Planning of multi-hub energy system by considering competition issue
Bibliographic record
Abstract
Energy hub concept has been emerged as a suitable tool to analyze multi-carrier energy systems. Deregulation and increasing competition in the energy industry have provided a suitable platform for developing the energy systems composed of competing energy hubs. Planning of energy hubs considering the competition between the hubs has not been sufficiently addressed, yet. A model has been proposed in this study for planning of a multi-hub energy system considering the competition between the hubs. The hubs are interconnected via an electric transmission system. While the heat demand for each hub must be supplied by deploying associated technologies, the electricity demand can be fulfilled by investing in technologies or exchanging through the transmission system considering the market price and line loading limits. A linear model has been developed to determine the optimal capacity development of heat and electricity generation technologies for energy hubs in a multi-period planning horizon to meet the heat and electricity demand for the defined load zone. The problem has been formulated and solved using Karush–Kuhn–Tucker (KKT) conditions. Once solved, the optimal capacity development in the hubs is determined as well as the amount and price of electricity interchange between the hubs for the load zones of the planning horizon. The proposed model has been applied to 3-Hub and 5-Hub energy systems. The effect of renewable generation and storage system have also been evaluated. The result have been presented and discussed to evaluate the validity of the results as well as the capabilities of the proposed model. It was observed that, due to the competition between the hubs, the electricity generation capacity in a hub can reach 23% higher that the peak demand of the same hub. The electricity price between the hubs differs by 25% while the difference between the gas price of the hubs is about 5 percent. It has also been observed that inclusion of renewable generation or storage technologies can alter the electricity generation capacity by 63 percent in HUB2.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".