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Energies 2017, 10(12), 2111; https://doi.org/10.3390/en10122111

Chance-Constrained Real-Time Dispatch with Renewable Uncertainty Based on Dynamic Load Flow

1
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Guangzhou Power Supply Co., Ltd., Guangzhou 510000, China
*
Author to whom correspondence should be addressed.
Received: 5 November 2017 / Revised: 29 November 2017 / Accepted: 8 December 2017 / Published: 12 December 2017
(This article belongs to the Section Electrical Power and Energy System)
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Abstract

In this paper, a comprehensive real-time dispatch model considering renewable uncertainty based on dynamic load flow (DLF) is proposed. Through DLF, the primary and secondary frequency regulation amount caused by the variation of renewable energy as well as the line flow when primary and secondary regulation are deployed can be obtained easily. Not only the frequency constraints, but also the regular constraints like generator production limits and line flow limits are respected under both primary and secondary frequency regulation. To solve the dispatch problem with renewable uncertainty, chance-constrained programming based on cumulants and Cornish-fisher expansions (CCP-CMCF) is adopted to get the probability of holding the chance constraints and then the real-time dispatch model can be transformed into a quadratic programming. The simulation results show that the dispatch model proposed in this paper can deal with both primary and secondary regulation well and has a fast computation speed. View Full-Text
Keywords: dynamic load flow (DLF); renewable energy; real-time dispatch; chance-constrained programming based on cumulants and Cornish-fisher expansions (CCP-CMCF); frequency regulation dynamic load flow (DLF); renewable energy; real-time dispatch; chance-constrained programming based on cumulants and Cornish-fisher expansions (CCP-CMCF); frequency regulation
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Bie, P.; Zhang, B.; Li, H.; Wang, Y.; Luan, L.; Chen, G.; Lu, G. Chance-Constrained Real-Time Dispatch with Renewable Uncertainty Based on Dynamic Load Flow. Energies 2017, 10, 2111.

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