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A Game-theoretic and Machine-learning Approach to Demand Response Management for the Smart Grid

Meng, Fanlin

[Thesis]. Manchester, UK: The University of Manchester; 2015.

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Abstract

Demand Response (DR) was proposed more than a decade ago to incentivise customers to shift their electricity usage from peak demand periods to off-peak demand periods and to curtail their electricity usage during peak demand periods. However, the lack of two-way communication infrastructure weakens the influence of DR and limits its applications. With the development of smart grid facilities (e.g. smart meters and the two-way communication infrastructure) that enable the interactions between the energy retailer and its customers, demand response shows great potential to reduce customers' bills, increase the retailer's profit and further stabilize the power systems. Given such a context, in this thesis we propose smart pricing based demand response programs to study the interactions between the energy retailer and its customers based on game-theory and machine learning techniques. We conduct the research in two different application scenarios: 1) For customers with home energy management system (HEMS) installed in their smart meters, the retailer will know the customers' energy consumption patterns by interacting with the HEMS. As a result, the smart pricing based demand response problem can be modelled as a Stackelberg game or bilevel optimization problem. Further, efficient solutions are proposed for the demand response problems and the existence of optimal solution to the Stackelberg game and the bilevel model is proved; 2) For customers without HEMS installed in their smart meters, the retailer will not know the energy consumption patterns of these customers and must learn customers' behaviour patterns via historical energy usage data. To realize this, two appliance-level machine learning algorithms are proposed to learn customers' consumption patterns. Further, distributed pricing algorithms are proposed for the retailer to solve the demand response problem effectively. Simulation results indicate the effectiveness of the proposed demand response models in both application scenarios.

Bibliographic metadata

Type of resource:
Content type:
Administered thesis
Form of thesis:
Traditional
Type of submission:
Doctoral level ETD - final
Thesis title:
A Game-theoretic and Machine-learning Approach to Demand Response Management for the Smart Grid
Degree type:
Doctor of Philosophy
Degree programme:
PhD Computer Science
Publication date:
2015-12-23T13:20:06
Institution:
The University of Manchester
Location:
Manchester, UK
Total pages:
173
Abstract:
Demand Response (DR) was proposed more than a decade ago to incentivise customers to shift their electricity usage from peak demand periods to off-peak demand periods and to curtail their electricity usage during peak demand periods. However, the lack of two-way communication infrastructure weakens the influence of DR and limits its applications. With the development of smart grid facilities (e.g. smart meters and the two-way communication infrastructure) that enable the interactions between the energy retailer and its customers, demand response shows great potential to reduce customers' bills, increase the retailer's profit and further stabilize the power systems. Given such a context, in this thesis we propose smart pricing based demand response programs to study the interactions between the energy retailer and its customers based on game-theory and machine learning techniques. We conduct the research in two different application scenarios: 1) For customers with home energy management system (HEMS) installed in their smart meters, the retailer will know the customers' energy consumption patterns by interacting with the HEMS. As a result, the smart pricing based demand response problem can be modelled as a Stackelberg game or bilevel optimization problem. Further, efficient solutions are proposed for the demand response problems and the existence of optimal solution to the Stackelberg game and the bilevel model is proved; 2) For customers without HEMS installed in their smart meters, the retailer will not know the energy consumption patterns of these customers and must learn customers' behaviour patterns via historical energy usage data. To realize this, two appliance-level machine learning algorithms are proposed to learn customers' consumption patterns. Further, distributed pricing algorithms are proposed for the retailer to solve the demand response problem effectively. Simulation results indicate the effectiveness of the proposed demand response models in both application scenarios.
Keyword(s):
Thesis main supervisor(s):
Degree grantor:
Language:
en

Institutional metadata

University researcher(s):
Academic department(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:293806
Created by:
Meng, Fanlin
Created:
23rd December, 2015, 13:20:07
Last modified by:
Meng, Fanlin
Last modified:
9th September, 2016, 13:04:31

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