TY - GEN
T1 - Iterative-Lengthening and Auxiliary Search Based Particle Swarm Optimization for Online Short-term Hydrothermal Scheduling
AU - Yi, Ruikang
AU - Luo, Wenjian
AU - Lin, Xin
AU - Xu, Peilan
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Short-term hydrothermal scheduling problems (SHSPs) are to determine the optimal schedule scheme for hydro generators and thermal generators, with the objective of minimizing the total fuel cost of thermal generators. Traditional methods often regards the SHSPs as offline problems, using the predicting data to determine the whole schedule scheme in advance. However, this might be inappropriate. On the one hand, actual data may deviate from the predicting data, thus only employing the predicting data might be a little inaccurate. On the other hand, actual data arriving during the scheduled period have been ignored, while this data could be applied to improve the decision quality to some extent. Therefore, the online character in the SHSPs should be emphasized. In this paper, after introducing the online short-term hydrothermal scheduling problems, we propose an Iterative-Lengthening and Auxiliary Search based Particle Swarm optimization (ILAS-PSO) to solve online SHSPs. Experimental results show that the proposed ILAS-PSO is effective for solving online SHSPs.
AB - Short-term hydrothermal scheduling problems (SHSPs) are to determine the optimal schedule scheme for hydro generators and thermal generators, with the objective of minimizing the total fuel cost of thermal generators. Traditional methods often regards the SHSPs as offline problems, using the predicting data to determine the whole schedule scheme in advance. However, this might be inappropriate. On the one hand, actual data may deviate from the predicting data, thus only employing the predicting data might be a little inaccurate. On the other hand, actual data arriving during the scheduled period have been ignored, while this data could be applied to improve the decision quality to some extent. Therefore, the online character in the SHSPs should be emphasized. In this paper, after introducing the online short-term hydrothermal scheduling problems, we propose an Iterative-Lengthening and Auxiliary Search based Particle Swarm optimization (ILAS-PSO) to solve online SHSPs. Experimental results show that the proposed ILAS-PSO is effective for solving online SHSPs.
KW - Online hydrothermal scheduling problems
KW - auxiliary search
KW - iterative-Lengthening search
KW - particle swarm optimization
UR - https://www.scopus.com/pages/publications/85062768431
U2 - 10.1109/SSCI.2018.8628849
DO - 10.1109/SSCI.2018.8628849
M3 - 会议稿件
AN - SCOPUS:85062768431
T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
SP - 1913
EP - 1920
BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
A2 - Sundaram, Suresh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Y2 - 18 November 2018 through 21 November 2018
ER -