Abstract

Building performance simulation (BPS) is a powerful tool for engineers working in building design and heating, ventilation and air-conditioning. Many case studies using BPS investigate the potential of demand response (DR) measures with heat pumps. However, the models are often simplified for the components of the heat pump system (i.e. heat pump, electric auxiliary heater and storage tank) and for their interactions. These simplifications may lead to significant differences in terms of DR performance so that more comprehensive models for a heat pump system may be necessary. The contribution of this work is twofold. Firstly, this work investigates the influence of the modeling complexity of the heat pump control on different key performance indicators for the energy efficiency, the DR potential and the heat pump operation. To this end, the performance of six different heat pump controls is compared. Secondly, it describes the implementation of a comprehensive control for a heat pump system in BPS tools. This control is not often documented in the BPS literature and is error-prone. Generic pseudo-codes are provided, whereas IDA ICE is taken as an example in the case study. A predictive rule-based control is implemented to study price-based DR of residential heating. It is shown that a realistic operation of the heat pump system can be achieved using the proposed modeling approach. The results prove that the modeling complexity of the system control has a significant impact on the performance indicators, meaning that this aspect should not be overlooked. For some performance indicators, e.g. the annual energy use for heating and average water tank temperature, it is shown that a proportional (P-) and proportional-integral (PI-) control can lead to similar results. If the heat pump operation is investigated in detail and a short-time resolution is required, the difference between P- and PI-controls and their tuning is important. As long as the heat pump operation and electrical power at short timescales are not of importance, the choice of controller (P or PI) is not crucial. However, the use of P-control significantly simplifies the modeling work compared to PI-control. If DR is performed for domestic hot water, it is also demonstrated that the prioritization of domestic hot water heating can indirectly influence the operation of auxiliary heaters for space-heating, significantly increasing the use of electricity. However, the electricity use is only slightly increased if DR control is only used for space heating.

Highlights

  • Demand response (DR) measures can be applied to building energy systems to achieve load shifting and peak power reductions, according to reviews on demand side flexibility [1] and demand response [2,3]

  • This paper investigates the influence of the modeling complexity of the heat pump control in the context of demand response (DR) and building energy flexibility

  • The case of a detached single-family house heated using an air-source heat pump is analyzed with a price-based predictive rule-based control (PRBC)

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Summary

Introduction

Demand response (DR) measures can be applied to building energy systems to achieve load shifting and peak power reductions, according to reviews on demand side flexibility [1] and demand response [2,3]. Applied Energy 255 (2019) 113847 min MPC OHP OTCC P PI PMV PPD PRBC PV RTSP SH SCOP SP Ti TM TSP τc ZEB minimum model-predictive control on-off heat pump outdoor temperature compensation curve proportional proportional integral predicted mean vote predicted percentage dissatisfied predictive rule-based control photovoltaic reference temperature set-point space heating seasonal coefficient of performance spot price integral time temperature measurement temperature set-point tuning parameter for PI-controller tuning zero emission building activation of the building energy flexibility using (predictive) rulebased control ((P)RBC), e.g. A heat pump is connected to a water tank to store domestic hot water (DHW) or provide space heating (SH)

Model complexity and simplifications
Physical characteristics of heat pumps
Main contribution of the study
Implementation of a detailed heat pump system model
Tank: detecting DHW heating and SH requirements
Heat pump
Heat pump and tank interaction
Tank: control of the auxiliary heaters
Price-based demand response measures
For SH room
Case study on demand response and model complexity
Description of the case study
Control principle of a price-based PRBC for demand response
Modeling complexity
Findings
Conclusions
Full Text
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