Research Of The Joint Optimization Of Primary Power Market And Ancillary Services Market

With the continuous development of power industry's marketization, ancillary services are gradually separated from the primary market. Against this background, the ancillary services market can be bidded in by power generation companies like the primary market. Therefore, how to balance these two markets and to obtain a combinational optimal bidding strategy between them for the maximum prospective return has become a hot spot of today's power market research.At first, the overview of the world's power market reform is introduced in this paper, as well as the current situation of the domestic ancillary services market, which focuses on the eastern area of China. Then the joint bidding strategy of primary and secondary market is discussed in this paper to reach the generation company's optimal expected benefits.Analysis showes that the generation company's total market revenue is influenced by generation costs, market prices, delivery capacities and bidding risk. Considering the above factors, the multi-market integrated optimization decision model based on the fuzzy combinatorial theory is proposed in this paper using fuzzy math method to deal with the uncertain information and bidding risk, and thus, simplify the multi-objective programming problem. The balance between benefit and risk is more fully considered, compared with other decision models and methods.But other market participants and competitors will have to be faced by single generation company in the actual power market environment. So the multi-market integrated optimization decision model based on the fuzzy game optimization theory is proposed to solve the two markets'optimal balanced decision, complementing and expanding the former fuzzy combinatorial optimization model.Both of the two models proposed in this paper are used for the power market simulation to get the generation company's combined optimization strategy. At the same time the corresponding strategies of the generation company were analysed, according to the changes of market prices, bidding risk and energy demand through the simulation. The final conclusion is consistent with the actual operation in the power market, verifing the two models'effectiveness and practicality.