Convex Primal Formulations for Convex Hull Pricing With Reserve Commitments

Convex hull pricing has been introduced recently to increase transparency and reduce uplift payments for the U.S. wholesale markets. A convex primal formulation approach is one of the most efficient methods to obtain a high-quality convex hull price. Even though significant progress has been made, t...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 36; no. 3; pp. 2345 - 2354
Main Authors: Yu, Yanan, Zhang, Tong, Guan, Yongpei, Chen, Yonghong
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Ancillary services
Combined cycle
Computational geometry
Computing time
convex hull pricing
Convexity
Cost function
Generators
integral formulation
MISO communication
Mixed integer
Optimization
Power system stability
Pricing
reserve commitment decisions
Spinning
Uplift
uplift payments
Upper bound
Upper bounds
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:Convex hull pricing has been introduced recently to increase transparency and reduce uplift payments for the U.S. wholesale markets. A convex primal formulation approach is one of the most efficient methods to obtain a high-quality convex hull price. Even though significant progress has been made, the co-optimization of energy and ancillary services is much more challenging due to the discrete nature in formulating ancillary services, for example, the regulating reserve commitment variables, which are binary introduced by some Independent System Operators (ISOs), such as Midcontinent ISO (MISO), to capture the special characteristics of certain generators, such as combined-cycle units. In this paper, we propose convex primal formulations for convex hull pricing considering regulating, spinning, and online/offline-supplemental reserves, in which the regulating reserve commitment variables are considered. Accordingly, we can solve a linear program, instead of a mixed-integer program, which is much harder to solve, to derive the minimum uplift payments and exact convex hull price for the case without general ramping constraints. For the case with general ramping constraints, an upper bound of the minimum uplift payment can also be derived. The final computational experiments on MISO cases verify the effectiveness of our approach in solution quality and computational time.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.3039980