Demand response in the New Zealand Electricity market
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines a proposal for the introduction of incentive based demand curtailment in the New Zealand Electricity market (NZEM). Today NZEM participants do not submit demand bids (except in pre-dispatch schedule) to the markets for energy and regulation reserve. Professional literature identifies demand response (DR) in a broad variety of uses. Such uses range across peak load management, transmission congestion management, regulating reserve, market efficiency, transmission and distribution investment deferral, among other things. Despite the apparent benefits of these various options demand side participation has been very limited in today's markets throughout the world. Many US and Canadian markets have embraced demand response, but many markets are still reluctant to implement demand management products in real time markets. The New Zealand power system has a limited Advance Metering Infrastructure (AMI) and a low penetration of smart appliances. In the installed AMI much of the communication capability is limited to remote meter reading. Consequent perhaps on the limited TOU capability of the AMI, tariffs are fixed rather than TOU based. Some adhoc incentives have been given to persuade consumers to participate in conservation initiatives during periods of energy shortage. At the time of writing New Zealand does not use price based demand response methods. Our discussion is centered on a generic incentive based demand response. This paper reviews the status of the various DR implementations in the United States Independent System Operators (ISO's) as a basis of comparison for the NZEM. The paper investigates the effect of introducing incentive based demand side participation in the NZEM. Demand participation in the form of dispatchable energy bids are considered with the objective of investigating the LMP formulation changes necessary to accommodate DR in the energy, contingency and regulating reserve markets in the NZEM.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it