Pricing and trading strategies in networked microgrid systems: A comprehensive review
Bibliographic record
Abstract
As microgrids evolve from isolated systems into interconnected networks, pricing and trading strategies have emerged as the economic backbone of networked microgrid systems. These strategies are essential to enable energy self-sufficiency, facilitate energy exchanges within the network, and support scalable coordination. However, the development of effective trading approaches remains a complex and challenging task for researchers. To address these challenges, several studies have been proposed in the literature to overcome the complexities of trading in networked microgrids. This article presents a comprehensive comparative review of existing studies on pricing and trading strategies in networked microgrids. The reviewed methods are classified into five major categories: mathematical optimization techniques, market mechanisms, game-theoretic approaches, reinforcement learning methods, and blockchain-based models. Each category is examined in terms of its technical foundations and the application of the respective strategy within networked microgrids. Furthermore, four key evaluation criteria—fairness, privacy, scalability, and computational efficiency—are identified to facilitate a detailed comparative analysis of the studies within each category. This review highlights the strengths and trade-offs of each approach based on these criteria. Finally, the article highlights real-world pilot projects that demonstrate the practical viability of each categorized approach, while also outlining key research gaps that hinder broader implementation of pricing and trading strategies in networked microgrid systems. • Reviews pricing and trading strategies in networked microgrid systems. • Classifies approaches into MO, market, GT, RL, and blockchain-based models. • Compares approaches using fairness, privacy, scalability, and efficiency criteria. • Identifies research gaps and opportunities for practical market implementation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".