Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

/pdf/a-particle-swarm-optimization-for-reactive-power-and-voltage-1g427relsp.pdf

A particle swarm optimization for reactive power and voltage control considering voltage security assessment

/pdf/a-survey-of-frequency-and-voltage-control-ancillary-services-32x7gwttpw.pdf

A Survey of Frequency and Voltage Control Ancillary Services—Part I: Technical Features

This two-part paper surveys the frequency and voltage control ancillary services in power systems from various parts of the world. In this second part, essential economic features that must be taken into account when designing markets for ancillary services are first discussed. The methods adopted in eight different systems (Australia, France, Germany, Great Britain, New Zealand, PJM, Spain, and Sweden) for trading frequency and voltage control ancillary services are then compared. Lastly, new indicators for comparing volumes and costs of ancillary services across systems are proposed and calculated for the systems included in this survey. The companion paper surveys the technical features of these ancillary services

The power grid is not only a network interconnecting generators and loads through a transmission and distribution system, but is overlaid with a communication and control system that enables economic and secure operation. This multilayered infrastructure has evolved over many decades utilizing new technologies as they have appeared. This evolution has been slow and incremental, as the operation of the power system consisting of vertically integrated utilities has, until recently, changed very little. The monitoring of the grid is still done by a hierarchical design with polling for data at scanning rates in seconds that reflects the conceptual design of the 1960s. This design was adequate for vertically integrated utilities with limited feedback and wide-area controls; however, the thesis of this paper is that the changing environment, in both policy and technology, requires a new look at the operation of the power grid and a complete redesign of the control, communication and computation infrastructure. We provide several example novel control and communication regimes for such a new infrastructure.

/pdf/designing-the-next-generation-of-real-time-control-51elyzn366.pdf

Designing the Next Generation of Real-Time Control, Communication, and Computations for Large Power Systems

In this letter, a novel autonomous control framework “Grid Mind” is proposed for the secure operation of power grids based on cutting-edge artificial intelligence (AI) technologies. The proposed platform provides a data-driven, model-free and closed-loop control agent trained using deep reinforcement learning (DRL) algorithms by interacting with massive simulations and/or real environment of a power grid. The proposed agent learns from scratch to master the power grid voltage control problem purely from data. It can make autonomous voltage control (AVC) strategies to support grid operators in making effective and timely control actions, according to the current system conditions detected by real-time measurements from supervisory control and data acquisition (SCADA) or phasor measurement units (PMUs). Two state-of-the-art DRL algorithms, namely deep Q-network (DQN) and deep deterministic policy gradient (DDPG), are proposed to formulate the AVC problem with performance compared. Case studies on a realistic 200-bus test system demonstrate the effectiveness and promising performance of the proposed framework.

Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations

To achieve a better voltage-VAr control in the electric power transmission system, different facilities are used. Generators are equipped with automatic voltage regulators to cope with sudden and random voltage changes caused by natural load fluctuations or failures. Other devices like capacitors, inductors, transformers with on load tap changers are installed on the network. Faced with the evolution of the network and operating conditions, electricity utilities are more and more interested in overall and coherent control systems, automatic or not. These systems are expected to co-ordinate the actions of local facilities for a better voltage control (more stable and faster reaction) inside different areas of the network in case of greater voltage and VAr variations. They afford a better use of existing reactive resources. Also, installation of new devices can be avoided allowing economy of investment. With this in mind, EDF has designed a system called Co-ordinated Secondary Voltage Control (CSVC). It is an automatic closed loop system with a dynamic of a few minutes. It takes into account the network conditions (topology, loads), the voltage limits and the generator operating constraints. This paper presents improvements which allow the CSVC to control the voltage profile and different kinds of reactive means on a large-scale power system, Furthermore, this paper presents a solution to spread out investment costs over several years, considering a deployment gradually extended.

An improved voltage control on large-scale power system

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An improved voltage control on large-scale power system