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Large Language Models as Strategic Bidding Agents in P2P Energy Trading Markets
Ismail Lotfi, Ali Ghrayeb, Haitham Abu-Rub
TL;DR
Repeated P2P energy auctions leave the strategic behavior of bounded-rational bidding agents insufficiently understood, especially under changing market conditions. The paper introduces LLM-based bidding agents and compares them with random and ε-greedy MAB strategies, finding stronger early trading performance and adaptation, alongside increasingly asymmetric surplus allocation in homogeneous LLM markets.
Problem
Strategic bidding behavior in repeated P2P energy auctions remains insufficiently explored, particularly under bounded rationality and changing market conditions.
Method
The paper compares LLM-based, random, and ε-greedy MAB bidding agents in repeated k-double P2P energy auctions using simulation-based evaluation.
Results
The LLM strategy achieves higher early cleared trading volume, adapts without a relearning period after market changes, and homogeneous LLM agents produce increasingly asymmetric surplus favoring sellers.
Takeaways & Limitations
Contextual LLM bidding can avoid statistical burn-in and remain effective across changed market conditions, but homogeneous deployment can intensify seller-side surplus concentration.
Takeaways & Limitations
The persistence of seller-side escalation across alternative auction mechanisms and the effectiveness of fairness methods remain open questions.
Abstract
from arXiv · showhide
Peer-to-peer (P2P) energy trading markets rely on double auction mechanisms to match prosumers and consumers in smart grid distribution networks. However, the strategic behavior of bidding agents in such markets remains not fully explored, particularly in repeated settings with bounded rationality. This paper proposes a novel framework that integrates large language models (LLMs) as reasoning-driven bidding agents in repeated P2P energy double auctions. We compare the performance of three bidding strategies: random bidding, an $\varepsilon$-greedy multi-armed bandit (MAB) approach, and an LLM-based strategy. Simulation results show that the LLM-based strategy achieves superior cleared trading volume over the first episodes compared to $\varepsilon$-greedy MAB and random bidding baselines, eliminating the exploration burn-in period that statistical learning algorithms inherently require before converging to productive price arms. Importantly, when tested in an environment different from the one used during learning, the performance of the $\varepsilon$-greedy strategy drops significantly, while that of the LLM-based bidding strategy continue to achieve higher surplus and successful trades. Nevertheless, the LLM's advanced contextual reasoning also gives rise to an important market dynamic. In a homogeneous population of LLM agents, sellers increasingly exploit buyers' rational outside options to drive clearing prices above the Nash equilibrium, resulting in a progressively more asymmetric allocation of surplus in favor of sellers that does not converge within the observed time horizon.
I. INTRODUCTION
P2P energy trading has emerged as a response to challenges in conventional compensation schemes, with double auctions matching buyers and sellers while balancing volume, budget balance, and surplus. Repeated-market bidding remains difficult because agents must adapt under bounded rationality and changing conditions.
- P2P energy trading enables direct exchanges among smart-grid participants as an alternative to traditional compensation schemes.
- Double auctions match buyers and sellers while balancing traded volume, budget balance, and surplus allocation.
- Repeated auctions require bidding agents to continuously adapt to changing market conditions under bounded rationality.
- Early P2P studies used heuristic or statistical learning strategies, including ZIP, UCB, and ε-greedy MAB agents.
- LLMs have mainly served as auxiliary smart-grid modules, leaving their direct participation in repeated P2P double auctions largely unexplored.
C. Research Gap and Contributions
The paper addresses whether LLMs can autonomously bid in repeated P2P energy markets and what market dynamics emerge when reasoning-capable agents interact. It proposes an LLM-driven framework and benchmarks it against random and ε-greedy strategies.
- Learning-based policies may require additional exploration or retraining after market conditions change, whereas LLMs may adapt through contextual reasoning without retraining.
- The paper asks whether LLMs can replace conventional bidding algorithms and participate autonomously in repeated P2P energy markets.
- The proposed framework uses an LLM as a standalone bidding agent that generates bid prices from observable market information through natural-language reasoning.
- The evaluation compares LLM, random, and ε-greedy MAB bidding under a k-double auction using volume, surplus, convergence, and bidding behavior.
- The study examines emergent strategic behavior among homogeneous populations of LLM bidding agents.
B. Auction Surplus and Learning
Auction surplus measures trading benefits relative to grid-based outside options, while normalized rewards support learning. Unmatched agents receive the utility fallback outcome of zero reward.
- Auction surplus measures each agent’s economic benefit relative to its grid-based outside option.Buyers can purchase electricity at PUR, while sellers can sell surplus energy at PFIT.
- Any clearing price within (PFIT, PUR) benefits both buyers and sellers.
- The cleared energy quantity is used in the surplus formulation, and rewards are normalized to [0, 1] for learning.
- An unmatched agent receives normalized reward zero, corresponding to the utility fallback outcome.
C. k-Double Auction Mechanism
The k-double auction sorts buyer bids and seller asks, determines traded quantity from their curve intersection, and computes one clearing price for matched agents. Repeated bidding creates an exploration–exploitation trade-off across three strategies.
- Buyer bids are sorted in descending order, while seller asks are sorted in ascending order.
- The demand–supply intersection determines traded quantity Q*, using the marginal buyer bid p_bL and marginal seller ask p_sH.
- The clearing price is P* = k p_bL + (1 − k) p_sH, with k ∈ [0, 1].
- All matched agents trade at P*, while unmatched agents trade with the grid.
- Repeated auctions require each agent to choose bids without future outcomes, creating an exploration–exploitation trade-off across random, ε-greedy, and LLM strategies.
D. Baseline Bidding Strategies
The baseline strategies include random bidding and ε-greedy MAB bidding. ε-greedy treats bid prices as arms and balances exploration with exploitation using empirical rewards.
- Random bidding selects buyer and seller bid values uniformly within the interval [PFIT, PUR].
- ε-greedy MAB: The MAB framework models each bid price as an arm with an unknown reward distribution.Agents track arm selections and empirical average normalized rewards.
- ε-greedy MAB: ε-greedy selects the highest-reward arm with probability 1 −ε and a uniformly random arm with probability ε.
III. METHODOLOGY
The proposed LLM agent reasons over role, reward history, and auction mechanism information to generate bids and explanations. The methodology also addresses privacy and deployment constraints arising from cloud-hosted models and limited HEMS hardware.
- LLM-Based Bidding Strategy: The LLM bidding agent receives its role, historical reward trajectory, and current auction mechanism type in a structured prompt.It outputs a recommended bid value b* and a natural-language explanation E.
- Practical Deployment Challenges: Cloud-hosted LLM bidding can require sensitive prosumer data to be transmitted off-premises.This creates a privacy challenge for deployment.
- Practical Deployment Challenges: Privacy-preserving local deployment faces severe computational constraints on residential HEMS hardware.
- Practical Deployment Challenges: Differentially private prompt construction adds calibrated Gaussian or Laplace noise to sensitive numerical features before they enter the prompt.The privatized feature is formed as x̃ = x + η under a privacy budget εpriv and failure probability δ.
- Practical Deployment Challenges: Edge-deployable small language models use compression methods such as quantization, pruning, and knowledge distillation to reduce computational cost.
IV. NUMERICAL RESULTS
The study evaluates LLM-based, ε-greedy, and random bidding in repeated k-double P2P energy auctions using cleared volume and buyer- and seller-specific surplus. The design uses 100 daily settlement rounds and notes that time-varying demand and supply remain outside scope.
- Simulation Setup: D = 100 auction rounds represent daily settlement periods in the repeated k-double auction simulations.The auction parameter is k = 0.51, with PFIT = 10 ¢/kWh and PUR = 20 ¢/kWh.
- Evaluation Metrics: Performance is evaluated using cleared trading volume Q̄h and agent surplus S̄t, reported separately for buyers and sellers.
- Compared Strategies: The simulations compare LLM-based, ε-greedy, and random bidding agents.
- Scope Boundary: Modeling time-varying demand and supply patterns across daily and seasonal horizons is left for future work.
A. Cleared Trading Volume
LLM bidding produces substantially higher cleared volume than ε-greedy and random bidding during the early episodes by generating bids within compatible clearing ranges immediately. After approximately episode 40, LLM and ε-greedy volumes converge, while random bidding remains lower and unstable.
- Early Phase: During approximately the first 40 episodes, LLM bidding achieves substantially higher cleared volume than ε-greedy and random bidding.LLM buyers bid within the clearing zone immediately, while ε-greedy incurs failed trades from exploratory extreme bids.
- Convergence Phase: After approximately episode 40, LLM and ε-greedy cleared volumes converge to a similar level.ε-greedy has identified locally optimal price arms and shifts from exploration to exploitation.
- Convergence Phase: Random bidding remains unstable and lower than both LLM and ε-greedy agents across the episodes.
- Deployment Implication: The LLM strategy eliminates the burn-in period that statistical learning algorithms inherently require before productive bidding.
B. Buyers’ and Sellers’ Surplus
The surplus dynamics differ across bidding strategies: LLM bidding shifts surplus progressively toward sellers through rising prices, while ε-greedy seller gains mainly reflect recovering cleared volume.
- LLM Surplus Dynamics: LLM bidding produces steadily increasing seller surplus and decreasing buyer surplus as clearing prices rise from approximately 14 ¢/kWh to 18 ¢/kWh without stabilizing.The observed redistribution continues throughout the simulation horizon.
- LLM Surplus Dynamics: Successful clearing signals LLM sellers that higher asks are feasible, creating continuous upward price drift beyond the Nash equilibrium of 15 ¢/kWh.LLM buyers continue bidding because their alternative is purchasing from the utility at PUR = 20 ¢/kWh.
- ε-Greedy Surplus Dynamics: Under ε-greedy bidding, seller surplus increases and stabilizes around episode 40 while buyer surplus remains approximately constant.The clearing price stabilizes at approximately 16.5 ¢/kWh.
- ε-Greedy Surplus Dynamics: For ε-greedy bidding, seller-surplus growth is driven primarily by increasing cleared volume as exploration suppression diminishes and volume recovers.The exploration tax initially limits the total surplus available to buyers and sellers.
C. Robustness to Post-Deployment Market Changes
After a post-deployment price shift, ε-greedy performance degrades substantially, whereas LLM bidding remains largely unchanged by interpreting the updated market context without retraining.
- Original Environment: ε-greedy and LLM strategies perform comparably in the original environment for successful trades and average agent surplus.These results are reported under the original market conditions in Figure 4.
- Post-Deployment Market Changes: After the distribution shift, ε-greedy performance degrades substantially, with random bidding becoming better than ε-greedy.The shift increases both the feed-in tariff and retail electricity price by 7 ¢/kWh.
- Post-Deployment Market Changes: The LLM strategy remains largely unchanged after the shift by generating bids within the new clearing region rather than rediscovering profitable price levels through exploration.Agents are evaluated without additional training or parameter updates.
V. CONCLUSION
The paper concludes that contextual LLM bidding can improve early and shifted-market performance, but homogeneous LLM populations may produce increasingly asymmetric surplus allocation favoring sellers.
- Conclusion: The framework uses contextual reasoning over historical auction outcomes to adapt bids across episodes without the MAB statistical burn-in period.The comparison includes random bidding and ε-greedy MAB under the k-double auction mechanism.
- Conclusion: LLM bidding achieves higher cleared trading volume and surplus during early deployment, corresponding to approximately 40 calendar days of suboptimal ε-greedy performance.The LLM agent maintains high surplus and trade success across deployment environments without relearning.
- Conclusion: After market conditions change, ε-greedy performance degrades because its converged price arms no longer match the environment, whereas LLM bidding adapts immediately.The LLM reasons over current market context at each episode.
- Conclusion: Seller surplus becomes increasingly disproportionate at buyers’ expense in the homogeneous LLM market, motivating further investigation of alternative auction mechanisms and fairness methods.The conclusion identifies this asymmetric outcome as requiring further investigation.