Open AccessA two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant
Author(s) -
Emarati Mohammadreza,
Keynia Farshid,
Rashidinejad Masoud
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12255
Subject(s) - cvar , virtual power plant , expected shortfall , mathematical optimization , stochastic programming , computer science , markov decision process , wind power , distributed generation , stochastic optimization , electric power system , energy storage , risk management , renewable energy , reliability engineering , power (physics) , markov process , engineering , mathematics , economics , statistics , management , physics , quantum mechanics , electrical engineering
Summary With the increasing use of distributed energy resources (DERs), new technical and economic issues have been raised in power systems. Integration of DERs and energy storage systems (ESSs) in the form of virtual power plant (VPP) resolves an important part of these issues. This paper proposes a risk‐based two‐stage stochastic optimization framework to address the energy management problem for a VPP. The objective of the proposed framework is to optimize the operation of a VPP in day‐ahead (DA) and real‐time (RT) markets. In order to include the risk parameter in the proposed decision‐making problem, conditional value at risk (CVaR) index is applied in the objective function. The considered uncertain parameters in the model are price in DA market, as well as wind and solar generation for the next day. Markov chain Monte Carlo (MCMC) method is applied to model these uncertain parameters through generation of different scenarios. Also, the effects of using ESS on daily operation of considered VPP is investigated. The performance of the proposed method is illustrated through a case study using real data. The obtained results guarantee the appropriate operation of a VPP considering different values for level of the risk.