Shuwei Zhong, Yanbo Che^*, Shangyuan Zhang

Energy Engineering, Vol.121, No.3, pp. 603-618, 2024, DOI:10.32604/ee.2023.044667

Abstract Electric vehicle (EV) is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future. However, a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff. Therefore, this paper proposes a dynamic time-of-use tariff mechanism, which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean (FCM) clustering algorithm, and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period. Based on the proposed… More >

Lifeng Wang¹, Jing Yu^2,*, Wenlu Ji¹

Energy Engineering, Vol.121, No.2, pp. 523-540, 2024, DOI:10.32604/ee.2023.041881

Abstract Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users, the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies. For this purpose, a power grid-flexible load bilevel model is constructed based on dynamic pricing, where the leader is the dispatching center and the lower-level flexible load acts as the follower. Initially, an upper-level day-ahead dispatching model for the power grid is established,… More >

Jinseok Kim¹, Babar Shah², Ki-Il Kim^3,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042

Abstract Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.… More >