Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller

Reino Ruusu*, Sunliang Cao, Benjamin Manrique Delgado, Ala Hasan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

Abstract

This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168%, or by 21–75% of the cost of imported electricity.

Original languageEnglish
Pages (from-to)1109-1128
Number of pages20
JournalEnergy Conversion and Management
Volume180
DOIs
Publication statusPublished - 15 Jan 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Energy cost minimization
  • Energy flexibility
  • Energy management
  • Model predictive control
  • Nonlinear optimization
  • Smart buildings

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