Open AccessEnhanced long‐run incremental cost charging considering the impact of demand response
Author(s) -
Zhang Wei,
Wang Xiuli,
Wu Xiong,
Li Furong,
Cao Chunlian
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6124
Subject(s) - flexibility (engineering) , demand response , incentive , computer science , transmission (telecommunications) , investment (military) , node (physics) , transmission system , operations research , computer network , economics , engineering , microeconomics , telecommunications , electricity , management , structural engineering , politics , law , political science , electrical engineering
The existing transmission charging methods like long‐run incremental cost give forward‐looking charges which reflect the impact of system congestion on required network investment, but the flexibility of demand is not considered. This study proposes a novel transmission charging method which takes into consideration the demand response (DR). The proposed method could reflect: (1) how DR influences the short‐run congestion management cost and long‐run investment cost; (2) how DR influences the transmission charges. To promote the effect of DR, an approach is proposed to determine the optimal nodes for the implementation of DR based on the available transfer capability and congestion cost. Through the approach, different plans for the implementation of DR will be produced in different demand levels. The optimal node determination approach not only makes DR more targeted for the system congestion but also improves the effect of the proposed transmission charging method. The proposed method is applied to the IEEE 14‐bus system and a practical system in Western China. The results indicate that the proposed charging method can offer consumers with economic incentives to alleviate congestion, especially when the DR implemented on the optimal nodes, and thus delay the network reinforcement investment.