Preface
Traditionally, electrical power systems worldwide have been planned and operated in a relatively conservative manner, in which power system security, in terms of stability (i.e. dynamic performance under disturbances), has not been considered a major issue. Most of the tools developed and applied for these tasks were conceived to deal with reduced levels of uncertainty and have proven to be helpful to identify optimal developmental and operational strategies that ensure maximum net techno-economic benefits, in which only the fulfilment of steady-state performance constraints has been tackled.
The societal ambition of a cleaner, sustainable and affordable electrical energy supply is motivating a dramatic change in the infrastructure of transmission and distribution systems in order to catch up with the rapid and massive addition of evolving technologies for power generation based on renewable energy sources, particularly wind and solar photovoltaics. In addition to this, the emergence of the prosumer figure and new interactive business schemes entail operations within a heterogeneous and rapidly evolving market environment.
In view of this, power system security, and especially the analysis of vulnerability and possible mitigation measures against disturbances, deserves special attention, since planning and operating the electric power system of the future will involve dealing with a large volume of uncertainties that are reflected in highly variable operating conditions and will eventually lead to unprecedented events.
This book covers the fundamentals and application of recently developed methodologies for assessment and enhancement of power system security in short-term operational planning (e.g. intra-day, day-ahead, a week ahead, and monthly time horizons) and real-time operation. The methodologies are based on advanced data mining, probabilistic theory and computational intelligence algorithms, in order to provide knowledge-based support for monitoring, control and protection tasks. Each chapter of the book provides a thorough introduction to the intriguing mathematics behind each methodology as well as a sound discussion on its application to a specific case study, which addresses different aspects of power system steady-state and dynamic security.
In order to properly follow the content of the book, the reader is expected to have a basic background in power system analysis (e.g. power flow and fault calculation), power system stability (e.g. stability phenomena and modelling needs), and basics of control theory (e.g. Fourier transforms, linear systems). This background is usually acquired in graduate programs in electrical engineering and dedicated training courses and seminars. Therefore, the book is recommended for formal instruction, via advanced courses, of postgraduate students as well as for specialists working in power system operation and planning in industry. The content of the book is organised into two parts as follows:
Part I: Dynamic Vulnerability Assessment
Chapter 1 provides general definitions and rationale behind power system vulnerability assessment and phasor measurement technology, with special emphasis on the fundamental relationship between these concepts as seen in modern control centres.
Chapter 2 addresses power system reliability management and provides a broad discussion on the challenges for reliability management due to uncertainties in different time frames, ranging from long-term system development to short-term system operation.
Chapter 3 concerns the fundamentals of probabilistic reliability analysis, with emphasis on the study of large transmission networks. Two common approaches are presented: enumeration and Monte Carlo simulation. The chapter also provides a comprehensive study of the impact of underground cables on the Dutch extra-high-voltage (EHV) transmission network.
Chapter 4 introduces an enhanced data processing method based on the Hilbert–Huang Transform technology for studying low-frequency power system oscillations. Application to a real case study in Japan is overviewed and discussed.
Chapter 5 concerns the application of Monte Carlo simulation to recreate a statistical database of power system dynamic behaviour, followed by empirical orthogonal functions to approximate the dynamic vulnerability regions and a support vector classifier for online post-contingency dynamic vulnerability status prediction. The tuning of the classifier via a mean–variance mapping optimisation algorithm is also outlined.
Chapter 6 addresses the challenge of real-time vulnerability assessment. It introduces the notion of real-time coherency identification and vulnerability symptoms, for both fast and slow dynamic phenomena, and their identification from PMU data based on key performance indicators and clustering techniques.
Chapter 7 focuses on the security constrained optimal power flow problem, discussing the challenges and proposed solutions to leverage the computational effort in light of the more frequent use of risk-based security assessment and criteria for massive integration of renewable generation and the associated volumes of uncertainty.
Chapter 8 presents the various reliability management actions (preventive and corrective) as well as their modelling and integration into a security constrained optimal power flow problem. The different actions are represented by using a suitable linearized formulation, which allows keeping the computational costs low while retaining a sufficiently accurate approximation of the behaviour of the system.
PART II: Intelligent Control
Chapter 9 is devoted to damping control to mitigate oscillatory stability threats by using model-based predictive control. This is an emerging method that is receiving increasing interest in the control and power engineering community for the design of adaptive and coordinated control schemes. In this chapter, a hierarchical model-based predictive control scheme is proposed to calculate supplementary signals that are superimposed on the inputs of the damping controllers that are usually attached to different devices such as synchronous generators and FACTS devices.
Chapter 10 introduces a combined approach of an artificial neural network and ant colony optimisation to provide a fast estimation of voltage stability margin and to define the necessary adjustments of set-points of controllable reactive power sources based on voltage stability constrained optimal power flow.
Chapter 11 presents a control scheme for voltage and power control in high-voltage multi-terminal DC grids used for the grid connection of large offshore wind power plants. The proposed control scheme employs a computational intelligence technique in the form of a fuzzy controller for primary voltage control and a genetic algorithm for the secondary control level.
Chapter 12 concerns the application of model-based predictive control for reactive power control to adjust power system voltages during normal (i.e. quasi-steady state) conditions. This kind of control scheme has a slow response from, say, 10 to 60 seconds, to small operational changes and does not provide any fast reaction during large disturbances to prevent undesirable adverse implications.
Chapter 13 proposes an optimisation approach in which the objective function is augmented to incorporate the global optimisation of a linearized large scale multi-agent power system using the Lagrangian decomposition algorithm. The aim is to maintain centralised coordination among agents via a master agent leaving loss minimization as the only distributed optimisation, which is analysed while protecting the local sensitive data.
Chapter 14 presents a basic formulation of model-based predictive control for voltage corrective control, as well as the management of congestion and thermal overloads in distribution networks in the presence of high penetration of distributed generation units.
Chapter 15 addresses the interplay between transmission and distribution networks from the point of view of long-term voltage stability. It introduces the notion of Volt-Var Control (VVC) and the application of model-based predictive control for coordination of reactive power support between distribution and transmission.
Chapter 16 overviews an approach for power system controlled islanding. The approach is based on the development and integration of novel algorithms and procedures for graph partitioning and frequency behaviour estimation. It helps in avoiding a system collapse by splitting the system into electrical islands with adequate generation-load balance.
Chapter 17 provides insight into the application and value of empirical orthogonal functions as a promising alternative for signal processing applied to fault diagnosis. A comprehensive case study evidences that fault signals decomposed in terms of these orthogonal basis functions exhibit well-defined patterns, which can be used for recognising the main features of fault events such as inception angle, fault type and fault location.
Chapter 18 presents the main developmental aspects and lessons learnt so far concerning the implementation of a real phasor based vulnerability assessment and control scheme in the Ecuadorian National Interconnected System.
The book has intentionally been designed to allow some overlap between the chapters; it is desired to illustrate how some of the presented approaches could share some common elements, implementations or even developments and applications, despite being conceived for different purposes and uses.
We hope that the book proves to be a useful source of information on the understating of dynamic vulnerability assessment and intelligent control, but at the same time provides the basis for discussion among readers with diverse expertise and backgrounds. Given the great variety of topics covered in the book, which could not be completely covered in a single edition, it is expected that a second edition of the book will be made available soon.
José Luis Rueda-Torres, Delft University of Technology, The Netherlands.
Francisco González-Longatt, Loughborough University, UK.