Abstract
A significant difficulty associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DC optimal power flow problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique by simulating dispatch operations on a synthetic test system over the course of a week.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - 2 Aug 2020 |
Event | 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 - Montreal, Canada Duration: 2 Aug 2020 → 6 Aug 2020 |
Conference
Conference | 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 |
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Country/Territory | Canada |
City | Montreal |
Period | 2/08/20 → 6/08/20 |
Bibliographical note
See NREL/CP-2C00-75363 for preprintNREL Publication Number
- NREL/CP-2C00-79036
Keywords
- Data-driven forecasting
- High penetrations of renewables
- Scenario-based optimization
- Stochastic optimization