Research On The Power System Optimization Based On The Swarm Intelligence Algorithms And The Power Market Stability

Power system planning is one of the most important issues in the area of power system research and operation. With the development of power systems, the mordern power systems have become more and more complex. With the nature of nonlinear, nonconvex and discontinuous, it is already very difficult to deal with the power system planning for the conventional mathematical optimization algorithms. It is, therefore, necessary to develop algorithms to solve these problems. In the recent years, the algorithms based on the artificial intelligence (AI) have been introduced to the power system planning as alternative methods such as the swarm intelligence algorithms inspired from the swarm behaviors of the nature. Although different swarm intelligence algorithms are developed from different backgrounds, some things are common to all these algorithms. The purposeful behaviors of these algorithms are achieved with the cooperation of a number of individual agents, which follow the simple local rules and interact with the environment. In such a way some complicated goal can be reached. Because these algorithms show very strong robustness and effectiveness, they have attracted great deal of attention. However, the dispersive research result currently presented in the literature show that it is time to carry out theoretical research on these algorithms. When design a policy for electric power market, it is necessary to consider whether the market under this policy is stable. Theoretically, the foundation of electricity markets is based on the classical economic theory of competitive markets and their benefits. However, it is widely recognized that electricity market differs from any other commodity market in the following aspects. Firstly, electricity as one kind of the special commodity can not be stored, i.e., the market has to be cleared instantaneously. Secondly, the load demand in electricity market displays a cyclic pattern. Finally, the discrete biding strategy of the power market should be considered when investigating the market's stability. The object of this paper is to investigate the power system planning by swarm intelligence algorithms and the stability of the power market. Following results are obtained. Firstly, a unified frame for swarm intelligence algorithms is proposed. Main operators in the frame are defined. Several sufficient conditions for the convergence of the algorithms are also given. Secondly, according to the frame proposed, a new versatile optimization algorithm called generalized ant colony optimization is developed to solve the discontinuous, nonconvex, nonlinear constrained optimization problems. Based on the fixed-point theorem, the sufficient conditions for the convergence of the algorithm are deduced. The effectiveness of the algorithm is verified by the power system economic dispatching and reactive power planning. Thirdly, a global convergence algorithm called enhanced particle swarm optimization algorithm is developed. Based on the stochastic analysis theory, a sufficient condition for the convergence of the algorithm is given. To increase the speed of convergence, the general ant colony optimization is integrated with the particle swarm optimization algorithm. The effectiveness of the algorithms developed above is tested by the power system economic dispatching. Encouraging results are obtained. Fourthly, a new swarm intelligence algorithm called quantum-inspired evolutionary algorithm is proposed to solve the optimal transmission network expansion planning. Some new operators are defined and used for the global searching. The performances of these operators are investigated by comparison. In order to increase the convergence, measures are proposed to optimize the parameters in the algorithm. Test results are used to show the accuracy and the efficiency of the algorithm. Fifthly, a new optimization algorithm called estimation of distribution algorithm is introduced to solve the transmission network expansion planning. Two kinds of EDA: population-based incremental learning (PBIL) and factorized distribution algorithm (FDA) are used. To increase the convergence, several enhanced operators are involved in the algorithm. Numerical results show that the proposed method is feasible and effective. Sixthly, the generation expansion planning of power system is solved by modified genetic algorithm and partheno-genetic algorithm. Several results of actual system validate the proposed algorithms.Seventhly, the stability of power market is investigated. Several realistic models with periodic factors of the electricity market that is characterized by dynamic system models are obtained. The problem of electricity market stability is transformed to the problem of the stability of a periodic solution of the dynamic system. To study the stability conditions, several lemmas are proven. Conditions for the existence of a unique stable periodic solution are obtained and the ultimate convergence boundary is estimated. Simulation results show the effectiveness of the models and the stability conditions obtained. Finally, conclusions about this dissertation are summarized and the further work is pointed out.