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

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 e...

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Veröffentlicht in:IEEE transactions on power systems Jg. 33; H. 6; S. 7292 - 7295
Hauptverfasser: Fang, Xin, Hodge, Bri-Mathias, Bai, Linquan, Cui, Hantao, Li, Fangxing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Economic forecasting
electricity market
Energy industry
ENERGY PLANNING, POLICY, AND ECONOMY
ENERGY STORAGE
Forecast uncertainty
Linear programming
locational marginal price (LMP)
Markets
Mathematical models
mixed-integer conic programming (MICP)
Optimization
Power markets
Real time
Real-time systems
Scheduling
second-order cone programming (SOCP)
Uncertainty
ISSN:0885-8950, 1558-0679
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
AC36-08GO28308
NREL/JA-5D00-70325
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2018.2852951