Optimizing CHP-based multi-carrier energy networks with advanced energy storage solutions
Bibliographic record
Abstract
This paper presents an advanced operational framework for large-scale combined heat and power (CHP)-based multi-carrier energy (MCE) networks integrating both electrical and gas energy storage systems (EESS and GESS). A novel coordinated controller is developed to regulate energy flows by managing charging and discharging cycles of storage units while stabilizing electricity and gas supply to CHP units. The operational optimization problem is solved using a parameter-free Teaching-Learning-Based Optimization (TLBO) algorithm, which efficiently minimizes total costs and enhances system flexibility. The proposed approach is validated on a comprehensive testbed comprising the IEEE 14-bus power system, the Belgian natural gas network, and district heating subsystems. Results showed that the presence of EESS reduced the total operation cost of the network by about 0.075%, while the use of GESS increased the operation cost by about 0.024%. Overall, the framework significantly improves operational cost efficiency, energy flow stability, and network resilience compared to existing methods. This work provides valuable insights into the integration and coordinated control of multi-energy storage in CHP-based MCE networks, contributing to the development of more sustainable and flexible energy systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".