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Comparative analysis of bidding strategies for auction-based local energy markets

Author

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  • Schölzel, Joel David
  • Henn, Sarah
  • Tings, Milena
  • Streblow, Rita
  • Müller, Dirk

Abstract

In view of the ongoing integration of distributed energy resources (DERs) and energy storage into the energy system, conventional consumers are transitioning into prosumers and flexumers. Local energy markets (LEMs) enables these end users to trade electricity directly with each other in order to obtain lower energy prices and to increase the local self-consumption. Since bidding strategies have a decisive impact on efficient trading in auction-based LEMs, the comparison and consistent evaluation of bidding strategies is an important research area. As novelty we propose an uniform evaluation methodology keeping all market features equal except the used bidding strategy. For the first time we evaluate a device-oriented (DO) strategy including an incentive price signal with a zero-intelligence (ZI) strategy, a learning-intelligence (LI) strategy employing the modified Roth–Erev learning algorithm. As evaluation we analyze the resulting market trading with various key performance indicators (KPIs), which consider both cost savings and local energy supply. Our findings reveal that the chosen bidding strategy has a decisive impact on cost savings and the distribution of gains between the buyer and seller side. In a photovoltaic (PV) and combined heat and power (CHP) scenario with different technology penetrations, the end users’ gains attain the highest values for the DO bidding strategy with 7909 € and 16454 €. Also the average market clearing prices are for the DO bidding strategy the highest with 0.269 €/kWh for the and 0.3032 €/kWh, which implies that the seller side predominantly obtains the gains.

Suggested Citation

  • Schölzel, Joel David & Henn, Sarah & Tings, Milena & Streblow, Rita & Müller, Dirk, 2024. "Comparative analysis of bidding strategies for auction-based local energy markets," Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:energy:v:291:y:2024:i:c:s0360544223036058
    DOI: 10.1016/j.energy.2023.130211
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    Citations

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    Cited by:

    1. Hussein, Noon & Musilek, Petr, 2025. "Enhancing fairness and efficiency in community energy systems: A forecast-driven approach," Energy, Elsevier, vol. 335(C).
    2. Oprea, Simona-Vasilica & Bâra, Adela, 2026. "An adaptive pricing mechanism framework for post-auction in the local electricity markets integrating a higher share of renewables," Renewable Energy, Elsevier, vol. 256(PB).
    3. Satpathy, Priya Ranjan & Ramachandaramurthy, Vigna Kumaran, 2026. "Artificial intelligence and machine learning for distributed energy resource management systems: Applications, frameworks, and future directions," Applied Energy, Elsevier, vol. 403(PB).
    4. Lan, Liuhan & Zhang, Xingping & Ng, Tsan Sheng & Wang, Teng & Tan, Qinliang, 2025. "Bidding behaviors of coal-fired units in China's electricity market considering previous capacity revenues based on prospect theory," Energy, Elsevier, vol. 322(C).
    5. Mahmud, Sakib & Sayed, Aya Nabil & Himeur, Yassine & Nhlabatsi, Armstrong & Bensaali, Faycal, 2026. "A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).

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