4) Time-Varying Rate Impact on System Investment

[sherryzuo]

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Objective

Quantify the system-wide benefits of implementing time-varying + seasonal heat pump (HP) rates, focusing on:

1. Impact on peak growth deferral (MW and years).
2. Value of deferred generation, transmission, and distribution capacity investment.
3. Net system benefits ($/year, NPV over 10 years).
4. Sensitivity to adoption, enabling technologies, and capacity value assumptions.

Primary Research Questions:

• If TOU + seasonal rates reduce peaks by X MW, what is the value of deferred capacity investment?
• How do these rates change the trajectory of HP adoption and peak growth (direct + indirect effects)?

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1. Methodology Dimensions

Two axes of methodology:

• Axis 1: Analysis Complexity

  • Easy: Map peak reductions to Brattle study benefit valuations ($/kW-yr).
  • Difficult: Run capacity expansion + dispatch modeling to endogenously value deferral.

• Axis 2: Attribution Ambition

  • Direct (Justifiable): Focus on load-shifting and DR impacts of existing HPs.
  • Direct + Indirect (Optimal): Add HP adoption response to rates and re-compute peak growth trajectory.

The combination produces four possible approaches:

	Direct (Justifiable)	Direct + Indirect (Optimal)
Easy Analysis	Map observed load/peak reductions to Brattle scenarios and extract benefits.	Use Brattle scenarios but adjust HP adoption trajectory to reflect bill impacts; scale benefits accordingly.
Difficult Analysis	Use capacity expansion to recompute marginal costs and peak deferral benefits, adoption fixed.	Full expansion + adoption feedback loop; equilibrium of rates ↔ load ↔ investment ↔ adoption.

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2. Methodology Options

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Easy + Justifiable (Brattle Benchmark)

• Core idea: Use Brattle study capacity benefit values to monetize peak reductions from TOU + seasonal rates.
• Inputs:
  • From Issue 3) TOU + Seasonal Rate Design #96 : Hourly load pre/post rates, adoption of enabling tech, behavioral response parameters.
  • From Brattle: $/kW-yr capacity benefit values (gen, trans, dist).
• Steps:
  1. Compute system peak reduction ΔD_system (MW).
  2. Map ΔD_system to the closest Brattle scenario.
  3. Extract benefit values ($/kW-yr).
  4. Multiply ΔD_system × 1000 × (benefit $/kW-yr).
  5. Calculate years of deferral = ΔD_system / Annual Peak Growth (MW/yr).
  6. Compute NPV of deferral with discount rate (5–7%).
• Use case: Fast, transparent, leverages existing valuations; good first-pass estimate.

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Easy + Optimal (Brattle + Adoption Adjusted)

• Core idea: Extend Brattle mapping to include HP adoption effects on peak growth.
• Inputs: Same as above, plus adoption elasticity estimates.
• Steps:
  1. Estimate adoption change (Δa) given bill impacts under TOU + seasonal rates.
  2. Adjust system load projections to reflect new adoption levels.
  3. Recompute ΔD_system and map to Brattle scenarios.
  4. Re-estimate system benefits (gen, trans, dist).
• Use case: Captures both direct (load-shift) and indirect (adoption-driven load growth) effects; still leverages Brattle for valuation.

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Difficult + Justifiable (System-Informed, Direct)

• Core idea: Use capacity expansion + dispatch modeling to value peak deferral with rate-shaped loads; adoption fixed.
• Inputs:
  • HP load profiles with TOU response.
  • Model inputs: fuel prices, technology costs, CLCPA policy constraints.
• Steps:
  1. Build hourly net loads with TOU + seasonal rates.
  2. Run capacity expansion model (e.g., GenX, Switch) to determine new build/dispatch.
  3. Extract marginal capacity values (MEC, MCC, MPC).
  4. Compute deferred build years vs. baseline scenario.
  5. Value benefits using model-consistent capex + O&M costs.
• Use case: Produces NY-specific system-consistent benefits, robust to future peak shifts (summer → winter).

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Difficult + Optimal (Full Equilibrium)

• Core idea: Model the full feedback loop between TOU rates, HP adoption, and system investment.
• Inputs: Same as above, plus adoption choice model (e.g., logit or diffusion).
• Steps:
  1. Given rates, compute HP adoption trajectory (Δa).
  2. Feed new adoption-driven loads into capacity expansion + dispatch.
  3. Extract updated marginal costs and system peaks.
  4. Recompute seasonal rate adjustments and iterate until convergence.
  5. Value benefits (generation, transmission, distribution, energy, emissions).
• Use case: Most precise, equilibrium-consistent representation of TOU impacts on adoption and system costs; long-term research path.

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3. Deliverables

• System Impact Outputs:

  • Peak reduction (MW) by season and system-wide.
  • Years of deferral in capacity investments.
  • Seasonal revenue requirement shares (winter vs. summer).

• Benefit Outputs:

  • Annual and NPV system benefits ($/yr, 10-yr horizon).
  • Breakdown by generation, transmission, distribution, energy savings, and emissions.
  • Benefit-cost ratio vs. alternative capacity options (peaker, storage).

• Policy Readouts:

  • Where TOU + seasonal rates provide material deferral and cost-reflective seasonal alignment.
  • Role of enabling technologies in amplifying system benefits.
  • Risks if adoption response is negative (winter bills too high).

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[linear]

SWI-80