A Study Of Optimization Algorithms And Some Optimization Problems In Thermal Power Plants

With the deregulating of electricity market and the emergency of energy supply, the study of optimization techniques, which can improve the whole efficiency of thermal power plants, is much more important and necessary. In this paper, the optimization algorithms, simulation tests and its applications in real world were investigated detailedly in order to optimize sub-systems in thermal power plants.The problems in the concerned documents and the research directions & contents were pointed out on the base of the analyzing large numbers of investigation and application situation of optimization technologies in thermal power plants. In view of optimization algorithms always being involved in the course of solving engineering optimization problems, some types of optimization algorithms were discussed in this paper and the fact that the Lemke algorithm is one of the most efficient tools to solve the quadratic program (QP) was presented. Furthermore, the extensively applied intelligent optimization algorithms such as were studied intensively. Two types of improved hybrid optimization algorithms were presented respectively, which can surmount the limitations of original RCGA and PSO. Varieties of simulation tests indicated that the improved algorithms were very effective. A set of software about optimization algorithms based on the COM technique was developed in order to realize the reuse of code really for the posterior research.On the base of investigating into optimization algorithms, problems such as economic dispatch, optimization of thermal engineering control system and unit optimal operation in thermal power plants were probed especially.Economic dispatch, which was studied frequently, is the precondition for the other systems'optimal operation in thermal power plants. Three new and available optimization methods were presented to solve the problem of economic dispatch. (1) A novel idea that a static optimization problem can be solved by the way of dynamic method was constructed. The convergence of the method included in the idea was proved. Simulation results indicated that the method could assure the optimal result being reliable and global. (2) In order to validate the efficiency of the improved PSO algorithm proposed in the paper, a simulation on a problem of economic dispatch among three thermal units was carried out. The exact result of economic dispatch was obtained once the algorithm was utilized along with the constraint-handling mechanism based on preserving feasibility of solutions. (3) For the first time, Lemke Algorithm was employed to solve the optimization problem of economic dispatch. Simulation results showed that Lemke algorithm was a very simply method and optimal results can obtained only involving in limited time of row transformation. In order to extend Lemke algorithm in this field, an important proposition was presented and proved.The load demand of units is variable and units'operation always burdening exterior disturbing because of the application of economic dispatch among thermal units. Especially, with the increasing of gap between peak and valley of grid, thermal generation units run in a large range of load alteration and face the more austere tribulation. It is a very important for thermal engineering control systems being optimized to ensure generation units'safety and economical operation in the rigorous circumstances. In essence, optimizing control systems is a design procedure that introduces some appropriate loops and advanced algorithms bringing better performance indices for control systems. In this paper, the general methods for optimizing control systems werepresented according to the characteristic of thermal engineering plants. The control system for regulating the reheating temperature in some a 350MW coal-fired unit was optimized based on one of these general methods. The better control system quality than that of the previous one was achieved. Furthermore, the predictive control, one of advanced control algorithms, is very fit for controlling thermal engineering plants. As the core component of this control algorithm, the receding horizon optimization especially under constraints carries into execution depending upon some types of nonlinear algorithms. In the course of simulation for testing predictive control algorithm, all types of the optimization algorithms studying in this paper were utilized to implement the receding horizon optimization for searching the optimal control sequence. The good control indices indicated that these optimization algorithms were very efficient to solve optimization problems. The online calculation burden is very heavy for receding horizon optimization when the controlled plants is very complicated. Motivated by this fact, some optimization strategies were introduced in this paper to release the online calculation burden.Large numbers of operation datum contain characteristic of units and associated rules of operation parameters. The scheme of implementing optimal operation on circulation water systems based on artificial neural networks was proposed in this paper. The acquirement of optimal values of operation parameters is bottleneck for implementing unit economical operation. Taking the obtaining feedwater temperature optimal value for example, the implementary scheme and steps of acquiring optimal values based on data mining technique were presented.