Experimental demonstration of grid-supportive scheduling of a polygeneration system using economic-MPC

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Parantapa Sawant - , Offenburg University of Applied Sciences (Author)
  • Adrian Bürger - , University of Freiburg, University of Applied Sciences Karlsruhe (Author)
  • Clemens Felsmann - , Chair of Building Energy Systems and Heat Supply (Author)
  • Jens Pfafferott - , Offenburg University of Applied Sciences (Author)

Abstract

Drawing off the technical flexibility of building polygeneration systems to support a rapidly expanding renewable electricity grid requires the application of advanced controllers like model predictive control (MPC) that can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints amongst other features. In this original work, an economic-MPC-based optimal scheduling of a real-world building energy system is demonstrated and its performance is evaluated against a conventional controller. The demonstration includes the steps to integrate an optimisation-based supervisory controller into a standard building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms for solving complex nonlinear mixed integer optimal control problems. With the MPC, quantitative benefits in terms of 6–12% demand-cost savings and qualitative benefits in terms of better controller adaptability and hardware-friendly operation are identified. Further research potential for improving the MPC framework in terms of field-level stability, minimising constraint violations, and inter-system communication for its deployment in a prosumer-network is also identified.

Details

Original languageEnglish
Article number111619
Number of pages18
JournalEnergy and buildings
Volume2022
Issue number254
Publication statusPublished - 1 Jan 2022
Peer-reviewedYes

Keywords

Sustainable Development Goals

Keywords

  • Mixed integer nonlinear optimal control, Model predictive control, Optimal scheduling of energy systems, Real-world trigeneration, Sector-coupling

Library keywords