Minimax: an incentive-driven pricing scheme in the electricity retail market

Abstract

Reducing peak demand is critically important in smartgrid as a significant fraction of the electric grids capital and operational expenses is affected by the peak power demands. Time of Use (ToU) and Real Time Pricing (RTP) pricing schemes have been used by power system operators to incentivize cus- tomers to reduce their peak energy demands during peak hours. However, ToU only provides a weak incentive for customers and does not promote adoption at scale. Similarly, day-ahead Real- Time Pricing (RTP) scheme might create peaks in previoulsy off-peak periods and causes some ping-pong effect in next day prices. In this paper, we introduce a new incentive-driven scheme called Minimax which encourages customers to flatten their daily load profiles such that they can reduce their electricity bill and help lowering the aggregate peak power demands. Using two real life energy usage datasets, we show via simulations how the peak energy usage and load factor vary with different choices of parameter values we select for the Minimax scheme. In addition, we present our optimal scheduling policy which yields the minimum energy bill assuming a certain percentage of load demands is schedulable. Our results using energy usage data of 100 homes from the UMASS dataset show that customers can save 13-17% of their electricity bills if the Minimax scheme is used but only about 2-3% if RTP or TOU scheme is used. Furthermore, the power system operators see a 10% reduction in peak power demand if appropriate parameter values are used for the Minimax scheme while the peak demands increase by more than 70% using RTP or TOU schemes.