Coordinated Multi-Objective Optimization to Increase Distributed Energy Resources Hosting Capacity in Active Distribution Systems
Bibliographic record
Abstract
The increasing installation of distributed energy resources (DERs) by end consumers introduces new operational challenges to distribution networks. Although several studies investigate these impacts, most proposed control strategies are implemented independently, focusing on individual elements such as substation voltage adjustment, network reconfiguration, or inverter-based DER controls. These approaches often overlook both the interactions among different control mechanisms and the interests of end consumers, concentrating primarily on the system operator’s perspective. As a result, their potential to achieve more balanced and effective solutions is limited. This paper proposes a coordinated multi-objective optimization approach that integrates substation voltage adjustment, network reconfiguration, and the determination of Volt-Var and Volt-Watt control setpoints. By combining these strategies, the search space is expanded, enabling the identification of superior solutions that better balance the competing objectives of minimizing power losses, increasing DER hosting capacity, and maximizing the active power supplied by DERs. The multi-objective optimization problem is formulated with these three objective functions and a set of operational constraints, explicitly considering the interests and requirements of both end consumers and system operators. Furthermore, uncertainties related to load demand and DER generation are addressed using a Monte Carlo method. The proposed methodology is applied to the IEEE 69-bus distribution system and the optimization problem is solved using the Multi-objective Cuckoo Search Algorithm. The results demonstrate that the coordinated strategy outperforms isolated approaches by completely eliminating violations and reducing losses by 47.60%, while incurring only a 7.27% reduction in active power injection from DERs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".