A simulation study for residential electricity user behavior under dynamic variable pricing with demand charge

ABSTRACT

Attempting to increase energy efficiency and improve system load factors in an electricity distribution system, Demand Response (DR) has long been proposed and implemented as a form of load management. Various pricing structures incentivizing consumers to shift energy consumption from on-peak to off-peak periods are evident in this field. Most DR methods currently used in practice belong to static variable pricing (e.g., Time of Use, Critical Peak Pricing) and the impact of such tariffs has been well established. However, dynamic variable pricing in general is less studied and much less practiced in the field, due to the lack of understanding of consumer behavior in response to price uncertainty. In this article, we study a novel dynamic variable pricing scheme that uses the coincident demand charge to reduce load consumption during peak events. We employ a multi-attribute utility function and model predictive control to simulate consumer behavior of utility maximization in home energy consumption. We use a conditional Markov chain to model and predict the system peak. Effects of the proposed residential electricity rate based on coincident demand charge are compared with other pricing schemes through simulation validated with real-world residential load profiles. Finally, we extend the simulations to study the impact of integrating renewable solar production in a DR program.

KEYWORDS:

• Demand response
• multi-attribute utility
• model predictive control
• coincident demand charge
• dynamic pricing

Additional information

Notes on contributors

Prajwal Khadgi

Prajwal Khadgi is currently working as a Senior Data Scientist at Cox Automotive Inc., Atlanta. He received his Ph.D. in industrial engineering from the University of Louisville in 2016 and his master's in industrial engineering from Northern Illinois University in 2009. His expertise includes discrete-event simulation, simulation optimization, decision theory, operations research, consumer behavior modeling, and data analysis. He is a member of INFORMS, IISE, and Alpha Pi Mu.

Lihui Bai

Lihui Bai is an Associate Professor in the Industrial Engineering Department at the University of Louisville. She received her Ph.D. in industrial engineering from the University of Florida. Her research interests are in the field of optimization modeling and computation in operations research, with focuses on applications in energy systems and transportation in logistics. Her research has been published in Operations Research, Networks, and Operations Research Letters, among others. She is a member of INFORMS, IISE, Alpha Pi Mu, and Alpha Iota Delta.