Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks

Wang, Haixin, Yang, Junyou, Chen, Zhe, Li, Gen, Liang, Jun, Ma, Yiming, Dong, Henan, Ji, Huichao and Feng, Jiawei 2020. Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks. Applied Energy 267 , 114879. 10.1016/j.apenergy.2020.114879
Item availability restricted.

[img] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 6 April 2021 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB)

Abstract

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the autocorrelation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0306-2619
Date of First Compliant Deposit: 26 March 2020
Date of Acceptance: 22 March 2020
Last Modified: 31 Jul 2020 15:24
URI: http://orca.cf.ac.uk/id/eprint/130605

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics