Control Of Distribution Networks Research Articles

Analytical target cascading based real-time distributed voltage control for MV and LV active distribution networks

This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV&LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV&LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV&LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV&LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme.

An online updated linear power flow model based on regression learning

AbstractThe linear power flow (LPF) model is widely used in the optimization, operation, and control of distribution networks. These applications require the LPF model to be accurate, fast, and simple in order to simplify calculations as well as to efficiently perform operations and scheduling. In addition, it is difficult to realize the online update of parameters in the existing LPF models. The model retraining brings serious data burden and inefficiency. To serve these applications and comply with requirements, a brand new LPF model is proposed in this paper. A quadratic power flow model is trained by regression learning first, and then the proposed LPF model is derived by Taylor expansion. After only one initial regression learning, the proposed LPF model no longer needs retraining when updated. The refreshed parameter is simply updated online according to the real‐time measurement data, which improves the generalization ability. In conclusion, the proposed LPF model is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies these superiorities.

Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system.

Optimal Volt/Var Control for Unbalanced Distribution Networks With Human-in-the-Loop Deep Reinforcement Learning

Optimal Volt/Var Control for Unbalanced Distribution Networks With Human-in-the-Loop Deep Reinforcement Learning

Key technologies and applications of cloud-edge collaborative integrated low and medium voltage distributed photovoltaic coordination control

The large-scale distributed photovoltaic access leads to overvoltage, equipment overload, and difficulty in on-site consumption, which causes power to be sent back to the high voltage level, posing hidden dangers to the safe operation of the distribution network. This article proposes optimizing the medium-voltage distribution network at the cloud side with the goal of maximum consumption and operating cost, forming a rolling strategy adjustment based on load forecasting within the day. At the edge side, the distributed photovoltaic control in the low-voltage distribution network within the jurisdiction is adjusted based on the daily adjustment strategy issued by the cloud side, completing the optimization and regulation of distributed photovoltaics on a distribution substation unit basis to achieve the control and integration of distributed power sources and consumption, coordination between operation and side, and coordinated control in the integrated medium and low-voltage zoning, achieving autonomous distribution substations and feeder lines. Field applications have proven that this solution solves problems such as line and transformer overload, and voltage quality caused by distributed photovoltaic power generation.

Signal Injection-Based Topology Identification for Low-Voltage Distribution Networks Considering Missing Data

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal injection-based topology identification algorithm is favored for its speed and efficiency. This research introduces an innovative topology identification algorithm based on signal injection, specifically designed to address the challenges of incomplete and inaccurate identifications caused by the missing data in feature signal records (FSRs). Based on the correlations among FSRs at various devices, the algorithm introduces a dual-axis completion strategy—both vertical and horizontal—to effectively address missing data. Subsequently, an inclusion detection process is devised to process the completed FSRs, culminating in an accurate topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the proposed algorithm markedly enhances the completeness and accuracy of topology identification. This advancement offers a robust solution tailored to accommodate the dynamic and swiftly changing topological configurations of LVDNs.

Study on Multivariable Dynamic Matrix Control for a Novel Solar Hybrid STIGT System

To construct a clean and efficient energy system, advanced solar thermal power generation technology is developed, i.e., a solar hybrid STIGT (Steam Injected Gas Turbine) system with near zero water supply. Such a system is conducive to the efficient use of solar energy and water resources, and to improvement of the performance of the overall system. Given that the strong correlation between multiple-input and multiple-output of the new system, the MDMC (Multivariable Dynamic Matrix Control) method is proposed as an alternative to a PID (Proportional-Integral-Derivative) controller to meet requirements in achieving better control characteristics for a complex power system. First, based on MATLAB/Simulink, a dynamic model of the novel system is established. Then it is validated by both experimental and literature data, yielding an error no more than 5%. Subsequently, simulation results demonstrate that the overshoot of output power on MDMC is 1.2%, lower than the 3.4% observed with the PID controller. This improvement in stability, along with a reduction in settling time and peak time by over 50%, highlights the excellent potential of the MDMC in controlling overshoot and settling time in the novel system, while providing enhanced stability, rapidity, and accuracy in the regulation and control of distribution networks.

Research on data-driven power flow calculation method based on undirected-graph delooping-backtracking

As the scale of the power grid expands and distributed energy sources are integrated, along with the emergence of random loads, topological control of distribution networks has become a novel means of control. Therefore, data-driven power flow calculations must be capable of rapidly and accurately computing power flow results even when there are changes in the network’s topology. In this paper, a data-driven power flow calculation method is proposed to take topological changes into account. Based on initial loop data, we employ an undirected-graph delooping-backtracking method to generate a set of feasible topological samples. Using the Monte Carlo method on this basis, we generate feasible samples for the network’s topology and power injection, thereby establishing a training dataset. By training a deep neural network on these samples and adjusting network parameters, we effectively address power flow calculations in the presence of topological changes. Case study results demonstrate that the data-driven power flow calculation method, considering topological changes, can rapidly and accurately compute power flow results when topology alterations occur.

Evaluation method of distributed photovoltaic maximum access capacity based on ADMM

With the increasing penetration rate of distributed photovoltaics in the distribution network, new requirements are put forward for the operation and control of distribution networks. In order to ensure that the distribution network can operate stably within a safe range, it is important to evaluate the maximum capacity of the distribution network that can be connected to distributed photovoltaics. Considering the high cost of centralized computing, high communication requirements, and poor reliability, it is difficult to adapt to the actual needs of the distribution network with large-scale access to distributed photovoltaics. Therefore, this paper proposes an ADMM-based distributed PV maximum access capacity evaluation method. This method takes the distributed photovoltaic access capacity and the active power loss of the distribution network line as the objective function and considers the power flow equation constraints, node voltage constraints, and line current carrying capacity constraints of the distribution network. By simplifying the model, the non-convex optimization problem is transformed into a convex quadratic programming problem, and the ADMM algorithm is used to solve the distributed solution to obtain the access strategy of distributed PV in various regions under different operating conditions. Finally, the IEEE-33 system is used as an example to simulate the calculation, and the experimental results verify the correctness and effectiveness of the method.

Dynamic Control and Time-Delayed Channel Scheduling Co-Design for Voltage Control in Active Distribution Networks

Dynamic Control and Time-Delayed Channel Scheduling Co-Design for Voltage Control in Active Distribution Networks

A Machine Learning-Assisted Distributed Optimization Method for Inverter-Based Volt-VAR Control in Active Distribution Networks

The number of smart inverters in active distribution networks is growing rapidly, making it challenging to realize a fast, distributed Volt/Var control (VVC). This work proposes a machine learning-assisted distributed algorithm to accelerate the solution of the VVC strategy. We first observe the convergence process of the Alternating Direction Method of Multipliers (ADMM)-based VVC problem and explore the potential relationships between the convergence and time-series regression. Then, the long short-term memory (LSTM) technique is applied to learn the convergence process and regress the converged values of the dual and global variables with previous ADMM observations. After that, the LSTM-assisted ADMM algorithm is proposed, where the regressions are used for ADMM parameter updates. In this algorithm, the inputs of the LSTM model are carefully designed since the complementary conditions implied in the conventional ADMM should be considered. Unlike existing methods, the proposed method does not use the LSTM to determine the VVC strategy directly, indicating that it is non-intrusive and can satisfy all safety constraints during operations. The proof of its optimality and convergence is also given. The numerical simulations on the 33-bus distribution system demonstrate the effectiveness and efficiency of the proposed method.

Distributed Voltage Reactive Power Control of Distribution Network Considering the Coordination Between DPV and SVC

With the increasing permeability rate of DPV in the distribution network, the safe and economic operation of the distribution network is faced with many challenges, among which the problem of voltage exceeding the limit must be solved urgently. A method for distributed voltage reactive power control, which involves coordinating DPV and SVC, is suggested to enhance the voltage regulation capacity in high-permeability distribution networks. Firstly, considering the limited reactive power regulation capacity of DPV, a coordinated control strategy is proposed to enable SVC when the reactive power capacity of DPV is insufficient. Secondly, a voltage-reactive power control model for distribution networks is established, with a focus on minimizing node voltage deviations and network losses. An adaptive penalty factor adjustment strategy, based on the alternating direction multiplier method, is introduced to achieve a comprehensive solution for this model. Finally, the enhanced IEEE 33-node system is utilized for verification and analysis. The outcomes demonstrate that the suggested coordination strategy involving DPV and SVC, alongside the distributed optimization control method, offers economic advantages and effectively achieves the safe and cost-effective operation of the distribution network.

Identification of Distribution Network Topology and Line Parameter Based on Smart Meter Measurements

Accurate line parameters are the basis for the optimal control and safety analysis of distribution networks. The lack of real-time monitoring equipment in grids has meant that data-driven identification methods have become the main tool to estimate line parameters. However, frequent network reconfigurations increase the uncertainty of distribution network topologies, creating challenges in the data-driven identification of line parameters. In this paper, a line parameter identification method compatible with an uncertain topology is proposed, which simplifies the model complexity of the joint identification of topology and line parameters by removing the unconnected branches through noise reduction. In order to improve the solving accuracy and efficiency of the identification model, a two-stage identification method is proposed. First, the initial values of the topology and line parameters are quickly obtained using a linear power flow model. Then, the identification results are modified iteratively based on the classical power flow model to achieve a more accurate estimation of the grid topology and line parameters. Finally, a simulation analysis based on IEEE 33- and 118-bus distribution systems demonstrated that the proposed method can effectively realize the estimation of topology and line parameters, and is robust with regard to both measurement errors and grid structures.

Distributed Energy Resources Based Two-Layer Delay-Independent Voltage Coordinated Control in Active Distribution Network

In order to solve serious voltage problem in active distribution networks and improve the utilization rate of renew-able energy resources, a distributed energy resources (DERs) based two-layer delay-independent voltage coordinated con-trol method is proposed. First, an upper-layer sensitivity-based voltage coordinated control strategy is designed by maximiz-ing the use of residual reactive power and minimizing active power reduction, where reactive power compensation and ac-tive power regulation from the most sensitive DERs are per-formed in a high priority. Then, a lower-layer delay-independ-ent coordinated controller is designed to dynamically regulate the reactive/active power of each DER unit, when communica-tion transmission delays are within allowable range estimated. A robust controller is designed with the local states of DER and the states of other coupling DERs with time delay, thereby overcoming the effects of coupling characteristics among DERs and parameter uncertainties. Finally, simulation results demonstrate the effectiveness of the proposed method.

Voltage Regulation Enhanced Hierarchical Coordinated Volt/Var and Volt/Watt Control for Active Distribution Networks with Soft Open Points

Voltage Regulation Enhanced Hierarchical Coordinated Volt/Var and Volt/Watt Control for Active Distribution Networks with Soft Open Points

Multi-Agent Safe Graph Reinforcement Learning for PV Inverters-Based Real-Time Decentralized Volt/Var Control in Zoned Distribution Networks

To realize real-time voltage/var control (VVC) in active distribution networks (ADNs), this paper proposes a new multi-agent safe graph reinforcement learning method to optimize reactive power output from PV inverters. The network is divided into several zones, and a decentralized framework is proposed for coordinated control of reactive power output in each zone to regulate voltage profiles and minimize network energy loss. The VVC problem is formulated as a multi-agent decentralized partially observable constrained Markov decision process. Each zone has a central control agent that embeds graph convolution networks (GCNs) in the policy network to improve the decision-making capability. The GCN extracts graph-structured features from the ADN topology, reflecting the relationship between VVC and grid topology, and can filter noise and impute missing data. The training process includes primal-dual policy optimization to rigorously satisfy voltage safety constraints. Simulations on a 141-bus distribution system demonstrate that the proposed method can effectively minimize network energy loss and reduce voltage deviations, even in the presence of noisy or incomplete input measurements.

Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control in Active Distribution Networks

Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control in Active Distribution Networks

Model-Free Distributed Voltage Control for Distribution Networks Based on State Space Mapping and Super-Linear Feedback

Model-Free Distributed Voltage Control for Distribution Networks Based on State Space Mapping and Super-Linear Feedback

Coordinated Voltage Control of Distribution Network Considering Multiple Types of Electric Vehicles

Coordinated Voltage Control of Distribution Network Considering Multiple Types of Electric Vehicles

Multiple Grid-Connected Microgrids with Distributed Generators Energy Sources Voltage Control in Radial Distribution Network Using ANFIS to Enhance Energy Management

Voltage conditions and power quality for customers and utility equipment are significantly impacted by the addition of microgrid-generating sources within distribution networks. Designing the right control for distributed generators for the various generating units of a Microgrid is important in enabling the synchronization of renewable energy generation sources, energy storage unity, and integration of Microgrids into a radial distribution network. This research provides control mechanisms based on an adaptive technique employing ANFIS, to reduce fluctuation of voltage and current difficulties faced when multiple renewable energy sources and storage systems are incorporated into a distribution network. A step-by-step Voltage Source Converter (VSC) Controller was designed for controlling the DC voltage power sources used. The ANFIS training, test system modeling, and the distributed energy source were modeled in MATLAB/SIMULINK 2021a Software. Four microgrids were developed each consisting of a Photovoltaic plant, Wind Turbine, and Battery Storage System. Non-critical and critical loads were considered during the system testing. The simulated result reveals that the proposed control system works effectively in maintaining a constant system voltage of 340VAC which significantly mitigates system voltage and current fluctuation without using any static synchronous compensator (STATCOM) and power system stabilizers.