pclm.0000918.t002 - <p> </p>

5.5 KB of Excel data from a 2030 case study of California and New England, analyzing demand response pricing strategies in multi-sector energy planning. Trevor Barnes authored this dataset, which was last updated on May 8, 2026. The data supports findings on how electrical and thermal demand response can reduce system costs in capacity-constrained energy systems.

Use Cases

• Compare energy carrier-independent versus carrier-specific demand response pricing strategies based on the described model implementation.
• Analyze system-level cost savings from thermal demand response for service sector space heating capacity, as mentioned in the description.
• Investigate net-load characteristics and system-level metrics for a future 2030 energy system in California and New England.
• Perform sensitivity analysis on demand response adoption levels to assess potential cost savings, as indicated in the study conclusions.

Strengths

• Data is derived from the open-source PyPSA-USA energy system optimization model, providing a transparent methodological foundation.
• The 5.5 KB size indicates a focused, non-redundant dataset suitable for targeted analysis.
• A specific 2030 case study for California and New England provides clear temporal and geographic scope.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale modeling.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.5 KB), indicating limited scope and likely containing summary or model output data rather than raw observations.

Provenance

Source

figshare, author Trevor Barnes

Collection Method

Likely contains model outputs from the PyPSA-USA energy system optimization model implementing demand response pricing strategies.

Time Range

Future scenario for 2030

Freshness

Last updated 2026-05-08 17:41:39; freshness should be verified.

Geography

California and New England, United States