Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters

Hafiz, Faeza; Chen, Bo; Chen, Chen; Rodrigo de Queiroz, Anderson; Husain, Iqbal

doi:10.1049/iet-gtd.2018.6866

Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters

Journal Article · Tue Jul 23 00:00:00 EDT 2019 · IET Generation, Transmission, & Distribution

DOI:https://doi.org/10.1049/iet-gtd.2018.6866· OSTI ID:1560041

Hafiz, Faeza ^[1]; Chen, Bo ^[2]; Chen, Chen ^[2]; Rodrigo de Queiroz, Anderson ^[1]; Husain, Iqbal ^[1]

North Carolina State Univ., Raleigh, NC (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

The increased frequency of power outages due to natural disasters in recent years has highlighted the urgency of enhancing distribution grid resilience. The effective distribution service restoration (DSR) is an important measure for a resilient distribution grid. In this work, the authors demonstrate that DSR can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a framework for this by considering integrated control of household-level flexible appliances to vary the load demand at the distribution-grid level to improve DSR. The overall framework of the proposed system is modelled as a three-step method considering three optimization problems to (i) calculate feasible controllable aggregated load range for each bus, (ii) determine candidate buses to perform DR and their target load demand, and (iii) maintain the load level in each house through home energy management during the DSR, considering uncertainties in load and solar generation sequentially. The optimization problems are formulated as linear programming, mixed-integer linear programming, and multistage stochastic programming (solved using the stochastic dual dynamic programming) models. Finally, case studies performed in the IEEE 123-node test feeder show improvements in resilience in terms of energy restored compared to the restoration process without DR.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE; USDOE Office of Electricity Delivery and Energy Reliability (OE), Advanced Grid Research and Development

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1560041

Alternate ID(s):: OSTI ID: 1786771

Journal Information:: IET Generation, Transmission, & Distribution, Journal Name: IET Generation, Transmission, & Distribution Journal Issue: 14 Vol. 13; ISSN 1751-8687

Publisher:: Institution of Engineering and TechnologyCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
IEEE 123-node test feeder system
aggregated load
demand response
demand side management
distribution grid resilience
distribution service restoration
distribution-grid level
domestic appliances
energy management systems
grid resiliency
home energy management
household-level flexible appliances
integrated control
mixed-integer linear programming
multistage stochastic programming
optimisation
optimisation problems
power distribution control
power grids
power outages
power system restoration
resilient distribution grid
solar generation
stochastic dual dynamic programming
target load demand

Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters

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References (28)

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