DR standalone demand response analysis

DR estimates how flexible resources can enroll, dispatch and change electricity load. You can run DR independently of DERP, using its own market, measure, program and load-profile inputs. You can also connect it to DERP when a study needs adoption-driven market changes and feedback into resource potential results.

What DR answers

How many units are eligible, enrolled and called under the program assumptions?
How does curtailment, pre-charging and rebound change a typical day or an annual hourly load curve?
How much peak reduction remains after resources interact?
Which eligible grid-service streams can dispatch serve, subject to caps and operating constraints?
How do incentives, participation, cost-effectiveness screening and program competition change the result?

Two execution contexts

Context	Market and measure preparation	Result
Standalone DR study	DR reads its study workbooks directly and constructs dispatch records	DR impact, load-curve, enrollment and financial reporting
DR connected to DERP	DERP supplies a handoff containing year/scenario market and impact records	DR reporting plus selected results written back to DERP

Both runners are distributed in the Python package named derp. A standalone DR study can read DER measure characterization for economics without executing the DERP technical, economic or achievable potential engine. The term standalone per-unit impact in DR output has a second, narrower meaning: a resource’s independently evaluated impact before portfolio interactions. That output metric is available within an analysis and must not be confused with whether the overall study was launched independently from DERP.

Run DR locally

After installing and configuring the package using the README, inspect the DR module’s CLI:

python -m derp.run_demand_response --help

Run a workbook-based standalone study with the actual input paths:

python -m derp.run_demand_response \
  --source write-to-file \
  --dr-measures "data/inputs/DR Measures.xlsx" \
  --der-measures "data/inputs/Measures.xlsx" \
  --markets "data/inputs/Markets.xlsx" \
  --programs "data/inputs/Programs.xlsx" \
  --settings "data/inputs/Settings.xlsx" \
  --valuations-and-rates "data/inputs/Valuations and Global Inputs.xlsx" \
  --adoption "data/inputs/Adoption.xlsx" \
  --output_file "data/outputs/DR_Outputs.zip"

The package currently registers a derp console command; the DR invocation above uses its separate Python module. Do not assume a separately installed drp console executable exists.

Choose the temporal and grid scope

Typical-day analysis uses 24-hour weekday, weekend and peak representations by period, including configured call-number evaluations. Annual analysis uses the 8,760-hour sequence with event-count, cooldown and availability constraints. Typical-day results are not a substitute for annual chronology. Local, regional-bulk and bulk modes change the grid aggregation used for load layering and dispatch. A node-level peak reduction and the reduction in an aggregated system peak answer different questions; report the active mode with the result.

Read the model specification

DR dispatch is a planning analysis using supplied profiles and program assumptions. The registered peak-chasing and system-needs strategies place resources sequentially; their results should not be interpreted as a guarantee of a globally optimal portfolio or as live device control.

​What DR answers

​Two execution contexts

​Run DR locally

​Choose the temporal and grid scope

​Read the model specification

What DR answers

Two execution contexts

Run DR locally

Choose the temporal and grid scope

Read the model specification