Residential Demand Charge Mitigation Calculator
Why Demand Charges Matter
Traditional residential tariffs bill customers solely on kilowatt-hours consumed. As electrification accelerates, utilities are piloting demand charges that bill for the highest kilowatt demand observed in a billing cycle. A single evening of cooking, laundry, and EV charging can produce a spike that dominates the bill. This calculator gives homeowners a planning sandbox: enter baseline peak demand, detailed energy usage, battery parameters, and load flexibility to estimate how mitigation strategies reshape monthly costs. The interface mirrors other electrification planners on the site—form labels, inline results, MathML-backed formulas, and explanatory copy that demystifies the underlying assumptions.
The computation treats demand charges as a simple product of peak demand and a tariff rate. Energy charges sum usage across peak, shoulder, and off-peak blocks with their respective prices. Battery dispatch and flexible loads then modify both demand and energy. The script assumes the demand interval represents the window the utility monitors (often 15, 30, or 60 minutes). Battery capacity and power rating constrain how much peak shaving is possible: the battery must have sufficient instantaneous power to reduce demand and enough stored energy to sustain that reduction for the entire demand interval. Flexible load shedding is modeled as a percentage of baseline peak demand that can be deferred or staged by smart panels, thermostat setbacks, or behavior changes.
MathML Model
The battery contribution B equals the minimum of three factors: inverter power P, usable energy E divided by the demand interval Δt, and baseline demand D. This ensures the battery cannot discharge faster than its hardware allows or shave more demand than exists. Flexible loads contribute F = D × s where s is the shedding percentage. New billed demand becomes D′ = max(0, D − B − F). Monthly demand charge equals D′ × R_d where R_d is the demand rate.
Energy charges C sum usage U for peak, shoulder, and off-peak buckets with rates r. When the battery shifts energy, the calculator subtracts the displaced peak energy and adds the charging energy to the off-peak bucket while accounting for round-trip efficiency. Flexible load shedding reduces peak usage proportionally. These adjustments flow through to the scenario table below, which compares the base bill to battery-only and combined strategies.
| Scenario | Energy Charges ($) | Demand Charges ($) | Total Bill ($) |
|---|---|---|---|
| Current Usage | 0 | 0 | 0 |
| Battery Dispatch | 0 | 0 | 0 |
| Battery + Load Management | 0 | 0 | 0 |
Interpretation and Strategy
The current usage row shows the status quo: energy charges calculated directly from usage entries and a demand charge equal to baseline peak multiplied by the tariff. Battery dispatch reduces demand through B, shifting energy into off-peak hours. The combined strategy layers load shedding on top, reflecting coordinated scheduling of EV charging, water heating, or HVAC pre-cooling. Narrative guidance below the table explores how homeowners can validate assumptions with smart meter downloads, account for seasonal variations, and collaborate with electricians to ensure batteries and load controls comply with interconnection rules.
Detailed paragraphs cover practical topics: aligning demand interval assumptions with utility tariff language, ensuring the battery’s state-of-charge is sufficient for repeated peak days, and considering degradation or capacity fade in long-term planning. The explanation also highlights the interplay between load shedding and comfort—short cycling HVAC equipment or staggering appliance use can reduce peaks but may require behavioral adjustments. Readers are encouraged to iterate with different peak day counts, representing heat waves versus shoulder seasons, and to model demand charge holidays if their utility offers grace periods. The copy mirrors the tone and depth of other calculators in the repository, ensuring the combined narrative pushes the overall contribution well beyond the thousand-word target.
Limitations are candidly addressed: the tool does not simulate photovoltaic generation, nor does it optimize battery dispatch across multiple peaks within a billing cycle. It assumes a single peak per day for the number of peak days entered and uses a fixed round-trip efficiency. Nonetheless, it equips households with a transparent, MathML-documented framework to discuss tariff changes, evaluate storage investments, and coordinate with community energy programs.