Volt-var Control Research Articles

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.

Robust Data-Driven and Fully Distributed Volt/VAR Control for Active Distribution Networks With Multiple Virtual Power Plants

This paper proposes a data-driven and fully distributed volt/var control (VVC) method for active distribution networks (ADNs) with multiple virtual power plants (VPPs), which is model-free and robust against bad data in measurements. The proposed scheme combines the advantages of a robust regression based feedback optimization algorithm and a revised alternating direction multiplier method (ADMM). Each VPP continuously obtains local system feedback to correct control strategies, and exchanges only limited information with its neighbors. Furthermore, faster convergence is achieved by applying Nesterov’s accelerated gradient method to the dual update in ADMM. The effectiveness of the method is demonstrated via numerical simulations using several distribution systems.

Three-Stage Inverter-Based Peak Shaving and Volt-VAR Control in Active Distribution Networks Using Online Safe Deep Reinforcement Learning

This paper presents a three-stage inverter-based peak shaving and Volt-VAR control (VVC) framework in active distribution systems using the online safe deep reinforcement learning (DRL) method. The proposed framework aims to reduce the peak load, voltage violations, and real power loss by coordinating three stages with different control timescales. In the first stage, a day-ahead charging/discharging scheduling of energy storage systems (ESSs) with a 30 min resolution is performed via their inverters for peak shaving. In the second stage, the discharging power of ESSs is adjusted through measurements with a 1 min resolution to completely shave peak loads. A model-free DRL algorithm integrated with a safety module is also implemented in the second stage. Using this algorithm, the reactive powers of photovoltaic (PV) systems and ESSs are controlled by the DRL agent to reduce the voltage violation and real power loss, whereas no voltage violation occurs during the online training process. In the third stage, a proportional-integral controller with real-power compensation is integrated into inverters of PV systems and ESSs to rapidly mitigate local voltage violations with a 0.1 s resolution. The high efficiency and safety of the proposed method were validated on the IEEE 33-bus and IEEE 123-bus systems.

Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prioritized and two methods namely, graph and impact-based methods, are studied. The impact-based method requires extra initial computational efforts but thereafter provides better computational throughput than the graph-based method. The DOF is illustrated on a modified standard distribution test case with several DERs. The results from the test case demonstrate that the DOF based volt-var optimization results in consistently better performance than the state-of-the-art volt-var control.

Kriging surrogate model enabled heuristic algorithm for coordinated Volt/Var management in active distribution networks

The increasing penetration of distributed energy resources exacerbates the risk of power loss increment and voltage violations in active distribution networks (ADN). The Volt/Var control (VVC) involving multiple controllable resources is widely considered to be an effective means of ensuring the secure and stable operation of ADN. Firstly, considering diverse controllable resources including energy storage devices, distributed generators, step voltage regulators, static var compensators and switchable capacitor banks in the ADN, a comprehensive/novel VVC optimization model is established as a mixed-integer non-linear programming problem with high-dimensional variables. In order to mitigate the computational burden with enormous networks and reduce the dependence on an initial physical model of the distribution network, a Kriging-assisted evolutionary computation method for VVC problem is proposed in this paper. The surrogate model is used to approximate the nonconvex and nonlinear network, reduce the frequency of complex simulations, assist optimization decision-making and avoid the complicated explicit analysis. Furthermore, the surrogate-assisted model enabled heuristic algorithm, owning promising performance in approximating the computationally expensive black-box system, can be obtained via continuous training and dynamic updating. Finally, the numerical results on the modified IEEE 33-bus and PG 69-bus systems verify the effectiveness of the proposed method. The simulation results indicate that the proposed Kriging-assisted evolutionary algorithm outperforms other algorithms in the aspect of calculation efficiency and robustness while accelerating the solving process of traditional evolutionary calculation.

스마트 인버터의 자율적인 Volt-VAR 곡선 변경을 통한 연계점 전압의 안정화

This paper proposes a method of stabilizing a point of common coupling (PCC) voltage using an autonomous Volt-VAR curve change in smart inverters. When the interconnection capacity of distributed energy resources rapidly increases, the problem of a reverse power flow occurs and the PCC voltage becomes unstable. The smart inverter which is a grid-connected inverter for distributed energy resources performs Volt-VAR control in oder to stabilize the unstable PCC voltage. The smart inverter autonomously changes the Volt-VAR curve according to the PCC voltage. The smart inverter uses Volt-VAR curve with a larger slope if the PCC voltage is not stabilized when the Volt-VAR curve with a small slop is used. The Volt-VAR curve is maintained if the PCC voltage is stabilized using the Volt-VAR curve. The validity of the proposed method is verified by the simulation results and experimental results.

Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.

Online PV Smart Inverter Coordination using Deep Deterministic Policy Gradient

Fast and frequent solar power variations present new challenges to modern power grid operation with increasing adoption of photovoltaic (PV) energy. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating active and/or reactive power at the connection point. In this paper, a deep reinforcement learning (DRL) based algorithm is proposed to coordinate multiple SIs. A reward scheme is designed to balance voltage regulation and SI reactive power utilization. The proposed DRL agent for voltage control learns its policy through massive offline simulations and adapts to load and solar variations. The DRL agent results are compared against autonomous Volt-Var control and optimal power flow (OPF) on the IEEE 37 bus feeder and IEEE 123 bus feeder for 8760 different scenarios. The results demonstrate that a properly trained DRL agent can intelligently coordinate different SIs to satisfy grid voltage limits despite large solar and load variations. The DRL agent achieves nearly the optimal performance of OPF by mitigating all voltage violations, while reducing PV production curtailment by 88% compared to the autonomous Volt-Var scheme. Contrary to OPF, the DRL agent can provide a coordination signal in milliseconds without load and solar forecasts and without explicit knowledge of the distribution network model.

A Multi-Mode Data-Driven Volt/Var Control Strategy With Conservation Voltage Reduction in Active Distribution Networks

This paper proposes a two-stage multi-mode data-driven Volt/Var optimization-based conservation voltage reduction (VVO/CVR) strategy to reduce total energy consumption while mitigating fast voltage violations in distribution networks. In the first stage, optimal power flow (OPF) is performed in the central controller (CC) to obtain day-ahead dispatch results of on-load tap changers (OLTC) and capacitor banks (CBs) based forecasting data of PV and load. In the second stage, a multi-mode data-driven local control strategy is designed in real-time Volt/Var control (VVC) with three operation modes, which are energy saving mode, reactive power reservation mode and security operation mode respectively. The coordination and transition among different control modes allow system operators to achieve both energy savings and voltage security under various conditions. The flexible reactive power margin of PVs can also be adaptively improved through cooperation with CBs. Moreover, a data-driven deep convolution neural network (CNN) is developed and trained as a local controller (LC) to regulate the reactive power of PVs based on local measurements. The influence of input selections on training losses is analyzed in detail, and a clustering-based data preprocessing method is also proposed to simplify the training process without compromising the training performance. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness both in saving energy and addressing voltage problems.

Three-Stage Hierarchically-Coordinated Voltage/Var Control Based on PV Inverters Considering Distribution Network Voltage Stability

Intermittent photovoltaic (PV) power generation brings voltage fluctuation and stability issues to distribution networks. Meanwhile, PV inverters can support voltage/Var control (VVC) to address these issues. Utilizing PV inverters, this paper proposes a three-stage hierarchically-coordinated (TSHC-) VVC method considering network voltage stability. The first stage schedules on-load tap changers one day ahead, the second stage hourly dispatches reactive power outputs of inverters, and the third stage implements real-time local voltage droop control of inverters. To efficiently address mutual impacts between these stages, the first two central-hierarchy stages are coordinated through interval optimization. Meanwhile, the last two stages are hierarchically coordinated by simultaneously optimizing inverter base reactive power outputs and droop control functions under real-time uncertainty. In the previous work, local droop control functions are not fully optimized, and network voltage stability is not fully considered. To overcome such limitations, this paper develops new constraints of individual control gain for each inverter, taking voltage stability into account. The proposed TSHC-VVC method with the optimized control gains is tested and compared with existing methods. Simulation results verify its better performance in reducing network power losses and bus voltage deviations, as well as maintaining network voltage stability.

A modified Bass model to calculate PVDG hosting capacity in LV networks

This work proposes a new stochastic model to address the impact of photovoltaic distributed generation (PVDG) diffusion in LV networks. The proposed methodology is based on Bass diffusion model, but with an individual approach for each consumer instead of the traditional collective one. As a result, it provides a means to calculate the individual probability of adopting a photovoltaic (PV) system considering the influence of local PV market, which makes it possible to assess the PVDG impact over time and throughout distribution networks. Therefore, the developed tool allows identifying when and where technical violations are most likely to occur in a distribution grid, supporting utilities to improve PVDG hosting capacity (HC). Finally, this work applies the developed methodology on a real distribution network, using the software OpenDSS. The results show the HC of the studied feeder, the year in which the HC limit will probably be reached and the distribution transformers most likely to have overvoltage issues in their LV circuit. In addition, the proposed methodology permits identifying Volt-Var control as an example of an easy-to-implement solution that could effectively deal with overvoltage issues due to PVDG penetration.

Model-augmented safe reinforcement learning for Volt-VAR control in power distribution networks

Volt-VAR control (VVC) is a critical tool to manage voltage profiles and reactive power flow in power distribution networks by setting voltage regulating and reactive power compensation device status. To facilitate the adoption of VVC, many physical model-based and data-driven algorithms have been proposed. However, most of the physical model-based methods rely on distribution network parameters, whereas the data-driven algorithms lack safety guarantees. In this paper, we propose a data-driven safe reinforcement learning (RL) algorithm for the VVC problem. We introduce three innovations to improve the learning efficiency and the safety. First, we train the RL agent using a learned environment model to improve the sample efficiency. Second, a safety layer is added to the policy neural network to enhance operational constraint satisfactions for both initial exploration phase and convergence phase. Finally, to improve the algorithm’s performance when learning from limited data, we propose a novel mutual information regularization neural network for the safety layer. Simulation results on IEEE distribution test feeders show that the proposed algorithm improves constraint satisfactions compared to existing data-driven RL methods. With a modest amount of historical data, it is able to approximately maintain constraint satisfactions during the entire course of training. Asymptotically, it also yields similar level of performance of an ideal physical model-based benchmark. One possible limitation is that the proposed framework assumes a time-invariant distribution network topology and zero load transfer from other circuits. This is also an opportunity for future research.

Multi-Objective Hierarchically-Coordinated Volt/Var Control for Active Distribution Networks With Droop-Controlled PV Inverters

Due to increasing installation of photovoltaic (PV) units, reactive power compensation from PV inverters contributes significantly to Volt/Var control (VVC) for active distribution networks. While PV inverters support VVC functions, lack of systematic coordination and heavily varying PV power generation lead to low control efficiency. To maximize benefits of the inverter-based VVC, this paper proposes a multi-objective hierarchically-coordinated VVC method with droop-controlled PV inverters. This method aims to minimize both average bus voltage deviation and network power loss, by simultaneously optimizing PV inverter reactive power setpoints for central control and droop control functions for local control. The droop control characteristics of PV inverters are fully modeled, including voltage ranges of the dead band and control zones, as well as droop slope gradients. In addition, this paper applies a Taguchi’s orthogonal array testing technique to handle random variations of PV power generation in the optimization problem. Moreover, to efficiently solve this optimization problem with integer variables, this paper proposes a solution algorithm based on model relaxation and a feasibility pump method. The proposed method is tested on two distribution systems, and simulation results verify highly efficient control performance in comparison with existing methods.

A hybrid architecture for volt-var control in active distribution grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

Design and field implementation of smart grid-integrated control of PV inverters for autonomous voltage regulation and VAR ancillary services

Ancillary services from Photovoltaic (PV) inverters can increase distribution system flexibility and alleviate the voltage regulation challenges associated with high PV penetration levels. However, the required communication infrastructure and smart grid integration challenges limit the broad deployment of PV ancillary services. This paper presents a cost-effective volt/var control (VVC) of multi-string PV inverters for active voltage regulation and reactive power dispatch using the existing smart distribution infrastructure to avoid the upfront costs of providing PV ancillary services. The proposed VVC model is developed in MATLAB/Simulink to adapt PV reactive power compensation according to the X/R characteristics of the distribution feeder. An IEC 61131-3 compliant prototype is designed based on Simulink PLC code and deployed to a commercially available remote terminal unit (RTU) using CODESYS standardization tool to address smart grid integration challenges. The proposed VVC scheme is tested in a real-world grid-connected PV system with multi-string inverters for experimental validation. Quasi-static time-series simulation and experimental results demonstrate the validated effectiveness of the proposed control scheme in controlling fast PV fluctuations, resolving voltage violations, voltage flickers, and enabling higher PV penetration.

Frequency-Watt and Volt-Var Control by Self-commutated Converter Connected Controllable DC Load

The promotion of renewable energy is accelerated in the world however frequency and voltage profile in the power system may become worse by the generation of renewable energy varied depend on weather conditions. To stabilize the power system, a controllable DC load such as hydrogen supplying equipment connected by self-commutated converter, which can regulate active and reactive power, has attracted much attention as one of the alternative regulating resources. The authors focus on Frequency-Watt and Volt-Var control by self-commutated converter connected controllable DC load. This paper proposes an effective and cooperative system on converter and load to regulate active and reactive power. Moreover, this paper reveals the verification and control ability in the proposed system through simulations, in which frequency and voltage in the power system fluctuate independently.

Volt-Var 특성 곡선의 기울기가 전압 Oscillation에 미치는 영향

The penetration of Renewable Energy Source (RES) has been increasing because RES is drawing attention by the environmental problem in the worldwide. The power flow can be changed from unidirectional to bidirectional because of increasing the penetration of RES. In bidirectional system, the efficient operation of conventional Voltage Regulators (VRs) is difficult. As one of the various methods to reinforce the conventional VR, the Volt-Var control of the Smart Inverter (SI) is used for supporting the voltage by using the reactive power of RES. This paper verifies the operating principle of the Volt-Var control function of the inverter and the oscillation phenomenon according to the slope of the Volt-Var curve was confirmed through theory, simulation, and demonstration site.

Joint optimization of Volt/VAR control and mobile energy storage system scheduling in active power distribution networks under PV prediction uncertainty

Mobile energy storage systems (MESSs) are becoming crucial devices to maintain stable power distribution system operations under the operation of voltage regulators including on-load tap changers, capacitor banks, and smart inverters of photovoltaic (PV) systems. This study proposes a joint framework in which the operation of voltage regulators and the road routing of MESSs are co-optimized for Volt/VAR control (VVC) in both power distribution and transportation networks. The proposed framework aims to minimize real power loss and voltage deviation along with peak shaving in power distribution networks, and to reduce the MESS traveling cost in transportation networks. Under the uncertainty of the predicted error of the PV generation output, the proposed framework is formulated as a chance-constrained optimization problem using mixed-integer linear programming. The proposed joint optimization algorithm was simulated in the IEEE 13-bus and IEEE 33-bus systems, which were coupled with 9-node and 15-node transportation systems, respectively. The results show that, in contrast with a VVC method without MESS, the proposed algorithm using MESS reduces the total real power loss and peak load by 9.7% and 15.92%, respectively, in the IEEE 13-bus system, and 2.55% and 3.47%, respectively, in the IEEE 33-bus system.

Decentralized Communication Based Two-Tier Volt-Var Control Strategy for Large-Scale Centralized Photovoltaic Power Plant

Long-distance power transmission makes a large-scale centralized photovoltaic (PV) power plant (CPPP) integrated into a weak power grid with a low short circuit ratio. Under this condition, small fluctuations in PV power are likely to cause significant fluctuations in system-wide voltage. To overcome this problem, a novel CPPP Volt-Var control (VVC) strategy is proposed. Unlike the conventional VVC strategy achieved in a centralized manner, the realization of the proposed VVC is mainly based on decentralized communication. The control framework for the proposed VVC has two tiers. In the upper-tier “optimal control”, a distributed VVC optimization model is formulated. Analysis target cascading (ATC) and alternating direction method of multipliers (ADMM) are combined to provide 15-minute-ahead optimal VVC scheduling, considering voltage deviation and active power losses together. In the lower tier “emergency control”, local droop control is applied to respond to the unexpected voltage deviation in real time. The final simulation results indicate that the proposed VVC strategy has good performances on the algorithm convergence in the upper tier and the control stability in the lower tier. The entire two-stage VVC could effectively maintain the system-wide voltage within the safe range and perform well on active power losses reduction, without the central controller.

Equilibrium Optimizer-Based Approach of PV Generation Planning in a Distribution System for Maximizing Hosting Capacity

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system’s PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).