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Liberalized electricity markets promise a cost-efficient operation and expansion of power systems but may as well introduce opportunities for strategic gaming for price-making agents. Given the rapid transition of today's energy systems, unconventional generation and consumption patterns are emerging, presenting new challenges for regulators and policymakers to prevent strategic behavior. The strategic offering of various price-making agents in oligopolistic electricity markets resembles a multi-leader-common-follower game. The decision problem of each agent can be modeled as a bi-level optimization problem, consisting of the strategic agent's decision problem at the upper-level, and the market clearing at the lower-level. When modeling a multi-leader game, i.e., a set of bi-level optimization problems, the resulting equilibrium problem with equilibrium constraints poses several challenges. Real-life applicability or policy-oriented studies are challenged by the potential multiplicity of equilibria and the difficulty of exhaustively exploring this range of equilibria. In this paper, the range of equilibria is explored by using a novel simultaneous solution method. The proposed solution technique relies on applying Scholtes' regularization before concatenating the strategic actor's decision problems' optimality conditions. Hence, the attained solutions are stationary points with high confidence. In a stylized example, different strategic agents, including an energy storage system, are modeled to capture the asymmetric opportunities they may face when exercising market power. Our analysis reveals that these models' outcomes may span a broad range, impacting the derived economic metrics significantly. [ABSTRACT FROM AUTHOR]

Many regulators are pushing for more cost-reflective distribution network charges to inform end users of the grid infrastructure costs their behavior causes. Since future investment costs can be avoided by reducing simultaneous peak loads, forward-looking, coincident peak charges are often proposed. Under the assumption of convex network costs, it has been shown that optimal charges signal long-run marginal network costs, triggering an optimal trade-off between network expansion and peak load reduction. In practice, however, network investments are lumpy, requiring engineering methods to estimate ill-defined marginal costs based on long-term peak demand forecasts. In this paper, we derive the optimal forward-looking network charge set by a social welfare maximizing regulator, endogenously considering investment lumpiness and uncertain consumer demand. While the optimal tariff still equals marginal network costs in essence, it now depends on a multitude of network- and demand-related parameters. Our results demonstrate that forward-looking network charges require accurate information on willingness to pay for peak demand, which currently is typically unknown to regulators. [ABSTRACT FROM AUTHOR]