Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical Engineering | en_US |
| dc.creator | Lau, Tat-wa | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/6172 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Application of quantum-inspired evolutionary algorithm in solving the unit commitment problem | en_US |
| dcterms.abstract | Unit commitment (UC) represents an important optimization problem in power systems. The UC problem is to schedule the on and off statuses of the generating units over a time horizon such that the production cost is minimized and all operation constraints are satisfied. The UC problem is characterized by being large-scale, mixed-integer, complicated and highly constrained. These characteristics make the UC problem one of the most difficult optimization problems in power systems. Coal-fired thermal plants account for a significant percentage of emissions from generation plants. With the increasing environmental awareness, emission performance becomes an important part to power utilities. With regards to environmental protection, emissions from thermal plants can be included into the UC problem to form a bi-objective UC problem considering both cost and emission objective functions. The two objective functions are conflicting in nature. The bi-objective UC gives a set of Pareto-optimal or compromise solutions, and these solutions facilitate the decision making of power system operators on plant scheduling. Nevertheless, the bi-objective UC problem involves binary and continuous control variables, non-linearity, bi-objectives and a lot of operating constraints. It is highly difficult to handle the bi-objective UC. In the literature, a lot of optimization techniques have been proposed to solve the UC problem. These techniques consist of deterministic approaches and computational intelligence algorithms. Deterministic methods include the priority list, dynamic programming, the branch-and-bound method, Lagrangian relaxation and mixed-integer programming. These methods may suffer from convergence problems. For example, the priority list approach is simple and fast, but it usually yields high production cost. The dynamic programming is flexible but suffers from the problem of high dimensionality. The Lagrangian relaxation method offers a faster solution; it may encounter numerical convergence problems. Recently, computational intelligence algorithms (CIAs) have been applied to cope with the UC problem, such as genetic algorithm (GA), simulated annealing (SA), evolutionary programming (EP), particle swarm optimization (PSO). CIAs are general-purpose stochastic optimization algorithms, and they offer great potential to attain global convergence, easy implementation and solution of complicated optimization problems. Nevertheless, CIAs are parameter-sensitive and computationally expensive. They often consume a considerable amount of computational time when dealing with large-scale UC problems. | en_US |
| dcterms.abstract | The UC problem is characterized by numerous operating constraints. These constraints make the problem difficult to solve. As a result, constraint handling also represents a vital part in the UC problem. Currently, constraint handling on UC can be divided into penalty-based method and feasibility-based method. The penalty-based constraint handling method is used to help generating feasible unit schedules, and it does not guarantee feasible unit schedules in UC problems. In addition, the penalty approach needs to set an appropriate penalty factor. In different UC problems, the penalty factor is needed to be fine tuned again. The feasibility-based method always creates feasible solutions. However, the feasibility-based technique may require a large computational time. This thesis presents a novel method for solving the UC problem based on quantum-inspired evolutionary algorithm (QEA). The proposed method applies QEA to handle the unit-scheduling problem and the Lambda-iteration technique to solve the economic dispatch problem. The QEA method is based on the concept and principles of quantum computing, such as quantum bits, quantum gates and superposition of states. QEA employs quantum bit representation, which has better population diversity compared with other representations used in evolutionary algorithms, and uses quantum gate to drive the population towards the best solution. The mechanism of QEA can inherently treat the balance between exploration and exploitation and also achieve better quality of solutions, even with a small population. In addition, an effective constraint handling technique is developed to ensure that feasible and potential UC solutions are produced in the optimization process. The proposed QEA-UC method is applied to systems with the number of generating units in the range of 10 to 100 in a 24-hour scheduling horizon and is compared to conventional methods in the literatures. Moreover, the proposed method is extended to solve a large-scale UC problem in which 100 units are scheduled over a 7-day horizon with unit ramp-rate limits considered. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm. Furthermore, the proposed QEA-UC is modified to solve the bi-objective UC considering two conflicting objective functions, 24-hour horizon and 10 generating units. The simulation results have shown the potential of QEA-UC to solve bi-objective UC problem. | en_US |
| dcterms.extent | xiv, 108 p. : ill. ; 30 cm. | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2011 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Phil. | en_US |
| dcterms.LCSH | Electric power systems -- Mathematical models. | en_US |
| dcterms.LCSH | Mathematical optimization. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| b24561836.pdf | For All Users | 1.11 MB | Adobe PDF | View/Open |
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