TY - JOUR
T1 - Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk
AU - Li, Yuanzheng
AU - Huang, Jingjing
AU - Liu, Yun
AU - Ni, Zhixian
AU - Shen, Yu
AU - Hu, Wei
AU - Wu, Lei
N1 - Publisher Copyright:
© 2013 State Grid Electric Power Research Institute.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. After-wards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.
AB - The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. After-wards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.
KW - Economic dispatch (ED)
KW - extreme learning machine
KW - optimization algorithm
KW - wind power
UR - http://www.scopus.com/inward/record.url?scp=85143520086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143520086&partnerID=8YFLogxK
U2 - 10.35833/MPCE.2020.000764
DO - 10.35833/MPCE.2020.000764
M3 - Article
AN - SCOPUS:85143520086
SN - 2196-5625
VL - 10
SP - 1459
EP - 1471
JO - Journal of Modern Power Systems and Clean Energy
JF - Journal of Modern Power Systems and Clean Energy
IS - 6
ER -