Off-site PPA as a tool to protect against electricity price spikes : Developing a framework for risk assessment and mitigation
Bibliographic record
Abstract
This dissertation was developed in collaboration with E&C Consultants, an international energy procurement consultancy, to gain a deeper understanding of the impact of the 2022 energy crisis on Power Purchase Agreements (PPAs) and their associated risks. Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The escalation of energy prices reached such levels that discussions emerged regarding an energy crisis comparable in magnitude to the 1973 oil shock. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food insecurity, business bankruptcies and recession. A well-known tool to protect energy consumers from energy price spikes, while at the same time contributing to the development of sustainable technologies, are Power Purchase Agreements. PPAs are long-term bilateral contracts for the purchase and sale of a certain amount of electricity, usually generated from renewable sources. Volatility in the energy market in 2022 negatively affected the PPA market, causing their prices to increase by up to 10-15% only in the first quarter of 2022. Such an environment was not ideal for the negotiation of PPAs, which influenced the formation of new trends related to certain parameters, such as the duration of PPAs and pricing mechanisms. The primary goal of this Master's Thesis is to assess how the risk associated with PPAs has evolved between 2020 and 2023. It aims to examine whether, after the events in 2022, PPAs remain a robust solution that protects the off-taker from energy price spikes, ensures greater energy budget stability, and enables savings. To achieve this, the probability of PPA prices being higher than market prices is evaluated, taking into account the changing market landscape. Furthermore, this Thesis intends to gain a thorough understanding of each risk related to PPAs and the best strategies for mitigating it, in order to maximize the protection of the off-taker
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".