Research On Multi-Objective TOU Pricing Optimization In DSM

Time of use(TOU) pricing is a significant method in the field of demand side management(DSM). According to the original load curve, the 24 hours of a day is sorted and then priced differently. It is aiming at change the users' consumption behavior by use of economic guidance, thus improving the distribution of the daily load,achieving the purpose of clipping the peak load to fill the valley load, and realizing energy-saving and emission-reduction. By implementing the TOU pricing, the power shortage in the peak period is overcome, and the conflict between demand side and supply side is eased. TOU pricing bright benefit to both sides in the market, and a win-win situation is achieved successfully.In this paper, the research of home and aboard on TOU pricing of DSM is analyzed, and there exist some problems. The design of price of each time-period lay too much emphasis on the effect of clipping the peak to fill the valley, but the feeling of the consumer is ignored, and so on. To solve the problems above, a comprehensive multi-objective TOU pricing progressive optimization scheme is established, in which kinds of factors are taken into consideration.This paper use the method of cluster analysis to realize the time-period partition. The partition result has more obvious distinction degree, which makes the result more scientific. After the time-period partition, the multi-objective TOU pricing optimization scheme based on user satisfaction index is come up with. In the process of pricing, he users satisfaction index and the acceptance degree is considered to correct the user response curve. The corrected user response curve makes the predicted load curve more accurate. Besides, fast and elitist non-dominated sorting genetic algorithm is adapted to optimize the multi-objectives at the same time, thus the objective-balanced Pareto solution set is obtained, and the optimal compromise solution with the highest comprehensive satisfaction is get by multiple attribute decision making principle. What's more, this paper come up with a progressive strategy to optimize the TOU pricing in further by replacing the original load curve with the optimized load curve. The purpose of progressive strategy is to explore more potential of clipping the peak to fill the valley and to realize it.By integrating the research above, this paper set up a complete multi-objective TOU pricing progressive optimization scheme with strong theoretical and practical features. According to different kinds of users and different areas, the research productions is generally practical. It makes reference for different area to design the TOU pricing to balance and optimize the load distribution. It is innovate to take the user acceptation degree into consideration and to make further optimization by progressive strategy. It makes a different for the widespread and optimization for TOU pricing.