A generalized class of locational pricing mechanisms for the electricity markets

Mariusz Kaleta

Abstract

In this paper, we consider a new class of mechanisms, LPR, which serves as a generalization of locational marginal pricing (LMP) and uniform pricing (CP) mechanisms. We analyze a space of possible mechanisms defined by the LPR class in the context of fairness, system balancing cost and market signals. We show that LMP is the only mechanism that produces signals of good quality. On the other hand, the uniform price mechanism enables minimization of system balancing costs and thereby, maximization of economic surplus, which is allocated under competitive rules. LMP generally works better than CP in terms of fairness. However, we show that there are other possibilities for constructing new and interesting mechanisms in the LPR class. We present an exemplary mechanism, LP+, which acts as a trade-off between LMP and CP, and presents sustainable properties. The numerical experiments we provide reveal that LP+ has interesting features. Finally, we claim that none of the following mechanisms – LMP, CP and LP+ – is dominated in LPR space in terms of considered quality measures.
Author Mariusz Kaleta (FEIT / AK)
Mariusz Kaleta,,
- The Institute of Control and Computation Engineering
Journal seriesEnergy Economics, ISSN 0140-9883, e-ISSN 1873-6181
Issue year2020
No86
Pages1-14
Publication size in sheets0.8
Article number103455
Keywords in EnglishLocational pricing mechanisms; System cost minimization; Fairness; Market signals; Parametric market mechanism; Networked auctions
ASJC Classification2002 Economics and Econometrics; 2100 General Energy
DOIDOI:10.1016/j.eneco.2016.09.023
URL http://www.sciencedirect.com/science/article/pii/S0140988316302687
Languageen angielski
File
Kaleta Energy Econom 2016.pdf 1.2 MB
Score (nominal)200
Score sourcejournalList
ScoreMinisterial score = 200.0, 06-09-2020, ArticleFromJournal
Publication indicators WoS Citations = 1; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.827; WoS Impact Factor: 2017 = 3.91 (2) - 2017=4.963 (5)
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