This document contains extended mathematical derivations for the communication-free and model-free algorithms that can actively control converter-connected devices, and can operate either as stand-alone or in combination with centralized optimization algorithms. We address the problem of loss minimization in distribution grids, and we analytically prove that our proposed algorithms reduce the total grid losses without any prior information about the network, requiring no communication, and based only on local measurements.
This paper shows that a relation can be found between the voltage at the terminals of an inverter-interfaced Renewable Energy Source RES and its optimal reactive power support. This relationship, known as Volt-Var Curve VVC, enables the decentral operation of RES for Active Voltage Management (AVM). In this paper, the decentralized AVM technique is modified to consider the effects of the realistic operational constraints of RES. The AVM technique capitalizes on the reactive power support capabilities of inverters to achieve the desired objective in unbalanced active Low-Voltage Distribution Sy…
This letter proposes a general decentralized control of cascaded inverters-power factor angle droop control. Compared to the existing control strategies, it has the following attractive benefits: 1) it is suitable for both grid-connected and islanded modes; 2) Seamless transition between different modes can be obtained; 3) stability condition in the grid-connected mode is independent of the transmission line impedance; 4) it is suited for any types of loads in islanded modes; 5) multi-equilibrium point problem is avoided; 6) it is suitable for four quadrant operation. The small signal stabilit…
Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters' fault-ride-through capability. This can become crucial for the reliable operation of the distribution grids, as they could lose valuable resources to support grid voltage at the time they need them the most. This paper explores how two novel loss-minimizing algorithms can both achieve high reduction of the system losses during normal operation and remain connected to support the voltage during faults. The algorithms we propose are decentralized and model-free: they r…
Chance-Constrained ADMM Approach for Decentralized Control of Distributed Energy Resources
Tandon School of Engineering, New York University（纽约大学坦顿工程学院）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）
Multilevel inverter converts different level DC voltage to AC voltage. It has wide interest in power industry especially in high power applications. In power electronic equipment the major drawback is the harmonics. Several control strategies are available to reduce the harmonic content and the most widely used measure of Total Harmonic Distortion (THD). In this project, the comparison has been made for the open loop and closed loop PI controller and neural network that predict the switching angle in order to reduce the harmonics. The mapping between Modulation Index and Switching angles are p…
Distribution grids are experiencing a massive penetration of fluctuating distributed energy resources (DERs). As a result, the real-time efficient and secure operation of distribution grids becomes a paramount problem. While installing smart sensors and enhancing communication infrastructure improves grid observability, it is computationally impossible for the distribution system operator (DSO) to optimize setpoints of millions of DER units. This paper proposes communication-free and model-free algorithms that can actively control converter-connected devices, and can operate either as stand-al…
Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation
2 Department of Electrical Engineering \& Computer Sciences, UC Berkeley, Berkeley, USA；3 Department of Mechanical Engineering, UC Berkeley, Berkeley, USA；4 Delft Center for Systems；Control, Delft University of Technology, Delft, The Netherlands
The distributed Volt/Var control (VVC) methods have been widely studied for active distribution networks(ADNs), which is based on perfect model and real-time P2P communication. However, the model is always incomplete with significant parameter errors and such P2P communication system is hard to maintain. In this paper, we propose an online multi-agent reinforcement learning and decentralized control framework (OLDC) for VVC. In this framework, the VVC problem is formulated as a constrained Markov game and we propose a novel multi-agent constrained soft actor-critic (MACSAC) reinforcement learn…
A graph theoretic view on small signal stability of inverter-based power grids
Forschungszentrum Jülich（于利希研究中心）；Institute of Climate and Energy Systems: Energy Systems Engineering (ICE-1)（气候与能源系统研究所：能源系统工程）；Institute of Physics Belgrade, University of Belgrade（贝尔格莱德大学物理研究所）；Potsdam Institute for Climate Impact Research (PIK)（波茨坦气候影响研究所）；Technische Universität Berlin（柏林工业大学）；Institute for Theoretical Physics, University of Cologne（科隆大学理论物理研究所）；Department of Environmental Sciences, Open University of the Netherlands（荷兰开放大学环境科学系）；Institute of Physics, Norwegian University of Life Sciences（挪威生命大学物理研究所）；RWTH Aachen（亚琛工业大学）
Microgrids are increasingly recognized as a key technology for the integration of distributed energy resources into the power network, allowing local clusters of load and distributed energy resources to operate autonomously. However, microgrid operation brings new challenges, especially in islanded operation as frequency and voltage control are no longer provided by large rotating machines. Instead, the power converters in the microgrid must coordinate to regulate the frequency and voltage and ensure stability. We consider the problem of designing controllers to achieve these objectives. Using…
Cluster-Based Distributed Small-Signal Stability Certificates for Grid-Forming Inverter Networks
Ralph O'Connor Sustainable Energy Institute (ROSEI), Johns Hopkins University（拉尔夫·奥康纳可持续能源研究所，约翰斯·霍普金斯大学）；Department of Electrical and Computer Engineering, Johns Hopkins University（电气与计算机工程系，约翰斯·霍普金斯大学）
A novel variable dc-link voltage technique is proposed to reduce the traction losses for electrical drive applications. A 100-unit cascaded multilevel converter is developed to generate the variable dc-link voltage. Experimental measurement shows that the machine additional losses and IGBT-inverter losses are reduced substantially. The system efficiency enhancement is at least 2%.
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…
In this paper, we introduce a new framework to address the problem of voltage regulation in unbalanced distribution grids with deep photovoltaic penetration. In this framework, both real and reactive power setpoints are explicitly controlled at each solar panel smart inverter, and the objective is to simultaneously minimize system-wide voltage deviation and maximize solar power output. We formulate the problem as a Markov decision process with continuous action spaces and use proximal policy optimization, a reinforcement learning-based approach, to solve it, without the need for any forecast o…
We study multi-hop data-dissemination in a wireless network from one source to multiple nodes where some of the nodes of the network act as re-transmitting nodes and help the source in data dissemination. In this network, we study two scenarios; i) the transmitting nodes do not need an incentive for transmission and ii) they do need an incentive and are paid by their corresponding receiving nodes by virtual tokens. We investigate two problems; P1) network power minimization for the first scenario and P2) social cost minimization for the second scenario, defined as the total cost paid by the no…
This paper studies a decentralized stochastic gradient tracking (DSGT) algorithm for non-convex empirical risk minimization problems over a peer-to-peer network of nodes, which is in sharp contrast to the existing DSGT only for convex problems. To ensure exact convergence and handle the variance among decentralized datasets, each node performs a stochastic gradient (SG) tracking step by using a mini-batch of samples, where the batch size is designed to be proportional to the size of the local dataset. We explicitly evaluate the convergence rate of DSGT with respect to the number of iterations …
In this work, we approach the problem of resource allocation in a team of agents through the framework of Centroidal Voronoi Tessellations. CVTs provide a natural way to embed a desired global trend in the team through probability distributions, and in one-dimensional spaces, CVTs offer an inherent line structure allowing for a simple communication graph and scalability. We first consider the amount of resource to be allocated to be a constant and provide an analytical solution to such static resource allocation problem by embedding the allocation constraint within the distribution through a s…
Decentralized solutions to finite-sum minimization are of significant importance in many signal processing, control, and machine learning applications. In such settings, the data is distributed over a network of arbitrarily-connected nodes and raw data sharing is prohibitive often due to communication or privacy constraints. In this article, we review decentralized stochastic first-order optimization methods and illustrate some recent improvements based on gradient tracking and variance reduction, focusing particularly on smooth and strongly-convex objective functions. We provide intuitive ill…
In this paper, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level variance reduction to remove the uncertainty caused by stochastic gradients, network-level gradient tracking to address the distributed nature of the data, and push-sum consensus to tackle the challenge of directed communication links. We show that Push-SAGA achieves linear convergence to the exact solution for smooth and strongly convex problems and is thus the first linearly-convergent stochastic algorithm over arbitrar…
