Active Distribution Network Research Articles

An enhanced algorithm for detection of HIAF in active distribution networks and real-time analysis

Identifying a downed conductor in distribution is challenging due to the insignificant variations in the current signal. Most of the distribution system faults are identified using overcurrent and earth fault protection. Fault path resistance decides the current magnitude while high resistance path may create timely issues in identifying a fault. Downed conductor and high resistance faults create arc in between the ground and conductor. Due to this the fault at the location is highly nonlinear with reduced magnitude. These faults are known as high impedance arcing fault (HIAF). Such faults can be identified effectively by conducting harmonic analysis of the signals. But in a modern distribution system apart from other renewable sources, the integration of electric vehicle (EV) may create HIAF detection challenges due to the inclusion of inverters and switching harmonics associated with EV. Considering the available advanced architecture of modern microgrid system, an effective solution is proposed in this work to securely detect HIAFs. The method is robust and highly reliable for HIAFs. The system stability and continuity can be unaffected with the help of the proposed method in case of various non-fault or transient/switching conditions. Comparison with other profound techniques is done to show the superiority of the proposed method.

A two-layer economic resilience model for distribution network restoration after natural disasters

Improving the resilience of energy networks and minimizing outages is crucial, making the development of self-healing strategies essential for ensuring a continuous power supply and boosting resilience. As decentralized prosumers become more integrated into smart grids, innovative coordination methods are required to fully leverage their potential and improve the system's self-healing capabilities. Hence, in this paper, we present a decentralized two-layer model for active distribution networks (ADNs) incorporating commercial energy hubs, Parking lots (PLs), hydrogen refueling stations, and mobile units (MUs). This model aims to maximize the flexible capacities of flexible prosumers under emergencies and includes a dynamic mechanism for transporting hydrogen to refueling stations via trucks. In the first layer of the model, the AND operator provides the required services to flexible prosumers, including commercial energy hubs, PLs and hydrogen refueling stations. During this layer, the movement paths of mobile units, such as battery-based mobile storage units (MSUs) and mobile hydrogen units (MHUs), are determined. In the second layer, prosumers independently plan and decide the extent of their service contributions. Subsequently, with the actual service capacities established, the AND operator re-optimizes the initial planning to finalize the system schedule and precise movement paths of the mobile units. The proposed model was tested on a modified 83-bus distribution network using the CPLEX solver in GAMS. Simulation results demonstrate a 54.9 % reduction in forced load shedding (FLS) due to the dispatch of mobile units and a further 84.4 %% reduction in FLS from leveraging the flexible capacities of commercial energy hubs and PLs.

Fault-Line Selection Method in Active Distribution Networks Based on Improved Multivariate Variational Mode Decomposition and Lightweight YOLOv10 Network

Fault-Line Selection Method in Active Distribution Networks Based on Improved Multivariate Variational Mode Decomposition and Lightweight YOLOv10 Network

Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface

AbstractActive distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

Collaborative operation of active distribution network considering PV-storage-charging-compensation

Abstract With the conjunction of clean energy generation and electric cars, the power distribution network is gradually shifting from a traditional one-way radiative network to an active network. Coordinated control of these controllable elements of “source-grid-load-storage” can improve the operating voltage level and economic benefits, and reduce the load peak and off-peak difference. A collaborative dispatch model for active power distribution networks including PV-storage-charging-compensation is constructed in this paper, with the goal of minimizing network losses and PV curtailment costs. The cone transformation is used to handle non-linear constraints of power flow. Compared with existing ordered charging control methods, the constructed model can maximize the control efficiency of the distribution grid, enhance the PV absorption capacity, and effectively slow down the upgrading and renovation of the distribution network.

Ground fault protection algorithm of active distribution network based on energy extremum direction

AbstractAfter large‐scale distributed power sources are connected to the distribution network, the fault current undergoes noticeable changes, affecting the accuracy of traditional single‐phase grounding fault algorithms. Therefore, the objective of this paper is to address the impact of distributed power integration on the grounding algorithms of distribution networks. The main contribution is: by establishing an electrical system structure and model for distribution network grounding faults that include distributed generation (DG), theoretically deriving and calculating the transient zero‐sequence current frequency changes at the moment of fault, analysing the directional characteristics of zero‐sequence currents under DG connection conditions, designing a local grounding fault judgment algorithm based on energy extremum direction, and providing a fault judgment and isolation process for grounding fault monitoring devices. The results show: through simulation and experimentation, the algorithm was tested, and the method can reliably judge grounding faults under various transition resistances, different numbers and capacities of connected distributed power sources, and different grounding switch‐on angles. The applicability of the algorithm covers both methods of neutral grounding through an arc suppression coil and ungrounded neutrals, adapting to scenarios with or without DG connections.

Lightning risk assessment of active distribution network with distributed photovoltaic system

Lightning strikes are the main reason for the fault of active distribution network. It is of great significance to study the risk assessment of lightning in active distribution network for lightning protection. Taking an active distribution network in a certain region as the research object, considering the mutual influence between the photovoltaic side and the distribution line side during lightning strikes, the method of lightning risk assessment for the active distribution network with photovoltaic system was proposed. Electrical geometric models were constructed to calculate the fault rate of the photovoltaic side and the trip rate of the distribution line side, and the risk assessment was conducted based on the calculation results. The results show that when the lightning strike is closest to the tower on the photovoltaic side, the lightning fault rate on the photovoltaic side increases from 0.957 times/year to 3.0766 times/year. When the photovoltaic side is struck by lightning, the intrusion of lightning impulse can double the lightning trip rate of the adjacent three towers on the distribution line side, indicating a higher risk level. It is recommended to focus on the protection measurement for the three towers near the photovoltaic system.

Collaborative optimization of electric-vehicle battery swapping stations based on energy storage sharing

Due to the extensive integration of distributed renewable energy resources, the Active Distribution Network (ADN) faces numerous challenges, including, for example, renewable energy curtailment and overloading. This paper proposes a collaborative optimization control method for electric-vehicle battery swapping stations that mitigates the mismatching between generation and load demand in the ADN. Energy storage sharing is considered in this study, that allows stations to exchange batteries via the traffic network, and this extends the capacity of Battery-Transferable Swapping Stations (BTSSs). First, the operational principles of the energy storage shared BTSS are carefully analyzed, including external and internal control mechanisms and energy storage sharing. Subsequently, a bi-level optimization model is proposed, whereby the upper level aims to minimize the total operational cost of the ADN and the lower level seeks to maximize the benefit of each BTSS. A two-stage transactive control is introduced to decompose the bi-level situation into two stages: the day-ahead stage and the real-time stage and this facilitates easier problem-solving. Finally, an IEEE 15-node system is utilized to verify the proposed method. The results indicate that the proposed method reduces the renewable energy curtailment, power shortages, and operational costs of the ADN, while at the same time increasing the earnings of BTSSs.

Co-optimization of virtual power plants and distribution grids: Emphasizing flexible resource aggregation and battery capacity degradation

Coordination between virtual power plants and active distribution networks is crucial as these plants increasingly aggregate distributed resources within the power system. This study introduces a bilevel optimization framework to coordinate the scheduling of multiple virtual power plants and an active distribution network using pricing strategies for energy and reserves. The upper-level optimization minimizes total operating costs by incorporating bidding plans of the active distribution network in various markets, its interactions with multiple virtual power plants, and operational costs. The lower-level optimization maximizes revenue for each virtual power plant, considering both battery capacity degradation costs and operational costs of various resources. To facilitate solutions, this research developed a nonlinear transformation method for modeling capacity degradation. Based on the dispatching strategy from the virtual power plant, this study uses the squared difference between energy consumption of equipment for controllable loads and the strategy as the optimization target to derive control strategies for two equipment types. Results show that the framework effectively integrates dispatch and control strategies without oversimplifying the system model, proving its applicability in various scenarios with diverse resource compositions.

A Two-Stage Fault Localization Method for Active Distribution Networks Based on COA-SVM Model and Cosine Similarity

A Two-Stage Fault Localization Method for Active Distribution Networks Based on COA-SVM Model and Cosine Similarity

Research on the three-phase load imbalance control of the active distribution network based on the distributed power flow controller

Research on the three-phase load imbalance control of the active distribution network based on the distributed power flow controller

Collaborative Optimization for Active Distribution Network with Soft Open Point and Energy Storage

Background: The increasing use of high-penetration distributed clean energy has led to voltage fluctuations in the distribution network that surpass safety limits and pose challenges to economic and low-carbon operation. Traditional voltage regulation devices are unable to meet the real-time high-precision voltage control needs when encountering frequent fluctuations due to physical limitations. The Soft Open Point (SOP) can provide continuous reactive power to achieve rapid voltage regulation. Objective: We propose a collaborative optimization approach that fully considers the regulatory capabilities of SOP and energy storage to eliminate voltage violations and reduce network losses. Methods: This study introduces an active distribution network reactive power voltage optimization approach that incorporates intelligent SOP and energy storage, combining conventional devices (such as on-load tap changers and capacitor banks) with contemporary devices (such as SOP, distributed generators, and energy storage) to establish synergy. A coordinated optimization model was developed, considering various operational constraints to minimize network losses and regulate the voltage of distribution network nodes. By using linearization and quadratic relaxation, the original non-convex mixed-integer non-linear optimization model was transformed into a Mixed-Integer Second-Order Cone Programming (MISOCP) model to meet the requirements of rapid voltage regulation. Results: A case study was conducted on the IEEE 33-node test system, showing that the proposed approach effectively reduces network losses and eliminates voltage violations. Conclusion: The feasibility and effectiveness of the proposed methodology have been validated, confirming its ability to ensure the safe and economic operation of active distribution networks.

Coordinated active-reactive power optimization considering photovoltaic abandon based on second order cone programming in active distribution networks

On the basis of predecessors’ coordination optimization of active and reactive power in distribution network, For the necessity of the optimal operation in the distribution network, part of power generated from photovoltaic (PV) cannot be sold to users, and cannot enjoy subsidies. Similarly, the network loss in the power transmission will also bring a certain economic loss. This paper comprehensively considers the economic loss caused by the network loss and PV abandon of the distribution system, and establishes a model to minimize the economic loss. To solve this problem efficiently, the method of DistFlow equation and mixed integer second order cone programming (MISOCP) is used to solve the problem, in this method, the original mixed integer nonlinear programming non-convex problem is transformed into a convex problem, which makes the optimization problem easy to solve. The modified IEEE 33 and IEEE 69 distribution networks are tested by the above method. The optimized results are able to meet the target and have very small relaxation gaps, and the voltage level is also optimized. This coordinated optimization approach helps to optimize the economic operation for active distribution networks with PVs.

Active and reactive power coordination optimization for active distribution network considering mobile energy storage system and dynamic network reconfiguration

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.

A Wide-Range TCSC Based ADN in Mountainous Areas Considering Hydropower-Photovoltaic-ESS Complementarity.

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

Voltage regulation and energy loss minimization for distribution networks with high photovoltaic penetration and EV charging stations using dual-stage model predictive control

The widespread integration of photovoltaic (PV) units and controllable loads like electric vehicles (EVs) might cause uncertain voltage fluctuations quite frequently. The reactive power from distributed generation (DG) units and EV charging stations (EVCSs) can effectively be used along with conventional devices like on-load tap changer (OLTC) to successfully control network voltages in real-time, with multi-time scale coordination. However, the control of a large number of available resources, with reliable communication among them becomes a complex task. Moreover, the substantial real-time data set amassed through the deployment of measurement devices across the network increases both cost and computational intricacies. This paper presents a novel approach, employing a time decomposition-based dual-stage model predictive control (MPC) with a reduced model control framework for voltage control and energy loss minimization in active distribution networks (ADNs), by significantly reducing the number of measuring devices. A minimum global set of available control resources is identified for real-time control, aiming to mitigate control complexity and minimize the demand for heavy communication. The proposed control strategy is validated on the 33-bus distribution network and modified IEEE 123-bus distributed network, under high PV penetration and EV charging stations. It is seen that the proposed reduced model framework with very limited measuring devices and control equipment can effectively regulate the voltages with a standard deviation of 0.0059 p.u. and 0.0035 p.u. as compared to the full order system model, for 33-bus network and IEEE 123-bus network, respectively. Furthermore, there is a net 23.24% reduction in energy losses when power loss minimization is considered along with the minimization of voltage deviations in the 33-bus network.

An Active Distribution Network Voltage Optimization Method Based on Source-Network-Load-Storage Coordination and Interaction

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system.

Coordinated Planning of Soft Open Points and Energy Storage Systems to Enhance Flexibility of Distribution Networks

With the large-scale penetration of distributed generation (DG), the volatility problems of active distribution networks (ADNs) have become more prominent, which can no longer be met by traditional regulation means and need to be regulated by introducing flexible resources. Soft open points (SOP) and energy storage systems (ESS) can regulate the tidal currents on spatial and temporal scales, respectively, to improve the flexibility of ADN. To this end, in-depth consideration of DG admission is given to establish flexibility assessment indicators from the power side of ADN. The conditional deep convolution generative adversarial network (C-DCGAN) is used to generate the output scenario of DG. On this basis, the SOP and ESS two-layer planning models, which take account of the potential for improvement in the flexibility of ADN, are constructed. Among them, the upper layer is the site selection and volume determination layer, which considers the economy of the system with the optimization objective of minimizing the annual integrated cost; the lower layer is the operation optimization layer, which considers the flexibility of the system and takes the highest average daily flexibility level as the optimization objective. The planning model is solved using genetic algorithm-particle swarm optimization (GA-PSO) and second-order cone programming (SOCP). The case analysis verifies the rationality and effectiveness of the planning model.

Conditional value at risk-based island partitioning and fault restoration reconfiguration of active distribution networks

Conditional value at risk-based island partitioning and fault restoration reconfiguration of active distribution networks

A novel distributed zero bus model for optimal sizing and siting of distributed generators in an active distribution network

A novel distributed zero bus model for optimal sizing and siting of distributed generators in an active distribution network