Household Customers’ Cost of Interruption: Survey Study on Summer Electricity Peak of Xi’an City

In light of China’s Carbon Neutrality Target and facing the fluctuating pressure of power supply brought on by new energy intermittent power generation, it is urgent to mobilize a large number of residential flexible loads that can respond instantaneously to mitigate peak–valley difference. Under a framework of demand-side management (DSM) and utility analysis, we empirically investigate customers’ costs from interrupting typical electrical terminals at the household level. Specifically, by using the contingent valuation method (CVM), we explore the factors that affect households’ Willingness to Accept (WTA) of voluntarily participating in the interruption management during the summer electricity peak and estimate the distribution of households’ WTA values. We find that given the value of WTA, households’ participation rate in the interruption management significantly decreases with the increase in interruption duration and varies with the type of terminal appliance that is on direct interruption management. Moreover, the majority of households are willing to participate in the interruption management even if the compensation amount is low. The factors that determine households’ WTA and the size of their influences vary with the type of electrical terminal. The results imply that differentiating the terminal electricity market and accurately locking on the target terminals by considering the household heterogeneity can reduce the household welfare losses arising from DSM.

Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data

Motivated by the challenges induced by the so-called Target Model and the associated changes to the current structure of the energy market, we revisit the problem of day-ahead prediction of power production from Small Hydropower Plants (SHPPs) without storage capacity. Using as an example a typical run-of-river SHPP in Western Greece, we test alternative forecasting schemes (from regression-based to machine learning) that take advantage of different levels of information. In this respect, we investigate whether it is preferable to use as predictor the known energy production of previous days, or to predict the day-ahead inflows and next estimate the resulting energy production via simulation. Our analyses indicate that the second approach becomes clearly more advantageous when the expert's knowledge about the hydrological regime and the technical characteristics of the SHPP is incorporated within the model training procedure. Beyond these, we also focus on the predictive uncertainty that characterize such forecasts, with overarching objective to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of power production. Finally, we discuss the use of the proposed forecasting procedure under uncertainty in the real-world electricity market.

The Electricity Rebound Effect: Empirical Evidence From the Chinese Chemical Industry

The Chinese government has adopted many policies to save energy and electricity in the chemical industry by improving technology and reforming its electricity market. The improved electricity efficiency and the electricity reform may indirectly reduce expected energy and electricity savings by decreasing the effective electricity price and the marginal cost of electricity services. To analyze the above issues, this paper employs the Morishima Elasticity of Substitution of the electricity cost share equation which is estimated by the DOLS method. The results show that: 1) There exists a rebound effect in the Chinese chemical industry, but it is quite large because the electricity price is being controlled by the government; 2) the reform of the electricity market reduces the rebound effect to 73.85%, as electricity price begins to reflect cost information to some extent; 3) there is still a lot of space for the reform to improve, and the rebound effect could be reduced further once the electricity price is adjusted to transfer the market information more correctly. In order to succeed in saving electricity and decreasing the rebound effect in the chemical industry, the policy implications are provided from perspectives of the improved energy efficiency and electricity pricing mechanism.