TY - GEN

T1 - Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators

AU - Sato, Shotaro

AU - Namerikawa, Toru

N1 - Publisher Copyright:
© 2018 Technical Committee on Control Theory, Chinese Association of Automation.

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

T3 - Chinese Control Conference, CCC

SP - 2304

EP - 2309

BT - Proceedings of the 37th Chinese Control Conference, CCC 2018

A2 - Chen, Xin

A2 - Zhao, Qianchuan

PB - IEEE Computer Society

T2 - 37th Chinese Control Conference, CCC 2018

Y2 - 25 July 2018 through 27 July 2018

ER -