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@PHDTHESIS{Ahmadifar:1015170,
author = {Ahmadifar, Amir},
othercontributors = {Monti, Antonello and Ulbig, Andreas},
title = {{R}esilient microgrid management in active distribution
systems for normal and contingency operations; 1. {A}uflage},
volume = {145},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {E.ON Energy Research Center, RWTH Aachen University},
reportid = {RWTH-2025-06239},
isbn = {978-3-948234-59-1},
series = {E.On Energy Research Center},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Druckausgabe: 2025. - Auch veröffentlicht auf dem
Publikationsserver der RWTH Aachen University; Dissertation,
RWTH Aachen University, 2025},
abstract = {With the proliferation of Distributed Energy Resources
(DER) in power systems, traditional passive distribution
systems are facing transformation towards more dynamic and
complex structures to cope with the integration of newly
emerging DER including local storage systems and Electrical
Vehicles (EVs). On the path towards this transformation,
Microgrids (MGs) play a crucial role as they facilitate DER
integration, allow local balancing between generation and
consumption, and can operate independently from the grid.
Thanks to their stand-alone, localized, and flexible
operation and their ability to operate in both
grid-connected and islanded modes, MGs constitute an
effective resilience source and if managed effectively in
both normal and contingency cases, they can enhance the
distribution system resilience. In light of these
considerations, this work focuses on the optimal MG
scheduling and energy management. It explores
resilient-oriented strategies that on one hand enable
self-sufficiency during normal operation and on the other
hand ensure system loads are sustained during contingencies
such as outages and islanding. The resilient-driven MG
management of this work is structured in two parts. In the
first part, particularly suited for smaller and less
interconnected systems, the focus lies on the Single MG
(SMG) management with the objective of minimizing grid
dependency and enhancing self-sufficiency. In the second
part, local autonomy and optimization is accompanied by
system-wide optimization for collective gains. In this part,
the more complex management of Networked MGs (NMGs) is
addressed by capturing the interactions between multiple MGs
and the grid in both normal and contingency cases. The
overarching aim is to develop resilient MG scheduling
strategies which facilitate the integration of Renewable
Energy Sources (RES), are suited for both single and
multiple MG setups, are adaptable to both normal and
contingency operations, and have special focus on EV
integration mechanisms. For the SMG management, the first
contribution is related to the development of an Energy
Management System (EMS) with the objective of maximizing
self-sufficiency and self-consumption. The proposed EMS,
deployed and tested in real-life conditions by an MG
Operator (MGO) uses (near) real-time measurements and
forecasts at the point of common coupling to actively manage
the MG's load, generation, and storage based on rule-based
and optimization control mechanisms. By minimizing the power
exchange with the grid, the proposed resilience-oriented
mechanisms reduce grid dependency, facilitate the operation
of a virtually islanded MG, and ensure smoother transitions
to full islanding when necessary. The second contribution
involves performing a comprehensive and systematic global
sensitivity analysis for the developed EMS and paves the
path for its re-usability in other EMS applications. This
analysis evaluates how the EMS model's outputs, i.e., its
key performance indicators that reflect MGO's interest and
objective are impacted by the uncertainties related to
inputs such as MG flexibility and RES generation prediction.
Furthermore, it helps identifying the EMS model influential
inputs and provides MGO with a support tool for the decision
making process in an uncertainty framework, e.g., to revise
the EMS model, to invest resources for uncertainty reduction
for certain inputs, etc. The third contribution extends the
proposed EMS framework for SMGs by integrating additional
flexibility, external compliance, and contingency
preparedness. The enhanced EMS enables SMGs to maintain
self-sufficient operation while dynamically responding to
external grid requests from the System Operator (SO),
including predefined import/export boundaries and bulk
energy transactions. Furthermore, the extended EMS
introduces an advanced EV management framework that
establishes a structured interaction between the Charging
Point Operator (CPO) and the MG Operator (MGO). This
framework preserves their distinct roles while allowing them
to impose bilateral operational boundaries on each other.
The MGO, considering system needs and SO-imposed
constraints, sets operational limits, within which the CPO
optimizes EV charging profiles. By seamlessly integrating EV
flexibility into the MG's total load, this approach enhances
both system efficiency and resilience, ensuring that EV
operations align with broader EMS objectives while
maintaining coordinated yet independent decision-making. For
the NMGs management, the first contribution is related to
proposing a framework that fully identifies available
flexibility at the MG level, integrates diverse flexibility
sources across both local and system levels, and balances
local autonomy with system-wide coordination while
respecting economic and operational constraints. The
proposed NMGs management framework incorporates Local EMSs
(LEMSs) and Central EMS (CEMS) for respectively MG and
system levels. At the local level, LEMSs optimize
MG-specific operations by considering internal constraints
and objectives, enabling MGs to independently manage their
resources while ensuring compliance with predefined
operational limits. At the system level, CEMS is responsible
for coordinating the contributions of multiple MGs, aligning
their operations with system-wide objectives, and ensuring
compliance with grid code. The second contribution extends
the proposed NMGs management framework to ensure effective
operation under contingency scenarios. In this regard, the
constraints and objective functions at both MG and system
levels are revisited to incorporate contingency-aware
optimization mechanisms. Special attention is given to load
criticality, ensuring that the adaptations made to the
respective LEMSs and CEMS facilitate a resilient, adaptive,
and well-coordinated response to contingency events. The
framework optimally utilizes available MG flexibilities,
including EVs, storage units, and local generation, to
support system loads during contingencies, ensuring
resilience across the NMGs network. The third contribution
focuses on the development of supportive strategies to
enhance NMGs resiliency during contingency operations by
leveraging EVs as additional support resources. In this
regard, the contingency support needs of the NMGs system are
first identified. Based on these needs, an
optimization-based selection process is introduced to
determine suitable supporting EVs, considering the
flexibility available in connected MGs and the specific
requirements of disconnected MGs. Provided that contractual
agreements are in place, two EV reallocation
mechanisms—complete EV assignment and dynamic EV
allocation—are proposed to effectively redistribute EVs
among MGs before managing them through their respective
LEMSs. In the complete assignment approach, each supportive
EV from the connected MG is fully reallocated to a
disconnected MG for the entire duration between its arrival
and departure. In contrast, the dynamic allocation approach
allows EVs to be flexibly moved between MGs while
maintaining their presence in the original MGs before and
after the contingency. Furthermore, EVs allocation within
each disconnected MG is determined with explicit
consideration of load criticality, ensuring that essential
loads receive prioritized support.},
cin = {616310 / 080052},
ddc = {621.3},
cid = {$I:(DE-82)616310_20140620$ / $I:(DE-82)080052_20160101$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2025-06239},
url = {https://publications.rwth-aachen.de/record/1015170},
}
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