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Author:

Wei, Dongni (Wei, Dongni.) | Xiao, Yanli (Xiao, Yanli.) | Wang, Zheng (Wang, Zheng.) | Liu, Shangke (Liu, Shangke.) | Zhang, Beibei (Zhang, Beibei.)

Indexed by:

CPCI-S

Abstract:

Based on TSP-CVaR risk aversion theory, this paper proposes an optimal dispatching model for wind-PV-hydro hybrid power system with complementarity characteristics. Under the framework of two-stage programming (TSP), the proposed method could effectively deal with the volatility of demand, the randomness of upstream traffic, and the complex connection of hydraulic resource in cascade hydropower station. Besides, conditional value at risk (CVaR) is introduced to characterize the risk aversion attitude of decision makers (DMs), and take full account of the minimum acceptable profit under certain confidence level. Finally, the feasibility and effectiveness of the proposed method is verified through a case study. The influence of the risk aversion attitude of DMs on the scheduling and operation plan of the multi-energy complementary power generation system is also analyzed and discussed.

Keyword:

hybrid power system two-stage programming CVaR uncertain programming energy complementarity

Author Community:

  • [ 1 ] [Wei, Dongni]State Grid Ningxia Elect Power Ecotech Res Inst, Ningxia 100126, Peoples R China
  • [ 2 ] [Xiao, Yanli]State Grid Ningxia Elect Power Ecotech Res Inst, Ningxia 100126, Peoples R China
  • [ 3 ] [Wang, Zheng]State Grid Ningxia Elect Power Ecotech Res Inst, Ningxia 100126, Peoples R China
  • [ 4 ] [Liu, Shangke]State Grid Ningxia Elect Power Ecotech Res Inst, Ningxia 100126, Peoples R China
  • [ 5 ] [Zhang, Beibei]Beijing Univ Technol, Sch Econ & Management, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Zhang, Beibei]Beijing Univ Technol, Sch Econ & Management, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019)

Year: 2019

Page: 1811-1816

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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