Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller. (15th January 2019) - Ruusu, Reino; Cao, Sunliang; Manrique Delgado, Benjamin; Hasan, Ala
Highlights: Novel energy management system for strongly nonlinear energy systems. Economic model-predictive control using successive linear programming. Continuous approximation in optimization of discrete control problem. Flexible system modelling framework in Matlab. Whole year simulation study of EMS performance on a building energy system. 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.