Chance-constrained Scheduling of Variable Generation and Energy Storage in a Multi-Timescale Framework
This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The...
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Main Authors: | , , |
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Format: | E-Article |
Language: | English |
Published: |
The Korean Institute of Electrical Engineers
2017
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Subjects: | |
Online Access: | http://ir.unimas.my/id/eprint/17323/1/Chance-constrained%20Scheduling%20of%20Variable%20Generation%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/17323/ http://doi.org/10.???/JEET.2017.12.3.1921 http://doi.org/10.???/JEET.2017.12.3.1921 |
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Institution: | Universiti Malaysia Sarawak |
Language: | English |
Summary: | This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS)
approach for generation scheduling of a power grid. SDMS considers flexible resource options
including conventional generation flexibility in a chance-constrained day-ahead scheduling
optimization (DASO). The prime objective of the DASO is the minimization of the daily production
cost in power systems with high penetration scenarios of variable generation. Furthermore, energy
storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO).
DASO simulation results are used as the base starting-point values in the hour-ahead online rolling
RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid
flexibility, to balance out the deviation between predicted and actual net load demand values.
Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the
effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load
demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the
framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind
forecasting. |
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