Mitigating Increase-Decrease Gaming with Alternative Connection Agreements: A Defender-Attacker-Defender Game

TL;DR

The paper tackles increase-decrease gaming in distribution-network redispatch by evaluating Alternative Connection Agreements (ACAs) within a Defender-Attacker-Defender framework. It formulates a novel trilevel optimization for ACA activation, day-ahead baselining, and intraday redispatch, then reformulates it into a tractable bilevel problem (LL*) and solves it with a custom branch-and-bound algorithm. Uncertainty from loads and prices is incorporated via Monte Carlo scenario analysis, using copula-based price scenarios and GOPACS data for redispatch spreads. A Dutch GOPACS-inspired case demonstrates that ACAs can substantially reduce redispatch costs (roughly 25% in many cases) with limited impact on FSP profits, and the gains depend on how often ACAs can be invoked and whether the DSO can anticipate gaming behavior.

Abstract

Redispatch markets are widely used by system operators to manage network congestion. A well-known drawback, however, is that Flexibility Service Providers (FSPs) may strategically adjust their baselines in anticipation of redispatch actions, thereby aggravating congestion and raising system costs. To address this increase-decrease gaming, Distribution System Operators (DSOs) could use Alternative Connection Agreements (ACAs) to conditionally limit the available connection capacity of market participants in the day-ahead stage. In this paper, we present a novel Defender-Attacker-Defender game to investigate the potential of this approach in distribution networks under load and price uncertainty. We solve the resulting trilevel optimization model using a custom branch-and-bound algorithm, and we demonstrate that it efficiently solves the problem without exploring many nodes in the branch-and-bound search tree for most simulated scenarios. The case study demonstrates that applying ACAs can substantially lower redispatch costs (e.g. by 25%) for the DSO with only a limited impact on FSP profits. The effectiveness of the approach critically depends on how often the DSO can invoke ACAs and on the extent to which the DSO can anticipate strategic bidding behavior of the FSP.

Figures (10)

• Figure 1: Diagram of the Defender–Attacker–Defender game, where the exercises as a preventive measure against increase–decrease gaming. The game consists of three sequential decisions: for the first two, we compare two alternative strategies, while for the final decision, only one strategy is considered. Each strategy is represented by a corresponding single- or multi-level optimization problem (see Section \ref{['sec: Other problems']}).
• Figure 2: Structure of the proposed trilevel defender-attacker-defender model. Variables propagating downwards are treated as parameters in the lower levels. Variables propagating upwards are the result of lower-level decisions, providing feedback to higher levels.
• Figure 3: Illustration of how the parameters $\bar{E}^{\mathrm{EV}}$, $\text{\b{$E$}}^{\mathrm{EV}}$, and $\bar{p}^{\mathrm{EV}}$ can be calculated for a fleet of two cars from their respective charging session information. The two figures in the left column present the areas containing all possible charging schedules of the individual EVs. These areas are characterized by the maximum charge power of the respective EVs, and the arrival/departure times and state of charge. In the right column, the figures present the three fleet parameters based on the charging sessions.
• Figure 4: Illustration of the convex restriction given by linear inequalities \ref{['eq: restriction']} to approximate the circular feasibility region given by equation \ref{['eq: S ub']} for some branch $(m, n) \in \Omega_B$ and some time step $t\in \Omega_T$
• Figure 5: Schematic representation of the CIGRE network with solar and wind generation under radial operation, adapted from rudion2006design. The simulated controls two fleets at buses 2 and 14.