DE-based reactive power planning with wind power penetration

Pao Yue-kong Library Electronic Theses Database

DE-based reactive power planning with wind power penetration

 

Author: Niu, Ming
Title: DE-based reactive power planning with wind power penetration
Degree: M.Sc.
Year: 2012
Subject: Wind energy conversion systems.
Wind power.
Electric power systems -- Planning.
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Engineering
Pages: x, 62 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2649305
URI: http://theses.lib.polyu.edu.hk/handle/200/7309
Abstract: In recent years, wind energy has beomce the fastest growing power generation resource in the world because of its economic and enviromental adavantages. The spectacular growth is due to the fact that wind generation costs have fallen drawmatically over the last 15 years, moving closer to the cost of conventional energy sources. Power rating, efficiency and reliability of wind turbines have been improved. However, the big amount of reactive power demand of a large wind farm may not be satisfied by the grid. Therefore, if this issue is not well planned in advance, the connection of a large wind farm would cause voltage instability as well as increased energy loss. The solution would be to supply suffciently and locally, as close as possible, the reactive power compensation to the wind farm. In an effort to solve this problem, flexible AC transmission system (FACTS) devices such as Static VAR Compensator (SVC) are commonly used to compensate the reactive power consumption of wind farms. Economic benefits of the SVC compensation depends mainly on where and how much the SVC should be installed. During the past few years, many techniques have been used to solve the optimization of reactive power control in power system operation. Recently, new method besed on Artificial Intelligence (AI) or Evolutionary Algorithms (EAs) have been used. These techniques include Artificial Neural Network (ANN), Tabu Search (TS), Simulated Annealing (SA), Expert System (ES), Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Programming (EP), etc. However, due to difficulties involved in the initialization for the control variables and the associated boundaries, these techniques may not be able to explore efficiently in the seraching space in order to prevent local minima.
This thesis proposed a DE-based two-stage algorithm to power system reactive power planning with wind farms integration. In the first stage, with the aid of the sensitivity analysis of Jacobin matrix from power flow program and the partial derivative of the reactive power injection to the node shunt susceptance, a new susceptance-based voltage sensitivity matrix consists of the partial derivative of the voltage to the shunt susceptance for each bus is obtained. The new matrix is used to narrow the boundaries of the compensation capacity with respect to the lower and upper bounds for the bus voltage. The second stage of the proposed method consists of a DE algorithm. The two-stage algorithm is designed to take advantage of the merits of each technique. The method of the first stage is employed to provide a good initial guess and to quickly and locally improve upon the solution provided by the DE with less computation. In this thesis, the SVC device is used to minimize the real power losses and to improve the voltage profile during various operation scenarios disturbed by wind power variations. The model is formulated as a constrained optimization problem, where the constraint is to let the voltage of each bus close to 1 p.u. The proposed DE-based two-stage method has been tested in a modified version of the IEEE 30 bus system for 6 scenarios of different wind speed levels and also compared with the typical DE one with satisfactory results.

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