Author: Denis Avetisyan
New research examines whether electricity suppliers manipulate bids in response to automated systems designed to prevent market abuse.
A regression discontinuity design analysis reveals limited strategic bidding under current automated mitigation procedures in electricity markets, suggesting potential for improved threshold calibration.
Despite widespread implementation of automated market power mitigation in electricity markets, evidence of firms strategically responding to these regulations remains scarce. This paper, ‘Strategic bid response under automated market power mitigation in electricity markets’, investigates how generation firms adjust their bids in response to price-cap-and-penalty mechanisms, using data from New York and New England. The analysis reveals that while 30-40% of bidders exhibit strategic behavior – lowering maximum bids to avoid penalties – the overall regulatory impact is statistically undetectable, suggesting current thresholds may be too lax. Could empirically-calibrated mitigation thresholds unlock substantial welfare gains and improve the efficiency of electricity market regulation?
Decoding Market Dynamics and the Potential for Strategic Manipulation
The efficiency of the electricity grid hinges on the Real-Time Market, a dynamic system where power is dispatched based on continually updated pricing. Accurate price discovery within this market is paramount; it functions as a signaling mechanism, reflecting the true cost of generating and delivering electricity at any given moment. This precise valuation allows grid operators to select the most cost-effective power sources to meet demand, minimizing overall expenses and ensuring a stable supply. Without reliable pricing, dispatch decisions become inefficient, potentially leading to increased costs for consumers and even jeopardizing grid reliability. Essentially, the Real-Time Market acts as a crucial feedback loop, continuously optimizing electricity flow based on real-time conditions and fostering a resilient energy infrastructure.
When a single electricity supplier possesses significant pivotality – meaning their actions demonstrably influence market prices – opportunities for market power abuse arise. These suppliers, rather than competing solely on cost and efficiency, can strategically withhold supply or inflate bids, knowing their actions will disproportionately affect the overall price. This manipulation doesn’t reflect genuine scarcity or increased production costs, but rather an exploitation of their critical position. Consequently, consumers may face artificially inflated electricity bills, while efficient competitors are disadvantaged, ultimately undermining the integrity and fairness of the Real-Time Market and reducing overall societal welfare. Detecting and preventing such behavior is crucial for ensuring a reliable and affordable energy supply.
The exertion of market power by electricity suppliers doesn’t simply inflate prices; it fundamentally corrupts the information that guides efficient energy distribution. When a supplier manipulates the market, the resulting prices no longer accurately reflect the true cost of electricity, leading to misallocation of resources and hindering the dispatch of the most cost-effective generation sources. This distortion diminishes overall economic welfare, as consumers pay inflated rates and the system operates with reduced efficiency. Consequently, continuous and sophisticated monitoring systems are crucial for detecting manipulative behavior, and proactive mitigation strategies – potentially including regulatory interventions or market design adjustments – are essential to safeguard against sustained abuse and ensure a reliably functioning, equitable energy market.
Proactive Regulation: Automated Mitigation as a Systemic Safeguard
Automated Mitigation Procedures leverage ex-ante tools – those applied before market interactions – to proactively identify and counteract potential exercises of market power. These procedures typically involve continuous monitoring of bidding behavior and system marginal prices, employing algorithms to detect anomalies indicative of strategic manipulation. By analyzing bids against pre-defined thresholds and historical data, these tools can flag potentially anti-competitive behavior in real-time. This allows system operators to intervene, often through automated adjustments to bid acceptance or price calculations, thereby preventing the exertion of undue influence over market outcomes and ensuring efficient price discovery.
Automated mitigation procedures frequently utilize price cap mechanisms to limit the price of a service or good, preventing suppliers from exploiting market power. These caps are often determined by calculating the cost of efficient production or referencing competitive benchmarks. Complementing price caps, detailed conduct assessments evaluate supplier behavior for anti-competitive practices, such as discriminatory pricing or tying arrangements. These assessments involve analyzing bidding data, market shares, and supplier strategies to identify and address potentially harmful conduct, often triggering pre-defined corrective actions or penalties.
Establishing a fair Reference Level is fundamental to accurate assessment within automated mitigation procedures. This Reference Level serves as the benchmark against which submitted bids are compared to detect potentially anti-competitive behavior. Typically, it’s derived from a historical average of market participant bids, adjusted for efficiency gains and cost reductions. The methodology for calculating this level must be transparent and consistently applied, accounting for factors like input costs, production capacity, and prevailing market conditions. Deviation from this Reference Level triggers further investigation and potential intervention, ensuring bids are evaluated objectively and preventing the exercise of undue market power. A robust Reference Level calculation minimizes false positives and ensures that only genuinely problematic bids are flagged for review.
Quantifying Influence and Isolating the Impact of Interventions
The Residual Supplier Index (RSI) is calculated to determine a supplier’s influence on market prices based on the difference between a supplier’s marginal cost and the locational marginal price (LMP) at a specific node. A higher RSI value indicates greater Pivotality, signifying that the supplier is frequently setting the market-clearing price. The index also quantifies a supplier’s dependence by assessing how much its output changes in response to price fluctuations; suppliers with inelastic supply curves exhibit higher dependence. Specifically, the RSI is computed as the sum, across all hours and locations, of the difference between the LMP and the supplier’s offer price, weighted by the quantity supplied. This calculation provides a quantifiable metric for identifying suppliers who exert disproportionate control over market outcomes and are therefore critical to monitor.
The combination of the Residual Supplier Index and Regression Discontinuity Design (RDD) provides a methodology for isolating the causal effect of Automated Mitigation Procedures (AMPs). RDD exploits the discrete nature of AMP application – suppliers exceeding a defined threshold are subject to mitigation, while those just below are not – to create a treatment and control group. By comparing outcomes (e.g., market prices) for suppliers immediately above and below the threshold, researchers can estimate the impact of AMPs while controlling for confounding factors. This approach differs from traditional methods by minimizing selection bias and providing a statistically rigorous evaluation of AMP effectiveness, allowing for precise measurement of the treatment effect and informing regulatory adjustments.
Analysis of currently implemented automated mitigation procedures in ISO-NE and NYISO reveals limited effectiveness. Estimated treatment effects range from -1 to -10 $/MWh, representing a reduction in mitigation benefits. However, this observed reduction is not statistically significant, indicating that the procedures do not reliably alter market outcomes. This finding suggests that the current automated systems are insufficient to consistently address manipulative bidding behavior and require further refinement or alternative strategies to ensure effective market monitoring and price stability.
The Merit-Order Model is a simulation tool utilized by regulatory bodies to forecast wholesale electricity market outcomes. This model constructs a supply curve based on generating unit operating costs, and then matches this supply with anticipated demand as defined by $Load Forecast$ data. Regulators employ this simulation to predict the impact of $Incremental Bid$ submissions – small changes in offered price or quantity – on locational marginal prices (LMPs) and overall market efficiency. By iteratively adjusting bid parameters within the model, they can estimate the potential financial and operational consequences of various supplier strategies before they are implemented in the actual market.
Beyond Intervention: Systemic Impacts and Future Directions for a Resilient Grid
Automated Mitigation Procedures (AMP) represent a crucial mechanism for safeguarding consumer welfare in energy markets dominated by suppliers wielding significant power. These procedures function by proactively addressing imbalances between energy supply and demand, thereby curtailing the ability of powerful suppliers to artificially inflate prices during peak demand or constrained conditions. Without such automated interventions, a substantial welfare transfer occurs – effectively, consumers pay a premium to suppliers simply due to market dominance, rather than genuine increases in the cost of production. By automatically adjusting supply or curtailing demand, AMP minimizes this transfer, ensuring a more equitable distribution of economic surplus and fostering a more efficient market. The effectiveness of these procedures hinges on their ability to preemptively address potential imbalances, preventing the exertion of undue market power and protecting consumers from inflated costs.
Proactive management of grid congestion, facilitated by automated mitigation procedures, demonstrably improves the stability and economic efficiency of power systems. By anticipating and resolving bottlenecks before they escalate into widespread disruptions, these procedures minimize the need for costly emergency measures and enhance overall system reliability. Simulations indicate that a shift towards preventative congestion management not only lowers operational costs-reducing the financial burden on consumers-but also unlocks substantial economic benefits. This approach allows for a more predictable and optimized flow of electricity, preventing imbalances that can lead to price spikes and service interruptions, and ultimately fostering a more resilient and affordable energy landscape.
Simulations of automated mitigation procedures reveal a currently limited operational impact, with fewer than 32 hours of grid congestion addressed annually under existing thresholds. Despite this constrained timeframe, the potential economic benefits are substantial; recalibrating these procedures to allow for stricter mitigation could generate up to $31 million in buyer surplus. This suggests that even modest increases in mitigated hours translate to significant gains for consumers, with each additional hour of addressed congestion potentially yielding between $350,000 and $980,000 in surplus. The analysis underscores a key opportunity: optimizing intervention thresholds could unlock considerable economic value from existing infrastructure, even with relatively infrequent activations of the automated mitigation protocols.
Analysis reveals a substantial economic incentive for refining automated mitigation procedures. Simulations demonstrate that each additional hour of successfully mitigated grid congestion could generate between $350,000 and $980,000 in buyer surplus – effectively transferring value from suppliers with market power to consumers. This suggests that even incremental improvements in mitigation effectiveness, achieved through recalibrated thresholds for intervention, could unlock significant economic benefits. While current mitigation efforts are limited, the potential for maximizing buyer surplus underscores the importance of optimizing these procedures to capture even small gains in grid reliability and cost reduction, potentially amounting to millions of dollars with each additional hour of congestion addressed.
The study’s findings, revealing limited strategic bidding responses to automated mitigation procedures, echo a sentiment akin to Marie Curie’s assertion: “Nothing in life is to be feared, it is only to be understood.” The research doesn’t portray manipulation as absent, but rather highlights the complexity of market participant behavior, suggesting that current price cap thresholds may not be finely tuned enough to elicit significant, measurable responses. This echoes the core idea of the paper – that understanding the system’s patterns requires a rigorous, empirical approach. Every deviation from expected bidding behavior, much like an anomaly in a scientific experiment, presents an opportunity to refine models and improve the efficacy of automated market power mitigation.
What’s Next?
The limited evidence of strategic bidding response to automated mitigation procedures presents a curious case – a system seemingly robust, yet potentially operating far from optimal efficiency. It recalls the principle of minimal energy in physical systems; just because a system exists in a stable state doesn’t imply it’s achieved the lowest possible energy configuration. Further investigation should not focus solely on detecting behavioral shifts, but rather on characterizing the shape of the response surface. Are bidders truly indifferent to the thresholds, or is the response simply muted within the current parameter space?
A compelling avenue for future work lies in viewing mitigation thresholds not as fixed parameters, but as adaptive elements within a larger control system. Much like the immune system calibrates its response to antigens, these thresholds should be empirically tuned, perhaps utilizing machine learning techniques to identify the boundaries between effective mitigation and undue market distortion. The current reliance on potentially arbitrary thresholds feels akin to diagnosing a disease based on a single symptom – a shortcut that may miss critical underlying mechanisms.
Ultimately, the effectiveness of any mitigation strategy hinges on a nuanced understanding of bidder behavior – a complex adaptive system in its own right. The challenge isn’t simply to prevent manipulation, but to encourage efficient bidding, fostering a market that reflects genuine supply and demand. This demands a shift from reactive control to proactive design, a move towards an electricity market that isn’t just stable, but truly intelligent.
Original article: https://arxiv.org/pdf/2511.20812.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-11-29 19:07