Power System Planning and Control

Control and planning approaches for secure and sustainable power systems Power System Planning covers vital information, from core concepts to identifying suitable power system models to ensure accurate simulations and predictions. The book reviews effective curve fitting techniques, including constructing simple straight lines, creating polynomial fits, and applying harmonic analysis and clearly defines peak demand in power systems, highlighting the significance of regression analysis for data-driven decisions. Utilize the advanced methods for simulating system loads, enabling a comprehensive and thorough analysis of the dynamic performance of real-world power system loads. Readers will learn the mathematics of variance, covariance, and correlation to analyze relationships, explore Box and Jenkins time series analysis, tackle autoregressive (AR), moving average (MA) and ARIMA models, assess generating unit merit orders, and engage in strategic unit commitment planning, balancing economic benefits with reliability. They will be equipped to examine the causes and implications of power system failures stemming from frequency decay due to generation loss, foreseeing potential disruptions This book discusses: Analysis of power flows through state estimation and determination of voltage angles using the Direct Current Power Flow (DCPF) techniqueModern practices for ensuring a high-quality power supply, with strategies for maintaining voltage quality, analyzing shunt capacitive compensation benefits, and determining capacitor placement for optimal performanceThe methodology of setting the line drop compensator (IDC) to maintain voltage quality at critical load points within a radial distribution feeder Advantages of shunt capacitive compensation for voltage regulation and skillfully determine the necessary amount of compensationAdvanced methods for simulating system load to analyze real-world dynamic performanceChallenges and solutions related to renewable energy sources and distributed generation, introducing new dynamics to traditional schemesFuture trends and innovations, such as the application of artificial intelligence and machine learning in predictive maintenance and fault diagnosis