Bi-level planning for integrated energy systems incorporating demand response and energy storage under uncertain environments using novel metamodel
Bibliographic record
Abstract
The optimal planning and design of an integrated energy system (IES) is of great significance to facilitate distributed renewable energy (DRE) technology and improve the overall energy efficiency of the energy system. With the increased penetration of distributed generation (DG), the power supply and load sides of an IES present more increased levels of uncertainties. Demand response (DR) and the energy storage system (ESS) serve as important means to shift energy supply and use across time to counter the indeterminate variations. However, the current IES planning methods are unable to effectively deal with the uncertainties of DREs and loads, and to optimize the operations of DG-DR-ESS due to the enormous possible combinations. In this paper, a new method for the optimal planning and design of an integrated energy system has been introduced and verified. The new method consists of three integrated elements. First, the method of the probability scenario has been used to model the uncertainties of the DREs and loads so as to better characterize the impact of uncertainty on the planning and design of the IES. Secondly, the optimal operation of the IES under different probability scenarios is ensured using the second-order cone optimization for quick solutions due to the simplicity of this sub-problem, serving as the bottom-level optimization. Thirdly, the optimal planning and design of IES through optimal sizing of the power generating components and ESS are performed using a special meta-model based global optimization method due to the complex, black-box, and computation intensive nature of this top-level optimization in a nested, bi-level global optimization problem. The combined approach takes full account of the interrelated operations of DG-DR-ESS under different design configurations to support a better optimal planning and design of the IES. The simulation has been carried out on an IES system modified from the IEEE 33-node distribution system. The simulation results show that the proposed method and model are effective.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".