Active Distribution Network Research Articles

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.

A Pseudo-Measurement Modelling Strategy for Active Distribution Networks Considering Uncertainty of DGs

A Pseudo-Measurement Modelling Strategy for Active Distribution Networks Considering Uncertainty of DGs

Dynamic capacity withholding assessment of virtual power plants in local energy and reserve market

The increasing utilization of distributed generation resources led to the formation of active distribution networks and virtual power plants (VPPs), which have changed the paradigms of electrical energy transactions in local energy markets. The VPPs can form capacity-withholding groups and impose market power to gain more profits, which may increase the costs of energy procurements for consumers. This paper presents an algorithm for the local electricity market operator in distribution networks to assess the dynamic capacity withholding of VPPs in the local energy and reserve markets. The main contribution of this paper is proposing indices to evaluate the dynamic capacity withholding of VPPs in energy and reserve markets. The other contribution of this paper is that it also quantitatively analyzes the impact of withholding processes on the flexibility of the distribution network. An optimization process is used to estimate coordinated offers of VPPs in the energy market in order to prevent the formation of withholding groups. The proposed algorithm was assessed for the 123-bus IEEE test system and the energy and reserve dynamic capacity-withholding indices were determined for different operating conditions.

Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting

Integrating distributed generations (DGs) into distribution networks poses a challenge for active distribution networks (ADNs) when managing distributed resources for optimal scheduling. To address this issue, this paper proposes a day-ahead and intra-day scheduling approach based on a multi-microgrid system. It starts with a CNN-LSTM-based generation and load forecasting model to address the impact of generation and load uncertainties on the power grid scheduling. Then, an optimal day-ahead and intra-day scheduling framework for ADN and microgrids is introduced using predicted generation and load information. The day-ahead scheduling is responsible for optimizing the power interactions between ADN and the connected microgrids, while intra-day scheduling focuses on minimizing the operational costs of microgrids. The effectiveness of the proposed scheduling strategy is verified via case studies performed on a modified IEEE 33-node ADN. The results show that the network loss of ADN and the operation costs of microgrids are reduced by 17.31% and 32.81% after the microgrid is integrated into the ADN. The peak-valley difference in microgrids decreased by 13.12%. The simulation shows a significant reduction in operational costs and load fluctuations after implementing the proposed day-ahead and intra-day scheduling strategy. The seamless coordination between the day-ahead scheduling and intra-day scheduling allows for the precise adjustment of transfer power, alleviating peak load demand and minimizing network losses in the ADN system.

A Distributed Coordination Approach for Enhancing Protection System Adaptability in Active Distribution Networks

A Distributed Coordination Approach for Enhancing Protection System Adaptability in Active Distribution Networks

Enhancing short-circuit current calculation in active distribution networks through Fusing superposition theorem and Data-Driven approach

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.

Economic optimal dispatch of active distribution network with CCHP multi-microgrid based on analytical target cascading

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

Fast power flow calculation for distribution networks based on graph models and hierarchical forward-backward sweep parallel algorithm

IntroductionIn response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm.MethodsFirstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis.Results and DiscussionResults from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

Distributed Robust Optimization Method for Active Distribution Network with Variable-Speed Pumped Storage

Distributed Robust Optimization Method for Active Distribution Network with Variable-Speed Pumped Storage

A multi‐objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources

AbstractWith the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach.

A multi-period restoration approach for resilience increase of active distribution networks by considering fault rapid recovery and component repair

As the frequency of extreme weather events continues to rise, there is an urgent need to strengthen the safe and stable operation of active distribution networks (ADNs), and it is of great value to establish highly resilient ADNs to withstand multi-faults caused by extreme weather events. This paper proposes a multi-period restoration approach for the resilience increase of ADNs by considering fault rapid recovery and component repair under typhoon disasters. Firstly, based on the structural reliability theory, the failure rate model of the main components is established, and in light of the system information entropy, the typical fault scenario selection strategy is designed to determine the branches with high fault probability. Then, according to the fault islanding division and network reconfiguration, a fault rapid recovery method is suggested for the ADNs, where the impact of typhoon disasters on the output features of distributed generators (DGs) are taken into account, and meanwhile, the network structure and the output power of the DGs are jointly optimized to minimize the operating cost of the ADNs. Further, a fault component repair model is formulated by adopting the adaptive ant colony algorithm, and a multi-period restoration approach is proposed for the ADNs to fulfill a rolling optimization of the network reconfiguration and fault component repair. The improved IEEE 33-node and IEEE 118-node systems are used for the approach verification, and the results show that the proposed approach can effectively improve the overall load restoration level and increase the component repair efficiency. Following a multi-criteria resilience evaluation system, the proposed approach enables the ADNs to more effectively withstand typhoon disasters, offering a resilience increase of 6.93 % and 32.24 % regarding the 33-node and 118-node systems.

Week-ahead dispatching of active distribution networks using hybrid energy storage systems

This paper presents a week-long scheduling approach to address the issues associated with uncertain stochastic generation. Specifically, the method is designed for active distribution networks (ADNs) hosting hybrid energy storages, composed by a hydrogen energy storage system (HESS) and a battery energy storage system (BESS). The inclusion of a pressurized HESS allows to balance energy over longer time periods, as opposed to methods considering only BESSs. To this end, this paper combines linearized models for the electricity grid with linearized models of the HESS to solve a tractable scheduling problem. The proposed optimal schedule consists of an active power trajectory at the grid connection point (GCP), called the dispatch plan, and the unit commitment schedule of a PEM fuel cell and electrolyzer system interfacing the electricity network with the HESS. Additionally, a bilevel model predictive control strategy is proposed, where the upper layer MPC computes a storage target accounting for the full horizon, while the lower layer computes the controllable resource setpoints to minimize the dispatch tracking error in each period. A numerical experiment shows the effectiveness of the proposed scheduling and control to accurately compute and track a dispatch plan over a full week. The results clearly show the benefits of combining a HESS with a BESS especially in periods where the prosumption is highly uncertain. Finally, we discuss the computational challenges associated with the weekly horizon and the use of a HESS that exhibits different dynamics than a BESS and propose an approach to mitigate the computational cost.

Voltage Hierarchical Control Strategy for Distribution Networks Based on Regional Autonomy and Photovoltaic-Storage Coordination

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

A survey on emergency voltage control of active distribution networks with PV prosumers

A survey on emergency voltage control of active distribution networks with PV prosumers