Models And Algorithms Of Electric Power Network Flexible Planning Under Multi Uncertainty

Flexible electric power network planning includes two aspects: one is to build up reasonable planning model; the other is to give an appropriate solving algorithm. In this paper, the planning models and corresponding solving algorithms of electric power network flexible planning are studied in detail. By investigating the difficulty of global convergence in solving flexible planning model, the ant colony algorithm (ACA) and its improved algorithms are introduced to solve it. By synthetically considering the economy, reliability and flexibility of the planning scheme, a multi-object planning model concerning the power resource abundance is proposed. By analyzing the characteristic of high-dimensioned variables in the multi-stage planning problem, a minimal expectant regret concerned electric power network planning model and the corresponding solving method are proposed. The main content is as follows:Considering the character of power network planning, the ACA is proposed to solve single-stage network planning. Because the existence of premature convergence in ACA, two improving strategies are brought forward. One is multi-group parallel ACA (MPACA), and the other is the schema recording parallel ACA (SRPACA). On one hand, MPACA can improve the convergence speed by the parallel search of multi colony. On the other hand, it can also reduce the influence of the parameters'improperly setting effectively. Thus, it can avoid the premature convergence adequately. Besides the advantages which MPACA has, SRPACA can partition the solution space through schema recording, and can identify, record and jump away from the local optimal solution. In this way, the reduplicate search can be reduced furthest, and the computation efficiency is greatly improved. The simulation results of two sample systems show that SRPACA has high computation efficiency and good local & global convergence.By analyzing the characteristics of the four kinds of uncertain information which directly influence network planning (load forecast, generation, power flow and investment of lines) in detail, the blind number models of them are established. Furthermore, the distribution of the blind numbers'reliability can be calculated by means of'judgment matrix analytical method'. Then, after introducing the direct current sensitivity analysis method of general ATC, the direct current sensitivity analysis method of blind number ATC is studied, and some predigestion tips are also proposed to decrease computational effort. Based on the calculation of blind number ATC, a multi-object planning model concerning the power resource abundance is formulated. By establishing the fuzzy evaluation model of blind information, the fuzzy integrated evaluation method is used to solve the multi-object model. This model can be solved easily by the SRPACA method proposed in Chap. 2. This optimal object of this model is the integration of blind number ATC, blind number investment and the probability of overload under flexible'N-1'constraint. The simulation results of a practical sample system show that this model can consider the abundance of power resource effectively. The solutions of this model have good character of economy, reliability and flexibility.In multi-stage power network planning problems, the difficulty is that the values of decision variables in each stage are restricted by each other. The computational effort is directly proportional to the stage number. In this paper, a minimal expectant regret concerned multi-stage planning model is proposed and the SRPACA is used to solve this model. The simulation results show that the planned network under this model has the minimal expectant invest regret in all the scenarios, and the curse of dimensionality is also prevented.