Open AccessOptimal scheduling of distributed energy resources as a virtual power plant in a transactive energy framework
Author(s) -
Qiu Jing,
Meng Ke,
Zheng Yu,
Dong Zhao Yang
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0268
Subject(s) - virtual power plant , computer science , mathematical optimization , scheduling (production processes) , particle swarm optimization , profit (economics) , solver , distributed generation , electricity market , distributed computing , demand response , operations research , reliability engineering , electricity , renewable energy , engineering , mathematics , algorithm , microeconomics , economics , electrical engineering
A transactive energy framework can provide an integral management scheme that facilitates power delivery with high efficiency and reliability. To close the gap between wholesale and retail markets, this study presents a two‐stage optimal scheduling model for distributed energy resources in the form of a virtual power plant (VPP) participating in the day‐ahead (DA) and real‐time (RT) markets. In the first stage, the hourly scheduling strategy of the VPP is optimised, in order to maximise the total profit in the DA market. In the second stage, the outputs of the VPP are optimally adjusted, in order to minimise the imbalance cost in the RT market. The conditional‐value‐at‐risk is used to assess the risk of profit variability due to the presence of uncertainties. Furthermore, formulated two‐stage models are solved by the enhanced particle swarm optimisation algorithm and a commercial solver. Case study results show that the proposed approach can identify optimal and accurate scheduling results, and is a useful decision‐making tool.