What is it about?
Smart metering in electricity markets offers an opportunity to explore more diverse tariff structures. In this article residential electricity demand and the System Marginal Price of Ireland’s Single Electricity Market are simulated to estimate the wholesale risk associated with possible tariffs. A genetic algorithm (GA) with a stochastic fitness function is proposed to search for time-of-use tariffs that minimise wholesale risk to the supplier in residential markets. Alternative search algorithms and fitness functions are investigated in detail, as well as trade-offs in GA and simulation parameter settings.
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Why is it important?
The energy sector is undergoing a transition to become secure, clean and efficient. Designing electricity tariffs that fit withing this new framework is a challenging task. This paper helps understand the risks to suppliers when setting time-of-use tariffs.
Perspectives
This paper is interesting because it looks at both the electricity tariff design problem, and applies the tariffs to a real world smart meter data set.
Paula Carroll
University College Dublin, Ireland
Read the Original
This page is a summary of: A genetic algorithm approach to the smart grid tariff design problem, Soft Computing, December 2017, Springer Science + Business Media,
DOI: 10.1007/s00500-017-2971-2.
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