This paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.
This paper presents a regression-based method for estimating voltages and voltage sensitivities for volt-var control on distribution circuits with limited data. The estimator uses power flow results for representative load and PV output scenarios as training data. Using linear regressions on power flow results, the voltages at critical nodes are calculated online based on power measurements at the feeder head and at each PV plant. The voltage sensitivity to changes in reactive power injection by each PV plant is also found online using regressions on power flow results. The estimator thus prov…
This paper focuses on the effective use of smart inverters for Volt/Var control (VVC) on a distribution system. New smart inverters offer Var support capability but for their effective use they need to be coordinated with existing Volt/Var schemes. A new VVC scheme is proposed to facilitate such coordination. The proposed scheme decomposes the problem into two levels. The first level uses Load Tap Changer (LTC) and Voltage Regulators (VRs) and coordinates their control with smart inverters to adjust the voltage level on the circuit to keep the voltages along the circuit within the desired rang…
This paper presents a zonal Volt/VAR control scheme that coordinates Photovoltaic (PV) inverters for providing voltage regulation on 3-phase unbalanced distribution feeders. Voltage sensitivity studies are conducted to uncover the dependency between nodal voltage changes and the reactive power injections at nodes with smart PV inverters. A fast incremental clustering method is developed to divide the distribution feeder into weakly coupled zones based on correlations of nodal voltage sensitivities. Because each zone is weakly coupled, voltage of each zone can be controlled independently. Thus,…
This paper presents a two-stage zonal Volt/VAR control scheme for coordinating inverter-based resources (IBR) with utility-owned voltage regulators (VR) to regulate voltage in unbalanced 3-phase distribution systems. First, correlations between nodal voltages are derived from nodal voltage sensitivity studies. Then, the feeder is partitioned into non-overlapping, weakly-coupled voltage control zones based on nodal voltage correlations. IBR are used in the first stage to regulate voltage changes continuously and VR are used in the second stage to regulate large voltage deviations. An online VR …
The increasing proliferation of distributed energy resources has posed new challenges to Volt/VAr control problems in distribution networks. To this end, this paper proposes an automatic self-adaptive local voltage control (ASALVC) by locally controlling VAr outputs of distributed energy resources. In this ASALVC strategy, each bus agent can locally and dynamically adjust its voltage droop function in accordance with time-varying system changes. The voltage droop function is associated with the bus-specific time-varying slope and intercept, which can be locally updated, merely based on local v…
The high R/X ratio of typical distribution systems makes the system voltage vulnerable to the active power injection from distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to the voltage control. This paper proposes a two-stage stochastic optimization strategy to optimally place the PV smart inverters with Volt-VAr capability for distribution systems with high photovoltaic (PV) penetration to mitigate voltage violation issues. The proposed optimization strategy enables a planningstage guide for upgrading the existin…
High-voltage direct current (HVDC) transmission applications and the growth of the dynamic load in large-scale receiving-end grids lead to a higher risk of short-term voltage instability. An effective way to address this problem is to improve the system's dynamic voltage support capability by changing the operation status of dynamic var devices, and the dynamic var reserve (DVR) is commonly used. Due to the time delay in synchronous machine excitation systems, the dynamic var reserved at steady state cannot be exploited immediately under large disturbances. In addition, some reactive power is …
This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high photovoltaic (PV) penetration and intermittency with a minimum investment cost. The tool considers all existing volt-ampere reactive (var) assets in both sub-transmission and distribution systems to reduce the need of new equipment. The planning tool coordinates with an operational volt-var optimization tool to determine all scenarios with voltage violations and verify the planning results. The planning result of each scenario is the solution of a propo…
This paper proposes a novel two-layer Volt/VAR control (VVC) framework to regulate the voltage profiles across an unbalanced active distribution system, which achieves both the efficient open-loop optimization and accurate closed-loop tracking. In the upper layer, the conventional voltage regulation devices with discrete and slow-response characteristics are optimally scheduled to regulate voltage profiles in an hourly timescale while improving economic operations based on the receding horizon optimization (RHO) in a centralized manner. A generalized linearized branch flow model (G-LBFM) is de…
This paper considers the problem of voltage regulation in distribution networks. The primary motivation is to keep voltages within preassigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises two main steps. In the first, by exploiting historical data and for each DER, we learn a function representing the desirable equilibrium points for the power network. These points approximate solutions of an Optimal Power Flow (OPF) problem. In the second, we pro…
Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits. However, these control functions can become unstable, introducing oscillations in system voltages when not appropriately configured or maliciously altered during a cyberattack. In the event that Volt-VAR and Volt-Watt control functions in a portion of PV smart inverters in a distribution grid are unstable, the proposed adaptation scheme utilizes the remaining and stably-behaving PV smart inverters and other Distribut…
This paper proposes an online voltage control strategy of distributed energy resources (DERs), based on the projected Newton method (PNM), for unbalanced distribution networks. The optimal Volt/VAr control (VVC) problem is formulated as an optimization program, with the goal of maintaining the voltage profile across the network by coordinating the VAr outputs of DERs. To overcome the slow convergence rate of conventional gradient-based methods, a PNM-based solution algorithm is developed to solve this VVC problem. It utilizes a non-diagonal symmetric positive definite matrix, developed from th…
This paper presents a method for controlling the voltage of inverter-based Microgrids by proposing a new scale-free distributed cooperative controller. The communication network is modeled by a general time-varying graph which enhances the resilience of the proposed protocol against communication link failure, data packet loss, and fast plug and play operation in the presence of arbitrarily communication delays. The proposed scale-free distributed cooperative controller is independent of any information about the communication system and the size of the network (i.e., the number of distributed…
In an active power distribution system, Volt-VAR optimization (VVO) methods are employed to achieve network-level objectives such as minimization of network power losses. The commonly used model-based centralized and distributed VVO algorithms perform poorly in the absence of a communication system and with model and measurement uncertainties. In this paper, we proposed a model-free local Volt-VAR control approach for network-level optimization that does not require communication with other decision-making agents. The proposed algorithm is based on extremum-seeking approach that uses only loca…
Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level opt…
Volt/VAR Optimization in Transmission Networks with Discrete-Control Devices
School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院工业与系统工程学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）
The rapidly increasing penetration of inverter-based resources into a power transmission network requires more sophisticated voltage control strategies considering their inherent output variabilities. In addition, faults and load variations affect the voltage profile over the power network. This paper proposes a Primal Dual Gradient Dynamics based optimal distributed voltage control approach that optimizes outputs of distributed reactive power sources to maintain an acceptable voltage profile while preserving operational limits. Case studies of this new approach on IEEE test systems have verif…
This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (MADRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in unbalanced distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions of the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the DQN agents. A delicat…
Voltage control devices are employed in power distribution systems to reduce the power consumption by operating the system closer to the lower acceptable voltage limits; this technique is called conservation voltage reduction (CVR). The different modes of operation for system's legacy devices (with binary control) and new devices (e.g. smart inverters with continuous control) coupled with variable photovoltaic (PV) generation results in voltage fluctuations which makes it challenging to achieve CVR objective. This paper presents a two-timescale control of feeder's voltage control devices to ac…
Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networks
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学-伯克利深圳学院，清华大学）；Southwest Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group（中国电力工程顾问集团西南电力设计院有限公司）
Volt-VAR control (VVC) is a critical application in active distribution network management system to reduce network losses and improve voltage profile. To remove dependency on inaccurate and incomplete network models and enhance resiliency against communication or controller failure, we propose consensus multi-agent deep reinforcement learning algorithm to solve the VVC problem. The VVC problem is formulated as a networked multi-agent Markov decision process, which is solved using the maximum entropy reinforcement learning framework and a novel communication-efficient consensus strategy. The p…
To facilitate the development of reinforcement learning (RL) based power distribution system Volt-VAR control (VVC), this paper introduces a suite of open-source datasets for RL-based VVC algorithm research that is sample efficient, safe, and robust. The dataset consists of two components: 1. a Gym-like VVC testing environment for the IEEE-13, 123, and 8500-bus test feeders and 2. a historical operational dataset for each of the feeders. Potential users of the dataset and testing environment could first train an sample-efficient off-line (batch) RL algorithm on the historical dataset and then …
Model-based Vol/VAR optimization method is widely used to eliminate voltage violations and reduce network losses. However, the parameters of active distribution networks(ADNs) are not onsite identified, so significant errors may be involved in the model and make the model-based method infeasible. To cope with this critical issue, we propose a novel two-stage deep reinforcement learning (DRL) method to improve the voltage profile by regulating inverter-based energy resources, which consists of offline stage and online stage. In the offline stage, a highly efficient adversarial reinforcement lea…
