A Data-Driven Methodology for Dynamic Pricing and Demand Response in Electric Power Networks

Abstract

The practice of disclosing price of electricity before consumption (dynamic pricing) is essential to promote aggregator-based demand response in smart and connected communities. However, both practitioners and researchers have expressed fear that wild fluctuations in demand response resulting from dynamic pricing may adversely affect the stability of both the network and the market. This paper presents a comprehensive methodology guided by a data-driven learning model to develop stable and coordinated strategies for both dynamic pricing as well as demand response. The methodology is designed to learn offline without interfering with network operations. Application of the methodology is demonstrated using simulation results from a sample 5-bus PJM network. Results show that it is possible to arrive at stable dynamic pricing and demand response strategies that can reduce cost to the consumers as well as improve network load balance.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Aggregated demand response; Bayesian demand prediction; Dynamic pricing; Electric power network

International Standard Serial Number (ISSN)

0378-7796

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Elsevier Ltd, All rights reserved.

Publication Date

01 Sep 2019

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