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CM: Economic Feasibility

Introduction

👉 Click here to access the online tutorial for this module

This Calculation Module (CM) enables users to conduct an economic and financial assessment of investments in space cooling (SC) facilities, considering various financing options. Users can either rely on default data on typical investment costs (€/kW) for different cooling technologies or input specific project data for a more tailored analysis. The module provides key financial insights based on cost-benefit analysis (CBA), evaluating projects under different financing support schemes.

This module offers two types of assessments:

  • National-level assessment: for policymakers and energy planners to analyze policy scenarios and investment strategies, focusing on the evolution of cooling demand and associated costs.
  • Building-level assessment: for property owners to evaluate cooling demand reductions and associated costs at an individual building scale.

The module works at NUTS1,NUTS2,NUTS3 and LAU2. However, it is to be noted that irrespective of the geographical selection, results are presented as national level average. The analysis can be done either to acquire National level for aggregated results or on Building levels for specific building types.

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Method

The approach allows the quantification of the effects of passive measures and technological advancements on space cooling demands, integrating environmental, economic, and societal impacts. The methodology is structured to utilize open-source data sets for evaluating EU-wide cooling demand trends, starting with a bottom-up estimation based on national building stock distribution. The process involves calibrating building physics parameters to reflect passive cooling measures, leading to estimates of Theoretical Useful Energy Demand (TUED) and Practical Useful Energy Demand (PUED). Specifically, the following parameters are estimated:

  • Theoretical Useful Energy Demand (TUED): This step models the potential cooling demand if all built floor area had space cooling technologies, serving as a baseline to assess energy savings from passive measures. This is taken as input from results generated by the Invert EE-lab model.
  • Diffusion Rates of Technology: The adoption rates of active cooling technologies are analyzed to translate theoretical potential into practical application, reflecting realistic market penetration. The diffusion rates are used to scale the TUED to practical useful energy demand.
  • Final Energy Demand: After considering the adoption of passive and active technologies, the actual energy consumption (Final Energy Demand) is estimated, accounting for different rates of technological advancement and the mix of technologies employed.
  • Impact of Passive Measures: The methodology rigorously accounts for the potential energy savings achievable through various scenarios of passive measure uptake, allowing for detailed projections of energy demand reductions across different EU regions and timeframes.
  • Economic Parameters: Based on the impact of the predefined scenarios of the active and passive measures, the module calculates costs for implementing such scenarios in terms of the cooling provided as well as the costs per unit of energy savings. The overall costs of the scenarios are categorized into three components:
    • Active CAPEX: The investment in new technology for each year is annualized over its lifetime.
    • Active OPEX: The operation costs of the technologies
    • Passive CAPEX: The investment costs on passive cooling measures

Based on these costs, the calculation module provides the economic indicators for the scenario in terms of the levelized cost of cooling supply and the levelized costs of savings in terms of the useful energy demand.

This approach not only provides insights into the direct impacts of cooling technologies but also highlights their broader implications for energy efficiency and social well-being within the EU-27 context.

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Input

The following input parameters must be selected:

  • Assessment type: The user can perform the assessment either on a national level (covers the overall cooling sector of the selected country) or a building level (provides financial indicators to a homeowner).
    • National-level assessments: allow users to compare demand trajectories under current policy frameworks with scenarios that envision both high and low uptake of active cooling technologies. It also examines how advancements in technology efficiency impact overall demand over time. For financial assessments, the tool evaluates the costs associated with energy savings and investments in active cooling technologies. Key parameters used in our analysis include the Levelized Cost of Cooling (LCOC) for cooling supply and the Levelized Cost of Energy Savings (LCES). By comparing these costs at a national scale, the tool helps determine the economic viability of investing in both passive and active cooling measures, guiding strategic decision-making in energy policy development.
    • Building-level assessment: enables users to select a specific building type within their region and define its size in square meters (m²). Based on these inputs, the module calculates the overall costs of cooling supply with the active measures, estimates the potential for energy savings, and provides an overview of the costs required to implement various passive cooling measures. This assessment supports informed decision-making by offering a first-level evaluation of economic feasibility, helping building owners explore cost-effective solutions for improving energy efficiency in their buildings.The module utilizes similar parameters as the national-level assessment, allowing for a consistent evaluation framework across different scales.
  • Passive measure efficiency: Based on literature and existing market shares of different passive cooling measures, options on pre-defined passive measures packages at different levels of efficiency can be selected (See Table Efficiency Levels of Passive Cooling Strategies). The future possibility of user-defined packages is foreseen.
  • Diffusion rates of Active Measures: The parameter indicates the rate of diffusion of cooling technologies. Scenarios can be developed based on different rates of diffusion of the technology.
  • Active measure efficiency: This parameter indicates the SEER values of the individual supply technology, allowing the user to define the development of cooling technologies.

Table Efficiency Levels of Passive Cooling Strategies

Category Baseline Moderate High
Shading No additional shading Manual shading Automated shading
Shading Technology Internal Venetian Blind Highly reflective inner screen External Venetian Blind
Window Glazing Single glazing window Double glazing low emissivity window Solar controlled window glazing
Night Ventilation No night ventilation Low level of night ventilation High level of night ventilation
Indoor Temperature Average indoor temperature Indoor temperature increase by 2°C Indoor temperature increased by 4°C

The user needs to select the input layer , i.e. the region where the analysis is to be performed. In case of an error regarding the size of the region, the user can select a sub-region (e.g. NUTS1) within the same country, which will still give results on the national level. The user needs to avoid selecting multiple regions at the same time.

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Output

The following outputs will be obtained from this analysis.

  • Indicators:
    • Total Final Energy demand from the scenario
    • Useful Energy saving potential from the selected scenario in the year 2050
    • Percentage Final Energy demand savings
    • Description of the Scenario
  • Charts:
    • Total Useful Energy demand of the scenario vs. Baseline for years 2030,2040 and 2050
    • Total Final Energy demand of the scenario vs. Baseline for years 2030,2040 and 2050
    • Break down of total investment in active and passive measures (scenario vs. Baseline)
    • Comparison of levelized cost of cooling (Passive - Euros per MWh of saved useful energy demand; Active - Euros per MWh of supplied useful energy demand)

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Sample Run

National Level

Here, the sample run of the calculation module for Czech Republic (Czechia) is performed on a national level.

Step 1: Area type selection

Select the NUTS1 on the top right area selection option.

Step 2: Region Selection option

Select the NUTS1 region Czechia.

Step 3: Go to the calculation module tab and select the CM Economic Feasibility.

Step 4: Provide input parameters

Now, we provide the input parameters to the CM (see Figure CM - Economic feasibility, National-level assessment, Input parameters, part 1 and Figure CM - Economic feasibility, National-level assessment, Input parameters, part 2). Here, we have simplified the user input requirements with a set of pre-defined combinations of technology and measures based on literature.

  • Enter the Session name, e.g. "Czechia national analysis".
  • Select the Assessment Type as National as we are performing a National level assessment. The results from the CM could be an input to the National Comprehensive Assessment Report.
  • Select the passive measure efficiency. Choose 'High Efficiency'. This means that we are assessing a scenario where we see a widespread application of passive measures with high efficiency (energy savings potential) in Austria up to 2050. The underlying assumptions and passive measure combination details are in the method section above.
  • Select Diffusion Rates of Active Measures. Choose 'Moderate Diffusion'. This means we are assessing a scenario where there is a moderate increase in the diffusion of technology until 2050. The underlying assumptions and rates of active measure uptakes are available in the method section above.
  • Select Active Measure Efficiency. Choose 'High Efficiency'. This means we are assessing a scenario where there is considerable improvement in the efficiency of the active cooling technology. The underlying assumptions and rates of active measure efficiency improvements are available in the method section above.
  • Select the Building type in [m2]. For National-level assessment, this parameter doesn't play a role.
  • Enter the Building Floor Area. This parameter is irrelevant for the National-level assessment
  • Define the discount rate. For this example, we assume 5%.
  • Lifetime of the technology and measures can be adjusted, based on literature and average value define this as 10 years for this example.

Figure CM - Economic feasibility, National-level assessment, Input parameters, part 1

Figure CM - Economic feasibility, National-level assessment, Input parameters, part 2

Step 5: Run the CM

Step 5: View and Download the Results

See Figure CM - Economic feasibility, National-level assessment, Example Results, Indicators and Figure CM - Economic feasibility, National-level assessment, Example Results, Charts. The following outpu types are available:

  1. Indicators:

    • Country name
    • Scenario Description
    • Baseline Seasonal Energy Efficiency Ratio (SEER): Based on literature the current SEER value for Czech Republic. Future scenarios are based on this.
    • Base year Useful energy demand for Base Scenario: Base Scenario refers to the no-change scenario. This is the comparison scenario where no passive and active measure uptake and no efficiency measures are assumed for the entire study horizon.
    • 2050 Useful energy demand for Base Scenario: This is the projected 2050 demand for the base scenario.
    • 2050 Useful energy demand for Selected Scenario: This is the projected 2050 demand for the scenario we have defined based on the input parameters.
    • Final Energy Demand (FED) savings estimated in the scenario 2050: Compared to the baseline, here are the savings in the final energy demand with the defined uptake and development of active and passive cooling measures.

  2. Charts:

Figure CM - Economic feasibility, National-level assessment, Example Results, Indicators

Figure CM - Economic feasibility, National-level assessment, Example Results, Charts

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Building Level

Step 1: Area type selection

Select the NUTS1 on the top right area selection option.

Step 2: Region Selection option

Select the NUTS1 region Czech Republic.

Step 3: Go to the calculation module tab and select the CM-Economic Feasibility

Step 4: Provide input parameters

Now, we provide the input parameters to the CM (see Figure CM - Economic feasibility, Building-level Assessment, Input parameters, part 1 and Figure CM - Economic feasibility, Building-level Assessment, Input parameters, part 2). Here, we have simplified the user input requirements with a set of pre-defined combinations of technology and measures based on literature.

  • Enter the Session name, e.g. "Czechia building analysis".
  • Select the Assessment Type as Building level as we are performing a building level assessment.
  • Select the passive measure efficiency. Choose 'High Efficiency'. This means that we are assessing a scenario where we see a widespread application of passive measures with high efficiency (energy savings potential) in the Czech Republic. The underlying assumptions and passive measure combination details are in the method section above.
  • Select Diffusion Rates of Active Measures: Here, for building level assessment, we just select "Yes- the presence of active cooling measure," indicating that the building under consideration has the existence of a cooling supply technology.
  • Select Efficiency of Active Measure: Here, you need to select the type of active measure, which is defined by the efficiency of the technology. A high level of efficiency indicates higher upfront investment but lower operation costs. Choose "Moderate Efficiency" for this sample run.
  • Select the building type: Here, you can choose the type of building based on which the space cooling demand for the building is calculated. Multiple residential and non-residential options of building archetypes can be selected. Choose "Multi Family Household".
  • Enter the gross floor area of the building: Here, you need to enter the total gross floor area of your building. Enter 600 square meters here.
  • Define the discount rate. For this example, we assume 5%.
  • Lifetime of the technology and measures can be adjusted, based on literature and average value define this as 10 years for this example.

Note: If in case you encounter an error in the Building level run of the CM, this is because the building stock data for this building type is missing in the database. This will updated when improved data is acquired. For now, we suggest using other similar building types to obtain tentative results.

Figure CM - Economic feasibility, Building-level Assessment, Input parameters, part 1

Figure CM - Economic feasibility, Building-level Assessment, Input parameters, part 2

Step 5: Run the CM

Step 6: Example Results

See Figure CM - Economic feasibility, Building-level Assessment, Example Results, Indicators and Figure CM - Economic feasibility, Building-level Assessment, Example Results, Charts. The results include:

  1. Indicators:

    • Country name
    • Scenario Description
    • Baseline Seasonal Energy Efficiency (SEER) Ratio: The value is based on literature (the current average SEER value for the Czech Republic.) Future scenarios are based on the scaling of these average SEER values.
    • Useful Energy Demand without interventions: This is the comparison scenario where no passive and active measure uptake and no efficiency measures are assumed for the entire study horizon.
    • Useful energy demand with selected active and passive measure: This is the calculated demand if there is the uptake of defined passive measure and technology improvement.
    • Useful Energy Demand Savings compared to Baseline: Percentage useful energy savings from the intervention of the measures.
    • FED Savings Compared to Baseline: Percentage of final energy savings from the intervention of the measures.

  2. Charts:

    • Chart 1: Comparison of Baseline and selected scenario.
    • Chart 2: Break down of the total investment required for the selected scenario compared against the baseline.
    • Chart 3: Comparison of levelized cost of cooling on useful energy demand of the two scenarios.

Figure CM - Economic feasibility, Building-level Assessment, Example Results, Indicators

Figure CM - Economic feasibility, Building-level Assessment, Example Results, Charts

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Repository of the calculation module

You can access the open-source code for this calculation module here.

How To Cite

Aadit Malla, in CoolLIFE-Wiki, CM Economic Feasibility

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Authors And Reviewers

This page was written by Aadit Malla EEG-TU WIEN.

This page was reviewed by Ardak Akhatova e-think.

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License

Copyright © 2024-2025: Aadit Malla

Creative Commons Attribution 4.0 International License

This work is licensed under a Creative Commons CC BY 4.0 International License.

SPDX-License-Identifier: CC-BY-4.0

License-Text: https://spdx.org/licenses/CC-BY-4.0.html

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Acknowledgement

We would like to convey our deepest appreciation to the LIFE Programme CoolLIFE Project (Grant Agreement number 101075405), which co-funded the present investigation.

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