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Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics. [ABSTRACT FROM AUTHOR]

This paper proposes a pricing scheme for the day-ahead market in power systems with a large percentage of renewable stochastic production. To clear the day-ahead market, instead of a simplistic deterministic model, we use a two-stage stochastic programming model that embodies a prognosis of future operating conditions. Non-convexities due to start-up costs and the on/off status of generators and their minimum power outputs are properly taken into account. Our goal is to obtain uniform day-ahead clearing prices that deviate in the least possible manner from marginal prices and that allow producers to recover their costs without uplifts. The proposed methodology is illustrated using a simple example and a realistic case study. [ABSTRACT FROM AUTHOR]

Auctions with non-convexities, as considered in this paper, use a multi-period optimal power flow (OPF) model based on generators' offers to calculate efficient schedules. The standard locational marginal prices (LMPs) may not support these schedules and uplifts are needed to help the generators break even. This paper presents a direct minimum-uplift (DMU) pricing model, which is formulated as a mixed integer linear programming problem with uplift minimization objective. New DMU prices are major variables constrained with decomposition formula based on shift factors, similar to the actual practice of developing LMPs. The remaining constraints reflect the generators' profits and uplifts modeled as functions of the DMU prices. The main feature of the DMU model is individual rationality attained in pool-based auction, under prices properly reflecting network constraints, complemented with minimum uplifts. The model is validated on several cases, including the IEEE 24-node system. [ABSTRACT FROM PUBLISHER]

This letter considers a stochastic electricity market in which the uncertainty is handled using a proportional control law and chance security constraints. We prove that in this framework, market clearing prices yielding a robust competitive market equilibrium can be computed and used for stochastic market settlements by risk-aware system operators. An illustrative case study corroborates the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In centralized day-ahead electricity markets with marginal pricing, unit commitment costs and capacity constraints give rise to non-convexities which may result in losses to some of the participating generating units. To compensate them for these losses, a recovery mechanism is required. In Part I of this two-part paper, we present certain recovery mechanisms that result in recovery payments after the market is cleared. We also propose a methodology for evaluating the bidding strategy behavior of the participating units for each mechanism. In this paper (Part II), we apply this methodology to evaluate the performance and incentive compatibility properties of each recovery mechanism on a test case model representing the Greek joint energy/reserve day-ahead electricity market. Lastly, we perform sensitivity analysis with respect to key parameters and assumptions and we provide directions for further research. [ABSTRACT FROM PUBLISHER]

In centralized day-ahead electricity markets with marginal pricing, unit commitment costs and capacity constraints give rise to non-convexities which may result in losses to some of the participating generating units. Therefore, a recovery mechanism is required to compensate them. In this paper, we present and analyze several recovery mechanisms that result in recovery payments after the market is cleared. Each of these mechanisms results in a different type and/or amount of payments for each participating unit that exhibits losses. We also propose a methodology for evaluating the bidding strategy behavior of the participating units for each mechanism. This methodology is based on the execution of a numerical procedure aimed at finding joint optimal bidding strategies of the profit-maximizing units. In a companion follow-up paper (Part II), we apply this methodology to evaluate the performance and incentive compatibility of the suggested recovery mechanisms on a simplified test case model of the Greek electricity market. [ABSTRACT FROM PUBLISHER]