Lead Investigator: Dr. Melvyn Weeks, Faculty of Economics, University of Cambridge, UK
Coauthor: Bowei Guo, Faculty of Economics, University of Cambridge, UK
Governments across Europe and beyond are planning to integrate large quantities of intermittent
wind and solar into the electricity grid. In this context, and with the timing
of renewable supply largely fixed, a major challenge for renewable energy consumption is
how to incentivise flexible demand, by encouraging consumers to reallocate consumption
through a number of mechanisms, including time-of-use (TOU) tariffs. Among demand
response programmes, the dynamic pricing schemes are believed to have the greatest potential
to both shift and reduce peak load, thus reducing the financial risks for electricity
retailers, but also benefit to consumers. In much of the current literature the focus has
been on estimating demand response for agivenset of prices. In this paper we consider
a different problem, namely how should a retailer decide the set of dynamic prices to
maximises profit, while guaranteeing gains for consumers.
We develop a game theoretic model in a dynamic pricing environment with two types of
decision makers: retailers and consumers. Working backward we first solve for demand
across heterogenous consumers for a fixed set of prices. We then solve the retailers problem,
namely and the optimal day-ahead prices that maximise profit, given the demand response
of consumers.
We utilise data from the Ireland Electricity Smart Metering Trials programme to evaluate
the gain for both consumers and retailers from switching from an existing flat tariff scheme
to the proposed set of optimal tariffs. There are two important by-products from this work.
First, we evaluate the economic value added in the improvement in forecast accuracy in
electricity demand. Second, we evaluate the economic value added when more personalised
and targeting tariffs are set for different groups of consumers.