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Research On Intelligent Optimization Algorithms For Energy-saving Generation Dispatch Of Thermal Power Units

Posted on:2017-07-03Degree:MasterType:Thesis
Country:ChinaCandidate:W W HuoFull Text:PDF
GTID:2322330488975959Subject:Electrical engineering
Abstract/Summary:PDF Full Text Request
The scale of electric power system in china leaps into the front ranks of the world. The thermal power is the main power supply supporting the development of electric power industry, and most of the thermal power is coal-fired. In the aspects of energy consumption and air pollution emission, China is the focus of the world because of the energy supply and demand structure dominated by coal. Based on the analysis of the primary energy consumption of electric power system in our country, in order to further promote the energy conservation and emission reduction of electric power system, the intelligent optimization algorithms for energy-saving generation dispatching of coal-fired thermal power units are researched. The following researches are carried out in this paper:The primary energy consumption of electric power system in China is analyzed. First, the concept of energy feedbacks is proposed. Some electricity users service for the electric power system, which are treated as non-end users. The electricity consumed by non-end users is called energy feedbacks. And then the parameter of energy feedbacks in China in 2012 is estimated, on the basis of which the energy consumption of end electricity users is assessed.An optimal algorithm for unit commitment based on different time intervals is presented. The time interval represents the time length of a period, and it is equal to the length of a cycle divided by the number of total dispatching periods. Unit commitment problem can be converted to two subproblems of the 0-1 integer programming for the condition of unit performance and continuous variables programming for load distribution. Determine the condition of unit performance using Ant Colony Optimization (ACO) algorithm in bigger time interval preliminarily, and then the enumeration method is adopted to further optimize the specific time of start/stop in smaller time interval. We make sure in which smaller period to start/stop, thus the condition of unit performance can be identified in smaller time interval, which makes the results better. The algorithm has been tested on a 10-unit generation scheduling system, and the whole cycle is divided into 96 periods. The simulation results demanstrate that the proposed method based on different time intervals can reduce the energy consumption, and effectively improve the computation efficiency.In terms of load distribution, the Chaotic Particle Swarm Optimization (CPSO) algorithm for emission economic load distribution is proposed.T he multi-objective particle swarm optimization (PSO) algorithm is improved. Particles are initialized with the idea of chaos to increase the diversity of the decision variables. Calculate fitness values of all the particles through multi-objective PSO algorithm, and then calculate the standardized satisfaction values. According to the standardized satisfaction values, some particles are selected to be dealed with chaos optimization, which can help the dicision variables to jump out of the local extreme regions. The algorithm has been tested on a 3-unit 6-bus system, and the simulation results demanstrate that the improved algorithm can jump out of local optimum in time to get the global optimal solution, and its convergence speed is fast. The case study verifies the feasibility and effectiveness of the CPSO algorithm in solving emission economic load distribution.
Keywords/Search Tags:Energy-saving and emission-reduction, Unit commitment, Emission economic load distribution, Time interval, Ant colony optimization, Particle swarm optimization, Chaos optimization
PDF Full Text Request
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