Stepwise power tariff (SPT), which has been put into practice, is a crucial way for energy saving and environment protecting. In this paper, a new optimal model of SPT based on residential demand response model is presented. The optimal decision is proposed to restrain high electricity consumption as well as safeguard benefits of both supply and demand sides. As a result, the objective is designed to minimize electricity consumption and constraints are taken into consideration thoroughly, including acceptable index of consumers, average price, sales profit of power providers and basic electricity demand, which serves as a foundation for smooth implement of SPT. To solve the constrained optimal problem, genetic algorithm (GA) is employed. The effectiveness of the model and algorithm is investigated and demonstrated based on real data of 300 residents by a numerical example. The study shows that the method can reduce power consumption obviously with little sacrifice of the benefits of consumers and power providers.Index Terms-Electricity markets, energy saving, stepwise power tariff. NOMENCLATUREElectricity quantity of the step .Upper limit of electricity quantity.Single price before implementing SPT.Unit price of the step after implementation of SPT.Upper limit of price.Clearing price per month.Steps of SPT.

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Understanding people's attitudes towards proenvironmental travel will help to encourage people to adopt proenvironmental travel behavior. Revealed preference theory assumes that the consumption preference of consumers can be revealed by their consumption behavior. In order to investigate the influences on citizens' travel decision and analyze the difficulties of promoting proenvironmental travel behavior in medium-sized cities in China, based on revealed preference theory, this paper uses the RP survey method and disaggregate model to analyze how individual characteristics, situational factors, and trip features influence the travel mode choice. The field investigation was conducted in Tangshan City to obtain the RP data. An MNL model was built to deal with the travel mode choice. SPSS software was used to calibrate the model parameters. The goodness-of-fit tests and the predicted outcome demonstrate the validation of the parameter setting. The results show that gender, occupation, trip purpose, and distance have an obvious influence on the travel mode choice. In particular, the male gender, high income, and business travel show a high correlation with carbon-intensive travel, while the female gender and a medium income scored higher in terms of proenvironmental travel modes, such as walking, cycling, and public transport.

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