Control Of Power Distribution Systems Research Articles

Optimal loop power flow control of power distribution system using advanced meta-heuristic algorithms

Optimal loop power flow control of power distribution system using advanced meta-heuristic algorithms

Sub Distribution Panel’s Design Rancangan SubDistribution Panel

Electrical panels play an important role as the center of electrical energy distribution, functioning to regulate the flow of electricity in a building. This distribution process requires something that can secure and divide the amount of power flowing, therefore the SDP (Sub Distribution Panel) panel is needed. This panel contains several components that are assembled into one unit so that it can function properly. This research uses a qualitative descriptive method that focuses on problems based on facts carried out by means of observation or observation, interviews, and studying documents. If all the techniques in this method are performed successfully, the accurate percentage of this research is 98%. This research produces a description of the process of making SDP panels from start to finish. This research resulted that the SDP panel has several functions, namely collecting and forwarding power to each load in each branch as needed, connecting and disconnecting the power distribution circuit and the safety of the power distribution system control.

Functional Failure Criticality Assessment and Control of Power Distribution Systems: Failure Prioritization Using AHP

Functional Failure Criticality Assessment and Control of Power Distribution Systems: Failure Prioritization Using AHP

A linear Distflow model considering line shunts for fast calculation and voltage control of power distribution systems

The line shunts are usually ignored by various linear power flow (PF) models in power distribution system analysis, planning and optimization. However, “charging effects” from line shunts of underground/submarine power cables would cause non-negligible model errors for these commonly used PF models. In this paper, we propose a modified Linear Distflow model (LinDist) with line shunts, i.e., LinDistS. We also further propose its extensions considering the ZIP load, weakly-meshed topology and unbalanced three-phase systems. Moreover, the linearization error of voltage component is theoretically analyzed. Case studies show that compared with other models, the proposed LinDistS achieves the descent calculation accuracy and efficiency. We also show the application scope of LinDistS by a Volt/VAr control (VVC) framework with distributed generators, mainly photovoltaic (PV), and the non-negligible “charging effect”. Simulation exhibits that with LinDistS, the VVC can optimally dispatch the shunt capacitors and also optimize the real-time reactive power output of PV. Moreover, with LinDistS, the VVC shows better solutions’ consistency and higher computing efficiency compared to traditional VVC methods.

Multi-agent graph reinforcement learning for decentralized Volt-VAR control in power distribution systems

Volt/Var control (VVC) is a crucial function in power distribution systems to minimize power loss and maintain voltages within allowable limits. However, incomplete and inaccurate information about the distribution network makes model-based VVC methods difficult to implement in practice. In this paper, we propose a novel multi-agent graph-based deep reinforcement learning (DRL) algorithm named MASAC-HGRN to address the VVC problem under partial observation constraints. Our proposed algorithm divides the power distribution system into several regions, each region treated as an agent. Unlike traditional model-based or global-observation-based DRL methods, our proposed method leverages a practical decentralized training and decentralized execution (DTDE) paradigm to address the partial observation constraints. The well-trained agents gather information only from their interconnected neighbors and realize decentralized local control. Numerical studies with IEEE 33-bus and 123-bus distribution test feeders demonstrate that our proposed MASAC-HGRN algorithm outperforms the state-of-art RL algorithms and traditional model-based approaches in terms of VVC performance. Moreover, the DTDE framework exhibits flexibility and robustness in extensive robustness experiments.

Estimate Three-Phase Distribution Line Parameters With Physics-Informed Graphical Learning Method

Accurate estimates of network parameters are essential for modeling, monitoring, and control in power distribution systems. In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems. Our proposed algorithm uses only readily available smart meter data to estimate the three-phase series resistance and reactance of the primary distribution line segments. We first develop a parametric physics-based model to replace the black-box deep neural networks in the conventional graphical neural network (GNN). Then we derive the gradient of the loss function with respect to the network parameters and use stochastic gradient descent (SGD) to estimate the physical parameters. Prior knowledge of network parameters is also considered to further improve the accuracy of estimation. Comprehensive numerical study results show that our proposed algorithm yields high accuracy and outperforms existing methods.

A graph policy network approach for Volt-Var Control in power distribution systems

Volt-var control (VVC) is the problem of operating power distribution systems within healthy regimes by controlling actuators in power systems. Existing works have mostly adopted the conventional routine of representing the power systems (a graph with tree topology) as vectors to train deep reinforcement learning (RL) policies. We propose a framework that combines RL with graph neural networks and study the benefits and limitations of graph-based policy in the VVC setting. Our results show that graph-based policies converge to the same rewards asymptotically, however at a slower rate when compared to vector representation counterpart. We conduct further analysis on the impact of both observations and actions: On the observation end, we examine the robustness of graph-based policies on two typical data acquisition errors in power systems, namely sensor communication failure and measurement misalignment. Furthermore, we study the robustness of graph-based policies to erroneous topological information in the graph representation. Our results reveal that graph-based policies are significantly more robust than policies with conventional dense network representations. On the action end, we show that actuators have various impacts on the system, thus using a graph representation induced by the physical power systems topology may not be the optimal choice. In the end, we conduct a case study to demonstrate that the choice of readout function architecture and graph augmentation can further improve training performance and robustness.

MPC-Based Decentralized Voltage Control in Power Distribution Systems With EV and PV Coordination

Distribution systems are growing rapidly in size and complexity with increased penetrations of distributed energy resources (DER) and electric vehicles (EVs), leading to operational challenges. Reactive power compensation from photovoltaic (PV) inverters and active power curtailment of PV output are commonly used to mitigate voltage-related problems. However, the charging/discharging flexibility of EVs can be used to avoid PV output curtailment and save energy motivating methods to coordinate EV charging with PVs. Unfortunately, a large number of control variables makes the centralized voltage control computationally expensive. In this work, a decentralized voltage control algorithm which considers the active and reactive power compensation from PV inverters and EVs is presented. The proposed approach helps to solve the voltage issues in a more effective way. The approach involves clustering of the distribution network based on modified modularity, an index that considers EV flexibility and prediction time period. A model predictive control (MPC)-based algorithm is proposed for each cluster to solve the voltage problem using the respective PVs and EVs, while ensuring that the EV charging demand is satisfied while contributing to the voltage regulation. The proposed algorithm is validated using the IEEE 123 node test systems under two PV and EV penetration levels and shown to be effective in solving the voltage regulation problem.

A New Second-Order Cone Programming Model for Voltage Control of Power Distribution System With Inverter-Based Distributed Generation

In this article, a novel second-order cone programming based branch flow model is proposed for optimal power flow (OPF) in radial distribution networks. The main advantage of the proposed architecture is that it can recover the bus voltage angle difference and, thus, control the reactive power flow (PF), leading to a better voltage regulation in the network. Based on the line PF and the line impedance, a convex equation is derived and included with the model to retrieve the bus voltage angle difference. Finally, using the retrieved bus voltage angle difference within the global optimal solution space, an algorithm is developed to control the reactive flow for voltage regulation. The proposed model has been tested on modified IEEE 32 bus (IEEE 33 bus network) and IEEE 123 bus power network systems for different test cases. The simulation results are verified with the OpenDSS PF and the MatPower nonlinear programming (NLP) based OPF solutions. The computational efficiency of the proposed model is superior to the NLP models. Also, the proposed reactive flow control algorithm improves the voltage regulation of a network and promises a globally optimal solution.

Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems

Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.

Two-Layer Collaborative Architecture for Distributed Volt/Var Optimization and Control in Power Distribution Systems

Two-Layer Collaborative Architecture for Distributed Volt/Var Optimization and Control in Power Distribution Systems

A Two-Stage Distributed Architecture for Voltage Control in Power Distribution Systems

In this paper, we propose an architecture for voltage regulation in distribution networks that relies on controlling reactive power injections provided by distributed energy resources (DERs). A local controller on each bus of the network monitors the bus voltage and, whenever there is a voltage violation, it uses locally available information to estimate the amount of reactive power that needs to be injected into the bus in order to correct the violation. If the DERs connected to the bus can collectively provide the reactive power estimated by the local controller, they are instructed to do so. Otherwise, the local controller initiates a request for additional reactive power support from other controllers at neighboring buses through a distributed algorithm that relies on a local exchange of information among neighboring controllers. We show that the proposed architecture helps prevent voltage violations and shapes the voltage profile in radial distribution networks, even in the presence of considerable penetration of variable generation and loads. We present several case studies involving 8-, 13-, and 123-bus distribution systems to illustrate the operation of the architecture.

Concepts of Reactive Power Control and Voltage Stability Methods in Power System Network

The intent of this paper is to present an analysis of reactive power control and voltage stability in power systems. It identifies a new model used to enhance voltage stability and exposes several key issues that had remained as research challenges in this area. The steady state voltage and reactive power control in distribution systems can be properly controlled by coordinating the available voltage and reactive power control equipment, such as on-load tap-changers, substation shunt capacitors and feeder shunt capacitors. It began with an overview of reactive power and voltage stability in transmission, distribution and load, and the importance of providing reactive power locally. The description of selected control features of shunt power systems such as SVC (Static Var Compensator) - static compensators of reactive power, STATCOM-type systems (Static Compensator), static reactive power generators and systems that combine both these solutions, which are referred to as SVC based on STATCOM were not left out. It explains the need to improve the voltage stability of Power system, as well as the increasing requirements for energy quality and security. It also discusses the techniques that were adopted in controlling and monitoring of the rate of power flow in the entire power system topology. This investigates the system to an optimal level in order to reduce losses and ensures sufficiency of reactive power control during normal and emergency conditions and to prevent voltage collapse.

Efficient Computation of Sensitivity Coefficients of Node Voltages and Line Currents in Unbalanced Radial Electrical Distribution Networks

The problem of optimal control of power distribution systems is becoming increasingly compelling due to the progressive penetration of distributed energy resources in this specific layer of the electrical infrastructure. Distribution systems are, indeed, experiencing significant changes in terms of operation philosophies that are often based on optimal control strategies relying on the computation of linearized dependencies between controlled (e.g. voltages, frequency in case of islanding operation) and control variables (e.g. power injections, transformers tap positions). As the implementation of these strategies in real-time controllers imposes stringent time constraints, the derivation of analytical dependency between controlled and control variables becomes a non-trivial task to be solved. With reference to optimal voltage and power flow controls, this paper aims at providing an analytical derivation of node voltage and line current flows as a function of the nodal power injections and transformers tap-changers positions. Compared to other approaches presented in the literature, the one proposed here is based on the use of the [Y] compound matrix of a generic multi-phase radial unbalanced network. In order to estimate the computational benefits of the proposed approach, the relevant improvements are also quantified versus traditional methods. The validation of the proposed method is carried out by using both IEEE 13 and 34 node test feeders. The paper finally shows the use of the proposed method for the problem of optimal voltage control applied to the IEEE 34 node test feeder.

The Evolution for Voltage and Reactive Power Control in Smart Distribution Systems

This paper has been arisen to show the necessity of the evolution for voltage and reactive power control in distribution systems from conventional to active control techniques. The paper addresses the drawbacks and conflicts that the conventional voltage and reactive power control schemes will face in future distribution systems, especially with high penetration of Distributed Generation (DG). Some of these drawbacks have been verified by carrying out various simulation studies for different IEEE unbalanced radial distribution test systems. The results show that applying the conventional utility voltage regulation control practices in smart grid configuration is intolerable. Therefore, the issue of voltage and reactive power control in smart distribution systems is significant and an evolution of the current control schemes from passive to active is necessary. Using the smart grid technologies, a distributed single layer cooperative control scheme between substation regulators, line regulators and DG units has been proposed. The proposed cooperative scheme is based on the concept of multi-agent systems. Simulation results have been carried out to show the effectiveness of the proposed control scheme.

AI Applications to Distribution System with Distributed Generation

ABSTRACT In this paper, Artificial Neural Network, one of the Artificial Intelligence (AI) techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG) is proposed. Artificial neural networks have been considered due to their ability for real time control, simpler calculations and adaptability to different operating conditions. Neuro-controllers are much more effective, fast acting than conventional controllers. Neural network for controlling Step voltage regulator (SVR) with line rise compensation (LRC) /line drop compensation (LDC) function has been presented. The neural network based controller has been simulated for a radial distribution system with DG and the neuro-controller shows promising results after testing. Keywords Artificial Intelligence, Artificial neural network, Dispersed generation, Distribution system, Line drop compensation, Line rise compensation, Step Voltage regulator, Voltage / Reactive power control.

Evolutionary Algorithm Based Optimal Control in Distribution System with Dispersed Generation

ABSTRACT In this paper, one of the evolutionary algorithm based method, Non-Dominated Sorting Genetic Algorithm (NSGA) has been presented for the Volt / Var control in power distribution systems with dispersed generation (DG). The proposed method is better suited for volt/var control problems. Genetic algorithm approach is used due to its broad applicability, ease of use and high accuracy. A multi-objective optimization problem has been formulated for the volt/var control of the distribution system. The non-dominated sorting genetic algorithm based method proposed in this paper, alleviates the problem of tuning the weighting factors required in solving the multi-objective volt/var control optimization problems. Based on the simulation studies carried out on the distribution system, the proposed scheme has been found to be simple, accurate and easy to apply to solve the multi-objective volt/var control optimization problem of the distribution system with dispersed generation.

Voltage and Reactive Power Control in Systems With Synchronous Machine-Based Distributed Generation

This paper investigates voltage and reactive power control in distribution systems and how the presence of synchronous machine-based distributed generation (DG) affects the control. A proper coordination among the onload tap changer (OLTC), substation switched capacitors and feeder-switched capacitors in order to obtain optimum voltage and reactive power control is proposed. It is assumed that there is no communication link between the OLTC and the capacitors, a normal case in distribution system operation these days. The results indicate that the proposed method decreases the number of OLTC operations, losses, and voltage fluctuations in distribution systems, with and without DG present. The power-flow reversal due to the DG is shown not to interfere with the effectiveness of the OLTC operation. Further, it is also shown that as long as the available capacitors are enough to compensate the reactive power demand, the DG operation mode does not give a significant effect to the distribution system losses. However, DG operating at a constant voltage is beneficial for a significant reduction of OLTC operation and voltage fluctuation in the distribution system.

An ordinal optimization based method for power distribution system control

This work presents an Ordinal Optimization Theory based method for power distribution system control. The solution procedure bases on an evolutionary search scheme. The method aims at minimizing the total power losses on network feeders while keeping the node voltages at near rated values against changing loading conditions. By relaxing the definition of optimality and softening the goal of optimization, ordinal optimization makes the problem easier as well as the solution process faster. A solution can be defined as good enough in performance value if it is one of the best m-percent solutions on the search space with probability P%. A solution can be defined as good enough also if its performance value approximates to the optimal value to a pre-defined extent. This work estimates the performance value, the total power loss, of the optimum state of power distribution network at a given loading condition. Then, with the two relaxed definitions for optimality, it finds the optimal settings of on-load tap changers and capacitors in a 24-h time frame. Results verify our method is proper for on-line and off-line operation.

An approximate solution to lower tap changing or capacitors regulator times in reactive power control in distribution systems

In the operation of distribution system, if the tap of transformer and capacitors regulators were frequently changed, these devices would be easily damaged. So it is necessary to lower the tap-changing and capacitors regulators times. Due to the fact that capacitors exert smaller influence on the voltage level than transformers do, it is appropriate to first adjust the capacitors settings with tap position fixed so as to reduce substantially the probability of voltage limits violations while minimizing losses, and then to adjust transformer taps to maintain voltage within the specified range. The problem of reactive power and voltage control is well decoupled. The objective function is not related to the taps changing of transformers, but it is only related to capacitive susceptance; therefore, the capacitors variables can be adjusted alone to reduce the objective function, then the taps changing of transformers to meet the voltage constraints. In this paper, a two-phase based loss optimization algorithm, which is suitable to distribution network, is presented. In this method, losses in the system can be optimized, and the tap-changing or capacitors regulators times can be lowered. IEEE 30-bus system indicates the proposed algorithm’s validity, fast convergence, and computation efficiency.