Open AccessUtilising demand response for distribution service restoration to achieve grid resiliency against natural disasters
Author(s) -
Hafiz Faeza,
Chen Bo,
Chen Chen,
Rodrigo de Queiroz Anderson,
Husain Iqbal
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6866
Subject(s) - demand response , grid , mathematical optimization , resilience (materials science) , computer science , linear programming , flexibility (engineering) , stochastic programming , reliability engineering , distributed computing , engineering , electricity , mathematics , electrical engineering , statistics , physics , geometry , thermodynamics
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. 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.