Medical Freezers as Flexible Load for Demand Response in a Business Park Microgrid with Local Solar Power Generation

Abstract

This work presents a day-ahead demand response (DR) scheduling framework that quantifies the flexibility in non-residential buildings by using thermodynamic modeling, and assesses the benefits of DR in terms of three separate optimization variants: net payment minimization, energy self-sufficiency, and peak power reduction. We test the framework in a case study of a medical research facility located in a business park with local solar power generation. The flexible loads are four groups of independently-controlled medical freezers. Our DR framework generates optimal freezer operation and solar power production/curtailment schedules that are compared against a business-as-usual scenario with no DR. We perform simulations for cases with and without end-ofhorizon temperature constraints. Results show that the flexibility harnessed from the freezers' thermal mass for DR actions improves the price-responsiveness, energy independence, and peak power consumption of the system with respect to the business-as-usual scenario. Furthermore, adding end-of-horizon constraints ensures that the thermal buffer of the flexible load will be full for the next simulation time window. Keywords: Demand response • Genetic algorithm • Smart grid • Local RES integration • Physical systems modeling This work was supported by Alliander N.V.