Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control Behrang Alimohammadisagvand a,⇑ , Juha Jokisalo a , Simo Kilpeläinen a , Mubbashir Ali b , Kai Sirén a a HVAC Technology, Department of Energy Efficiency and Systems, School of Mechanical Engineering, Aalto University, P.O. Box 14400, FI-00076 Aalto, Finland b Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Finland highlights  The cost-optimal solution is obtained based on DR for thermal storage with a GSHP.  The three DR control algorithms are developed for space heating and storage tank.  The storage tank of the IDA ICE simulation tool is regulated by measured data.  The maximum savings of annual delivered energy and cost are 12% and 10%. article info Article history: Received 11 February 2016 Received in revised form 1 April 2016 Accepted 6 April 2016 Keywords: Demand response Thermal storage tank LCC Control algorithm Energy Building abstract This study aims to define a cost-optimal solution based on demand response (DR) actions for a thermal energy storage system with a ground source heat pump in detached residential houses in a cold climate. This study finds out the minimum life cycle cost (LCC) of thermal energy storage over the period of 20 years by observing different temperature set points (55–95 °C) and sizes (0.3–1.5 m 3 ) of a hot water storage tank with developed DR control algorithms. Three different control algorithms were studied: (A) a momentary DR control algorithm based on real-time hourly electricity price (HEP), (B) a backwards-looking DR control algorithm based on previous HEPs and (C) a predictive DR control algo- rithm based on future HEPs. This research was carried out with the validated dynamic building simula- tion tool IDA Indoor Climate and Energy. The results show that by using the predictive DR control algorithm the maximum annual savings in total delivered energy and cost are about 12% and 10%, respec- tively. The minimum LCC can be achieved by the smallest studied storage tank size of 0.3 m 3 with 60 °C as the temperature set point, but the effect of storage tank size on LCC is relatively small. Ó 2016 Elsevier Ltd. All rights reserved. 1. Introduction Buildings consume over 40% of the overall energy consumed in the world and play a major role in sustaining electric grid power balance [1,2]. Demand response (DR) control algorithms on build- ings have been widely accepted as effective methods to improve energy efficiency of buildings and to minimize energy consump- tion and cost [3,4]. DR control is an approach to match generated electricity and demand by controlling heating, ventilation and air conditioning (HVAC) systems and by reducing electricity demand when so required. Yang et al. [5] reviewed and discussed thermal comfort of the occupants and the implications for building energy efficiency. They identified a better understanding of thermal comfort and energy conservation in buildings based on social-economic, cultural stud- ies and consideration of future climate scenarios. Sehar et al. [6] used the predicted mean vote index to maintain thermal comfort of the occupants. One important feature of DR in relation to build- ings is the ability to maintain thermal comfort conditions by adjusting the various indoor temperature set points considered [7–9]. Demand for heating energy is decreased with increasing ther- mal mass, due to the beneficial effects of fabric energy storage [10]. For example, Kensby et al. [11] concluded that the heavy buildings can tolerate relatively large variations in heat deliveries while still maintaining a good indoor climate. Also, thermal energy storage has been shown to be advantageous in increasing energy efficiency of buildings and in reducing their energy cost [12–14]. Arteconi et al. [15] found that combination of the DR and storage tank can have the potential market. It can be noted that since the thermal energy storage is combined with different types of heat http://dx.doi.org/10.1016/j.apenergy.2016.04.013 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail address: Behrang.alimohammadi@aalto.fi (B. Alimohammadisagvand). Applied Energy 174 (2016) 275–287 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy