(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

Demand response (DR) has proved to be an inevitable part of the future grid. Much research works have been reported in the literature on the benefits and implementation of DR. However, little works have been reported on the impacts of DR on dynamic performance of power systems, specifically on the load frequency control (LFC) problem. This paper makes an attempt to fill this gap by introducing a DR control loop in the traditional LFC model (called LFC-DR) for a single-area power system. The model has the feature of optimal operation through optimal power sharing between DR and supplementary control. The effect of DR communication delay in the controller design is also considered. It is shown that the addition of the DR control loop increases the stability margin of the system and DR effectively improves the system dynamic performance. Simulation studies are carried out for single-area power systems to verify the effectiveness of the proposed method.

Introducing Dynamic Demand Response in the LFC Model

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In
practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed
using a linear model, the non-linearities of the system are not accounted for and they are incapable of
gaining good dynamical performance for a wide range of operating conditions in a multi-area power system.
A strategy for solving this problem because of the distributed nature of a multi-area power system is
presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each
power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias
estimation and the controller agent uses reinforcement learning to control the power system in which genetic
algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the
system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal
based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is
realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a
three-area power system example is given with two scenarios.

Load–frequency control : a GA-based multi-agent reinforcement learning

The Smart Grid (SG) is a technological transformation from conventional electric grid, electro-mechanically controlled system, to smart, intelligent, and electronically controlled system called the “Smart Grid” (SG). There are about 20–30% losses present in the conventional electric grid due to substandard operations at generation, transmission and distribution side. The major players in the transformation are: (a) increased electricity cost, (b) aging infrastructure, (c) carbon footprint, (d) Green House Gas emissions, (e) climate change, and (f) less efficient electrical network. The promising features of the Smart Grid are: (a) intelligent de-centralized control, (b) resilience, (c) flexibility, (d) sustainability, (e) digitalization, (f) intelligence, (g) consumer empowerment, (h) green energy, and (i) smart infrastructure. The fundamental issues and open challenges in the SG are lack of awareness, consumer acceptance, cyber terrorism, data collection management, energy metering, dynamic optimization and energy control. Considering above, in this paper, a comprehensive review exploring information of development, technologies, and techniques in the SG. The main goal is to investigate and reveal the key enabling technologies, to obtain better picture about the current status of SG development. The focus areas of this review study are Architectural Model focusing Consumer Empowerment (CE), Demand Response Program (DRP), and Demand Side Management (DSM). Our survey discusses in detail the Communication Technologies, such as Wireless Advance Metering Infrastructure (AMI), Phasor Measurement Unit (PMU), Supervisory Control and Data Acquisition (SCADA), and Machine to Machine Communication (M2M). The power systems such as Micro Grid, Nano Grid, Pico Grid, Inter Grid, Virtual Power Plants, and Distributed Generation are also elaborated in this review study. Renewable Energy Resources (RERs) Integration with the SG and Integration issues related to Distributed generation (DG) are presented in this survey. This survey also analyzes Architectural Model of the Smart Grid focusing consumer empowerment and prosumers interaction. The aim of this study is to provide deep understanding of technologies and their applications in the SG.

A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid

This paper reviews the literature on the subject albeit approaching hybrid micro-generation power systems from a systems thinking (ST) and cybernetics standpoint, viewing them as dynamically complex adaptive systems (CAS) coupled with and supplying to a set of homes termed a sustainable block in a rural setting. Here homeostatic regulation (HR) and control play a vital role in reaching efficient equilibrium towards reconciling power supply and demand response management. Unlike most of the work reviewed in the literature, the focus here is on supervisory control of grid-connected micro-generation systems without energy storage, aiming towards building energy efficiency, thriftiness and sustainability in energy consumption. Building on homeostatic control (HC) principles first introduced by F.C. Schweppe in 1979, the paper explores the concepts of sustainability and sustainable hybrid energy systems (SHES) applied to micro-generation, focusing on operational aspects rather than on socio-economic, environmental or regulatory ones. A concrete theoretical model for building a SHES is presented for a proposed grid-connected renewable microgrid. The model seeks to reconcile power supply and demand towards efficient equilibrium (homeostasis) proposing reward-based criteria for controlling renewable electricity supply and consumption in rural communities in Chile. Discussion and recommendations are also offered stating that energy sustainability (ES) is essentially a systems issue, and one where ES is first and foremost a structural, organizational and operational property which is in the very nature of the system itself – it is built into it – rather than explained by exogenous factors.

Review of grid-tie micro-generation systems without energy storage: Towards a new approach to sustainable hybrid energy systems linked to energy efficiency

Modern power system is moving towards a smart and competitive grid, with competing generating companies, power retailers, and strategically behaving consumers playing a crucial role in the daily operation of the system Independent System Operator (ISO) monitors these daily operations and procure required services through market operations Active and reactive power pricing in real-time at the wholesale and retail level is considered an efficient energy management method However, procurement of reactive power through a market mechanism in real-time is not being implemented, despite its crucial role in maintaining system parameters within the permissible limits This paper attempts to detail the challenges faced in implementing such reactive power markets and presents a review of existing reactive power market mechanisms to address these challenges A framework suitable for reactive power ancillary service in a smart grid is also detailed in this paper

A comprehensive survey on reactive power ancillary service markets

In deregulated power systems, reactive power ancillary service through electricity market is becoming relevant where private generation companies participate in maintaining system wide bus voltage within the permissible limits. Marginal cost price (MCP) based real time reactive power ancillary service market faces several challenges due to the localized nature of reactive power. In this paper, a market mechanism for real time reactive power ancillary service market based on Stackelberg game model is proposed considering voltage-apparent power coupled subsystems. In the proposed Stackelberg game model, Independent System Operator (ISO) is considered as the leader, and GENCOs as followers. In the formulation, each GENCO is associated with a relevance factor in the partitioned subsystem so as to consider the real time voltage support requirement in the system. The market is then formulated as Mathematical Program with Equilibrium Constraints problem (MPEC). Existence of equilibrium, incentive compatibility, and individual rationality of the proposed market mechanism is then analysed in this work. The numerical examples are illustrated in PJM 5-bus system, and tested on IEEE 30- bus system, and Nordic 32 Bus-system. The mechanism induces truth-telling behavior of GENCOs, yields a non-negative profit, and the system wide bus voltage is improved.

Game Theoretical Approach to Novel Reactive Power Ancillary Service Market Mechanism

Smart grid enables active participation by consumers in Demand Response. In order to match the power demand and power supply, Dynamic Demand Control (DDC) with AGC in Smart Grid Environment is used. A load frequency control using DDC, was modeled for a two area system in this study. The frequency deviation and tie line flow deviation was compared with the deviations obtained when generation control, using PI controller, alone was implemented for frequency control. The PI controller parameters in the new scheme are then optimized using Lyapunov technique. Thus DDC alone is required to maintain the system frequency, during small load variations. DDC will play a major role in the economic operation of GENCOs under a Smart Grid environment.

Frequency restoration using Dynamic Demand Control under Smart Grid Environment

Demand response is a dynamic mechanism which helps to manage customer participation in response to power supply conditions under smart grid environment thereby contributing in system operation studies like Automatic Generation Control. This paper, which consists of two parts, presents Automatic Generation Control scheme under a deregulated environment using demand response. The first part proposes a transfer function model for power gain that can be achieved from price based demand response of thermostatically controlled loads. The second part proposes an Automatic Generation Control scheme under a deregulated system involving competing generation companies. In this paper Automatic Generation Control has been modeled as a collaborative stochastic game using Reinforcement based Learning. The advantage of this model is that the generation companies produce optimal amount of power that is required to minimize their cost of power production, while preserving the spirit of deregulation. Reinforcement Learning based Automatic Generation Control scheme incorporating demand response transfer function was simulated. The simulation results show that this approach can minimize the frequency deviation during load frequency control. The main contribution of the paper is the proposal of an AGC scheme that is suitable under smart grid (Demand Response) and deregulated environment (Competition among Generation companies).

Demand Response based Automatic Generation Control in smart-grid deregulated market

This work proposes a novel relative electrical distance measure that provides information of coupling between voltage and apparent power between two buses in power systems. Relative electrical distance measure is derived from the bus admittance matrix which can be obtained in real time using Phasor Measurement Units. Based on the relative electrical distance measure, in this work, an isoperimetric clustering based algorithm for partitioning power systems into voltage–apparent power coupled areas is proposed. The advantage of the partitioning algorithm proposed in this work is that large networks can be represented as a weighted graph with number of vertices equal to number of generators in the system which is much lesser than the size of system, thereby reducing the computational effort for partitioning. Isoperimetric clustering technique along with k-means is then applied to the graph to obtain voltage–apparent power coupled areas. Simulations carried out on New England 39-bus system and IEEE 118-bus system demonstrate the effectiveness of the proposed methodology for partitioning the system into voltage–apparent power coupled areas, subject to changes in the operating condition of the system. The quality of clustering is analysed and compared with Cheeger inequality bounds, which ensures that power system is well partitioned.

Devika Jay

Papers

A comprehensive survey on reactive power ancillary service markets

Game Theoretical Approach to Novel Reactive Power Ancillary Service Market Mechanism

Frequency restoration using Dynamic Demand Control under Smart Grid Environment

Demand Response based Automatic Generation Control in smart-grid deregulated market

Isoperimetric clustering-based network partitioning algorithm for voltage–apparent power coupled areas