ENEFIRST Plus' CBA Excel Tool

ENEFIRST Plus has received funding from the European Union’s LIFE programme under grant agreement No 101120880 1 / 7 Technical Model Description Enefirst Plus CBA Excel Tool for Local Heating Planning Purpose and relationship to the Guidance The Enefirst Plus CBA Excel Tool for local heating planning is an Excel-based calculation model that operationalises key concepts from the guidance “Making Informed Decisions – A Practitioner’s Guide to Capturing the EE1st Principle in Cost–Benefit Analysis” for the case of residential space and water heating. The tool is designed to: • Compare, for a given residential building stock, Energy Efficiency First (EE1st) options with NoEff (limited-efficiency) options on a consistent cost–benefit basis. • Illustrate in practice how to: o apply societal and private CBA perspectives, o identify the energy-efficiency gap, and o test simple policy levers that align private decisions with the societal optimum. Its primary role is educational and illustrative. It is methodologically robust and grounded in existing EU data and methods, but it is not intended to replace specialised planning models or to serve as the sole basis for formal national/regional strategies. The tool complements the Guidance; it is accompanied by this Model Description, which documents its scope, assumptions, methods, and limitations. Scope and system boundaries Geographic and sectoral scope • Sector: Residential buildings only; commercial and industrial sectors are outside the scope. • Geographic coverage: EU-27 Member States. The user defines a local building stock based on national archetypes and default data for each country. • Level of analysis: Local / municipal / regional heating planning for existing building stocks. No explicit intra-municipal spatial zoning is modelled. Building stock segmentation The building stock is represented as the combination of: • 4 building types: o single-family, o terraced, o multi-family, o apartment buildings; • 7 construction periods / age classes, following the TABULA typology. 2 / 7 Users specify the number of buildings in each type–age segment to reflect their local context. The physical characteristics of each segment (geometry, U-values, insulation level, etc.) are taken from predefined archetypes and are not user-edited in the standard version. Technologies and measures represented The tool distinguishes two overarching decision “spaces”: • EE1st option space o Envelope retrofits: ▪ Light retrofit (moderate improvement in thermal performance) ▪ Deep retrofit (substantial improvement in thermal performance) o Efficient heating systems: ▪ Air-to-water heat pumps ▪ Ground-source heat pumps • NoEff option space o Continued standard maintenance of the existing envelope (no substantial performance improvement) o Conventional boilers: ▪ gas boilers, ▪ oil boilers, ▪ biomass boilers. For each boiler type, users can define a time-varying fuel blend, e.g.: • Gas: natural gas, biomethane, hydrogen, synthetic methane. • Oil: fossil fuel oil, bio-oil, synthetic oil. • Biomass: pellets, wood chips. Out-of-scope elements (current version) The current version does not include: • District heating systems (networks, central plants and losses); • On-site renewables (PV, solar thermal, etc.); • Smart-building controls and demand-side flexibility (no explicit load shifting or peak reduction module); • Commercial and public buildings or industrial heat uses. Energy efficiency solutions (EE1ST) = Demand-side options that reduce the energy required for the same service or convert it far more efficiently Standard retrofit packages Ground-water heat pumps Air-water heat pumps Deep retrofit packages Refurbishment /maintenance without energy improvement Biomass boilers (wood chips, wood pellets) Oil boilers (fuel oil, synthetic fuel, biofuel) Gas boilers (natural gas, H2, P2G, biomethane) EE1st requires testing demand-side options as full alternatives to supply-side status quo. Comparing least-cost EE1ST vs least-cost NOEFF per segment (age class × building type) reveals the value of efficiency, plus the efficiency gap to private choices. 3 / 7 Time structure and discounting Assessment horizon Users select a decision year between 2024 and 2050. The default evaluation horizon is 20 years (e.g. 2025–2045), but this can be modified. Time resolution All quantities are annual (annual heat demand, annualised investments, yearly operating costs, yearly emissions). No seasonal or hourly time slices are represented. Discount rates The tool applies constant discount rates over the horizon, separately for: • Societal perspective: default 3%, • Private perspective: default 6%. Users can overwrite these defaults. Data, demand modelling and technology representation Heat demand calculation Baseline useful heat demand per building archetype is calculated via the TABULA Calculation Method [1] using: • Pre-defined building geometries, • U-values and insulation levels by age class, • Climate data per Member State. This yields annual useful heat (MWh) per building for space heating. Total useful heat demand is obtained by multiplying per-building demand by the number of buildings in each segment. Users cannot directly change envelope parameters (e.g. U-values) in the standard interface, but they can adjust the building stock composition. Baseline heating technologies For each segment, a baseline heating system is defined (typically gas boiler, sometimes oil boiler). These baselines reflect current dominant technologies; they form the starting point for the NoEff and EE1st comparisons. The tool does not model stepwise renovation. It evaluates a single investment decision (“before vs after”) at a given point in time. Technology performance Heat pumps Performance is represented through a seasonal performance factor (SPF). SPF values are derived parametrically based on: • average ambient temperature (by country), • assumed supply/return temperatures for each building segment. 4 / 7 Hourly temperature profiles and detailed COP curves are not modelled explicitly; the approach is intentionally simplified but consistent. Boilers Fuel-specific efficiencies and cost/emission factors are attached to each boiler type and fuel blend. Users can modify the shares of fossil vs renewable vs synthetic fuels in each blend over time. Cost–benefit calculation and perspectives Cost categories and externalities For each measure and building segment, the model quantifies and discounts: Investment costs • Equipment (envelope measures, heat pumps, boilers), • Installation, • Design and project development, • Decommissioning / end-of-life where applicable. Operation and maintenance (O&M): • Fixed O&M (e.g. €/kW·year, €/building·year), • Variable O&M where relevant. Energy and network costs: • Fuel and electricity purchases, • Network charges (e.g. gas and electricity network tariffs). Public charges and transfers • Energy and equipment taxes, • CO₂ prices / ETS payments, • Other levies and surcharges. Monetised externalities and co-benefits: • Climate externalities via a Social Cost of Carbon or shadow price trajectory; • Air pollutant damage costs associated with fossil and biomass combustion; • Health impacts of thermal comfort changes: thermal-discomfort-related morbidity and mortality are monetised based on changes in indoor conditions induced by renovation and efficient heating. Programme administration costs (e.g. for public support schemes) are not explicitly represented. Energy prices and emission trajectories Energy prices and emission factors are pre-filled for each EU-27 Member State and are user-adjustable. The model distinguishes: • Energy commodity price, • Network tariffs, • CO₂ prices, • Other energy taxes and levies. 5 / 7 Two default pathways are provided: • BAU (business-as-usual), and • NetZero (trajectory consistent with climate-neutrality by 2050). These affect both energy prices and emission factors. The tool uses annual average values only; timevarying marginal prices or emission factors are not modelled. Societal vs private perspectives The tool implements two distinct perspectives in line with the Guidance: Societal perspective • Includes real resource costs (capex, O&M, energy, networks) and externalities (climate, air pollution, health). • Excludes transfer payments such as taxes, levies, subsidies and ETS payments, which are treated as redistributions rather than net resource use. Private perspective • Reflects the cash flows of the decision-maker (owner-occupier), • Includes taxes, levies, subsidies and carbon-price payments, as these drive investment decisions. Workflow Analytical workflow The workbook is organised into four user-facing steps: Step 1: Societal added value of energy efficiency For each building segment, identify the least-cost EE1st option and least-cost NoEff option from the societal perspective and compare them. Key outputs: ▪Why: Show that retrofit + heat pumps can deliver lower total societal cost and impacts. ▪How: For each building segment, compare the least-cost EE1st option vs least-cost NoEff option under the societal perspective (externalities in, transfers out). ▪Result: Lifetime net benefit (NPV), levelised heat cost (LCOH), cost-effective useful/final energy savings (%; GWh) - plus a chart of which segments deliver the most value. ❹Non-monetary dashboards ❶Societal added value of energy efficiency ❷The energy-efficiency gap (societal vs private) ❸Closing the gap with policy levers ▪Why: Private choices (owner-occupiers) often diverge from the social optimum due to taxes, tariffs, financing, and unpriced externalities. ▪How: P ’ private least-cost option and compare to the socially optimal option ▪Result: Unrealised cost-effective savings (GWh) and Gap % showing where socially cost-effective savings are not realised privately. ▪ : ▪ : ▪ : ▪Why: Some factors matter but are hard to monetise—security of supply and affordability. ▪How: Display energy security (import exposure, price-volatility indicators) and energy poverty metrics alongside core CBA results. ▪Result: A balanced view that pairs CBA with practical policy considerations for local heating plans. 6 / 7 • Net present value (NPV) of EE1st vs NoEff, • Levelised cost of heat (LCOH), • Cost-effective energy savings (GWh, %), • Charts showing which segments deliver the highest societal net benefits. Step 2: The energy-efficiency gap Compare the private least-cost option with the socially optimal option for each segment. Outputs: • Unrealised cost-effective savings (GWh), • Indicators of the share of cost-effective savings not realised under current price and tax conditions. Step 3: Closing the gap with policy levers Users adjust “slider” parameters for grants, carbon prices, surcharges, soft-loan interest rate reductions and network-tariff rebates. The model recalculates when the private least-cost choices switch to EE1st-consistent options and reports: • Gap closed (%), • Required instrument level (e.g. €/m², €/MWh) Step 4: Non-monetary dashboards Presents selected non-monetary indicators, notably: • Energy security / import-exposure proxies, • Energy poverty metrics, including an estimate of people lifted out of energy poverty, based on an approach inspired by the MICATool. Uncertainty and scenario handling The model is deterministic. There are no probabilistic features. Users explore uncertainty by: • Duplicating the workbook for different assumptions (e.g. BAU vs NetZero prices, alternative discount rates, technology cost changes); • Adjusting inputs manually to create low/central/high or policy scenarios. No automated sensitivity charts (e.g. tornado diagrams) are currently implemented. Implementation and data sources • Platform: Microsoft Excel with a small number of macros/VBA for “Calculate” buttons; core calculations use standard formulas. Macros must be enabled for full functionality. • Structure: four main user sheets for the workflow, plus hidden data sheets (prices, building archetypes, technology data, climate). • Default data sources include Eurostat, Danish Energy Agency (DEA) technology catalogues and EU/academic studies (e.g. “Clean Planet for All”). Key limitations and appropriate use • Annual time step only; no hourly or seasonal resolution; no peak capacity or network reinforcement modelling; 7 / 7 • Average, not marginal, emission factors and prices; no explicit power-system feedbacks from additional electricity demand; • No staged renovation or replacement cycles; only a single decision point; • No representation of district heating, on-site renewables or demand-side flexibility beyond average energy savings. The tool is well suited as a learning and screening instrument that applies the EE1st CBA concepts to local residential heating. For detailed network planning, power-system analysis, or formal national strategies, it should be complemented by more specialised models. References [1] Loga T, Diefenbach N, Dascalaki E, Balaras C, Zavrl M, Rakušček A et al. TABULA Calculation Method: Energy Use for Heating and Domestic Hot Water. Reference Calculation and Adaptation to the Typical Level of Measured Consumption. Darmstadt: Institut Wohnen und Umwelt (IWU); 2013.