- Title
- Distributed optimal dispatch of virtual power plant based on elm transformation
- Creator
- Yang, Hongming; Yi, Dexin; Zhao, Junhua; Luo, Fengji; Dong, Zhaoyang
- Relation
- Journal of Industrial and Management Optimization Vol. 10, Issue 4, p. 1297-1318
- Publisher Link
- http://dx.doi.org/10.3934/jimo.2014.10.1297
- Publisher
- American Institute of Mathematical Sciences (AIMS)
- Resource Type
- journal article
- Date
- 2014
- Description
- 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 dis-patch 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.
- Subject
- distributed primal-dual sub-gradient algorithm; extreme learning machine; Gumbel-Copula; optimal dispatch; virtual power plant
- Identifier
- http://hdl.handle.net/1959.13/1306042
- Identifier
- uon:21141
- Identifier
- ISSN:1547-5816
- Language
- eng
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