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The recent development of shared-bike systems in China has brought convenience for users, as well as great pressure on bicycle parking management. There is limited empirical evidence regarding the effectiveness of incentive measures from the perspective of guiding people to park their shared bikes regularly. By defining a mixed logit model based on the theory of planned behaviour, this study focuses on exploring the conformity effects of policy compliance among dockless shared-bike users in the behavioural decisions of bicycle parking. A total of 453 respondents who had just finished parking a shared bike were invited to provide intentional information by participating in a stated preference survey. The empirical results indicated that, as a positive and negative incentive measure, both monetary rewards and financial penalties can motivate people to park the shared bikes in an unsaturated place near their destinations. However, the significant difference is that, with increasing incentive intensity, a monetary reward can motivate shared-bike users to shift the bicycles away more effectively than a financial penalty, especially when the shifting distance requires more than 10 min of walking. In addition, some factors used to characterize the individual heterogeneity, such as gender, education and searching time for dockless shared bikes, also have obvious impacts on policy compliance regarding bicycle parking guidance. These findings can help policy makers to create appropriate measures in the form of incentives to reduce illegal parking by shared-bike users. [ABSTRACT FROM AUTHOR]

Demand response can reduce peak load, transfer part of peak load to valley load, and reduce system risk at peak time. In this study, a calculation model of power grid loss risk is proposed, which is suitable for evaluating the risk changes caused by the demand response, and calculating the corresponding risk value under the demand response load curve. In the calculation of demand response load, considering the impact of two measures of time-of-use (TOU) price and incentive measure, the TOU and incentive load curve are obtained based on the user's maximum benefit. In order to gain the corresponding load risk index, Monte Carlo simulation method is used, demand response load curve is taken as input, and load point fault time, load point fault rate and expected energy not serve indexes are introduced. Moreover, compared with the risk ratio of the original load curve, the simulation results verify the effectiveness of the proposed risk model, and prove that the demand response measures can transfer the risk of partial high load and balance the risk distribution. [ABSTRACT FROM AUTHOR]

Demand response can reduce peak load, transfer part of peak load to valley load, and reduce system risk at peak time. In this study, a calculation model of power grid loss risk is proposed, which is suitable for evaluating the risk changes caused by the demand response, and calculating the corresponding risk value under the demand response load curve. In the calculation of demand response load, considering the impact of two measures of time-of-use (TOU) price and incentive measure, the TOU and incentive load curve are obtained based on the user's maximum benefit. In order to gain the corresponding load risk index, Monte Carlo simulation method is used, demand response load curve is taken as input, and load point fault time, load point fault rate and expected energy not serve indexes are introduced. Moreover, compared with the risk ratio of the original load curve, the simulation results verify the effectiveness of the proposed risk model, and prove that the demand response measures can transfer the risk of partial high load and balance the risk distribution.