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August 19, 2026Applied SciencesOpen Access

A Review of Mathematical Models for Trading Decision-Making in Electricity Markets

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Authors

XCXiaotao ChenHFHang FanSWShuaikang Wang

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Overview

Systematic review evaluates trading decision models in electricity markets, showing decision-dependent optimization improves aggregator profit by 6.8%, highlighting enhanced market adaptation.

Key Points

  • To systematically review, compare, and identify practical limitations of mathematical trading decision-making frameworks operating under high renewable penetration and spot market constraints.
  • Followed the PRISMA 2020 protocol to systematically screen 129 Chinese and English studies covering trading optimization models.
  • Evaluated seven modeling frameworks—including stochastic, robust, distributionally robust, decision-dependent uncertainty (DDU), risk-based, evolutionary algorithms, and reinforcement learning—against Shandong spot market rules.
  • Evaluated practical defects and bottlenecks across seven mathematical trading frameworks applied to locational marginal pricing and dual-settlement rules.
  • Numerical simulations showed that DDU distributionally robust optimization (DDU-DRO) improved aggregator profit by 6.8% compared to exogenous uncertainty models.

Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a8563d703308d306e2d74b8https://doi.org/10.3390/app16168194
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