Equilibrium models to analyse the impact of different coordination schemes between TSO and DSOs on market power in sequentially-cleared energy and ancillary services markets under load and renewable generation uncertainty

The current massive installation of distributed resources in electricity distribution systems is transforming these systems into active dispatching subjects. At the same time, the need to compensate for the intermittent generation of an increasing amount of renewable sources creates the need to acquire more ancillary services. Flexible resources in the distribution system could provide these services not only within the perimeter of the distribution network to which they are connected but also for the benefit of the transmission system. However, this requires Transmission System Operators (TSOs) and Distribution System Operators (DSOs) to coordinate their dispatching actions effectively. One critical aspect of this coordination is establishing a market architecture that limits market power. This paper presents an innovative game-theoretic approach to compare different TSO-DSO coordination models for acquiring ancillary services from distribution resources. Several schemes are considered: some with coordinated market management by TSOs and DSOs, others with sequential or independent local markets. For each scheme, the dispatching problem is formulated as a two-stage stochastic sequential game, where the first stage is the day-ahead market and the second stage is the balancing market. Nash equilibrium solutions are obtained by iteratively solving the profit maximization problem of each market player. Numerical tests on a CIGRE benchmark network show that coordination schemes enabling distribution resources to provide ancillary services to the transmission system can significantly increase system costs when congestion occurs in the transmission network.

💡 Research Summary

The paper addresses the pressing challenge of integrating a rapidly growing fleet of Distributed Energy Resources (DERs) into electricity markets while preserving efficient operation and limiting market power. The authors focus on the coordination between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) and propose a game‑theoretic framework to compare three distinct coordination schemes for ancillary service provision.

Scheme A assumes a single, common ancillary services market (ASM) that aggregates all flexibility resources—both those connected to the transmission network and those attached to distribution networks. Scheme B separates the ASM into a transmission‑level market and independent distribution‑level markets, each serving only the resources physically connected to the respective network. Scheme C is similar to B but allows unused flexibility from the distribution networks to be offered additionally in the transmission‑level ASM.

The interaction between market participants is modeled as a two‑stage stochastic sequential game. The first stage is the day‑ahead market (DAM), where generators and flexible loads submit price‑quantity bids and the system operator clears the market to minimize total cost while satisfying forecasted demand and renewable generation. The second stage is the real‑time ASM, which balances actual deviations from forecasts and resolves line congestion by procuring upward/downward regulation and load curtailment. Uncertainty in load and renewable output is captured through a set of scenarios.

Each market participant (aggregator) is treated as a leader in a multi‑leader, common‑follower game. Leaders choose bidding strategies to maximize their own profit; the market operator, acting as the follower, clears the markets given all submitted bids and seeks to minimize overall system cost. This structure yields a Mathematical Program with Equilibrium Constraints (MPEC). By replacing the follower’s optimality conditions with Karush‑Kuhn‑Tucker (KKT) conditions, the authors obtain a set of interrelated bilevel problems. They linearize bilinear terms and complementarity conditions using Big‑M techniques, converting the MPEC into a Mixed‑Integer Linear Program (MILP). An iterative best‑response algorithm is then employed to converge to a Nash equilibrium, where no player can improve its profit by unilaterally deviating.

Numerical experiments are conducted on the CIGRE 33‑bus benchmark network, extended with multiple distribution feeders. The authors introduce artificial congestion on selected transmission lines and vary the proportion of flexible DER capacity. Results show that schemes A and C, which permit distribution‑connected DERs to provide ancillary services to the transmission system, lead to significantly higher system costs under congestion. The reason is that the additional flexibility is often procured at high marginal prices, and strategic aggregators can exploit congestion to inflate those prices. Scheme B, by keeping transmission and distribution markets separate, limits the ability of distribution‑side DERs to affect transmission‑level prices, resulting in a more modest cost increase.

The study highlights the importance of accounting for market power when designing DER participation rules. Allowing DERs to enter the transmission‑level ancillary market without appropriate safeguards can create arbitrage opportunities for large aggregators, exacerbating congestion costs. The authors suggest that regulators may need to implement price caps, congestion‑cost allocation mechanisms, or participation limits to mitigate such risks.

Key contributions of the paper are: (1) a formal two‑stage stochastic game‑theoretic model that captures TSO‑DSO coordination and strategic bidding; (2) a tractable MILP reformulation of the resulting MPEC and an iterative algorithm for equilibrium computation; (3) a quantitative comparison of three coordination schemes using a realistic network, demonstrating how DER integration can unintentionally increase system costs and market power under certain designs. The insights provided are valuable for policymakers, system operators, and market designers seeking to integrate DERs while preserving competitive, efficient, and reliable electricity markets.