Hierarchical and Decentralized Stochastic Energy Management for Smart Distribution Systems With High BESS Penetration
Bibliographic record
Abstract
In this paper, we propose a hierarchical and decentralized stochastic energy management scheme for smart distribution systems with high battery energy storage system (BESS) penetration. An energy management problem is formulated based on a two-layer hierarchical architecture for the joint optimization of distribution system operator (DSO) and customers. In the lower layer, the stochastic energy management problem of individual BESS is formulated as a Markov decision process to minimize the electricity cost. In the upper layer, the solutions of individual BESS stochastic energy management problems are used for the energy management of smart distribution systems to minimize the line losses while maintaining the voltage levels within required range. Considering the partial communications among households, this problem can be transformed into a decentralized partially observable Markov decision process with stochastic controllers. Accordingly, an energy management scheme based on exhaustive backups is proposed to solve the formulated problem in a decentralized manner. To reduce the computational complexity caused by high BESS penetration, a heuristic search and pruning method is further proposed. The case study results based on IEEE 5-bus test feeder and IEEE European low voltage test feeder indicate that the proposed scheme can reduce the electricity costs of both DSO and customers, while having the voltage levels regulated. Also, the computational complexity is much lower for a smart distribution system with high BESS penetration, in comparison with existing BESS energy management schemes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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".