TY - GEN
T1 - Day-Ahead Optimal Reserve Capacity Planning Based on Stochastic RE and DR Models
AU - Tang, Wen Jun
AU - Shih, Sin Yi
AU - Yang, Hong Tzer
N1 - Funding Information:
This work was supported by Ministry of Science and Technology (MOST 106-3113-E-006 -010), Taiwan. 978-1-5386-4505-5/18/$31.00 ©2018 IEEE
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/10
Y1 - 2018/12/10
N2 - Renewable energy (RE) is commonly used nowadays not only to fulfill the increasing power demand but also to reduce global warming and environmental pollution. However, the uncertain characteristics of renewable energy heavily affect the capacity planning of operating reserve and thus reduce the reliability and security of power system. Appropriate planning of reserve capacity is, therefore, needed to solve these problems while maintaining cost minimization and power system stability. The proposed planning is performed based on a day-Ahead market with the reserve providers including external grid, automatic generation control, demand response (DR) program and RE curtailment. Stochastic models including independent uncertainty-related factors of RE generation and load are constructed in Monte Carlo simulations. To keep the dynamic reserve adequate and solve the aforementioned risk and cost balance problem, a chance-constrained optimal power flow is employed as a probabilistic constraint to enforce operating reserve to offer a certain extent backup capacity and risk tolerance. Moreover, the effectiveness of DR is also imitated with cross-elasticity and self-elasticity for the amount and price a consumer will bid in DR market. To verify the proposed approach for reserve capacity planning, the proposed method is tested in a modified IEEE 30-bus system with high RE penetration. The result shows a day-Ahead arrangement of operating reserve with good efficiency and economy.
AB - Renewable energy (RE) is commonly used nowadays not only to fulfill the increasing power demand but also to reduce global warming and environmental pollution. However, the uncertain characteristics of renewable energy heavily affect the capacity planning of operating reserve and thus reduce the reliability and security of power system. Appropriate planning of reserve capacity is, therefore, needed to solve these problems while maintaining cost minimization and power system stability. The proposed planning is performed based on a day-Ahead market with the reserve providers including external grid, automatic generation control, demand response (DR) program and RE curtailment. Stochastic models including independent uncertainty-related factors of RE generation and load are constructed in Monte Carlo simulations. To keep the dynamic reserve adequate and solve the aforementioned risk and cost balance problem, a chance-constrained optimal power flow is employed as a probabilistic constraint to enforce operating reserve to offer a certain extent backup capacity and risk tolerance. Moreover, the effectiveness of DR is also imitated with cross-elasticity and self-elasticity for the amount and price a consumer will bid in DR market. To verify the proposed approach for reserve capacity planning, the proposed method is tested in a modified IEEE 30-bus system with high RE penetration. The result shows a day-Ahead arrangement of operating reserve with good efficiency and economy.
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U2 - 10.1109/ISGTEurope.2018.8571523
DO - 10.1109/ISGTEurope.2018.8571523
M3 - Conference contribution
AN - SCOPUS:85060223565
T3 - Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018
BT - Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018
Y2 - 21 October 2018 through 25 October 2018
ER -