Day-Ahead Dynamic Assessment of Consumption Service Reserve Based on Morphological Filter

Abstract

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations.

Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources

“…The research presented in the article may be helpful to renewable energy developers who plan to build new generating capacity to power large fleets of electric vehicles [45]. They can also be helpful to private individuals or local governments that have problems with balancing the energy network in larger or smaller areas [46]. Large numbers of electric vehicles with traction batteries with high energy capacity may be helpful for this purpose.…”