS) framework in an islanded distribution network to schedule power among prioritized loads under limited generation conditions. The scheduling problem is solved in two stages: in the first stage, AHP is used to determine the priority ranking of loads by assigning weights that are calculated based on the relative importance of loads and their attributes; in the second stage, optimal load scheduling is implemented by minimizing the total cost of load curtailment, subjected to network constraints and security limits. The scheduling problem is solved in four different cases by varying the total power generation using General Algebraic Modeling System (GAMS) to supply power to the loads in the order of priority. Taking the modified IEEE 34 node distribution network as an example, a simulation study is performed to show that the total cost of load curtailment according to AOLS is 18.51%, 24.86%, 28.65%, and 31.54% less than that of optimal load scheduling framework with equal weightage to all loads (OLSEW) in cases 1, 2, 3 & 4, which verifies the effectiveness of the proposed AOLS framework.

In the context of vigorously developing new energy sources, the economic dispatch(ED) of active distribution network (ADN) is essential. Due to the single fault affecting the global and high communication cost in centralized scheduling, we propose a distributed gradient method, namely the fast Nesterov accelerated gradient method(FNAGM), which can solve the economic dispatch problem(EDP) in ADN. The distributed architecture does not need to collect global information and only uses a sparse communication network to complete communication exchanges. It is the distributed architecture that can protect users’ private information and reduce communication pressure. By constructing the acceleration matrix based on the upper limit of the second derivative, the convergence speed can be effectively improved while satisfying the equality constraints. Eventually, the FNAGM combined with the historical momentum information and the forecast gradient, which is simulated in the bi-layer model of ADN via MATLAB. The verification results show that the algorithm with the historical momentum information and the forecast gradient can complete the optimal scheduling of controllable distributed generator(DG). What is more, the convergence efficiency performance is greatly improved.

The integration of distributed energy resources (DERs) in electric distribution networks originates the Active Distribution Networks (ADNs). The presence of DERs introduces challenges to conventional protection schemes due to bidirectional power flows and variations in fault current magnitudes; furthermore, the connection and disconnection of DERs result in multiple ADN operating modes (OMs), deteriorating the effective operation of protective relays. Considering the above, this document presents an adaptive protection strategy to address the challenges related to the variable nature of the ADN operation. The main contribution of the proposed approach is accurate islanded mode detection without requiring communication links, enabling fault detection in all ADN operating modes. The protection strategy maintains directional overcurrent relays' coordination using local voltage and current measurements to continuously estimate the Thevenin equivalent, fault currents, and adaptive coefficients.Relay coordination is maintained through tests conducted on a modified IEEE 34-bus feeder, considering several OMs, fault types, and locations. A comparison between the proposed adaptive protection strategy and a conventional overcurrent scheme shows that relay operating time is better in 100% of the analysed cases for the former strategy. On the other hand, the maximum errors in the fault current estimation using the Thevenin equivalent parameters are between 0.05% to 2.6% for all of the analysed operating modes when compared with the true fault current magnitudes. Finally, the adequate results validate the potential of the proposed communication-less protection strategy for modern protective relaying applications.

Integration of Distributed Energy Resources (DER) into the Active Distribution Networks (ADN) causes issues in conventional overcurrent protection. Additionally, another conventional protection, such as the distance function, has been used, but ADN characteristics require new well-fitted approaches. This paper proposes a strategy to improve conventional distance relays when applied in unbalanced distribution lines. The strategy is based on the relay’s current and voltage measurements and the line series impedance matrix. Different overhead line cases are studied considering several pole geometrical arrangements and fault types. Based on the results, the new set of equations for distance-based protection can achieve a more accurate fault impedance estimation, with a maximum error of 0.1% in the analysed situations and despite the fault conditions. The high proposal performance and the straightforward application make this proposal suitable for the nowadays ADN protection.

Distribution network (DN) topology identification (TI) is the basis of distribution network state estimation. However, the connection of high-penetration renewable energy makes TI of DN more challenging. Thus, a TI method of active distribution network (ADN) based on one-dimensional convolutional neural network is proposed in this manuscript. Based on the sensitivity of node voltage to topology changing of DN, the characteristics of nodes are analyzed to select the key nodes to place the distribution network phasor measurement unit (DPMU), which can save investment and reduce the redundancy of model training. Several tests are carried out with the modified IEEE-33 bus DN with photovoltaic (PV) units. The results show that the proposed distribution network topology identification method can realize the high accuracy TI in ADN under limited DPMU measurement.

With a high proportion of distributed renewable energy source (DRES) integrated into the electrical distribution network, the problems of voltage violations and branch overload, stemming from the randomness and volatility of DRES, have gradually become substantial. To solve such issues, a bi-stage optimization model is proposed for the active distribution networks with soft open point (SOP), which considers the violation risk, thus enhancing the economics and security of distribution network operation. Stage 1 corresponds to a reconfiguration model focusing on the economics and safety of operation, while stage 2 refers to a stochastic optimization model that manages the coordination and optimization of multiple adjustment methods. Meanwhile, to simulate the output uncertainty of distributed power supply, optimal scenarios are constructed based on Wasserstein distance. To limit the computational complexity, a hybrid algorithm Lévy flight and chaos disturbed beetle antennae search based on grey target decision technology and second-order cone optimization was used to solve the proposed model. Finally, the correctness and superiority of the proposed bi-stage optimization model are verified on the modified IEEE 33 and 123-bus distribution systems. The simulation results show that compared with the original system, the economic risk is reduced by 7.51% and 96.92%, and the security risk is reduced by 69.64% and 76.56%, respectively.

As the differences among inverter-based distributed generators (DGs) in the active distribution network (ADN), the aggregated dynamic model under large disturbances cannot effectively represent transient characteristics of the ADN. To overcome this issue, in this paper, a generic dynamic equivalent model of DGs is proposed. First, a simulation platform for multiple DGs is built based on electromagnetic transient simulation tool. The simulation results show that the aggregated transient characteristics under large disturbances are mainly determined by the fault ride-through control strategy of each inverter. Based on this, a control strategy is proposed to characterize the differences between inverters, and the equivalent model is derived. To achieve engineering applications, the equivalent structure is simplified, and multiple disturbance curves are used to identify the parameters. The validity and generalization ability of the proposed model is verified by simulation analysis under different disturbance strengths.

Phasor measurement units (PMUs) are being widely installed in transmission networks. Many PMU based power system applications have been developed or are under development in academia and industry. For these applications, data status update is usually a key factor affecting its performance, especially for distribution network equipment information acquisition and control computing delay performance cannot accurately ensure the timely processing of services. Therefore, an index of information freshness is proposed—age of information(AoI). According to the model of intelligent computing system of distribution network equipment, the average peak information age of the system is calculated. Finally, the DQN method is adopted to minimize the peak information age. Simulation results show that the proposed service unloading optimization scheme based on DQN can effectively reduce the peak AoI of the system.

With the development of active distribution network (ADN), the situation that multiple users such as distributed generations and loads are connected to multiple nodes of ADN at the same time may occur. In this case, the power flow of ADN will change a lot when compared with the situation that a user is connected to only one node of ADN. Therefore, a maximum openable capacity optimization model of ADN considering multiple users access is proposed in this paper to deal with the situation that multiple users such as distributed generations and loads are connected to multiple nodes of ADN at the same time. The improved non-dominated sorting genetic algorithm (NSGA-II) is utilized to solve the proposed model, and case study based on IEEE 33-bus ADN is performed for verifying the effectiveness of the proposed maximum openable capacity optimization method.

Due to the existence of bidirectional fault currents in active distribution networks (ADN), a key issue of its protection is how to identify the fault direction. In traditional power systems, the positive sequence fault component (PSFC) based protection schemes are widely used, since they have good performance under different types of fault. However, due to the diversity of voltage variants and the influence from low-voltage ride-through (LVRT) requirement, the PSFC impedance angle of an inverter-interfaced distributed generator (IIDG) is difficult to determine. To solve this problem, this paper proposed a solution based on the cooperative design of the IIDG fault control strategy. Within a few power cycles after the fault occurs, each IIDG is controlled to output a specific positive sequence current according to the voltage variant. In this way, the PSFC impedance angle of IIDG is fixed to a given value under any fault scenario, thereby ensuring the applicability of PSFC-based protection in ADN. On this basis, the fault direction identification method and trip logic for the protection device are proposed. The effectiveness of the proposed method is verified by Matlab simulation results. The PSFC impedance angle of IIDG stabilizes at the desired value, and the output current of IIDG is always limited within a safe range during the whole process. Based on the sampled signals in the second power cycle after the occurrence of a fault, the protection device can use the PSFC-based criterion to accurately determine the direction of the fault.

The soft open point (SOP) replaces the traditional feeder switch based on controllable electronic converter, it provides real-time and accurate active and reactive power flow control. To address the optimization effects of SOP on the operating efficiency of active distribution network(ADN), this paper firstly establishes the access topology and mathematical model of four-port SOP with energy storage (SSOP) considering the operating constraints of four-port SSOP converter and DC-side energy storage device; secondly, an optimization model of the operating efficiency of ADN based on four-port SSOP is established, where power loss reduction, feeder load balancing and voltage profile improvement were taken as objectives. Semi-definite programming is used to simplify the model and convert nonlinear constrains into linear quantities. Case studies of a four-port SSOP in a ADN are conducted to verify the effectiveness and efficiency of the proposed method.

With increasing distributed renewable source and active loads, the active distribution network (ADN) would suffer dynamic instability problems. Therefore, a novel strategy of intelligent coordinated damping control to improve the dynamic characteristics of ADN with multiple devices is proposed in this paper. Firstly, the detailed model of the target ADN system with multiple VSCs is built, and the related dynamic instability problem is analyzed. Then, the intelligent coordinated damping control composed of the supplementary control and the coordinated strategy is proposed. The supplementary control feedbacks the additional signals through the current sampling loop of voltage source converter (VSC), while the coordinated strategy based on particle swarm optimization (PSO) is developed to achieve the optimal design of the supplementary control. After the successful implementation of the coordinated damping control in the target system, the eigenvalue analyses and time domain simulation are conducted to verify the effectiveness of the proposed strategy. The theoretical and simulation results show that the proposed strategy is able to strengthen the dynamic stability of the target system effectively. Furthermore, the coordinated damping control can provide an effective solution to improve the dynamic characteristics of ADN with multiple VSCs.

This paper proposes a stochastic optimal operation method for active distribution networks to improve system resilience. The source–network–load uncertainties are handled with a set of typical scenarios, and the system post-contingency security level is quantitatively evaluated by security distance. The formulated problem determines the on-load tap-changer tap position, the distributed generator (DG) output power, the DG power factor angle, the demand side management participation rate and the switch status to achieve the economic operation solution. The off-the-shelf solver is deployed to solve the proposed mixed-integer nonlinear programming model. Numerical simulations studied on a modified 104-bus distribution network validate the effectiveness and high performance of the presented method. The solutions indicate that the security-aware operation scheme can simultaneously improve system economy and resilience compared with the existing operation model. Moreover, the operation solution accommodates more renewable DGs.

Since Chinese government work report in 2010 put forward the content of strengthening the construction of smart grids, the construction of smart grids has become one of the important contents of the national development strategy. In the in-depth study of smart grids, the issue of security defense systems and disaster prevention frameworks has always been Research hotspots by scholars at home and abroad, but most of my country’s existing security defenses are aimed at large-scale interconnected systems such as transmission grids, and there is a lack of research on disaster prevention of distribution networks. Therefore, in this research context, this article starts from the perspective of multi-source information fusion, takes into account the uncertainty of distribution network information and other factors, constructs a distribution network disaster response resilience index system and a distribution network resilience assessment model, and uses accidents at the same time. The scenario optimization method improves the disaster warning capability of the distribution network, and provides a scientific basis for the distribution network to take active defense measures. Then, a coordinated defense evolution strategy for the distribution network considering the optimization of accident scenarios was established. This strategy takes limited resources as the constraint, takes the minimum load loss as the objective function, and considers the economics of strategy conversion. Power supply equipment output, load transfer, and switching linkage mechanism between the three load is established, forming islands of security, and network transfer, emergency coordination of the three islands of the distribution network evolution strategy of active defense. This strategy looks for a directional evolution path from real-time accident scenarios to defense strategies from both horizontal and vertical perspectives, and realizes the coordinated control of the distribution network’s active defense strategy locally and globally. Finally, this paper takes the ice disaster climate as an example for simulation verification to prove the comprehensiveness of the risk assessment model and the rationality of the security defense method, and prove that the defense strategy can meet the online adaptive evolution requirements of the active defense of the distribution network, while taking into account The local autonomy and global optimal coordinated control of the active defense of the distribution network make up for the shortcomings of the current active defense strategy and are of great significance to further improve the security defense level of the active distribution network.

Distributed generators are being widely integrated into China’s urban distribution network unevenly, which accounts for different load rates of distribution transformer cluster in spatial dimension and low net load simultaneity rate in temporal dimension. Therefore, it is necessary to carry out flexible dispatching for the distribution transformer cluster in active distribution network to effectively reduce the grid loss. Different from the existing research on dynamic reconfiguration of the network supplied by a single transformer or a single feeder, the grid loss of distribution transformer cluster is optimized in the paper. Firstly, a 0-1 integer optimization model is established. The model is characterized by calculating the total grid loss of distribution transformer cluster based on the connectivity of power supply path and considering the spatio-temporal constraints such as total change times of switches. Secondly, uncertainty of renewable energy output is considered with k-means clustering algorithm effectively. The reduced scenarios are used for flexible dispatching simulation. Then, the discrete monkey algorithm, characterized by the cross iteration of local optimization and global search, is improved for solving 0-1 integer programming problem. The improved method can deal with the temporal coupling constraints in the model. Finally, the effectiveness of the proposed method is identified by case study. Flexible dispatching of distribution transformer cluster can greatly reduce the grid loss of active distribution network, and the optimal dispatching scheme will change along with the change of distributed generator output.

The uncertainty of distributed energy generation and the uncertainty risk of system components failure are important means to ameliorate the economy and reliability of Active Distribution Network (ADN). In this paper, a two-stage optimal scheduling method for ADN considering uncertainty risk is proposed. Considering the uncertainty risk of renewable resources, load and electricity price, and the uncertainty risk of system component failure, the two-stage optimal dispatching process of ADN with energy storage system is investigated. Firstly, the first stage model is established to minimize the cost of operation of ADN under normal operation conditions, and then the second stage model is established to minimize the load reduction under emergency operation. Particle Swarm Optimization (PSO) algorithm is proposed for model optimization. The simulation outcomes on IEEE-33 bus system simulation demonstrated high effectiveness of the proposed method.

With the increasing installed capacity of new energy generation, the randomness and volatility of new energy generation has seriously affected the stable operation of the grid. And the traditional generation side dispatching method can no longer meet the demand of the grid to maintain stable operation, which greatly affects the consumption of new energy. In order to improve the rate of new energy consumption and economic efficiency, this paper proposed a regional active distribution network economic evaluation index that takes construction costs, operating costs, and new energy subsidies into account. What is more, the paper summarized the physical constraints of each equipment in the regional active distribution network. Combining the economic evaluation index and the physical constraints, the paper raised a regional active distribution network planning and scheduling model based on robust optimization. Finally, the regional active distribution network planning model based on robust optimization is validated in terms of the new energy consumption rate and the economic benefits by optimizing the examples of load-based regional active distribution network.

In order to effectively solve the problems of resource waste and environmental pollution caused by the gradual increase of power battery decommissioning scale, retired power batteries used in active distribution network (ADN) is one of the solutions. Considering the difference of initial state of each cell, a capacity allocation method of energy storage system(ESS) for ADN considering health risk assessment is proposed in the paper. Firstly, the assessment method of health risk for retired power battery based on state of health (SOH) is in the paper studied considering the influence of different discharge depth and times on the life loss. Secondly, the ESS capacity requirements of different time levels are determined based on the input and output characteristics of ADN. The state of charge (SOC) dynamic safety margin of decommissioned power batteries is set up taking the health risks during the operation of decommissioned power batteries into account. Finally, the location and capacity of ESS is determined considering the balance of supply and demand characteristics of ADN and echelon utilization ESS of residual life cycle cost. Simulation analysis and experimental results show that the configuration method proposed in the paper can effectively reduce the configuration cost and improve the service life of power battery.

Interaction between microgrid (MG) and active distribution network (ADN) can effectively improve the operating economics and reliability of power system. A bi-level interactive optimization model for ADN with MGs is studied in this paper. The upper level is a multi-objective problem that minimizes both the operation cost and voltage deviation of each node. The lower level aims to minimize the operation cost of MG. The interaction between adjacent MGs ensures a relatively lower electricity price. To achieve a low-carbon system, carbon trading cost is also considered at both levels. And the model is decoupled into several subproblems and solved using a distributed method. Therefore, the privacy of each entity is ensured. The results prove the effectiveness of the proposed method.

The distribution network is active for the high integration rate of sustainable sources and provides excess electricity to meet transmission demand. This paper proposes a two-layer hierarchical transmission extension formula for coordinated distribution network. The upper layer of the distributed optimization framework relates to the goals and limits of the transmission level, while the lower layer corresponds to the distributed viewpoint. The approximation method is deployed to linearize the distribution network operation constraints, which ensures that the formulated two-layer problem can be handled by a dual-based method. Numerical studies conducted on two integrated transmission and distribution networks show that the proposed model based on hierarchical duality can reduce investment costs, provide operational economic benefits and accommodate renewable energy, with its effectiveness and high performance.