Mean-Variance Optimization-Based Energy Storage Scheduling Considering Day-Ahead and Real-Time LMP Uncertainties

Xin Fang, Brian Hodge, Linquan Bai, Hantao Cui, Fangxing Li

Research output: Contribution to journalArticlepeer-review

39 Scopus Citations


In this letter, a new mean-variance optimization-based energy storage scheduling method is proposed with the consideration of both day-ahead (DA) and real-time (RT) energy markets price uncertainties. It considers the locational marginal price (LMP) forecast uncertainties in DA and RT markets. The energy storage arbitrage risk associated with the LMP forecast uncertainty is explicitly modeled through the variance component in the objective function. The quadratic term of this variance is transformed into a second-order cone constraint using the charging and discharging power complementarity of the energy storage system. Finally, the proposed model is formulated as a mixed-integer conic programming problem. Numerical case studies demonstrate the effectiveness of the proposed model for energy storage scheduling considering price uncertainty.

Original languageAmerican English
Article number8409961
Pages (from-to)7292-7295
Number of pages4
JournalIEEE Transactions on Power Systems
Issue number6
StatePublished - 2018

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

NREL Publication Number

  • NREL/JA-5D00-70325


  • electricity market
  • Energy storage
  • locational marginal price (LMP)
  • mixed-integer conic programming (MICP)
  • second-order cone programming (SOCP)


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