  In the present work we tackle the problem of finding the optimal price tariff
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 sufficiently large smart-meter dataset from which it can statistically
characterize the relationship between the tariff price and the demand load of
its clients. Three different models are analyzed to predict the aggregated load
as a function of the electricity prices and other parameters, as humidity or
temperature. More specifically, 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 offered. This optimization problem accounts for
different 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 profits 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.
