The Evolution of Electricity Spot Markets, Strategy, Behavior, and Settlement: A Comparative Review
This review paper synthesizes recent research on the evolution of electricity spot markets, focusing on the transition from national power exchanges to local peer-to-peer (P2P) trading systems(Ahmad 2019; Poplavskaya 2020) . By analyzing market designs in India, Europe, China, and Switzerland, the paper evaluates the effectiveness of various pricing mechanisms, including Locational Marginal Pricing (LMP), zonal pricing, and Marginal Pricing versus Pay-as-Bid (Ableitner 2020; Wang 2022) . Using advanced multi-agent-based simulations (MABS) and reinforcement learning (RL), the review demonstrates that while granular pricing mechanisms like LMP provide superior location signals, they often require hybrid settlement methods—such as System Average Pricing (SAP)—to ensure consumer fairness and acceptance(Ableitner 2020; Wang 2022) . Furthermore, the review explores the behavioral aspects of market participation, identifying a heterogeneous user base in local markets that values a balance between active price-setting agency and automated convenience(Poplavskaya 2020) . The research also highlights innovative frameworks like Iterative Proportional Adjusting (IPA) and Renewable Energy Certificates (RECs) designed to maximize the integration of variable renewable energy sources (RES) while mitigating the impact of strategic bidding and transmission congestion(Ahmad 2019; Ableitner 2020; Wang 2022) . Ultimately, the paper provides a comprehensive framework for understanding how adaptive pricing and robust market modeling can enhance liquidity, stability, and sustainability across diverse electricity market environments(Ahmad 2019; Poplavskaya 2020; Ableitner 2020; Wang 2022; Oprea 2026) .
Authors
- Vrundar More
- Sharmeen Sardar
- Mohd. Maaz Shaikh
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-31
- DOI
- https://doi.org/10.5281/zenodo.23832515
- Primary Topic
- Electric Power System Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00