arXiv subjects

Conleigh Byers

Publications and source records attributed to Conleigh Byers.

2 recordsLinked to original sources

arXiv2026

Modeling Coincident Peak Pricing in Electricity Markets: Challenges and Peak Shaving Effectiveness

Coincident Peak (CP) pricing is widely used in U.S. electricity markets to allocate capacity and transmission costs. This paper develops a behavioral game-theoretic framework for CP-driven load shifting that couples a nonlinear cost-allocation model with day-ahead (one-shot) and real-time (sequential-learning) decision processes. We examine two update rules, namely best-response dynamics (BRD) and fictitious-play dynamics (FPD), across continuous and finite action spaces to quantify how flexibility, action resolution, and participation influence peak outcomes. Using ERCOT peak-day data, we find that FPD reliably reduces system peaks, whereas BRD is more variable and can increase peaks under tight-capacity conditions. Finer action resolution improves peak shaving, while the number of participants is largely neutral when aggregate flexibility is fixed. Meanwhile, information-provider signals can induce herding, whereas response-aware or diverse signals improve peak shaving. These results highlight both the potential and limits of CP pricing: smoothing information and enabling granular control are as important as the amount of available flexibility. The framework offers practical guidance for system operators and consumers: For ISOs, broadcasting smoothed CP signals and setting minimum controllable-capacity thresholds enhance coordination. For consumers, greater flexibility and finer control resolution improve both cost savings and peak-shaving performance.

eess.SY↗

arXiv2024

Auction designs to increase incentive compatibility and reduce self-scheduling in electricity markets

The system operator's scheduling problem in electricity markets, called unit commitment, is a non-convex mixed-integer program. The optimal value function is non-convex, preventing the application of traditional marginal pricing theory to find prices that clear the market and incentivize market participants to follow the dispatch schedule. Units that perceive the opportunity to make a profit may be incentivized to self-commit (submitting an offer with zero fixed operating costs) or self-schedule their production (submitting an offer with zero total cost). We simulate bidder behavior to show that market power can be exercised by self-committing/scheduling. Agents can learn to increase their profits via a reinforcement learning algorithm without explicit knowledge of the costs or strategies of other agents. We investigate different non-convex pricing models over a multi-period commitment window simulating the day-ahead market and show that convex hull pricing can reduce producer incentives to deviate from the central dispatch decision. In a realistic test system with approximately 1000 generators, we find strategic bidding under the restricted convex model can increase total producer profits by 4.4\% and decrease lost opportunity costs by 2/3. While the cost to consumers with convex hull pricing is higher at the competitive solution, the cost to consumers is higher with the restricted convex model after strategic bidding.

econ.GN↗