arxiv: 2604.16229 · v2 · submitted 2026-04-17 · 📡 eess.SY · cs.SY

Recognition: unknown

Simulating Arbitrage Optimization for Market Monitoring in Gas and Electricity Transmission Networks

Noah Rhodes , Sachin Shivakumar , Luke S. Baker , Kaarthik Sundar , Anatoly Zlotnik

Authors on Pith no claims yet

Pith reviewed 2026-05-10 07:40 UTC · model grok-4.3

classification 📡 eess.SY cs.SY

keywords market powergas-electricity interactionoptimal power flowoptimal gas flowbid monitoringarbitrageenergy networksmarket mitigation

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The pith

Gas-fired generators can use bids in the natural gas market to influence electricity prices, and optimization methods can detect this cross-market market power.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper models two linked energy markets where the same participants act as producers in electricity and consumers in gas. It creates simulation scenarios in small test networks to demonstrate how strategic gas bids can change power market outcomes such as prices and supply availability. From these models the authors build optimization routines that scan bids to flag when a participant is likely exerting market power by withholding or inflating across the two systems. Market monitors could apply the routines to replace suspect bids with reference levels and avoid artificial shortages. The approach treats the markets as coupled rather than separate, which changes how monitoring and mitigation are performed.

Core claim

We develop a framework based on DC optimal power flow and steady-state optimal gas flow formulations to represent two interacting markets with structured exchange of price and quantity bids. We formulate optimization-based methods to identify market power in a power grid, as well as to identify market conditions that indicate market power being exerted by a generator using gas market bids.

What carries the argument

The coupled-market simulation framework that exchanges bids between a DC optimal power flow model and a steady-state optimal gas flow model, which enables both arbitrage scenario testing and the formulation of detection optimizations for cross-market power.

Load-bearing premise

Small simulated test networks are sufficient to show that the optimization methods will correctly identify market power when gas bids are used to affect power outcomes in real systems without other intervening factors.

What would settle it

Running the detection optimizations on historical bid and outcome data from an actual coupled gas-electricity market region and checking whether they correctly flag documented cases of market power or produce many false positives.

Figures

Figures reproduced from arXiv: 2604.16229 by Anatoly Zlotnik, Kaarthik Sundar, Luke S. Baker, Noah Rhodes, Sachin Shivakumar.

**Figure 1.** Figure 1: Diagram gas-fired generator bids in the OPF and OGF. D. Iterative Bidding Strategy For the iterative scheme, we assume that the electricity and gas markets are cleared at different times so generators can use additional information from the dispatch of one market to update the bids into the other. Algorithmically, this translates to the following steps: For the 0th iteration, initial bids for the electrici… view at source ↗

**Figure 2.** Figure 2: Power grid network with congestion. The LMP at each node is set by the bid price of a generator at the node. Each generator could bid higher to increase profit, but could not triple their bid or increase the bid by $100 and still be cleared in the market. Gen 1 and 2 do not have market power despite being in a congested region. TABLE I: Market Power Scenarios Network Changes LMP 1 LMP 2 LMP 3 Increase gen … view at source ↗

**Figure 3.** Figure 3: shows the networks with no congestion. In the gas network, the generator at Delivery node 1 and 2 receive their full gas nomination. In the electrical network, generator 1 and 2 are both marginal, and produce less than their maximum power. We use a pass-through electric bid price for simplicity, resulting in generators 1 and 2 making a small loss by selling slightly less electricity than they could generat… view at source ↗

**Figure 4.** Figure 4: Bidding a higher price and quantity in the gas network, generator 1 has more capacity in the electrical network, but the capacity at generator 2 is reduced. This increases the LMP at node 2 due to electrical network congestion, without generator 2 changing its price bid. to congestion, the LMP at node 2 can be increased above market monitoring thresholds. If the reference bid (average cleared bid in the la… view at source ↗

**Figure 5.** Figure 5: Coupling of IEEE-14 and GasLib-11 cases. Arrows indicate generators connections to gas and power nodes [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

read the original abstract

We examine market outcomes in energy transport networks with a focus on gas-fired generators, which are producers in a wholesale electricity market and consumers in the natural gas market. Market administrators monitor bids to determine whether a participant wields market power to manipulate the price of energy, reserves, or financial transmission rights. If economic or physical withholding of generation from the market is detected, mitigation is imposed by replacing excessive bids with reference level bids to prevent artificial supply shortages. We review market monitoring processes in the power grid, and present scenarios in small interpretable test networks to show how gas-fired generators can bid in the gas market to alter outcomes in a power market. We develop a framework based on DC optimal power flow (OPF) and steady-state optimal gas flow (OGF) formulations to represent two interacting markets with structured exchange of price and quantity bids. We formulate optimization-based methods to identify market power in a power grid, as well as to identify market conditions that indicate market power being exerted by a generator using gas market bids.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper couples DC OPF with steady-state OGF to simulate how gas-market bids can signal or enable market power in electricity markets, but all demonstrations stay on small hand-crafted networks.

read the letter

The core contribution here is a framework that links the two markets through structured bid exchange so that gas bids from generators can be used to detect conditions where market power is being exerted in the power grid. They build this on standard DC optimal power flow and optimal gas flow models, then add optimization-based detectors for market power and for gas-bid manipulation. That coupling is the new piece, and the small-network scenarios do show concrete ways a generator could use gas bids to shift power outcomes in interpretable cases. The formulations look clean and directly usable for anyone already running OPF or OGF tools. Credit to the authors for keeping the models transparent and for focusing on a real monitoring problem rather than abstract theory. The soft spot is exactly what the stress-test flags: validation is confined to tiny test networks. Nothing in the work checks whether the detectors remain reliable when node count grows, when multiple strategic players interact, when constraints bind in non-obvious patterns, or when data are incomplete or noisy. Because the identification methods are themselves optimization problems whose behavior changes with scale and topology, the claim that they can identify market power in a power grid rests on an untested jump from toy examples. No numerical performance numbers or robustness checks appear in the abstract either. This is for researchers who build or extend coupled energy-market models and want a starting point for cross-commodity monitoring tools. A reader already working on gas-power interactions could pull the bid-exchange structure or the detector formulations and adapt them. It is not yet ready for practitioners who need methods that have been stress-tested at realistic scale. I would send it to peer review. The modeling foundation is solid and the topic matters for infrastructure monitoring, so referees can usefully push on the scaling and validation gaps rather than starting from scratch.

Referee Report

2 major / 2 minor

Summary. The paper develops a coupled modeling framework based on DC optimal power flow (OPF) and steady-state optimal gas flow (OGF) to represent interacting electricity and natural gas markets, with structured exchange of price and quantity bids. It formulates optimization-based detectors for market power in the power grid and for conditions indicating market power exerted by gas-fired generators via gas-market bids. The central claims are illustrated through scenarios on small, interpretable test networks showing how gas bids can alter power-market outcomes, and the work reviews existing market-monitoring practices.

Significance. If the identification methods can be shown to generalize, the framework would offer a concrete, optimization-based approach for market administrators to detect strategic bidding across coupled energy networks, particularly for gas-fired units. The reliance on standard DC-OPF and OGF formulations is a strength, as is the explicit modeling of bid exchange between markets; these elements could support reproducible extensions once scaling and robustness are addressed.

major comments (2)

[Simulation scenarios and results] The validation of the market-power detectors rests entirely on small, hand-crafted test networks (a handful of nodes and participants). No experiments examine scaling with network size, simultaneous strategic behavior by multiple agents, binding constraints in non-obvious topologies, or the effects of measurement noise and incomplete information typical of real market data. Because the feasible sets and objective landscapes of the identification optimizations change with topology and participant count, the claim that the methods “can identify market power in a power grid” remains an untested extrapolation.
[Formulation of market-power identification methods] The optimization-based identification methods are formulated but no quantitative performance metrics (detection accuracy, false-positive rates, sensitivity to bid perturbations) are reported even on the toy networks, nor are baseline comparisons provided against simpler statistical or threshold-based monitors.

minor comments (2)

[Framework description] Notation for the structured bid exchange between the OPF and OGF models could be clarified with an explicit diagram or table showing the information flow at each time step.
[Introduction] The review of existing market-monitoring processes would benefit from additional citations to recent FERC or ISO reports on gas-electric coordination.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and for recognizing the potential of the coupled framework for market monitoring. Our manuscript focuses on formulating the DC-OPF/OGF coupling with bid exchange and optimization-based detectors, using small interpretable networks to illustrate interactions rather than providing large-scale empirical validation. We respond to each major comment below.

read point-by-point responses

Referee: [Simulation scenarios and results] The validation of the market-power detectors rests entirely on small, hand-crafted test networks (a handful of nodes and participants). No experiments examine scaling with network size, simultaneous strategic behavior by multiple agents, binding constraints in non-obvious topologies, or the effects of measurement noise and incomplete information typical of real market data. Because the feasible sets and objective landscapes of the identification optimizations change with topology and participant count, the claim that the methods “can identify market power in a power grid” remains an untested extrapolation.

Authors: We agree that the simulations use small, hand-crafted test networks selected for clarity in demonstrating market interactions and detector behavior. The paper does not present the scenarios as comprehensive validation or claim empirical proof across all scales; the core contribution is the general formulation of the coupled markets and identification optimizations. We will revise the manuscript to explicitly describe the examples as illustrative, tone down any broad claims, and add a dedicated discussion on scalability challenges, extensions to multiple agents, non-obvious topologies, and robustness considerations. The mathematical structure of the optimizations is topology-agnostic, supporting broader applicability, though we acknowledge that full scaling studies would require additional work beyond the current scope. revision: partial
Referee: [Formulation of market-power identification methods] The optimization-based identification methods are formulated but no quantitative performance metrics (detection accuracy, false-positive rates, sensitivity to bid perturbations) are reported even on the toy networks, nor are baseline comparisons provided against simpler statistical or threshold-based monitors.

Authors: The identification methods are formulated as optimization problems that detect minimal deviations from competitive outcomes consistent with observed bids and flows. The toy-network scenarios demonstrate that the detectors correctly identify strategic gas-market bidding by gas-fired generators through elevated objective values or infeasibility under competitive assumptions. We acknowledge that no statistical metrics such as accuracy rates, false-positive rates, or sensitivity analyses are provided, nor are comparisons to threshold-based or statistical monitors, because the work is not designed as a statistical detection benchmark requiring labeled data or Monte Carlo trials. In revision we will report the specific objective values, constraint slacks, and solution characteristics from the identification problems in the examples to strengthen the presentation. Broader benchmarking is outside the paper's scope. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivation builds on standard models

full rationale

The paper constructs a coupled framework from established DC OPF and steady-state OGF formulations, then formulates separate optimization-based detectors for market power and demonstrates them via explicit simulations on small test networks. No equations or claims reduce a prediction to a fitted input by construction, no self-definitional loops appear, and no load-bearing steps rely on self-citations whose validity is assumed rather than independently verified. The central claims rest on model construction and scenario testing rather than tautological equivalences, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no details on specific parameters, axioms, or new entities; relies on standard assumptions of OPF and OGF models.

pith-pipeline@v0.9.0 · 5491 in / 1021 out tokens · 71789 ms · 2026-05-10T07:40:43.185557+00:00 · methodology

discussion (0)

Reference graph

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