E3S Web Conf.
Volume 95, 2019The 3rd International Conference on Power, Energy and Mechanical Engineering (ICPEME 2019)
|Number of page(s)||5|
|Section||Energy and Power System|
|Published online||13 May 2019|
A Cascaded Algorithm Incorporating Knowledge Transfer Q-learning and Interior Point Method for Coordinated Operation of Integrated Energy System
Nantong Power Supply Company, State Grid Jiangsu Electric Power Company, 226000 Nantong, China
2 Suzhou Huatian Power Technology Company, 215000 Suzhou, China
The recent development of the Energy Internet has urged the conventional inefficient utilization of single energy to change towards the more developed energy usage of optimal dispatch of the integrated energy system. In this context, the joint optimization scheduling framework of integrated energy system is established based on the energy hub. Then a typical integrated energy system model is developed considering carbon emission and energy supply costs with valve point effect. To solve this non-linear problem with non-convex, discontinuously differentiable characteristic, the cascaded algorithm combined with the knowledge transfer based Q-learning algorithm and interior point method is applied on the model. Meanwhile, the efficiency is greatly improved by knowledge transfer. Case studies have been carried out on a 33energy hubs test system to verify the effectiveness of the proposed model and algorithm.
© The Authors, published by EDP Sciences, 2019
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.