TY - GEN
T1 - Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System
AU - Zhang, Han
AU - Wang, Xiuli
AU - Huang, Yongxi
AU - Gou, Xiaokan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - The large-scale renewable energy(RE) integration into power systems in China poses an additional challenge to the day-ahead stochastic scheduling due to the uncertainty. Moreover, when power markets are considered, trading energy in day-ahead scheduling cannot be disregarded. In this paper, a two-stage stochastic day-ahead scheduling model considering market transactions is established, with the goal to use the most economical way to ensure the safe and reliable operation of the power grid and maximize the consumption of new energy. Mid- and long-term electricity contract decomposition, ancillary service transactions, and inter-provincial transactions are considered in the proposed model. Finally, a numerical example based on a real system of a province of China verifies the reasonableness of the proposed model. Four cases are analyzed to understand the effect of market transactions on the consumption of new energy. Furthermore, the results show the superiority of the stochastic method over an equivalent deterministic model.
AB - The large-scale renewable energy(RE) integration into power systems in China poses an additional challenge to the day-ahead stochastic scheduling due to the uncertainty. Moreover, when power markets are considered, trading energy in day-ahead scheduling cannot be disregarded. In this paper, a two-stage stochastic day-ahead scheduling model considering market transactions is established, with the goal to use the most economical way to ensure the safe and reliable operation of the power grid and maximize the consumption of new energy. Mid- and long-term electricity contract decomposition, ancillary service transactions, and inter-provincial transactions are considered in the proposed model. Finally, a numerical example based on a real system of a province of China verifies the reasonableness of the proposed model. Four cases are analyzed to understand the effect of market transactions on the consumption of new energy. Furthermore, the results show the superiority of the stochastic method over an equivalent deterministic model.
KW - day-ahead scheduling
KW - multi-energy system
KW - power market
KW - two-stage stochastic optimization
UR - https://www.scopus.com/pages/publications/85094327100
U2 - 10.1109/APPEEC48164.2020.9220412
DO - 10.1109/APPEEC48164.2020.9220412
M3 - 会议稿件
AN - SCOPUS:85094327100
T3 - Asia-Pacific Power and Energy Engineering Conference, APPEEC
BT - 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
PB - IEEE Computer Society
T2 - 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
Y2 - 20 September 2020 through 23 September 2020
ER -
