Active Distribution Network Research Articles

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

Optimal dispatch of unbalanced distribution networks with phase-changing soft open points based on safe reinforcement learning

Distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks, which may harm the health of power equipment and increase the operational costs. There is emerging opportunity to dispatch soft open points to improve the operation performance of active distribution network. This paper proposes an optimal dispatch strategy to improve the network balancing performance, where a new type of phase-changing soft open point is installed. First, a new type of phase-changing soft open point with full-phase changing ability is introduced to balance the three-phase power flow. Then, the optimization model is formulated for phase-changing soft open points dispatching to minimize the total cost of distribution network. Furthermore, the model is formed as a constrained Markov decision process and efficiently solved by the augmented Lagrangian-based safe deep reinforcement learning algorithm featuring the soft actor-critic method. Finally, numerical simulations are conducted to validate the effectiveness, accuracy, and efficiency of the proposed method.

Coupled partition and configuration for cohesive and self-sufficient virtual microgrids

Active Distribution Network shows promise in operating and controlling distributed generations. Based on ADN structural and electrical differences, ADN could be partitioned to reveal innate heterogeneity for better control. Virtual Microgrid works as such a partition mode that each VM serves both as a part of ADN and as an individual operating agent. To identify VM boundaries, previous research utilized community detection methods and then configured ADN based on stationary partition. In this paper, VM partition and configuration are simultaneously determined in the proposed Coupled-Configuration-Partition of Active Planning. Also, a new definition of electrical edge betweenness is proposed to depict interaction among power system buses to improve the partition algorithm. Based on this proposal, ADN is planned to be cohesive in Net-ability, Flexibility Supply Index, and Capacity Fitness, in the meantime to minimize the cost in a multi-objective optimization, iteratively in Genetic Algorithm to reach Pareto Front. Case studies are designed to denote the benefits of the proposed Coupled-Configuration-Partition of Active Planning in modularity and system cohesion intensity.

Reconfiguration of active distribution networks as a means to address generation and consumption dynamic variability

AbstractThe integration of alternative energy sources, storage systems, and modern loads into the distribution grid is complicating its operation and maintenance. Variability in individual generation and consumption elements dynamically affects voltage profiles, which in turn undermines efficiency and power quality. This study proposes to address this dynamical variability using an online reconfiguration approach that involves opening and closing switches to modify the grid's topology and adjust voltage levels in response to load/generation variations. Other grid optimization techniques, based on reconfiguration, typically focus on static, fully instrumented grids with predictable parameters and homogeneous changes, aiming to minimize power losses but overlooking the dynamics of variable grid elements. This study proposes a testing approach that is dependent on the estimated transient status of the grid only using a limited number of measurement units and considering the individual‐stochastic variations of loads and generators. The proposed approach was tested on the IEEE 33‐bus test feeder with up to five varying distributed generators. The results confirm that the algorithm consistently finds a reconfiguration alternative that could enhance system efficiency and voltage profiles, even in the face of dynamic load/generator behavior, demonstrating its effectiveness and online adaptability for grid operation and management tasks.

Consensus-Based Model Predictive Control for Active Power and Voltage Regulation in Active Distribution Networks

In this paper, a consensus-based model predictive control (Cb-MPC) scheme is proposed to control the active power and voltage at all nodes in grid-connected active distribution networks (ADNs) with multiple distributed energy resources (DERs). The proposed design methodology is based on a multiple-input multiple-output (MIMO) model of an ADN which accounts for both the internal and external interactions among the control loops of the DERs. To achieve the control objective, each DER unit is equipped with a controller–observer system. In particular, the observer implements the consensus algorithm to estimate the collective system state by exchanging data only with its neighbors. The scope of the controller is to solve the MPC optimal problem based on its collective state estimate, and, due to the presence of an integral term in the control action, it is robust against any unknown scenarios of the ADN, which are represented by uncertainty in the model parameters. The results of numerical simulations validate the effectiveness of the proposed method in the presence of unknown changes in the operating conditions of the ADN and of communication using a sample and hold function.

Real-time voltage control of centralized and distributed coordination in active distribution network under multiple coupling

Abstract At present, the “source-load interaction” distribution network operation mode has gradually replaced the “source-load operation” mode. The uncertainty and volatility of the distribution system have been enhanced, the controllability has been weakened, the voltage fluctuations have been frequent, and the power quality has been decreased. Voltage regulation is crucial to fully absorbing new energy generation and improving the operation safety of the active distribution network. To solve the problem of real-time control of distribution networks, this paper proposes a distributed voltage control method of active distribution networks with global sensitivity, establishes a distributed communication mechanism, and builds a distributed forward push-back communication rule to transfer power and voltage information between nodes. The global sensitivity of voltage, active power, and reactive power is used for real-time, independent, and effective control of multiple devices at each node, and the distributed control process is simulated according to actual production conditions. Furthermore, the centralized and decentralized real-time voltage control methods are proposed, and the centralized and distributed methods are implemented in medium voltage primary distribution network (MVPN) and low voltage secondary distribution network (LVSN), respectively, to play the advantages of high computing efficiency and fast control response. Based on model predictive control (MPC), intra-day rolling time scale voltage regulation is constructed, and second-level control is used to adapt the MPC results, finally promoting the optimal scheduling and rapid response of various controllable flexible resources in the multi-level active distribution network.

Integrated planning research of active distribution networks considering electric vehicle integration in the context of dual carbon

Abstract Against the backdrop of the dual carbon strategy, electric vehicles are experiencing rapid growth in popularity due to their environmentally friendly characteristics. However, their load volatility, randomness, and impact also present new challenges to the carrying capacity of the distribution network. Addressing the inadequacies of existing research in tackling these challenges from the perspective of distribution network planning, this paper proposes a method that integrates the optimization planning of distributed energy supply systems, flexible DC systems, and charging piles in the active distribution network by analyzing the spatiotemporal characteristics of electric vehicle charging loads and ensuring the basic charging needs of electric vehicles, utilizing flexible regulation resources such as V2G, flexible DC systems, and energy storage, and comprehensively considering system carrying capacity, green and low-carbon level, and economic factors through the analytic hierarchy process. Typical case studies demonstrate that through this method, the planning system can effectively enhance the carrying capacity of the distribution network, improve the self-consistency of green energy, and reduce the overall life-cycle energy costs in the region during operation.