A Markov chain-based model for wind power prediction in congested electrical grids

Abstract

The large penetration of wind generators in existing electrical grids induces critical issues that are pushing the system operators to improve several critical operation functions, such as the security analysis and the spinning reserve assessment, with the purpose of mitigating the effects induced by the injected power profiles, which are ruled by the intermittent and not-programmable wind dynamics. Although numerous forecasting tools have been proposed in the literature to predict the generated power profiles in function of the estimated wind speed, further and more complex phenomena need to be investigated in order to take into account the effects of the forecasting uncertainty on power system operation. In order to deal with this issue, this paper proposes a probabilistic model based on Markov chains, which predicts the wind power profiles injected into the grid, considering the real generator model and the effects of the power curtailments imposed by the grid operator. Experimental results obtained on a real case study are presented and discussed in order to prove the effectiveness of the proposed method.

Keywords

• markov processes
• power engineering computing
• power grids
• wind power plants
• probability
• wind power
• penetration
• wind generators
• critical issues
• system operators
• critical operation functions
• security analysis
• spinning reserve assessment
• injected power profiles
• not-programmable wind dynamics
• numerous forecasting tools
• generated power profiles
• estimated wind speed
• complex phenomena
• forecasting uncertainty
• power system operation
• probabilistic model
• markov chains
• wind power profiles
• generator model
• power curtailments
• grid operator
• markov chain-based model
• wind power prediction
• congested electrical grids