| In competitive electricity market environment, generation companies and consumers are becoming the protagonists of the electricity market participants, and their market behaviors have also attracted increasing attention. Under the single-purchaser pool market model, the problem of the optimal bidding strategies for generation companies in day-ahead market is subjected to attention by both generation companies and market regulators, it has been a research focus in the field of electricity market. In addition, as the electricity purchasing option for the consumer side to gradually open up, large consumers could purchase electricity freely from different power providers. But at the same time the uncertainties in the market will bring different levels of risk to large consumers in their electricity purchasing process. How to face these uncertainties and build reasonable electricity purchasing strategy under the premise of controlling risk become one of the key issues facing large consumers.This dissertation, based on credibility theory and considering the random and fuzzy uncertain information in the market, focuses on researching the optimal bidding strategies for generation companies in day-ahead market under the pool model and the optimal electricity purchasing strategies for large consumers under direct electricity-purchase model for large consumers. Main achievements are presented as follows:(1) According to the problem that random or fuzzy uncertainty information in the market can only be considered separately in the past optimal bidding strategy research, based on credibility theory, comprehensively considering random and fuzzy uncertainty information, builded two level uncertain programming model which is composed of independent system operator optimal model and generation company optimal bidding strategy model. The two level model takes transmission capacity constraints into account for closing the actual auction environment. To solve the new model, proposed a hybrid solution algorithm combined with complementary direct optimization, uncertainty simulation, neural network and genetic algorithm, because the new model is a two level model that contains random variables and fuzzy variables synchronously, it is difficult to be solved by translating the model into certainty equivalent model. Used3-bus system,11-bus system and30-bus system to demonstrate the proposed model and solution algorithm.(2) According to the precocious problem encountered in basic genetic algorithm for solving the generation companies optimal bidding strategies, proposed an improved genetic algorithm based on introducing infeasible population and improving selection, crossover and mutation strategy. The introduction of the infeasible population and the new evaluation and selection strategy can simplify the problem of constraints handling. Under the premise of ensuring the continuity of the population fine mode, the new crossover and mutation strategy can increase the diversity of population, expand the search space and prevent the occurrence of precocious problem. The numerical examples in this dissertation and the classic test functions are used to verify the improved effect of the new genetic algorithm.(3) Based on the credibility theory, researched the problem of optimal electricity purchasing strategy for large consumers preliminary. comprehensively considering electricity purchasing transactions of large consumers in the primary energy market and reserve market, according to the value at risk thought, based on the primitive chance constraint, proposed an optimal electricity purchasing strategy model for large consumers taking risk into account. The random and fuzzy uncertain information, as well as the risks arising from these uncertain information are synchronously considered in the model. The hybrid algorithm based on random fuzzy simulation, neural networks and genetic algorithm is employed to solve the model. Numerical examples illustrate the feasibility of the proposed risk management method and model.
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