Abstract
This paper addresses photovoltaics (PV) power prediction and energy storage problem which are known to be a key technology in energy management systems (EMS). Extending results of the point prediction of PV power, we first describe a prediction interval (PI) method using a copula, which can express the relation between a multivariable joint distribution and each marginal distribution. Then, resorting to the PI method, the energy storage optimization problem in a building is developed. A scenario robust (SR) optimization theorem, which calculates the robustness of the optimal solution, is applied to the proposed PI method, and hence we obtain an optimal energy storage solution taking the robustness of the solution into account. Additionally, we propose a method which combines a model predictive control (MPC) technique and SR to reduce the total electricity costs. The simulation results finally illustrate the cost reduction and robustness of the proposed method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 37th Chinese Control Conference, CCC 2018 |
| Editors | Xin Chen, Qianchuan Zhao |
| Publisher | IEEE Computer Society |
| Pages | 2304-2309 |
| Number of pages | 6 |
| Volume | 2018-July |
| ISBN (Electronic) | 9789881563941 |
| DOIs | |
| Publication status | Published - 2018 Oct 5 |
| Event | 37th Chinese Control Conference, CCC 2018 - Wuhan, China Duration: 2018 Jul 25 → 2018 Jul 27 |
Other
| Other | 37th Chinese Control Conference, CCC 2018 |
|---|---|
| Country | China |
| City | Wuhan |
| Period | 18/7/25 → 18/7/27 |
Fingerprint
Keywords
- Energy Management
- Prediction Intervals
- Robust Optimization
ASJC Scopus subject areas
- Computer Science Applications
- Control and Systems Engineering
- Applied Mathematics
- Modelling and Simulation
Cite this
Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators. / Sato, Shotaro; Namerikawa, Toru.
Proceedings of the 37th Chinese Control Conference, CCC 2018. ed. / Xin Chen; Qianchuan Zhao. Vol. 2018-July IEEE Computer Society, 2018. p. 2304-2309 8483209.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators
AU - Sato, Shotaro
AU - Namerikawa, Toru
PY - 2018/10/5
Y1 - 2018/10/5
N2 - This paper addresses photovoltaics (PV) power prediction and energy storage problem which are known to be a key technology in energy management systems (EMS). Extending results of the point prediction of PV power, we first describe a prediction interval (PI) method using a copula, which can express the relation between a multivariable joint distribution and each marginal distribution. Then, resorting to the PI method, the energy storage optimization problem in a building is developed. A scenario robust (SR) optimization theorem, which calculates the robustness of the optimal solution, is applied to the proposed PI method, and hence we obtain an optimal energy storage solution taking the robustness of the solution into account. Additionally, we propose a method which combines a model predictive control (MPC) technique and SR to reduce the total electricity costs. The simulation results finally illustrate the cost reduction and robustness of the proposed method.
AB - This paper addresses photovoltaics (PV) power prediction and energy storage problem which are known to be a key technology in energy management systems (EMS). Extending results of the point prediction of PV power, we first describe a prediction interval (PI) method using a copula, which can express the relation between a multivariable joint distribution and each marginal distribution. Then, resorting to the PI method, the energy storage optimization problem in a building is developed. A scenario robust (SR) optimization theorem, which calculates the robustness of the optimal solution, is applied to the proposed PI method, and hence we obtain an optimal energy storage solution taking the robustness of the solution into account. Additionally, we propose a method which combines a model predictive control (MPC) technique and SR to reduce the total electricity costs. The simulation results finally illustrate the cost reduction and robustness of the proposed method.
KW - Energy Management
KW - Prediction Intervals
KW - Robust Optimization
UR - http://www.scopus.com/inward/record.url?scp=85056115618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056115618&partnerID=8YFLogxK
U2 - 10.23919/ChiCC.2018.8483209
DO - 10.23919/ChiCC.2018.8483209
M3 - Conference contribution
AN - SCOPUS:85056115618
VL - 2018-July
SP - 2304
EP - 2309
BT - Proceedings of the 37th Chinese Control Conference, CCC 2018
A2 - Chen, Xin
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
ER -