This paper presents quantitative component criticality importance indices applicable for identifying and ranking critical components in the phase of thermal power plants design. Identifying critical components for power plant reliability provides one important input to decision-making and guidance throughout the development project. The study of components criticality importance indices to several characteristic structural schemes of conventional thermal power plant is presented and discussed. Keywords—Component criticality importance measures, discrete event, reliability, thermal power plant.

This paper presents one comprehensive modelling approach for maintenance scheduling problem of thermal power units in competitive market. This problem is formulated as a 0/1 mixed- integer linear programming model. Model incorporates long-term bilateral contracts with defined profiles of power and price, and weekly forecasted market prices for market auction. The effectiveness of the proposed model is demonstrated through case study with detailed discussion.

This paper addresses the thermal unit commitment problem for power generation companies that operating in the market environment. The model for this problem is formulated as a deterministic optimization task where the optimal solution obtained using the 0/1 mixed-integer linear programming technique. The main feature of the model is that it provides a comprehensive and accurate representation of operating costs and operating constraints for thermal units. The model have been incorporated a long-term bilateral contracts with defined profiles power and price, and forecasted market for hourly prices for day-ahead auction. Solution is achieved using the homogeneous and self-dual interior point method for linear programming with a branch and bound optimizer for binary programming. The effectiveness of the proposed model is demonstrated through case study with detailed discussion.

This paper presents a combined Genetic Algorithm - Fuzzy Optimization approach to the Unit Commitment problem. The Unit Commitment problem is a high complex combinatorial optimization task, nonlinear and large-scale. In order to obtain a near optimal solution in low computational time and storage requirements, with respect to all specified constraints, a Genetic Algorithm using real-coded chromosomes is proposed in opposite to the more commonly used binary coded scheme. Gathering data from a list of strict priority order the Genetic Algorithm generates different candidate solutions to the problem, whereas Fuzzy Optimization guides the whole search process under an uncertain environment (varying load demand, renewable energy sources). A system consisting of 10 generating units is presented to demonstrate application of the proposed algorithm to the Unit Commitment problem. The obtained results show satisfactory outcome in total cost, compared to Dynamic Programming based applications and the sole Genetic Algorithm based solution to the Unit Commitment problem.