An efficient co-simulation and control approach to tackle complex multi-domain energetic systems: concepts and applications of the PEGASE platform
Bibliographic record
Abstract
In this paper, we present a novel research software, called PEGASE, suitable for the design, validation and deployment of advanced control strategies for complex multi-domain energy systems. PEGASE especially features a highly efficient cosimulation engine, together with integrated solutions for defining both rule-based control strategies and Model-Predictive Control (MPC). The main principle behind the PEGASE platform is divide-and-conquer. Indeed, rather than trying to solve a problem as a monolithic entity, which can be highly complex for multi-domain large-scale systems, it is often more efficient to decompose it into several domains or sub-problems, and to simulate them in a decoupled way. To provide its cosimulation capabilities, we based PEGASE on two main components. The first one is a framework for integrating simulation models, which can be either compatible with the FMI standard or interfaced through an Application Programming Interface (API). The second one is a multi-threaded sequencer enabling several simulation sequences with different time steps. To provide advanced control capabilities, we also equipped PEGASE with a framework for MPC combining a comprehensive management of predictions data and a modeler dedicated to the formulation of Mixed Integer Linear Programs. We implemented this framework in C++ providing low formulation and resolution times for typical applications. Connection to hardware is also available via standard industry protocols thereby allowing PEGASE to control real energy systems. In this paper, we show how these basic functionalities, combined with dedicated modeling tools, enable setting up simulation and control applications suitable for tackling the complexity of various kinds of energy systems. To illustrate this, we present four application examples from our recent research work. These examples cover several domains, from concentrated solar thermal plants to optimal control of district heating networks. The variety of examples demonstrates the robustness and genericity of the approach.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".