Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

Yerzhigit Bapin, Vasilios Zarikas

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach. Design/methodology/approach: The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours. Findings: The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%. Originality/value: The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

Original languageEnglish
JournalInternational Journal of Energy Sector Management
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Bayesian networks
  • Bivariate probability density function
  • Demand response
  • Energy sector
  • Interconnected power system
  • Linear programming
  • Markov model
  • Mixed integer programming
  • Monte Carlo simulation
  • Renewable energies
  • Reserve allocation adjustment algorithm
  • Solar
  • Spinning reserves
  • Wind
  • Wind-PV

ASJC Scopus subject areas

  • Energy(all)
  • Strategy and Management

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