Towards Data-Leveraged Behavioral Policy Design for Alleviating Peak Electricity Demand

Abstract

The problem of managing peak electricity demand is of significant importance to utility providers. In Ontario, electricity consumption achieves its peak during the afternoon hours in summer. Electricity generation units are provisioned for these few days of the year, which is expensive. In the past, researchers have studied several approaches to curb peak electricity demand by providing consumers with incentives to reduce their load.

We study using non-cash (or behavioral) incentives to motivate consumers to set their thermostats a few degrees higher during the summer, thereby reducing aggregate peak demand. Such incentives exploit cognitive biases and find their foundations in behavioral economics and psychology. We mathematically model the effect of non-cash incentives using utility functions. To build an accurate utility model, we devise and conduct a large-scale survey to elicit consumers' behavioral preferences. At a high level, we propose an analytical Big-Data based approach to evidence-based policy design, where a mechanism design framework uses a data-driven utility model to inform incentive policies.