District Energy Optimization Calculator

Inputs

Annual heating demand (MWh/yr)

Total useful heat delivered over the year.

Annual cooling demand (MWh/yr)

Total useful cooling delivered over the year.

Diversity factor (0–1)

Reduces coincident peak to reflect load diversity.

Peak heating load (kW)

Design peak for the district heating loop.

Peak cooling load (kW)

Design peak for the district chilled-water loop.

Heat source

Select the main heating technology to model.

Boiler efficiency (0–1)

Used only when heating source is boiler plant.

Cooling source

Use heat pump for cooling if reversible systems apply.

Chiller COP

Higher COP reduces electricity for cooling.

Heat pump COP

Applied when heat pump is selected for heating or cooling.

Distribution loss (%)

Thermal losses in distribution relative to delivered energy.

Pumping fraction (%)

Electricity as a fraction of delivered thermal energy.

Optimization goal

Balanced uses the weights below for cost and emissions.

Electricity price ($/kWh)

Use blended tariff if multiple rates apply.

Fuel price ($/kWh_fuel)

1 MMBtu ≈ 293.07 kWh; divide $/MMBtu by 293.07.

Electricity emissions (kgCO2/kWh)

Use your grid factor or contractual emission factor.

Fuel emissions (kgCO2/kWh_fuel)

Choose an appropriate factor for gas, oil, or biomass.

Cost weight (balanced)

CO2 weight (balanced)

Max COP gain (%)

Upper bound for plant efficiency improvements.

Max loss reduction (%)

Upper bound for network loss reduction actions.

Max pump reduction (%)

Upper bound for pumping and hydraulics improvements.

Thermal storage shift (%)

Percent of cooling electricity eligible for off‑peak shifting.

Off‑peak discount (%)

Cost reduction applied to shifted cooling electricity.

Reset

Example Data Table

Scenario	Heat (MWh/yr)	Cool (MWh/yr)	Loss (%)	Elec ($/kWh)	Fuel ($/kWh_fuel)
Mixed‑use campus	18,000	12,000	8	0.16	0.045
Healthcare district	26,000	9,500	10	0.19	0.050
High‑density residential	14,500	6,800	6	0.14	0.040

Use these examples to sanity‑check inputs before running optimization.

Formula Used

Distribution loss multiplier

LossMult = 1 + (Loss% / 100) × (1 − LossReduction% / 100)

Generated energy increases to cover network losses.

Pumping multiplier

PumpMult = 1 + (Pump% / 100) × (1 − PumpReduction% / 100)

Pumping electricity scales with delivered thermal energy.

Cooling electricity

ElecCool = Qc_gen / COP_cool_eff

Higher COP reduces electricity for the same cooling.

Boiler fuel

FuelHeat = Qh_gen / BoilerEff

Efficiency reduces fuel input for delivered heat.

Annual cost

Cost = Elec_kWh×Price + Fuel_kWh×Price − ShiftDiscount

ShiftDiscount applies to eligible off‑peak cooling electricity.

CO2 emissions

CO2 = Elec_kWh×ElecEF + Fuel_kWh×FuelEF

Use local emission factors for reliable comparisons.

How to Use This Calculator

Enter annual heating and cooling demand from your load model.
Set peak loads and a diversity factor for coincident sizing.
Choose plant types and enter realistic performance values.
Estimate distribution losses and pumping fraction for the network.
Add local energy prices and emission factors for your location.
Select an optimization goal: cost, emissions, or balanced scoring.
Set improvement bounds to reflect feasible project interventions.
Click Optimize, then download CSV or PDF if needed.

Project Guidance Article

1) Define the annual energy balance

Start with verified annual heating and cooling loads from a calibrated model. For a mixed-use campus, 18,000 MWh/yr heating and 12,000 MWh/yr cooling are plausible early-stage values. Convert to kWh for transparent calculations and keep seasonal profiles available for later refinement and commissioning. Pair annual totals with peaks, such as 9,000 kW heating and 8,000 kW cooling, then apply a 0.85 diversity factor to estimate coincident plant sizing.

2) Select realistic plant performance targets

Plant performance is the largest lever in most districts. A chiller COP of 5.2 and heat pump COP of 3.6 represent efficient modern equipment under favorable temperatures. The optimization tests up to a 20% COP gain, which can reflect improved controls, condenser water resets, heat recovery, or equipment replacement strategies.

3) Quantify distribution losses and pumping effort

Network losses often range from 6% to 12% depending on pipe length, insulation, and operating temperatures. If losses are 8% and pumping is 2.5% of delivered thermal energy, the model increases generated energy using a loss multiplier and adds electrical pumping energy. Reducing losses by 40% lowers wasted thermal generation and improves financial and emissions outcomes.

4) Incorporate tariffs and carbon factors for decisions

Use local prices and emission factors so comparisons remain credible. Example tariffs of $0.16 per kWh electricity and $0.045 per kWh fuel highlight why heat pumps can reduce fuel consumption but may increase electrical demand. With a grid factor of 0.55 kgCO2/kWh, savings depend on both efficiency and decarbonization pathways.

5) Use optimization outputs to set construction actions

Translate recommended improvements into actionable scope. A 30% pumping reduction may imply balancing valves, variable-speed drives, pipe resizing, or improved differential pressure control. A 10% storage shift represents chilled-water storage or operational load shifting that captures off-peak discounts. Document baseline versus optimized cost and CO2 to prioritize procurement, phasing, and performance verification.

FAQs

1) What does “optimization” mean in this calculator?

It searches combinations of efficiency gains, loss reductions, pumping reductions, and storage shifting within your limits, then selects the option that best meets your chosen goal.

2) Why does reducing distribution loss change both cost and emissions?

Lower losses reduce generated thermal energy needed to deliver the same load. That cuts boiler fuel or heat-pump electricity, which reduces operating cost and associated CO2.

3) Does thermal storage reduce energy use here?

In this simplified model, storage mainly reduces cost by moving eligible cooling electricity to discounted periods. Energy use is unchanged, but operational scheduling can lower utility bills.

4) How should I choose a diversity factor?

Use measured or simulated coincidence of building peaks. Districts with mixed occupancy often use 0.7–0.9. If uncertain, start with 0.85 and refine using load profiles.

5) When should I model heating with a heat pump instead of a boiler?

Choose a heat pump when low-carbon electricity or heat recovery is available and the project can support suitable supply temperatures. Compare cost and CO2 under your local tariffs and factors.

6) What inputs most affect results?

Annual loads, plant COP or boiler efficiency, distribution loss percentage, electricity and fuel prices, and emission factors typically dominate outcomes. Keep these inputs evidence-based whenever possible.

7) Can I use this for detailed design?

It is best for early planning, option screening, and documentation. For final design, use hourly simulations, hydraulic modeling, and equipment selection to confirm performance and controls.