E3S Web Conf.
Volume 69, 2018International Conference Green Energy and Smart Grids (GESG 2018)
|Number of page(s)||6|
|Section||Properties, Regimes and Development of Renewable Energy Sources|
|Published online||27 November 2018|
Volterra Models in Load Leveling Problem
Melentiev Energy Systems Institute, Siberian Branch of Russian Academy of Sciences, Irkutsk, Russia
2 Institute of Solar-Terrestrial Physics, Siberian Branch of Russian Academy of Sciences, Irkutsk, Russia
3 School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, UK
4 Department of Computer Engineering, Penza State University, Penza, Russia
5 Irkutsk Division of Main Computer Centre, JSC Russian Railways, Irkutsk, Russia
6 College of Electrical and Information Engineering, Hunan University, Changsha, China
* Corresponding author: firstname.lastname@example.org
Further growth in renewable energy and planned electrification and decentralization of transport and heating loads in future power systems will result in a more complex unit commitment problem (UCP). This paper proposes an adaptive approach to load leveling problem using novel dynamic models based on the Volterra integral equations of the first kind with piecewise continuous kernels. This approach employs a direct numerical method. The considered collocation-type numerical method has the second-order accuracy and enjoys self-regularization properties, which is associated with confidence levels of system demand. This adaptive approach is suitable for energy storage optimization in real time. The efficiency of the proposed methodology is demonstrated on the Single Electricity Market of the Island of Ireland.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.