DOI:https://doi.org/10.1007/s40565-018-0412-6 |
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An efficient stochastic algorithm for mid-term scheduling of cascaded hydro systems |
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Net amount: 657 |
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Author:
Xiaolin GE1, Shu XIA2, Wei-Jen LEE3
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Author Affiliation:
1. Electric Power College, Shanghai University of Electric
Power, Shanghai, China
2. Shibei Electricity Supply Company of State Grid Shanghai
Municipal Electric Power Company, Shanghai, China
3. Energy Systems Research Center, University of Texas at
Arlington, Arlington, TX 76019, USA
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Foundation: |
This work was supported in part by National Natural Science Foundation of China (No. 51507100), in part by Shanghai Sailing Program (No. 15YF1404600), and in part by “Chen Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (No. 14CG55). |
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Abstract: |
Due to the stochastic and correlated attributes of
natural inflows, the mid-term generation scheduling problem
for cascaded hydro systems is a very challenging issue.
This paper proposes a novel stochastic optimization algorithm
using Latin hypercube sampling and Cholesky
decomposition combined with scenario bundling and sensitivity
analysis (LC-SB-SA) to address this problem. To
deal with the uncertainty of natural inflows, Latin hypercube
sampling is implemented to provide an adequate
number of sampling scenarios efficiently, and Cholesky
decomposition is introduced to describe the correlated
natural inflows among cascaded stations. In addition, to
overcome the difficulties in solving the objectives of all the
scenarios, scenario bundling and sensitivity analysis algorithms
are developed to improve the computational effi-
ciency. Simulation results from both two-station and tenstation
systems indicate that the proposed method has the
merits in accuracy as well as calculation speed for the midterm
cascaded hydro generation scheduling. The consideration
of natural inflow correlation makes the formulated
problem more realistic. |
Keywords: |
Cascaded hydro systems, Mid-term scheduling,
Stochastic optimization algorithm, Correlation, Sensitivity |
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Online Time:2019/01/28 |
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