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Research on Model and Algorithm of Generation and Transmission Maintenance Scheduling

Author: WenYuanXi
Tutor: YanWei
School: Chongqing University
Course: Electrical Engineering
Keywords: Maintenance plan Genetic Algorithms Quantum evolutionary algorithm Transmission Equipment
CLC: TM73
Type: Master's thesis
Year: 2009
Downloads: 156
Quote: 1
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Abstract


Power system and transmission equipment maintenance program is the power system planning and operation of the important part. With the constant expansion of grid interconnection and the development of the electricity market, the development of rational and transmission equipment maintenance plan safe and economic operation of the system has more and more significance. Transmission Equipment Maintenance Plan for purchase and sale of electricity has an important influence, however, the model of the existing maintenance plan did not consider the purchase and sale of electricity efficiency, no coordination optimizing maintenance programs and power purchase plan. On the other hand, in a mature electricity market environment, power generation companies and independent transmission company hopes to develop maintenance plans in order to obtain greater economic benefits. Safe and reliable operation from the system point of view, the independent system operator (ISO) and transmission company declared the need for an overhaul plan for coordination. This requires a fair and transparent coordination mechanisms. To solve these problems, this paper conducted in-depth research, as follows: This paper established a new benefit considering power purchase and transmission equipment maintenance plan optimization model. The model takes into account not only the objective function of the system and transmission equipment production costs and minimize maintenance costs, but also consider the maintenance plan scheme of the system with the purchase and sale of electricity outside the network to maximize economic benefits. Specifically considered the hydro-thermal coordination, hydropower generating capacity constraints, reservoir regulation, the key line transmission stability limit, cluster Minimal Boot and other practical constraints. Model reflects the maintenance program and send purchase plan to coordinate and optimize the production of the year on the power system operation planning, purchase and sale of electricity outside the network has an important role in guiding contract strategies, constraints into account a lot of the actual operation of the system thus the model has greater practical value. The optimization model, the design of effective genetic algorithms and heuristic methods for solving method of combining. Original optimization problem into sub-problems overhaul discrete variables and continuous variables generating operation sub-problems. Sub-problems for maintenance design combined with the characteristics of the problem encoding and genetic operators. And uses a fast heuristic method for solving operational sub-problems. Through the assessment of individual fitness for coordination of the two sub-problems. Generating units containing 119 actual power system simulation calculations, numerical results show that the model and algorithm is effective and feasible. Competition in the existing turbine overhaul method based on bidding, this paper establishes a competitive bidding and transmission equipment maintenance scheduling optimization model with the goal of maximizing the bid amount. The model can be achieved on a fair and transparent coordination overhaul plan, the parties to the overhaul to meet the market requirements of fairness. In addition, for the first time introduced the quantum evolutionary algorithm for solving optimization problems overhaul plan. And the introduction of crossover on quantum evolutionary algorithm has been improved, the crossover operator is not on the quantum chromosome crossover, but to save the optimal solution of each generation group to cross. IEEE-RTS simulation examples show that the improved quantum evolutionary algorithm for solving optimization problems overhaul plan, compared to the quantum evolutionary algorithm and genetic algorithm populations evolve faster, more powerful search features.

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Power system scheduling, management, communication
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