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Optimal electricity tariff design with demand-side investments

  • Felipe A. CastroEmail author
  • Duncan S. Callaway
Original Paper
  • 6 Downloads

Abstract

This paper proposes a method for evaluating tariffs based on mathematical programming. In contrast to previous approaches, the technique allows comparisons between portfolios of rates while capturing complexities emerging in modern electricity sectors. Welfare analyses conducted with the method can account for interactions between intermittent renewable generation, distributed energy resources and tariff structures. We explore the theoretical and practical implications of the model that underlies the technique. Our analysis shows that a regulator may induce the welfare maximizing configuration of the demand by properly updating portfolios of tariffs. We exploit the structure of the model to construct a simple algorithm to find globally optimal solutions of the associated nonlinear optimization problem; a computational experiment suggests that the specialized procedure can outperform standard nonlinear programming techniques. To illustrate the practical relevance of the rate analysis method, we compare portfolios of tariffs with data from two electricity systems. Although portfolios with sophisticated rates create value in both, these improvements differ enough to advise different portfolios. This conclusion is beyond the reach of previous techniques to analyze rates, illustrating the importance of using model-based data-driven approaches in the design of rates in modern electricity sectors.

Keywords

Rate design Energy systems modeling Mathematical programming 

Notes

Acknowledgements

We are grateful for the insightful comments and observations of Shmuel Oren of the Industrial Engineering and Operations Research Department, and Pravin Varaiya of the Electrical Engineering and Computer Science Department, both at UC Berkeley. We would also like to thank the anonymous reviewers. Their comments helped to significantly improve this work.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Fiscalía Nacional EconómicaSantiagoChile
  2. 2.Energy and Resources Group, University of California at BerkeleyBerkeleyUSA

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