Genetic algorithm and universal generating function technique for solving problems of power system reliability optimization

• Published in: Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2000): 2000 International Conference On pp. 582 - 586
• Main Authors: Levitin, G., Lisnianski, A., Haim, H.B., Elmakis, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2000
• Subjects: Constraint optimization; Cost function; Energy measurement; Genetic algorithms; Investments; Power engineering and energy; Power system measurements; Power system reliability; Redundancy; Reliability engineering
• ISBN: 9780780359024; 078035902X
• DOI: 10.1109/DRPT.2000.855730

To provide a required level of power system reliability, redundant elements are included. Usually engineers try to achieve this level with minimal cost. The problem of total investment cost minimization, subject to reliability constraints, is well known as the redundancy optimization problem. When applied to power systems (PS), reliability is considered as a measure of the ability of the system to meet the load demand, i.e. to provide an adequate supply of electrical energy. In this case the outage effect will be essentially different for units with different nominal generating (transmitting) capacity. It will also depend on consumer demand. Therefore the capacities of PS components should be taken into account as well as the consumer load curve. To solve the redundancy optimization problem for a system with different element capacities, a genetic algorithm is used which is a technique inspired by a principle of evolution. A procedure based on the universal generating function method is used for fast reliability estimation of multi-state PS with series-parallel structure. Using the composition of the genetic algorithm and the universal generating function technique provides solutions of the following problems of reliability optimization of series-parallel multi-state PS: structure optimization subject to reliability constraints, optimal expansion, maintenance optimization and optimal multistage modernization.