Abstract
This chapter formulates an optimization problem that minimizes the microgrid operating cost. However, since some of the parameters are uncertain, e.g., wind turbine output, photovoltaics output, and market prices, the initial deterministic formulation is expanded to capture this uncertainty. Thus, stochastic, robust, and interval optimization models are presented as well. An illustrative case study is derived in order to show the mechanics and specifics of all four formulations.
| Original language | English |
|---|---|
| Title of host publication | Control of Power Electronic Converters and Systems |
| Editors | Frede Blaabjerg |
| Number of pages | 33 |
| Volume | 2 |
| Publisher | Academic Press |
| Publication date | 2018 |
| Pages | 201-233 |
| Chapter | 19 |
| ISBN (Print) | 978-0-12-816136-4 |
| DOIs | |
| Publication status | Published - 2018 |
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Keywords
- day-ahead market
- microgrid optimization
- interval optimization
- Robust optimization
- stochastic optimization
Cite this
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Control of Smart Grid Architecture. / Pandžić , Hrvoje; Dragicevic, Tomislav.
Control of Power Electronic Converters and Systems . ed. / Frede Blaabjerg. Vol. 2 Academic Press, 2018. p. 201-233.Research output: Contribution to book/anthology/report/conference proceeding › Book chapter › Research › peer-review
TY - CHAP
T1 - Control of Smart Grid Architecture
AU - Pandžić , Hrvoje
AU - Dragicevic, Tomislav
PY - 2018
Y1 - 2018
N2 - One of the main microgrid requirements is to operate in the most cost-effective way in a long run. Generally, microgrid, besides utilizing the local resources, can trade electricity in energy market, either directly or through an aggregator. Therefore, an optimization layer that optimizes microgrid resources with respect to their technical constraints, as well as external electricity prices, is essential for guiding the control layer toward the goal of operating at minimum cost. The optimization layer is usually a 24-h look-ahead optimization model, which is compatible with today's market structures and energy trading framework.This chapter formulates an optimization problem that minimizes the microgrid operating cost. However, since some of the parameters are uncertain, e.g., wind turbine output, photovoltaics output, and market prices, the initial deterministic formulation is expanded to capture this uncertainty. Thus, stochastic, robust, and interval optimization models are presented as well. An illustrative case study is derived in order to show the mechanics and specifics of all four formulations.
AB - One of the main microgrid requirements is to operate in the most cost-effective way in a long run. Generally, microgrid, besides utilizing the local resources, can trade electricity in energy market, either directly or through an aggregator. Therefore, an optimization layer that optimizes microgrid resources with respect to their technical constraints, as well as external electricity prices, is essential for guiding the control layer toward the goal of operating at minimum cost. The optimization layer is usually a 24-h look-ahead optimization model, which is compatible with today's market structures and energy trading framework.This chapter formulates an optimization problem that minimizes the microgrid operating cost. However, since some of the parameters are uncertain, e.g., wind turbine output, photovoltaics output, and market prices, the initial deterministic formulation is expanded to capture this uncertainty. Thus, stochastic, robust, and interval optimization models are presented as well. An illustrative case study is derived in order to show the mechanics and specifics of all four formulations.
KW - day-ahead market
KW - microgrid optimization
KW - interval optimization
KW - Robust optimization
KW - stochastic optimization
U2 - 10.1016/B978-0-12-816136-4.00019-1
DO - 10.1016/B978-0-12-816136-4.00019-1
M3 - Book chapter
SN - 978-0-12-816136-4
VL - 2
SP - 201
EP - 233
BT - Control of Power Electronic Converters and Systems
A2 - Blaabjerg, Frede
PB - Academic Press
ER -