Optimal Power Flow and Optimal Reactive Power Dispatch Incorporating TCSC-TCPS Devices Using Different Evolutionary Optimization Techniques

Introduction

The developing countries like India, commercial as well as industrial power demand grows unpredictably; thereby supplying reliable and secure power to the consumer is a challenging task. The McKinsey & Company’s ‘Electric Power and Natural Gas Practice’ made a survey on India of ‘Powering India: The Road to 2017’. Above analyses suggested that if India grows at an average rate of 8% for the next 10 years, then India’s demand for power is likely to soar from around 120 GW at present to 315–335 GW by 2017, 100 GW higher than most current estimates. Four main factors behind the analysis are: (i) Rise of Industrial Power demand, (ii) Commercial demand rising at 14% over the next 10 years, (iii) ‘Power for all by 2012’ and (iv) the realization of demand suppressed due to load shedding. To achieve the demand by 2017, India needs a generation capacity of 415–440 GW (McKinsey & Company, 2017).

In recent years Environment, deregulation of power utilities has delayed the construction of both generation facilities and new transmission lines. However, due to the increasing load demand, more and more power is required to be pushed over the existing transmission lines but the increase in the power flow above a certain operating level increases power loss, reduces the voltage profile and decreases the overall stability of the system. To achieve better power flow control over the existing transmission lines without violating the stability margin of the system, application of flexible ac transmission system (FACTS) may be adopted.

The optimal power flow (OPF) problem has been well known since 1960s. The first person to introduce the concept of OPF was Carpentier in the year 1962 (Carpentier, 1962). Nowadays, modern methods like differential evolution (DE) (Panda, 2009), evolutionary algorithm (EA) (Panda, 2009), genetic algorithm (GA) (Rashed et al., 2007; Narayana et al., 2012), particle swarm optimization (PSO) (Singh et al., 2015; Shayeghi et al., 2010), bacctoria foraging algorithm (BFA) (Edward et al., 2013), symbiotic organisms search algorithm (SOA), (Prasad & Mukherjee, 2016), Brainstorm optimisation algorithm (BSOA) (Jordehi, 2015), Cat Swarm Optimization (CSO) (Nireekshana et al., 2016), whale optimization algorithm (WOA) (Raj, 2017) to solve OPF must be adjusted in order to satisfy the new challenges of the power industry coming from market and technological transformations. In fact, the development of competitive electricity markets has had an important impact on classical analysis tools.

In this chapter, thyristor based FACTS controllers (TCSC and TCPS) have been introduced for the OPF and ORPD formulations. The basic objective of OPF is to find proper adjustments of the control variables that would minimize the fuel cost. Reactive power optimization is a sub problem of the optimal power flow (OPF) calculation, which determines all kinds of controllable variables, such as reactive power outputs of generators and static VAr compensators, tap ratios of transformers, outputs of shunt capacitors/reactors, etc., and minimizes transmission losses or other appropriate objective functions, while satisfying a given set of physical and operating constraints. Since transformer tap ratios and outputs of shunt capacitors/reactors are discrete variables, the reactive power optimization problem can be exactly formulated as a mixed integer nonlinear programming one.

However, the task of searching and finding the optimal values of control variables for the FACTS controllers is a complex optimization problem which cannot be easily solved.