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Demand response optimized heat pump control for service sector buildings

A Modular Framework for Simulation and Building Operation

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With an increasing amount of volatile renewable electrical energy, the balancing of demand and supply becomes more and more demanding. Demand response is one of the emerging tools in this new landscape. Targeting service sector buildings, we investigated a tariff driven demand response model as a means to shave electrical peak loads and thus reducing grid balancing energy. In this paper is presented a software framework for load shifting which uses a tariff signal for the electric energy as minimization target. The framework can be used both on top of an existing building management system to shift heat generation towards low-tariff times, as well as to simulate load shifting for different buildings, heat pumps and storage configurations. Its modular architecture allows us to easily replace optimizers, weather data providers or building management system adapters. Our results show that even with the current TOU tariff system, up to 34 % of cost savings and up to 20 % reduction in energy consumption can be achieved. With Sub-MPC, a modified MPC optimizer, we could reduce computing times by a factor 50, while only slightly affecting the quality of the optimization.

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Without the knowledge and financial support of our partners from SFOE (Swiss Federal Office of Energy), BKW, MeteoSchweiz, Siemens and Swissgrid, this work would not have been realized. This complete set of stakeholders in the field of DR gives iHomeLab the chance to derive simulation and controller software and corresponding models. Special thanks go to our colleagues Thierry Prud’homme and Stefan Ineichen from the CC Electronics of the Lucerne University of Applied Sciences and Arts, who provided the thermal building model.

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Correspondence to Edith Birrer.

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Birrer, E., Picard, C., Huber, P. et al. Demand response optimized heat pump control for service sector buildings. Comput Sci Res Dev 32, 25–34 (2017).

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  • ICT in buildings and housing
  • Building energy operating systems
  • Heating devices and energy networks
  • Demand response
  • Dynamic electricity prices
  • Load shifting
  • Simulation
  • Building automation