Optimal power flow solution of wind integrated power system using intelligent techniques

Abstract

To reduce the burden on conventional energy sources in meeting the growing load newlinedemands and the effects of combustion based power generation on environment, clean newlinerenewable sources of energy are gaining ground in their share. With time, the wind energy is newlineexpected to contribute more significantly and should be used as per the maximum utilization newlinepolicy. But, the uncertain nature of wind power has the risk of over estimating (OE) or under newlineestimating (UE) the available capacity of wind power. This may make the operation of wind newlineintegrated power system insecure. Therefore, in these systems, the nature of wind flow makes newlinethe above problem to be different in its modelling. This thesis aims at suitable formulation of newlinethe uncertain nature of wind in terms of some costs for improper estimation of the same in a newlinecombined generation scheduling problem of a wind thermal test system. Three of the smaller newlinesize thermal generators of IEEE 30-bus test system are replaced with equivalent sized wind newlinefarms. The corresponding added costs of UE and OE are conceptualized in the form of newlinemonetary penalties or levy paid by the concerned entity (power producer or system operator) newlinefor violating the policy of maximum utilization of wind energy during operation. The newlineproblem is formulated within the Optimal Power Flow (OPF) framework, so that the optimum newlineoperation is both cost effective and voltage secure. The wind farms are assumed to be using newlinedoubly fed induction generators (DFIG) for their several other advantages. However, the newlinelimitation of reactive power generation capability of DFIG during UE scenario, a STATCOM newlineis installed at the system bus. Different optimization problems are solved with a modified newlineBacteria foraging algorithm (MBFA) and Hybrid Algorithm (HA) combining the Bacteria newlineForaging Algorithm with Genetic algorithm (GA). The optimized values of different costs and newlineoptimal system operations are compared with the similar results obtained with GA, Particle newlineSwarm Optimization (PSO) etc. newline