Distributed optimal dispatch of virtual power plant based on ELM transformation

Hongming Yang; Dexin Yi; Junhua Zhao; Fengji Luo; Zhaoyang Dong

doi:10.3934/jimo.2014.10.1297

Article Contents

2014, Volume 10, Issue 4: 1297-1318. Doi: 10.3934/jimo.2014.10.1297

This issue Previous Article A hybrid method combining genetic algorithm and Hooke-Jeeves method for constrained global optimization Next Article A note on preinvexity

Distributed optimal dispatch of virtual power plant based on ELM transformation

1.
Hunan Province Key Laboratory of Smart Grids Operation and Control, School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114
2.
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114
3.
The Centre for Intelligent Electricity Networks, The University of Newcastle, NSW 2308
4.
Centre for Intelligent Electricity Networks, The University of Newcastle, Callaghan, NSW 2308
5.
School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006

Received: August 2013

Revised: January 2014

Published: October 2014

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Abstract

To implement the optimal dispatch of distributed energy resources (DER) in the virtual power plant (VPP), a distributed optimal dispatch method based on ELM (Extreme Learning Machine) transformation is proposed. The joint distribution of maximum available outputs of multiple wind turbines in the VPP is firstly modeled with the Gumbel-Copula function. A VPP optimal dispatch model is then formulated to achieve maximum utilization of renewable energy generation, which can take into account the constraints of electric power network and DERs. Based on the Gumbel-Copula joint distribution, the nonlinear functional relationship between the wind power cost and wind turbine output is approximated using ELM. The approximated functional relationship is then transformed into a set of equality constraints, which can be easily integrated with the optimal dispatch model. To solve the optimal dispatch problem, a distributed primal-dual sub-gradient algorithm is proposed to determine the operational strategies of DERs via local decision making and limited communication between neighbors. Finally, case studies based on the 15-node and the 118-node virtual power plant prove that the proposed method is effective and can achieve identical performance as the centralized dispatch approach.

Keywords:

Distributed primal-dual sub-gradient algorithm,
extreme learning machine,
Gumbel-Copula,
optimal dispatch,
virtual power plant.

Mathematics Subject Classification: Primary: 90B35, 90B18; Secondary: 90C30.

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