Optimal energy management via MPC considering photovoltaic power uncertainty

Toru Namerikawa, Shunsuke Igari

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

2 Citations (Scopus)

Abstract

In this paper, we propose a method that uses model predictive control (MPC) to predict photovoltaic (PV) power generation, plan for the electricity demand in a building using the predicted value, and apply it online to correct the prediction error. First, we construct the regression model using a PV experimental unit and past data obtained from the Meteorological Agency. Next, we predict the PV power using grid point power (GPV) data of the next day. Second, the air conditioning or heating of the building is modeled to determine the electricity demand so that it increases the profits to the consumer and reduces the peak in time-varying electric cost. The error between the predicted and true value is considered via MPC. Finally, we show the advantages of the proposed method by performing simulations.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-62
Number of pages6
ISBN (Electronic)9781509040759
DOIs
Publication statusPublished - 2016 Dec 8
Event7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 - Sydney, Australia
Duration: 2016 Nov 62016 Nov 9

Other

Other7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
CountryAustralia
CitySydney
Period16/11/616/11/9

Fingerprint

Energy Management
Model predictive control
Energy management
Model Predictive Control
Electricity
Uncertainty
Air conditioning
Power generation
Predict
Profitability
Prediction Error
Heating
Conditioning
Profit
Regression Model
Time-varying
Grid
Costs
Unit
Simulation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Control and Optimization
  • Signal Processing

Cite this

Namerikawa, T., & Igari, S. (2016). Optimal energy management via MPC considering photovoltaic power uncertainty. In 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 (pp. 57-62). [7778738] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SmartGridComm.2016.7778738

Optimal energy management via MPC considering photovoltaic power uncertainty. / Namerikawa, Toru; Igari, Shunsuke.

2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 57-62 7778738.

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

Namerikawa, T & Igari, S 2016, Optimal energy management via MPC considering photovoltaic power uncertainty. in 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016., 7778738, Institute of Electrical and Electronics Engineers Inc., pp. 57-62, 7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, 16/11/6. https://doi.org/10.1109/SmartGridComm.2016.7778738
Namerikawa T, Igari S. Optimal energy management via MPC considering photovoltaic power uncertainty. In 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 57-62. 7778738 https://doi.org/10.1109/SmartGridComm.2016.7778738
Namerikawa, Toru ; Igari, Shunsuke. / Optimal energy management via MPC considering photovoltaic power uncertainty. 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 57-62
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