Recovery mechanisms in day-ahead electricity markets with non-convexities - Part I: Design and evaluation methodology

Andrianesis, Panagiotis; Liberopoulos, George; Kozanidis, George; Papalexopoulos, A.

doi:10.1109/tpwrs.2012.2207920

Cited by 28 publications

(20 citation statements)

References 37 publications

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“…Literature on pricing non-convexities is extensive, see for example O'Neill et al (2005) or Andrianesis et al (2013) for the recent study. Various approaches have been proposed so far, but the elastic demand typically is neglected, and transmission constraints are omitted.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand shifting bids in energy auction with non-convexities and transmission constraints

Żółtowska

2016

Energy Economics

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Section: Literature Reviewmentioning

confidence: 99%

Demand shifting bids in energy auction with non-convexities and transmission constraints

Żółtowska

2016

Energy Economics

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“…Literature on the pricing non-convexities is extensive; see for example [3] or [4] for the recent study. Various approaches have been proposed so far, yet there are problems to be addressed, like a network-constrained market, among others [2].…”

Section: A Literature Reviewmentioning

confidence: 99%

“…In (3), the power flow constrained with line's transmission capacity limit is expressed using shift factors (SFs) (by definition, the shift factor at the reference node equals zero). The counterflow capacity limit is stated in (4). Each transmission constraint has its own shadow prices (3), (4).…”

Section: Pool-based Auction Clearing Modelmentioning

confidence: 99%

Direct Minimum-Uplift Model for Pricing Pool-Based Auction With Network Constraints

Żółtowska

2016

IEEE Trans. Power Syst.

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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.

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“…The full optimization model for this example is presented in [9,Section II]. For the needs of our analysis we make the following adjustments.…”

Section: Numerical Examplementioning

confidence: 99%

Market design for the simultaneous optimization of the Day-Ahead market and the reliability unit commitment applications

Papalexopoulos¹,

Andrianesis²

2013

2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid

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The design of restructured electricity markets requires a mechanism to ensure that differences between the bid-in demand that clears in the Day-Ahead Market (DAM) and the ISO's demand forecast do not compromise reliability requirements. This mechanism is usually called Reliability (or Residual) Unit Commitment (RUC), and is deployed to procure additional resources, beyond the DAM energy schedules, to meet the demand forecast. In this paper, we present the theoretical foundation of the RUC process and key important considerations in implementing the RUC application. We also provide a description of the general sequential approach in which the DAM application is executed first and then the RUC process is completed. We then offer the design framework for the implementation of an integrated approach which combines the functionality of the DAM and the RUC into one market application. The integrated approach offers substantial efficiencies by procuring all DAM products simultaneously.

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Recovery mechanisms in day-ahead electricity markets with non-convexities - Part I: Design and evaluation methodology

Cited by 28 publications

References 37 publications

Demand shifting bids in energy auction with non-convexities and transmission constraints

Demand shifting bids in energy auction with non-convexities and transmission constraints

Direct Minimum-Uplift Model for Pricing Pool-Based Auction With Network Constraints

Market design for the simultaneous optimization of the Day-Ahead market and the reliability unit commitment applications

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