Study On Ancillary Service In Power Market

In recent years, the deregulation of power system is carried out in some countries. The ancillary service is an important part of the power market. It is vital to the safety, stability and efficiency of power system. This paper discusses the reactive power service and the allocation of power losses. Reactive power service is the indispensable service of power market. Pricing of reactive power and the fuzzy reactive power optimization are studied in this paper. Because of its specialties, the pricing of reactive power is more complex. This paper proposes a novel method for reactive power pricing. The superposition theorem is used to calculate the contributions of individual generators to loads in a given status and the active and reactive power price of loads is determined by cost apportioning. It is applicable to the network in which the circumfluence exists. Pricing of active and reactive power is integrated efficiently. The problem that how to recover the cost of the generators which operate in the under excitation condition is resolved successfully. The simplicity and practicability of the method is evidence. Finally with the IEEE-14 and IEEE-30 bus systems as the examples, the simulation computing is carried out. The validity and feasibility of the method is demonstrated by the results.In this paper, the method of current superposition is used to allocate the power losses. The apportionment of each power source is calculated on the basis of its actual use of each branch. The losses allocation between the power sources and loads in certain proportion is calculated on the basis of the factor of contributions of individual generators to loads. Not only can the allocation of the active power losses but also that of the reactive power losses be calculated at the same time. The simplicity and good adapbility of the method is obvious. The rationality of the method is proved by the examples.The model of the fuzzy reactive power optimization is established in order to consider the uncertainty of the loads. The fuzzy set is introduced into the reactive power optimization, so the optimization becomes more reasonable and practical. The fuzzy theory and the genetic algorithms are combined to solve the fuzzy nonlinear optimization problem. A real system is used as an example to test the method.