Optimal pricing for electricity retailers based on data-driven consumers' price-response Articles uri icon

published in

  • Top  Journal

publication date

  • February 2022

start page

  • 340

end page

  • 464


  • 30

International Standard Serial Number (ISSN)

  • 1134-5764

Electronic International Standard Serial Number (EISSN)

  • 1863-8279


  • In the present work, we tackle the problem of fnding the optimal price tarif to be
    set by a risk-averse electric retailer participating in the pool and whose customers
    are price sensitive. We assume that the retailer has access to a sufciently large
    smart-meter dataset from which it can statistically characterize the relationship
    between the tarif price and the demand load of its clients. Three diferent models
    are analyzed to predict the aggregated load as a function of the electricity prices
    and other parameters, as humidity or temperature. More specifcally, we train linear
    regression (predictive) models to forecast the resulting demand load as a function of
    the retail price. Then, we will insert this model in a quadratic optimization problem
    which evaluates the optimal price to be ofered. This optimization problem accounts
    for diferent sources of uncertainty including consumer's response, pool prices and
    renewable source availability, and relies on a stochastic and risk-averse formulation.
    In particular, one important contribution of this work is to base the scenario generation and reduction procedure on the statistical properties of the resulting predictive
    model. This allows us to properly quantify (data-driven) not only the expected value
    but the level of uncertainty associated with the main problem parameters. Moreover, we consider both standard forward-based contracts and the recently introduced
    power purchase agreement contracts as risk-hedging tools for the retailer. The results
    are promising as profts are found for the retailer with highly competitive prices and
    some possible improvements are shown if richer datasets could be available in the
    future. A realistic case study and multiple sensitivity analyses have been performed
    to characterize the risk-aversion behavior of the retailer considering price-sensitive
    consumers. It has been assumed that the energy procurement of the retailer can be
    satisfed from the pool and diferent types of contracts. The obtained results reveal
    that the risk-aversion degree of the retailer strongly infuences contracting decisions,
    whereas the price sensitiveness of consumers has a higher impact on the selling
    price ofered.


  • Business
  • Economics
  • Industrial Engineering
  • Statistics


  • electricity retailer; price-sensitive consumers; risk aversion; smart meter data; stochastic programming; time-of-use rate