A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a schedu...

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Main Authors: Sean Williams, Michael Short, Tracey Crosbie, Maryam Shadman-Pajouh
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/16/4191
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spelling doaj-c899fa597a2842d2a08a47067ddcc4362020-11-25T02:54:51ZengMDPI AGEnergies1996-10732020-08-01134191419110.3390/en13164191A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response ServicesSean Williams0Michael Short1Tracey Crosbie2Maryam Shadman-Pajouh3School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UKSchool of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UKSchool of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UKTeesside University Business School, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UKThis paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.https://www.mdpi.com/1996-1073/13/16/4191decentralizeddemand responseoptimizationcommunity energy management
collection DOAJ
language English
format Article
sources DOAJ
author Sean Williams
Michael Short
Tracey Crosbie
Maryam Shadman-Pajouh
spellingShingle Sean Williams
Michael Short
Tracey Crosbie
Maryam Shadman-Pajouh
A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services
Energies
decentralized
demand response
optimization
community energy management
author_facet Sean Williams
Michael Short
Tracey Crosbie
Maryam Shadman-Pajouh
author_sort Sean Williams
title A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services
title_short A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services
title_full A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services
title_fullStr A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services
title_full_unstemmed A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services
title_sort decentralized informatics, optimization, and control framework for evolving demand response services
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2020-08-01
description This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.
topic decentralized
demand response
optimization
community energy management
url https://www.mdpi.com/1996-1073/13/16/4191
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