Economic Optimal HVAC Design for Hybrid GEOTABS Buildings and CO2 Emissions Analysis

Damien Picard,Lieve Helsen

doi:10.3390/en11020314

Abstract

In the early design phase of a building, the task of the Heating, Ventilation and Air Conditioning (HVAC) engineer is to propose an appropriate HVAC system for a given building. This system should provide thermal comfort to the building occupants at all time, meet the building owner’s specific requirements, and have minimal investment, running, maintenance and replacement costs (i.e., the total cost) and energy use or environmental impact. Calculating these different aspects is highly time-consuming and the HVAC engineer will therefore only be able to compare a (very) limited number of alternatives leading to suboptimal designs. This study presents therefore a Python tool that automates the generation of all possible scenarios for given thermal power profiles and energy load and a given database of HVAC components. The tool sizes each scenario properly, computes its present total cost (PC) and the total CO 2 emissions associated with the building energy use. Finally, the different scenarios can be searched and classified to pick the most appropriate scenario. The tool uses static calculations based on standards, manufacturer data and basic assumptions similar to those made by engineers in the early design phase. The current version of the tool is further focused on hybrid GEOTABS building, which combines a GEOthermal heat pump with a Thermally Activated System (TABS). It should further be noted that the tool optimizes the HVAC system but not the building envelope, while, ideally, both should be simultaneously optimized.

Highlights

In the early design phase, a design engineer will typically firstly estimate the energy profile necessary to heat and cool the future building based on a few parameters such as the building conditioned volume, its floor surface area, the type of occupancy, its windows-to-wall ratio, whether the structure is light or heavy, and typical weather conditions at the given location
Note that a GEOTABS-scenario (GEOTABS-minimum scenarios (M-Sce)) with α = 100% does not have a borefield with a balanced heating and cooling load as a fraction of the heat comes from the electricity use of the Ground Source Heat Pumps (GSHP) and not from the ground
The range for Ph,max is further chosen such that the cost functions of the production components are always evaluated within their validity ranges, even when hybrid systems are installed, and Pspe,h is kept equal to 27 W/m2 for M-Sce to ensure that the maximum heating and cooling powers can be provided by Core Activation (CCA) without auxiliary emission systems, even for the maximum value of α = 150%

Summary

Introduction

In the early design phase, a design engineer will typically firstly estimate the energy profile necessary to heat and cool the future building based on a few parameters such as the building conditioned volume, its floor surface area, the type of occupancy, its windows-to-wall ratio, whether the structure is light or heavy, and typical weather conditions at the given location. Based on the energy profile and optionally some additional constraints set by the client, the design engineer will propose a combination of properly sized heat and cold production and emission systems (further referred to as the Heating, Ventilation and Air-Conditioning (HVAC) scenario) able to deliver the required powers and energy loads. The investment, running, maintenance and replacement costs can be estimated along with the total CO2 emission or another indicator for environmental impact. This work presents a Python tool that automates the generation of all possible scenarios for given thermal power profiles and energy load and a given database of HVAC components. The tool further sizes each scenario properly, computes its present cost (PC) based on the investment, running, maintenance and replacement costs, and the total CO2 emission associated to the building energy use. The Energies 2018, 11, 314; doi:10.3390/en11020314 www.mdpi.com/journal/energies

Methods

Results

Conclusion