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Improved Regulatory Approaches for the Remuneration of Electricity Distribution Utilities with High Penetrations of Distributed Energy Resources

Jesse D. Jenkins and Ignacio J. Pérez-Arriaga

Year: 2017
Volume: Volume 38
Number: Number 3
DOI: 10.5547/01956574.38.3.jjen
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Abstract:
Under increasing penetration of distributed resources, regulators and electricity distribution utilities face greater uncertainty regarding the evolution of network uses and efficient system costs. This uncertainty can threaten revenue adequacy and challenges both cost of service/rate of return and incentive/performance-based approaches to the remuneration of distribution utilities. To address these challenges, this paper proposes a novel methodology to establish allowed utility revenues over a multi-year regulatory period. This method combines several "state of the art" regulatory tools designed to overcome information asymmetries, manage uncertainty, and align incentives for utilities to cost-effectively integrate distributed energy resources while taking advantage of opportunities to reduce system costs and improve performance. We use a reference network model to simulate a large-scale urban distribution network, demonstrate the practical application of this regulatory method, and illustrate its performance in the face of both benchmark and forecast errors.



Regulating Heterogeneous Utilities: A New Latent Class Approach with Application to the Norwegian Electricity Distribution Networks

Luis Orea and Tooraj Jamasb

Year: 2017
Volume: Volume 38
Number: Number 4
DOI: 10.5547/01956574.38.4.lore
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Abstract:
Since the 1990s, electricity distribution networks in many countries have been subject to incentive regulation. The sector regulators aim to identify the best performing utilities as frontier firms to determine the relative efficiency of firms. This paper develops a nested latent class (NLC) model approach where unobserved differences in firm performance are modelled using two `zero inefficiency stochastic frontier' (ZISF) models nested in a `latent class stochastic frontier' (LCSF) model. This captures the unobserved differences due to technology or environmental conditions. A Monte Carlo simulation suggests that the proposed model does not suffer from identification problems. We illustrate the proposed model with an application to Norwegian distribution network utilities for the period 2004-2011. We find that the efficiency scores in both LCSF and ZISF models are biased, and some firms in the ZISF model are wrongly labelled as inefficient. Conversely, inefficient firms may be wrongly labelled as being fully efficient by the ZISF model.





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