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

Shotaro Sato, Toru Namerikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages2304-2309
Number of pages6
Volume2018-July
ISBN (Electronic)9789881563941
DOIs
Publication statusPublished - 2018 Oct 5
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 2018 Jul 252018 Jul 27

Other

Other37th Chinese Control Conference, CCC 2018
CountryChina
CityWuhan
Period18/7/2518/7/27

Fingerprint

Interval Methods
Energy management systems
Prediction Interval
Energy Management
Energy Storage
Model predictive control
Model Predictive Control
Generator
Robustness
Scenarios
Optimization Theorems
Energy storage
Robust Optimization
Prediction
Costs
Copula
Marginal Distribution
Electricity
Joint Distribution
Express

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

Sato, S., & Namerikawa, T. (2018). Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators. In X. Chen, & Q. Zhao (Eds.), Proceedings of the 37th Chinese Control Conference, CCC 2018 (Vol. 2018-July, pp. 2304-2309). [8483209] IEEE Computer Society. https://doi.org/10.23919/ChiCC.2018.8483209

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 proceedingConference contribution

Sato, S & Namerikawa, T 2018, Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators. in X Chen & Q Zhao (eds), Proceedings of the 37th Chinese Control Conference, CCC 2018. vol. 2018-July, 8483209, IEEE Computer Society, pp. 2304-2309, 37th Chinese Control Conference, CCC 2018, Wuhan, China, 18/7/25. https://doi.org/10.23919/ChiCC.2018.8483209
Sato S, Namerikawa T. Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators. In Chen X, Zhao Q, editors, Proceedings of the 37th Chinese Control Conference, CCC 2018. Vol. 2018-July. IEEE Computer Society. 2018. p. 2304-2309. 8483209 https://doi.org/10.23919/ChiCC.2018.8483209
Sato, Shotaro ; Namerikawa, Toru. / Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators. Proceedings of the 37th Chinese Control Conference, CCC 2018. editor / Xin Chen ; Qianchuan Zhao. Vol. 2018-July IEEE Computer Society, 2018. pp. 2304-2309
@inproceedings{ca924b8634d64496a9849198bb08d2f3,
title = "Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators",
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.",
keywords = "Energy Management, Prediction Intervals, Robust Optimization",
author = "Shotaro Sato and Toru Namerikawa",
year = "2018",
month = "10",
day = "5",
doi = "10.23919/ChiCC.2018.8483209",
language = "English",
volume = "2018-July",
pages = "2304--2309",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
publisher = "IEEE Computer Society",

}

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 -