Demand Response Simulation Tool | Knowledge Base

Overview

A simulation tool that runs a full annual time-series simulation of a heat pump water heater under one or more demand response control strategies, then compares results against an uncontrolled baseline. The user configures a DR scenario (timer control, controlled load tariff, dynamic signal response) and receives a detailed breakdown of how the control strategy changes annual energy cost, heat pump vs boost element usage, and the volume of load shifted into or out of peak periods.

The primary audience is EnergyAE consultants assessing DR value propositions for manufacturer clients, and potentially manufacturers evaluating the impact of DR functionality on their product’s energy star rating or scheme entitlement. Access is gated to registered users; this is not a public tool.

User Stories

As a consultant, I want to simulate a VPP-enrolled HPWH under a representative DR signal schedule and see the annual cost impact vs uncontrolled operation so I can quantify the value proposition for a client.
As a manufacturer, I want to test how adding a timer lockout to my product affects annual energy consumption and COP so I can understand any unintended efficiency trade-offs.
As a consultant working on a grid flexibility study, I want to see how much kWh can be shifted from a 7am-9am morning peak across a fleet of HPWHs so I can estimate aggregate demand reduction potential.
As a user, I want to save a DR simulation against a product so I can compare multiple control strategies for the same product.

Context: Demand Response Strategies

The tool must support at least the following DR control modes. Alastair to confirm which are in scope for v1 and provide any additional modes required.

Controlled load (off-peak timer): HP and/or boost element only permitted to operate during defined hours (e.g. 10pm–6am). Represents the most common existing DR arrangement in AU/NZ.

Morning peak lockout: HP and boost locked out during a defined morning peak window (e.g. 7am–9am). Represents a soft DR signal without off-peak restriction.

Time-of-use tariff optimisation: System uses a TOU tariff schedule and shifts heating to the cheapest period. Represents what a smart controller could achieve with price visibility.

Dynamic DR signal schedule: User defines a weekly schedule of DR event hours (e.g. 2-hour event on 50 weekdays per year) during which the HP is locked out. Represents a VPP or network signal-based DR arrangement.

Pre-heat strategy: HP activates ahead of a defined peak window to pre-heat the tank, then locks out during the peak. Paired with any of the above.

Inputs

Product parameters

Same as the performance simulator and residential simulation tool. Sourced from the product database (preferred) or entered manually.

Climate and usage profile

Ambient temperature profile: full-year hourly profile (select from pre-defined climate profiles or enter a constant ambient)
Draw-off profile: pre-defined household profiles or custom daily volume and timing

Baseline control settings

Standard operating control: HP-only or HP + boost
Standard setpoint (°C)
Thermostat cut-in differential (°C)

DR control scenario

DR mode: select from the list above (one or more can be stacked)
For timer/lockout modes: define permitted operating hours as a weekly schedule (hour-by-hour for each day of week)
For TOU tariff optimisation: input tariff schedule (peak, shoulder, off-peak rates by hour)
For dynamic DR signal: define signal event hours per year and average duration
Pre-heat strategy: yes/no, and if yes, how many hours ahead of peak window

Tariff inputs (for cost calculation)

Flat tariff rate ($/kWh) for baseline comparison
TOU tariff rates (peak/shoulder/off-peak $/kWh) if TOU mode selected
Controlled load rate ($/kWh) if applicable

Simulation Outputs

All outputs shown as comparison between DR scenario and uncontrolled baseline.

Annual energy summary

Metric	Baseline	DR Scenario	Change
Annual HP compressor energy (kWh)
Annual boost element energy (kWh)
Annual total energy consumed (kWh)
Average annual COP
Annual energy cost ($)

Demand flexibility metrics

Total kWh shifted out of peak periods (annual)
Total kWh shifted into off-peak periods (annual)
Number of days the tank temperature dropped below setpoint due to DR lockout (comfort impact)
Number of cold water delivery events caused by DR lockout
Peak demand reduction per unit (kW) during the defined peak window — average and maximum

Time-series chart

A representative week’s simulation (user-selectable week) showing:

Tank temperature over time for both baseline and DR scenario (overlaid)
HP operating status (on/off) for both scenarios
Boost element operating status for both scenarios
DR event windows marked as shaded bands

Monthly breakdown

Bar chart showing monthly energy cost for baseline vs DR scenario. Seasonal variation in DR effectiveness is often significant.

Fleet Scaling

User can input an estimated fleet size (number of units). Tool scales per-unit results to fleet level:

Total kWh shifted annually across fleet
Total annual cost savings (or cost impact) across fleet
Peak demand reduction (kW) at fleet level during peak window

Useful for network operator studies and VPP business case development.

Out of Scope (v1)

Real-time DR signal integration (simulated historical signal schedule only in v1)
Multi-zone household simulation (single zone only)
Battery or solar PV interaction modelling
Optimisation engine (user defines the strategy, tool simulates it; no auto-optimise in v1)
Export as a structured DR technical report
CO2 emissions impact (add in v2)

Data Model (indicative)

dr_simulation_runs
  run_id
  product_id (nullable)
  user_id
  created_at
  baseline_parameters (JSON: product params, climate, draw-off, control settings)
  dr_scenario_parameters (JSON: DR mode, schedule, tariff inputs)
  output_baseline (JSON: annual energy, cost, COP)
  output_dr_scenario (JSON: annual energy, cost, COP, kwh_shifted, comfort_events)
  output_timeseries_week (JSON: representative week time-series for both scenarios)
  output_monthly (JSON array: month, baseline_cost, dr_cost)
  fleet_size (nullable)

Acceptance Criteria

Open Questions

Does the existing Python simulation engine support DR lockout schedules as an input parameter? Alastair to confirm before build begins — if not, this feature requires engine modifications which changes the scope significantly.
What is the representative peak event schedule for a VPP scenario? Alastair to define a standard reference case for acceptance testing.
Should the tool support stacking multiple DR strategies (e.g. controlled load off-peak timer plus a separate morning peak lockout)? This adds significant input complexity.
Is a pre-heat strategy (heating ahead of a lockout window) implemented in the existing engine or would it need to be added?
For the cold water delivery metric: what is the minimum delivery temperature threshold that defines a “cold water delivery event”? This should be consistent with the performance simulator definition.