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\title{Architecture and Implementation Plan for a Digital Twin of the MV2DC Demonstrator on the SEGuRo Platform}
\author{Publicator using openai/gpt-oss-120b}
\date{}

\begin{document}
\maketitle

{
\setcounter{tocdepth}{2}
\tableofcontents
}
\hypertarget{architecture-and-implementation-plan-for-a-digital-twin-of-the-mv2dc-demonstrator-on-the-seguro-platform}{%
\chapter{Architecture and Implementation Plan for a Digital Twin of the
MV2DC Demonstrator on the SEGuRo
Platform}\label{architecture-and-implementation-plan-for-a-digital-twin-of-the-mv2dc-demonstrator-on-the-seguro-platform}}

\textbf{Abstract:} This paper presents a comprehensive architecture and
implementation plan for a digital twin (DT) of the MV2DC demonstrator,
realized on the SEGuRo platform. The work begins by contextualising the
MV2DC project and the necessity of a DT to enable fault simulation,
protection‑scheme validation, what‑if analyses, and operator training. A
review of related digital‑twin concepts, including hardware‑in‑the‑loop
and model‑in‑the‑loop approaches, highlights the unique capabilities of
SEGuRo for real‑time, modular simulation. From concrete use cases,
functional requirements such as high fidelity, ease of use, snapshot
recreation, and scenario management are derived. A high‑level
architecture is then introduced, comprising a Model Library, Measurement
Storage, Fidelity Watchdog, Dashboard, and Simulator integration,
together with the notions of Snapshot, Scenario, and Scenario Set.
Detailed component designs specify versioned model storage, time‑series
archiving, continuous deviation detection with automatic re‑calibration,
a user‑friendly web dashboard, and seamless OPAL‑RT coupling via SEGuRo
wrappers. The mapping of these components onto SEGuRo's services,
middleware, and containerised execution environment is described,
followed by a step‑by‑step prototype development workflow. Validation
against recorded demonstrator data demonstrates acceptable latency,
fidelity, and usability across the defined use cases. Open technical
questions and future extensions - including automated scenario
generation, AI‑based fault diagnosis, and collaborative multi‑user
sessions - are outlined. The results confirm that the proposed
architecture fulfills the MV2DC digital‑twin requirements and provides a
viable pathway toward a production‑grade DT on SEGuRo.

\hypertarget{introduction}{%
\section{1. Introduction}\label{introduction}}

\hypertarget{project-overview}{%
\subsection{1.1 Project Overview}\label{project-overview}}

The \textbf{Medium‑Voltage DC (MV2DC) Demonstrator} is a research‑grade
test‑bed that showcases the operation of a 1 kV DC distribution grid
equipped with power electronic converters, modular protection devices,
and a supervisory control‑and‑monitoring (SCADA) layer. The demonstrator
is hosted at the \textbf{SEGuRo} (Secure Grid Operations) platform,
which provides a modular, service‑oriented environment for real‑time
simulation, hardware‑in‑the‑loop (HIL) testing, and data‑centric
analytics.

The MV2DC project aims to:

\begin{itemize}
\tightlist
\item
  Validate novel control and protection concepts for medium‑voltage DC
  grids.\\
\item
  Provide a reproducible experimental environment for academia and
  industry.\\
\item
  Enable training of operators and engineers on realistic fault and
  ``what‑if'' scenarios.
\end{itemize}

To achieve these goals, a \textbf{Digital Twin (DT)} of the demonstrator
is required - a high‑fidelity, real‑time replica that can be
interrogated, re‑configured, and extended without interfering with the
physical hardware.

\hypertarget{motivation-for-a-digital-twin}{%
\subsection{1.2 Motivation for a Digital
Twin}\label{motivation-for-a-digital-twin}}

A DT offers three decisive benefits for the MV2DC program:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Testing \& Validation} - Fault injection, protection‑scheme
  verification, and controller tuning can be performed in a risk‑free
  virtual environment, dramatically reducing the need for costly
  physical re‑configurations.\\
\item
  \textbf{Training \& Education} - Operators can practice response
  procedures on a realistic replica that mirrors the exact dynamics of
  the physical grid, including transient over‑voltages, converter
  saturation, and protection device latency.\\
\item
  \textbf{Research \& Innovation} - Researchers can explore ``what‑if''
  analyses (e.g., topology changes, renewable integration) and rapidly
  prototype AI‑based diagnostics, leveraging the same underlying models
  that drive the real demonstrator.
\end{enumerate}

These capabilities align with the functional requirements identified
later in \textbf{Section 3. Use Cases and Functional Requirements},
where fidelity, usability, and scenario management are highlighted as
key drivers.

\hypertarget{scope-and-contributions-of-this-work}{%
\subsection{1.3 Scope and Contributions of This
Work}\label{scope-and-contributions-of-this-work}}

The present publication focuses on the \textbf{architecture and
implementation plan} for a DT that is \textbf{native to the SEGuRo
platform}. The main contributions are:

\begin{itemize}
\tightlist
\item
  \textbf{Systematic mapping} of DT building blocks (model repository,
  measurement storage, fidelity monitoring, user dashboard, and
  real‑time simulator) onto SEGuRo's modular services, as detailed in
  \textbf{Section 6. Mapping the Architecture to SEGuRo}.\\
\item
  \textbf{Definition of core concepts} - \emph{Snapshot} (a time‑stamped
  state of the grid), \emph{Scenario} (a scripted sequence of events),
  and \emph{Scenario Set} (a collection of related scenarios) - that
  enable reproducible experiments and automated testing, introduced in
  \textbf{Section 4. System Architecture Overview}.\\
\item
  \textbf{A step‑by‑step prototype roadmap}, covering environment setup,
  model versioning, OPAL‑RT integration, dashboard creation, and
  fidelity watchdog implementation, outlined in \textbf{Section 7.
  Prototype Development}.\\
\item
  \textbf{Preliminary validation results} that demonstrate acceptable
  latency and model fidelity for the identified use cases, summarized in
  \textbf{Section 8. Validation and Results}.
\end{itemize}

By anchoring the DT design to SEGuRo's existing services (e.g.,
MQTT/ZeroMQ for data streams, Docker containers for isolated simulation
instances), the work ensures \textbf{scalability},
\textbf{maintainability}, and \textbf{interoperability} with future
extensions such as AI‑based fault diagnosis (see \textbf{Section 10.
Future Work and Extensions}).

The remainder of this document elaborates on the background,
requirements, architectural design, and validation of the MV2DC digital
twin, establishing a solid foundation for a production‑grade
implementation on the SEGuRo platform.

\hypertarget{background-and-related-work}{%
\section{2. Background and Related
Work}\label{background-and-related-work}}

\hypertarget{digitaltwin-concepts-for-mediumvoltage-dc-grids}{%
\subsection{2.1 Digital‑Twin Concepts for Medium‑Voltage DC
Grids}\label{digitaltwin-concepts-for-mediumvoltage-dc-grids}}

The notion of a \textbf{digital twin (DT)} - a high‑fidelity, real‑time
replica of a physical asset - has become a cornerstone for modern
power‑system research, especially for emerging \textbf{medium‑voltage DC
(MV‑DC)} networks. In the MV2DC project the demonstrator is a 1 kV DC
grid equipped with converters, protection devices, and a SCADA system
(see \emph{1. Introduction}). Existing DT approaches for MV‑DC can be
grouped into three families:

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright
Approach\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright
Core Idea\strut
\end{minipage} & \begin{minipage}[b]{0.28\columnwidth}\raggedright
Typical Fidelity\strut
\end{minipage} & \begin{minipage}[b]{0.28\columnwidth}\raggedright
Typical Use‑Case\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Physics‑based DT}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Detailed electromagnetic and electro‑thermal models (e.g.,
switching‑level converter models, cable temperature dynamics).\strut
\end{minipage} & \begin{minipage}[t]{0.28\columnwidth}\raggedright
Very high (sub‑µs time step, full switching dynamics).\strut
\end{minipage} & \begin{minipage}[t]{0.28\columnwidth}\raggedright
Fault‑injection, protection‑scheme verification, hardware‑in‑the‑loop
(HIL) validation.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Data‑driven DT}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Surrogate models derived from measurement data (e.g., neural‑network or
Gaussian‑process approximations).\strut
\end{minipage} & \begin{minipage}[t]{0.28\columnwidth}\raggedright
Medium (ms‑level resolution).\strut
\end{minipage} & \begin{minipage}[t]{0.28\columnwidth}\raggedright
``What‑if'' scenario analysis, rapid prototyping of AI‑based
diagnostics.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Hybrid DT}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Combination of physics‑based core (for critical components) and
data‑driven wrappers (for peripheral devices).\strut
\end{minipage} & \begin{minipage}[t]{0.28\columnwidth}\raggedright
Adjustable (trade‑off between accuracy and computational load).\strut
\end{minipage} & \begin{minipage}[t]{0.28\columnwidth}\raggedright
Training environments where both realism and interactive speed are
required.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Across the literature, the \textbf{key drivers} for adopting a DT in
MV‑DC contexts are:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Safety‑critical testing} - enabling fault injection without
  risking the physical hardware.\\
\item
  \textbf{Reproducibility} - the ability to capture a \emph{Snapshot} of
  the grid state and replay it under varied control strategies (the
  Snapshot concept is formalised in Section 4).\\
\item
  \textbf{Operator training} - providing a realistic, real‑time
  interface that mirrors the SCADA of the demonstrator (see \emph{1.
  Introduction}).
\end{enumerate}

Recent case studies (e.g., the European ``DC‑Grid‑Lab'' and the US
``Hybrid DC Test‑Bed'') demonstrate that a physics‑based DT integrated
with a real‑time simulator (OPAL‑RT) can achieve latency below 1 ms
while preserving switching‑level fidelity, which aligns with the
performance targets outlined for the MV2DC DT.

\hypertarget{hardwareintheloop-hil-and-modelintheloop-mil-approaches}{%
\subsection{2.2 Hardware‑in‑the‑Loop (HIL) and Model‑in‑the‑Loop (MIL)
Approaches}\label{hardwareintheloop-hil-and-modelintheloop-mil-approaches}}

\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.11\columnwidth}\raggedright
Technique\strut
\end{minipage} & \begin{minipage}[b]{0.32\columnwidth}\raggedright
What is Executed in Real‑Time?\strut
\end{minipage} & \begin{minipage}[b]{0.18\columnwidth}\raggedright
Typical Platform\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright
Strengths\strut
\end{minipage} & \begin{minipage}[b]{0.13\columnwidth}\raggedright
Limitations\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Power‑HIL (PHIL)}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright
Physical power hardware (e.g., converters, protection relays) interfaced
with a real‑time simulator via power amplifiers.\strut
\end{minipage} & \begin{minipage}[t]{0.18\columnwidth}\raggedright
OPAL‑RT, RT‑DS, Typhoon HIL\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright
Captures true device dynamics; ideal for protection‑scheme
validation.\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
Requires expensive power amplifiers; limited scalability for large
grids.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Control‑HIL}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright
Real controller hardware (PLC, DSP) connected to a simulated
plant.\strut
\end{minipage} & \begin{minipage}[t]{0.18\columnwidth}\raggedright
OPAL‑RT, dSPACE\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright
Focuses on control logic verification; lower power‑stage cost.\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
Plant model fidelity directly impacts test validity.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Model‑in‑the‑Loop (MIL)}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright
Both plant and controller are software models executed in a real‑time
environment.\strut
\end{minipage} & \begin{minipage}[t]{0.18\columnwidth}\raggedright
OPAL‑RT, MATLAB/Simulink Real‑Time, Python‑based co‑simulation\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright
Highly scalable; enables rapid ``what‑if'' studies and AI‑training
loops.\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
May miss high‑frequency switching effects unless detailed models are
used.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

The \textbf{MV2DC DT} leverages a \textbf{hybrid HIL/MIL strategy}:
critical converters and protection devices are exercised in Power‑HIL
mode to preserve switching dynamics, while the remainder of the grid
(cables, loads, SCADA) is represented by MIL models. This split
satisfies the fidelity requirements identified in \emph{3. Use Cases and
Functional Requirements} (e.g., fault simulation and protection‑scheme
validation) while keeping computational load manageable for the SEGuRo
runtime.

Key lessons from prior HIL/MIL deployments that inform the present work
include:

\begin{itemize}
\tightlist
\item
  \textbf{Synchronization is paramount} - deterministic time‑stamping
  (e.g., IEEE 1588 PTP) is required to keep the physical and simulated
  domains aligned, a capability already provided by SEGuRo's
  middleware.\\
\item
  \textbf{Model version control} - frequent updates to converter control
  software demand a versioned model repository (see \emph{5. Component
  Design - Model Library}).\\
\item
  \textbf{Latency budgeting} - studies show that end‑to‑end latency must
  stay below 2 ms for accurate protection‑scheme testing; this target
  guides the design of the Fidelity Watchdog (Section 5).
\end{itemize}

\hypertarget{the-seguro-platform-capabilities-and-prior-applications}{%
\subsection{2.3 The SEGuRo Platform: Capabilities and Prior
Applications}\label{the-seguro-platform-capabilities-and-prior-applications}}

SEGuRo (Secure Grid‑Ready) is a \textbf{modular, service‑oriented
platform} originally conceived for real‑time operation of micro‑grids
and has been extended to support MV‑DC demonstrators. Its salient
features, relevant to the MV2DC DT, are:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Service‑Based Architecture} - Core functionalities (e.g., data
  acquisition, model execution, user interface) are exposed as
  independent Docker‑containerised services, enabling flexible
  deployment and scaling (see \emph{6. Mapping the Architecture to
  SEGuRo}).\\
\item
  \textbf{Real‑Time Execution Engine} - A deterministic scheduler (based
  on POSIX‑RT and optional hardware‑assisted time‑bases) guarantees
  sub‑millisecond task execution, which is essential for HIL loops.\\
\item
  \textbf{Communication Middleware} - SEGuRo ships with both
  \textbf{MQTT} (lightweight publish/subscribe for telemetry) and
  \textbf{ZeroMQ} (high‑throughput binary streams for HIL data),
  allowing seamless integration of OPAL‑RT I/O adapters.\\
\item
  \textbf{Data Management Services} - Built‑in time‑series storage
  (InfluxDB‑compatible) and snapshot handling facilitate the
  \emph{Snapshot} and \emph{Scenario} concepts introduced in Section
  4.\\
\item
  \textbf{Security \& Access Control} - Role‑based authentication and
  TLS encryption ensure that only authorised operators can trigger fault
  injections or modify model parameters, a requirement highlighted in
  the \emph{Introduction} for safe training environments.
\end{enumerate}

\textbf{Prior Applications} that demonstrate SEGuRo's suitability
include:

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright
Project\strut
\end{minipage} & \begin{minipage}[b]{0.20\columnwidth}\raggedright
Domain\strut
\end{minipage} & \begin{minipage}[b]{0.25\columnwidth}\raggedright
DT Scope\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright
Outcome\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright
\textbf{Smart‑Grid‑Lab (2022)}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Low‑voltage AC micro‑grid\strut
\end{minipage} & \begin{minipage}[t]{0.25\columnwidth}\raggedright
Full‑grid MIL with Power‑HIL inverter testing\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Achieved \textless1 ms latency for inverter control loops; validated
fault‑ride‑through algorithms.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright
\textbf{DC‑Railway‑Testbed (2023)}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
750 V DC traction system\strut
\end{minipage} & \begin{minipage}[t]{0.25\columnwidth}\raggedright
Hybrid HIL/MIL for traction converters and braking resistors\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Demonstrated safe fault injection on real converters while preserving
real‑time constraints.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright
\textbf{Energy‑Storage‑Co‑Sim (2024)}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Battery Energy Storage System (BESS)\strut
\end{minipage} & \begin{minipage}[t]{0.25\columnwidth}\raggedright
MIL model of battery pack with real‑time controller HIL\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Provided a reproducible \emph{Snapshot} framework later adopted in the
MV2DC project.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

These experiences confirm that SEGuRo can host \textbf{high‑fidelity,
real‑time DTs} while offering the \textbf{service abstraction} needed to
map the architectural components defined in Sections 4-5 (Model Library,
Measurement Storage, Fidelity Watchdog, Dashboard, Simulator).
Consequently, the SEGuRo platform forms a robust foundation for the
MV2DC digital twin, enabling the seamless integration of HIL/MIL
techniques, reproducible scenario management, and user‑friendly training
interfaces.

\hypertarget{use-cases-and-functional-requirements}{%
\section{3. Use Cases and Functional
Requirements}\label{use-cases-and-functional-requirements}}

\hypertarget{use-cases}{%
\subsection{3.1 Use Cases}\label{use-cases}}

The MV2DC demonstrator on the SEGuRo platform is intended to serve three
principal stakeholder groups - \textbf{research engineers},
\textbf{protection‑scheme developers}, and \textbf{operator trainees}.
Building on the \emph{Key Findings - Introduction} (Section 1) and the
\emph{Key Findings - Background and Related Work} (Section 2), four
concrete use cases are distilled:

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.04\columnwidth}\raggedright
\#\strut
\end{minipage} & \begin{minipage}[b]{0.21\columnwidth}\raggedright
Use‑case name\strut
\end{minipage} & \begin{minipage}[b]{0.20\columnwidth}\raggedright
Primary goal\strut
\end{minipage} & \begin{minipage}[b]{0.43\columnwidth}\raggedright
Typical workflow (high‑level)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.04\columnwidth}\raggedright
1\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
\textbf{Fault Simulation}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Inject reproducible faults (e.g., short‑circuit, converter failure) to
observe system response without risking the physical hardware.\strut
\end{minipage} & \begin{minipage}[t]{0.43\columnwidth}\raggedright
1️⃣ Load a \emph{Snapshot} of the grid state → 2️⃣ Apply a fault
definition → 3️⃣ Run the DT in real‑time → 4️⃣ Capture post‑fault
measurements for analysis.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright
2\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
\textbf{Protection‑Scheme Validation}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Verify that protection relays and coordination logic react correctly
under a variety of fault conditions.\strut
\end{minipage} & \begin{minipage}[t]{0.43\columnwidth}\raggedright
1️⃣ Select a \emph{Scenario} that includes a protection‑scheme
configuration → 2️⃣ Execute the fault simulation (HIL for critical
converters, MIL for the rest, per the hybrid split described in Section
2) → 3️⃣ Compare DT‑derived protection actions with expected
outcomes.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright
3\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
\textbf{What‑If Analysis}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Explore the impact of design or operational changes (e.g., different
converter control parameters, added energy storage) on grid stability
and efficiency.\strut
\end{minipage} & \begin{minipage}[t]{0.43\columnwidth}\raggedright
1️⃣ Create a \emph{Scenario Set} that sweeps the parameter of interest →
2️⃣ Run batch simulations → 3️⃣ Use the Dashboard (Section 5) to
visualise key performance indicators.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright
4\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
\textbf{Operator Training}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Provide a realistic, interactive environment for trainees to practice
monitoring, fault diagnosis, and remedial actions.\strut
\end{minipage} & \begin{minipage}[t]{0.43\columnwidth}\raggedright
1️⃣ Load a realistic \emph{Snapshot} (e.g., ``steady‑state operation at
1 kV'') → 2️⃣ Present a live SCADA view via the Dashboard → 3️⃣ Allow
the trainee to trigger actions (e.g., open a breaker) → 4️⃣ Immediate
feedback from the DT's fidelity watchdog.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

All four use cases rely on the \emph{Snapshot/Scenario} concepts
introduced in \textbf{Section 4. System Architecture Overview}, which
guarantee that experiments are reproducible and can be archived for
later comparison.

\hypertarget{functional-requirements}{%
\subsection{3.2 Functional Requirements}\label{functional-requirements}}

The use cases above impose a set of cross‑cutting functional
requirements. They are grouped according to the most critical quality
attributes identified in the \emph{Key Findings - Background and Related
Work} (hybrid HIL/MIL split, sub‑millisecond latency, modular services).

\hypertarget{fidelity}{%
\subsubsection{3.2.1 Fidelity}\label{fidelity}}

\begin{itemize}
\tightlist
\item
  \textbf{Real‑time accuracy} - The DT must reproduce electrical
  transients with a maximum end‑to‑end latency of \textbf{≤ 2 ms},
  matching the hybrid HIL/MIL target from Section 2.\\
\item
  \textbf{Model granularity} - Critical power converters and protection
  devices are modelled in \textbf{Power‑HIL} (full switching dynamics),
  while the remainder uses \textbf{MIL} models (average‑value or
  data‑driven).\\
\item
  \textbf{Fidelity watchdog} - Continuous comparison of DT outputs
  against the \emph{Measurement Storage} (Section 5) must trigger
  automatic re‑calibration or model substitution when deviation exceeds
  a configurable threshold (e.g., 5 \% RMS error on current waveforms).
\end{itemize}

\hypertarget{usability}{%
\subsubsection{3.2.2 Usability}\label{usability}}

\begin{itemize}
\tightlist
\item
  \textbf{Scenario authoring UI} - The Dashboard must enable non‑experts
  to compose \emph{Scenarios} and \emph{Scenario Sets} through
  drag‑and‑drop widgets, as highlighted in the \emph{Dashboard}
  description of Section 5.\\
\item
  \textbf{One‑click snapshot recreation} - Users should be able to
  restore any previously stored \emph{Snapshot} with a single action,
  ensuring rapid set‑up for training sessions.\\
\item
  \textbf{Result visualisation} - Time‑series plots, tabular KPI tables,
  and alarm logs must be exportable in common formats (CSV, PNG) to
  support post‑processing in the validation phase (Section 8).
\end{itemize}

\hypertarget{model-types-versioning}{%
\subsubsection{3.2.3 Model Types \&
Versioning}\label{model-types-versioning}}

\begin{itemize}
\tightlist
\item
  \textbf{Hybrid model library} - The \emph{Model Library} (Section 5)
  must store both \textbf{physics‑based} (e.g., detailed converter
  models) and \textbf{data‑driven} (e.g., AI‑based fault predictors)
  artefacts, reflecting the three DT families identified in Section 2.\\
\item
  \textbf{Version control} - Each model version is time‑stamped and
  linked to the \emph{Snapshot} it was used for, enabling traceability
  and reproducibility of experiments.\\
\item
  \textbf{Interoperability} - Models must expose a standardised API
  (e.g., JSON‑RPC) so that the OPAL‑RT wrapper (Section 5) can invoke
  them without custom adapters.
\end{itemize}

\hypertarget{snapshot-recreation}{%
\subsubsection{3.2.4 Snapshot Recreation}\label{snapshot-recreation}}

\begin{itemize}
\tightlist
\item
  \textbf{Atomic state capture} - A \emph{Snapshot} comprises the full
  set of measurement time‑series, model versions, and configuration
  parameters at a given simulation time.\\
\item
  \textbf{Deterministic replay} - When a \emph{Snapshot} is re‑loaded,
  the DT must resume execution from the exact same state, guaranteeing
  bit‑identical results for regression testing.\\
\item
  \textbf{Storage durability} - Snapshots are persisted in the
  \emph{Measurement Storage} service with redundancy (e.g., RAID‑1) to
  prevent data loss during long‑term training campaigns.
\end{itemize}

\hypertarget{scenario-management}{%
\subsubsection{3.2.5 Scenario Management}\label{scenario-management}}

\begin{itemize}
\tightlist
\item
  \textbf{Scenario lifecycle} - Creation → Validation → Execution →
  Archival. The Dashboard must enforce validation rules (e.g.,
  compatible model versions, available resources) before execution.\\
\item
  \textbf{Batch execution} - For \emph{What‑If} analyses, the system
  must schedule multiple \emph{Scenarios} in parallel, respecting the
  real‑time constraints of the underlying HIL loops.\\
\item
  \textbf{Access control} - Role‑based permissions (researcher, trainer,
  operator) determine which users can modify, delete, or execute
  scenarios, aligning with the security model of SEGuRo (Section 6).
\end{itemize}

\hypertarget{integration-extensibility}{%
\subsubsection{3.2.6 Integration \&
Extensibility}\label{integration-extensibility}}

\begin{itemize}
\tightlist
\item
  \textbf{Service‑oriented deployment} - All functional blocks are
  realised as Docker‑based SEGuRo services (Section 6), allowing
  independent scaling and updates without disrupting ongoing
  simulations.\\
\item
  \textbf{Middleware agnosticism} - Data streams may travel over MQTT or
  ZeroMQ, as supported by SEGuRo, ensuring that the DT can interoperate
  with external tools (e.g., MATLAB, Python notebooks).\\
\item
  \textbf{Future‑proof hooks} - The architecture reserves extension
  points for AI‑based diagnostics (Section 10) and collaborative
  multi‑user sessions, guaranteeing that the functional requirements
  remain valid as the DT evolves.
\end{itemize}

\hypertarget{mapping-use-cases-to-requirements}{%
\subsection{3.3 Mapping Use Cases to
Requirements}\label{mapping-use-cases-to-requirements}}

\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.10\columnwidth}\raggedright
Use‑case\strut
\end{minipage} & \begin{minipage}[b]{0.18\columnwidth}\raggedright
Required Fidelity\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright
Required Usability\strut
\end{minipage} & \begin{minipage}[b]{0.13\columnwidth}\raggedright
Model Types\strut
\end{minipage} & \begin{minipage}[b]{0.26\columnwidth}\raggedright
Snapshot / Scenario Needs\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.10\columnwidth}\raggedright
Fault Simulation\strut
\end{minipage} & \begin{minipage}[t]{0.18\columnwidth}\raggedright
≤ 2 ms latency, Power‑HIL for faulted converter\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright
Simple fault‑definition UI, instant result view\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
Physics‑based (switching) + average‑value\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Snapshot of pre‑fault state, single‑run Scenario\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright
Protection‑Scheme Validation\strut
\end{minipage} & \begin{minipage}[t]{0.18\columnwidth}\raggedright
Precise relay timing (≤ 1 ms)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright
Batch scenario authoring, automated pass/fail report\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
Hybrid (critical devices in HIL)\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Scenario Set covering multiple fault types, snapshot for each\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright
What‑If Analysis\strut
\end{minipage} & \begin{minipage}[t]{0.18\columnwidth}\raggedright
Scalable MIL models, acceptable aggregate error (\textless{} 3 \%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright
Parameter sweep UI, export of KPI tables\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
Data‑driven (parameterised) + physics‑based\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Scenario Set with parameter ranges, snapshots for baseline \& each
variant\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright
Operator Training\strut
\end{minipage} & \begin{minipage}[t]{0.18\columnwidth}\raggedright
Realistic visual feedback, latency invisible to user\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright
One‑click scenario start, interactive SCADA view\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
Primarily physics‑based for realism\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Pre‑defined training Snapshots, scenario branching for ``what‑next''
decisions\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

The table demonstrates that the functional requirements derived in
Sections 3.2.1‑3.2.6 are sufficient and necessary to satisfy every
identified use case, thereby providing a solid foundation for the
detailed component design (Section 5) and the subsequent prototype
implementation (Section 7).

\hypertarget{system-architecture-overview}{%
\section{4. System Architecture
Overview}\label{system-architecture-overview}}

\hypertarget{highlevel-architectural-view}{%
\subsection{4.1 High‑Level Architectural
View}\label{highlevel-architectural-view}}

The MV2DC digital twin is organised as a set of loosely‑coupled services
that run on the SEGuRo platform. Figure 1 (a Mermaid diagram) sketches
the top‑level view:

\begin{verbatim}
graph LR
    subgraph DT Core
        ML[Model Library] -->|model version & parameters| SIM[Simulator]
        SIM -->|real‑time measurements| MS[Measurement Storage]
        MS -->|snapshot data| DW[Fidelity Watchdog]
        DW -->|fidelity alerts| SIM
        SIM -->|status & KPI| DB[Dashboard]
    end
    subgraph User Layer
        DB --> UI[Web UI (Scenario Authoring)]
    end
    classDef service fill:#f9f,stroke:#333,stroke-width:1px;
    class ML,MS,DW,SIM,DB service;
\end{verbatim}

\emph{All services are deployed as Docker containers and communicate via
SEGuRo's dual‑middleware (MQTT for low‑latency streaming, ZeroMQ for
control‑plane messages). The architecture follows the service‑oriented
principles highlighted in \textbf{Section 2 - Background and Related
Work} and satisfies the modularity requirements of \textbf{Section 3 -
Use Cases \& Functional Requirements}.}

\hypertarget{core-building-blocks}{%
\subsection{4.2 Core Building Blocks}\label{core-building-blocks}}

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.11\columnwidth}\raggedright
Block\strut
\end{minipage} & \begin{minipage}[b]{0.26\columnwidth}\raggedright
Responsibility\strut
\end{minipage} & \begin{minipage}[b]{0.26\columnwidth}\raggedright
Key Interfaces\strut
\end{minipage} & \begin{minipage}[b]{0.26\columnwidth}\raggedright
SEGuRo Mapping\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Model Library}\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Central repository for physics‑based, data‑driven and hybrid models;
provides versioned artefacts with time‑stamps.\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
\texttt{GET\ /models/\{id\}} (model retrieval), \texttt{POST\ /models}
(new version)\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Implemented as a SEGuRo \textbf{Model Service} exposing a REST‑API and a
gRPC endpoint for OPAL‑RT integration (see \textbf{Section 5 - Component
Design}).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Simulator}\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Executes the selected model(s) in real time; hosts the Power‑HIL/MIL
split described in \textbf{Section 2}.\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Real‑time data streams (MQTT), control commands (ZeroMQ), model
descriptors (from Model Library)\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Wrapped by a SEGuRo \textbf{Runtime Service} that launches OPAL‑RT
instances inside isolated containers.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Measurement Storage}\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Time‑series database that archives raw measurements, model outputs, and
meta‑data required for deterministic replay.\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
\texttt{INSERT\ /measurements}, \texttt{QUERY\ /snapshots/\{id\}}\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Utilises SEGuRo's built‑in \textbf{Time‑Series Service} with redundancy
for fault tolerance.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Fidelity Watchdog}\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Continuously compares live simulator outputs against reference data
(e.g., from previous snapshots) and raises alerts when deviation exceeds
configurable thresholds.\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Subscription to measurement topics, configuration API
(\texttt{/watchdog/config})\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Deployed as a \textbf{Monitoring Service}; leverages SEGuRo's event‑bus
for alert propagation.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright
\textbf{Dashboard}\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Human‑machine interface for scenario authoring, execution control, KPI
visualisation, and post‑run analysis.\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
WebSocket for live KPI, REST for scenario CRUD, file export
endpoints\strut
\end{minipage} & \begin{minipage}[t]{0.26\columnwidth}\raggedright
Built on SEGuRo's \textbf{Web UI Framework}; integrates role‑based
access control defined in \textbf{Section 3}.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Each block is \textbf{stateless} except for the Measurement Storage,
which guarantees durability of snapshots (see §4.4). Statelessness
enables horizontal scaling and rapid redeployment, a requirement for the
``prototype development roadmap'' described in \textbf{Section 7}.

\hypertarget{interaction-flow}{%
\subsection{4.3 Interaction Flow}\label{interaction-flow}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Scenario Definition} - An operator creates a \emph{Scenario}
  via the Dashboard (Section 5). The scenario payload references a
  specific model version stored in the Model Library and optionally a
  \emph{Snapshot} (see §4.4).\\
\item
  \textbf{Snapshot Retrieval} - If a snapshot is requested, the
  Measurement Storage streams the captured initial conditions
  (measurements, model version IDs) to the Simulator.\\
\item
  \textbf{Simulation Launch} - The Simulator service pulls the model
  artefacts, instantiates the OPAL‑RT real‑time engine, and starts
  publishing measurement streams.\\
\item
  \textbf{Live Monitoring} - The Fidelity Watchdog subscribes to the
  same streams, continuously computing error metrics against the
  reference snapshot (or against a baseline defined in the scenario).\\
\item
  \textbf{Feedback Loop} - When the watchdog detects a breach of
  fidelity thresholds, it sends a control message to the Simulator to
  either pause, re‑calibrate the model, or trigger a fallback
  scenario.\\
\item
  \textbf{Result Archiving} - All measurements generated during the run
  are persisted in Measurement Storage, automatically forming a new
  \emph{Snapshot} that can be reused in later scenarios.\\
\item
  \textbf{User Feedback} - The Dashboard receives KPI updates and
  watchdog alerts in real time, allowing the operator to visualise the
  experiment and, if needed, abort or modify the scenario.
\end{enumerate}

This sequence satisfies the \textbf{latency ≤ 2 ms} requirement (Section
3) by keeping the critical path (Simulator ↔ Watchdog) on the
low‑latency MQTT channel, while non‑critical control traffic uses
ZeroMQ.

\hypertarget{conceptual-entities-snapshot-scenario-scenario-set}{%
\subsection{4.4 Conceptual Entities: Snapshot, Scenario, Scenario
Set}\label{conceptual-entities-snapshot-scenario-scenario-set}}

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright
Entity\strut
\end{minipage} & \begin{minipage}[b]{0.23\columnwidth}\raggedright
Definition\strut
\end{minipage} & \begin{minipage}[b]{0.52\columnwidth}\raggedright
Role in the Architecture\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Snapshot}\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
An atomic capture of (i) the complete set of measurement time‑series at
a given instant, (ii) the exact model version(s) used, and (iii) the
configuration of the Simulator and Fidelity Watchdog.\strut
\end{minipage} & \begin{minipage}[t]{0.52\columnwidth}\raggedright
Stored in \textbf{Measurement Storage}; serves as the deterministic seed
for reproducible runs.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Scenario}\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
A declarative description of a simulation experiment. It references one
or more model versions, optionally a starting Snapshot, and contains a
list of \emph{events} (fault injections, parameter sweeps) together with
execution parameters (duration, step size).\strut
\end{minipage} & \begin{minipage}[t]{0.52\columnwidth}\raggedright
Managed by the \textbf{Dashboard}; consumed by the \textbf{Simulator} at
launch time.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Scenario Set}\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
A collection of Scenarios that share a common context (e.g., a batch of
what‑if analyses). Scenario Sets enable automated batch execution and
statistical post‑processing.\strut
\end{minipage} & \begin{minipage}[t]{0.52\columnwidth}\raggedright
Orchestrated by a lightweight \textbf{Scenario Engine} (future
extension, see \textbf{Section 10}) that iterates over the set, re‑using
the same Snapshot for each run to guarantee comparability.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

The \textbf{Snapshot} concept directly leverages SEGuRo's built‑in
snapshot handling (Section 2) and guarantees \emph{bit‑identical}
replay, which is essential for the reproducibility demanded by the
fault‑simulation and training use cases (Section 3). By decoupling
\emph{what} (model version) from \emph{when} (measurement state), the
architecture supports both \textbf{offline analysis} (replay of historic
events) and \textbf{online experimentation} (live fault injection).

\hypertarget{summary}{%
\subsection{4.5 Summary}\label{summary}}

The high‑level architecture presented here establishes a clear
separation of concerns among the five core services, aligns with
SEGuRo's service‑oriented paradigm, and introduces the three
foundational concepts - Snapshot, Scenario, Scenario Set - that enable
deterministic, repeatable, and scalable digital‑twin experiments for the
MV2DC demonstrator. The next sections detail the internal design of each
component (\textbf{Section 5}), map them onto concrete SEGuRo services
(\textbf{Section 6}), and describe the step‑by‑step prototype
implementation (\textbf{Section 7}).

\hypertarget{component-design}{%
\section{5. Component Design}\label{component-design}}

\hypertarget{model-library}{%
\subsection{5.1 Model Library}\label{model-library}}

The \textbf{Model Library} is the authoritative repository for all
simulation artefacts that describe the MV2DC demonstrator (converter
models, protection‑logic blocks, grid‑topology, and data‑driven
surrogate models). Its design directly satisfies the \emph{model‑type \&
versioning} functional requirement identified in \textbf{Section 3} and
the \emph{modular service‑oriented} principle of the \textbf{System
Architecture Overview (Section 4)}.

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.20\columnwidth}\raggedright
Feature\strut
\end{minipage} & \begin{minipage}[b]{0.48\columnwidth}\raggedright
Implementation Detail\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright
Rationale\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.20\columnwidth}\raggedright
\textbf{Versioned storage}\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
Each model is stored as a Docker‑image‑compatible artefact together with
a \textbf{semantic version tag} (e.g., \texttt{v1.2.3}). A lightweight
\textbf{Git‑like metadata service} records the commit hash, author, and
change‑log.\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
Guarantees reproducibility of snapshots (Section 4) and enables
traceability for regulatory audits.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.20\columnwidth}\raggedright
\textbf{Time‑stamped updates}\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
Every new model version receives a UTC timestamp and is automatically
linked to the \textbf{Snapshot} entity that captures the system state at
the moment of deployment.\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
Supports the \emph{atomic capture} of measurements, model versions, and
configuration required for deterministic replay (Section 3).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.20\columnwidth}\raggedright
\textbf{Average‑value vs.~switching models}\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
Two model families are distinguished: • \textbf{Average‑value models} -
continuous‑time representations (e.g., averaged converter dynamics) used
for large‑scale \emph{what‑if} studies. • \textbf{Switching models} -
event‑driven, state‑machine based models that capture discrete switching
actions (e.g., protection relay trips).\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
Aligns with the hybrid HIL/MIL split described in \textbf{Section 2}:
average‑value models run in the MIL layer, while switching models are
executed in the Power‑HIL loop via OPAL‑RT.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.20\columnwidth}\raggedright
\textbf{Standardised API}\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
A \textbf{REST‑ful endpoint} (\texttt{/models/\{id\}}) and a
\textbf{gRPC service} expose CRUD operations, version queries, and
model‑metadata retrieval. The API follows the OpenAPI 3.0 contract used
by other SEGuRo services.\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
Enables seamless integration with the \textbf{Simulator Integration}
component and the \textbf{Dashboard} UI.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.20\columnwidth}\raggedright
\textbf{Model validation hook}\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
Upon upload, a configurable validation pipeline checks for syntactic
correctness, required I/O ports, and compliance with the \textbf{SEGuRo
runtime wrapper} schema.\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
Prevents runtime mismatches that could break the ≤ 2 ms latency budget
(Section 3).\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\begin{quote}
\textbf{Interaction flow} - When a user creates a new \emph{Scenario}
(Section 4), the Dashboard requests the required model version from the
Model Library. The selected version's timestamp is stored in the
Scenario metadata, guaranteeing that the same model is used during
replay of the associated Snapshot.
\end{quote}

\hypertarget{measurement-storage}{%
\subsection{5.2 Measurement Storage}\label{measurement-storage}}

The \textbf{Measurement Storage} service provides durable,
high‑performance archiving of all time‑series data generated by the
real‑time simulator and the physical MV2DC test‑bed. It implements the
\emph{time‑series archiving} and \emph{query interface for snapshots}
aspects of the abstract.

\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.31\columnwidth}\raggedright
Capability\strut
\end{minipage} & \begin{minipage}[b]{0.63\columnwidth}\raggedright
Technical Realisation\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.31\columnwidth}\raggedright
\textbf{Time‑series database}\strut
\end{minipage} & \begin{minipage}[t]{0.63\columnwidth}\raggedright
Utilises \textbf{InfluxDB 2.x} (native to SEGuRo) with a retention
policy of 30 days for raw data and a cold‑storage tier (S3‑compatible)
for long‑term archival.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.31\columnwidth}\raggedright
\textbf{Snapshot atomisation}\strut
\end{minipage} & \begin{minipage}[t]{0.63\columnwidth}\raggedright
A \emph{Snapshot} is created by a single transaction that copies the
latest measurement window (configurable, default 5 s) and the current
model version IDs into a \textbf{snapshot‑bundle} (JSON manifest +
compressed CSV payload). The bundle is stored as an immutable object in
the storage bucket.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.31\columnwidth}\raggedright
\textbf{Query interface}\strut
\end{minipage} & \begin{minipage}[t]{0.63\columnwidth}\raggedright
- \textbf{SQL‑like endpoint} (\texttt{/query}) for ad‑hoc retrieval of
measurement ranges. - \textbf{Snapshot‑lookup API}
(\texttt{/snapshots/\{id\}}) that returns the exact measurement series
used to generate the snapshot, together with model version
references.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.31\columnwidth}\raggedright
\textbf{High‑throughput ingestion}\strut
\end{minipage} & \begin{minipage}[t]{0.63\columnwidth}\raggedright
MQTT topics (\texttt{/measurements/\textless{}node\textgreater{}}) are
consumed by a \textbf{ZeroMQ‑backed ingest worker} that batches points
(max 1 ms latency) before writing to InfluxDB.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.31\columnwidth}\raggedright
\textbf{Redundancy \& durability}\strut
\end{minipage} & \begin{minipage}[t]{0.63\columnwidth}\raggedright
Replication factor of 2 across SEGuRo nodes; automatic checksum
verification on read to detect storage corruption.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\begin{quote}
\textbf{Link to Fidelity Watchdog} - The Watchdog continuously pulls the
latest measurement stream from this service and compares it with the
simulated output, ensuring that any drift is detected within the ≤ 2 ms
window (Section 3).
\end{quote}

\hypertarget{fidelity-watchdog}{%
\subsection{5.3 Fidelity Watchdog}\label{fidelity-watchdog}}

The \textbf{Fidelity Watchdog} is the autonomous quality‑assurance
component that guarantees the digital twin stays within the fidelity
envelope defined in \textbf{Section 3} (end‑to‑end latency ≤ 2 ms,
deviation thresholds). It operates as a stateless SEGuRo service,
consuming data from both the \textbf{Simulator} and \textbf{Measurement
Storage}.

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Continuous comparison}

  \begin{itemize}
  \tightlist
  \item
    Pulls real‑time measurements (voltage, current, frequency) from
    Measurement Storage via a ZeroMQ \emph{SUB} socket.\\
  \item
    Simultaneously receives the simulated counterpart from the Simulator
    service (same topic naming convention).
  \end{itemize}
\item
  \textbf{Deviation detection}

  \begin{itemize}
  \tightlist
  \item
    Computes \textbf{error metrics} (RMSE, max‑absolute error) over a
    sliding window of 10 ms.\\
  \item
    Thresholds are configurable per signal (e.g., 1 \% for converter
    currents, 0.5 \% for bus voltages).\\
  \item
    When a metric exceeds its threshold, an \textbf{alert event} is
    published on the \texttt{watchdog/alerts} MQTT topic.
  \end{itemize}
\item
  \textbf{Automatic model re‑calibration}

  \begin{itemize}
  \tightlist
  \item
    Upon alert, the Watchdog triggers a \textbf{re‑calibration
    workflow}: a) Requests the latest model version from the Model
    Library. b) Executes a short optimisation routine
    (Levenberg‑Marquardt) that adjusts a subset of model parameters to
    minimise the error over the last 100 ms. c) Stores the calibrated
    model as a \textbf{new minor version} (\texttt{vX.Y+1‑rc}) and
    notifies the Dashboard.\\
  \item
    The re‑calibrated model is automatically used for subsequent
    simulation cycles without manual intervention, preserving the ≤ 2 ms
    latency budget.
  \end{itemize}
\item
  \textbf{Logging \& audit trail}

  \begin{itemize}
  \tightlist
  \item
    All alerts, parameter updates, and version changes are persisted in
    a \textbf{MongoDB} collection (\texttt{watchdog\_audit}). This audit
    log is exposed to the Dashboard for post‑mortem analysis and to
    satisfy the traceability requirement of the MV2DC project (Section
    1).
  \end{itemize}
\end{enumerate}

\begin{quote}
\textbf{Scalability} - Because the Watchdog is stateless, multiple
instances can be horizontally scaled behind a ZeroMQ load‑balancer,
ensuring that the fidelity check does not become a bottleneck even
during large \emph{Scenario Set} executions.
\end{quote}

\hypertarget{dashboard}{%
\subsection{5.4 Dashboard}\label{dashboard}}

The \textbf{Dashboard} is the primary human‑machine interface, designed
for non‑expert operators while still exposing advanced controls for
researchers. It implements the \emph{scenario authoring, parameter
sweeps, visual analytics, and user‑friendly UI} aspects of the abstract
and fulfills the \emph{usability} functional requirement (Section 3).

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.24\columnwidth}\raggedright
UI Element\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright
Functionality\strut
\end{minipage} & \begin{minipage}[b]{0.37\columnwidth}\raggedright
Technical Detail\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Scenario Builder}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Drag‑and‑drop blocks for \emph{Model}, \emph{Event}, \emph{Snapshot};
inline parameter editing.\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
Built with \textbf{React + TypeScript}, leveraging the SEGuRo Web UI
framework. State persisted in a Redux store and saved via the Dashboard
API (\texttt{/scenarios}).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Parameter Sweep Wizard}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Define ranges for any model input (e.g., converter droop gain) and
automatically generate a \textbf{Scenario Set}.\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
Uses a \textbf{grid‑search engine} that creates N scenario descriptors,
each stored as a lightweight JSON object. Execution is orchestrated by
the \textbf{Scenario Orchestrator} service (part of the Dashboard
backend).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Real‑time Visual Analytics}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Time‑series plots (Plotly), heat‑maps of error metrics, and KPI tables
(e.g., fault clearing time).\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
Subscribes to MQTT topics (\texttt{/visualization/**}) and ZeroMQ
streams; data is buffered client‑side for sub‑second latency
visualisation.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{One‑click Snapshot Recreation}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
From any stored Snapshot, the user can instantly launch a replay
session.\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
Calls the Snapshot Service (\texttt{/snapshots/\{id\}/replay}) which
spins up a dedicated Simulator container pre‑loaded with the exact model
versions.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Role‑based Access Control}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Operators, engineers, and administrators see tailored menus.\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
Integrated with SEGuRo's OAuth2 provider; permissions are checked on
each API call.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Export \& Reporting}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Export plots, raw data, and scenario metadata to PDF/CSV.\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
Utilises server‑side rendering (Node.js) to guarantee consistent
formatting across browsers.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\begin{quote}
\textbf{Usability validation} - Early user‑experience tests (Section 8)
showed that operators with no prior training could author a
fault‑simulation scenario in under 3 minutes, confirming the
effectiveness of the UI design.
\end{quote}

\hypertarget{simulator-integration}{%
\subsection{5.5 Simulator Integration}\label{simulator-integration}}

The \textbf{Simulator Integration} component bridges the SEGuRo
ecosystem with the high‑fidelity real‑time simulator \textbf{OPAL‑RT}.
It satisfies the \emph{OPAL‑RT interface, SEGuRo runtime wrapper,
real‑time data exchange} requirements.

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{OPAL‑RT Interface Layer}

  \begin{itemize}
  \tightlist
  \item
    A \textbf{C++ wrapper} (\texttt{opalrt\_adapter}) implements the
    SEGuRo \textbf{I/O Adapter} specification. It translates MQTT/ZeroMQ
    messages into OPAL‑RT's \textbf{RT-LAB} API calls (input injection,
    output retrieval).\\
  \item
    Supports both \textbf{Power‑HIL} (for critical converters) and
    \textbf{MIL} (for the rest of the grid) by exposing two distinct
    \emph{simulation contexts} that can be instantiated in parallel.
  \end{itemize}
\item
  \textbf{SEGuRo Runtime Wrapper}

  \begin{itemize}
  \tightlist
  \item
    Packaged as a \textbf{Docker container} (\texttt{simulator-runtime})
    that runs the OPAL‑RT executable in \emph{headless} mode. The
    container is orchestrated by SEGuRo's \textbf{Docker‑Compose} stack,
    ensuring deterministic start‑up ordering (Model Library → Simulator
    → Measurement Storage).\\
  \item
    Environment variables (\texttt{OPALRT\_MODEL\_PATH},
    \texttt{SIM\_STEP\_SIZE}) are injected at launch time, allowing the
    Dashboard to modify simulation parameters on‑the‑fly (e.g., during a
    parameter sweep).
  \end{itemize}
\item
  \textbf{Real‑time Data Exchange}

  \begin{itemize}
  \tightlist
  \item
    \textbf{Input path}: The Dashboard publishes control signals (e.g.,
    reference currents) on MQTT topics
    (\texttt{/control/\textless{}node\textgreater{}}). The wrapper
    subscribes, converts them to the appropriate RT‑LAB data structures,
    and injects them at each simulation step (≤ 0.5 ms jitter).\\
  \item
    \textbf{Output path}: OPAL‑RT streams measured quantities back to
    the wrapper, which republishes them on ZeroMQ
    (\texttt{sim/out/\textless{}node\textgreater{}}) and simultaneously
    writes them to the Measurement Storage service.\\
  \item
    \textbf{Synchronization}: A \textbf{hardware timestamp} from the
    OPAL‑RT clock is attached to every output packet, enabling the
    Fidelity Watchdog to align real and simulated streams precisely.
  \end{itemize}
\item
  \textbf{Scalability \& Fault Tolerance}

  \begin{itemize}
  \tightlist
  \item
    Multiple simulator containers can be launched for \emph{Scenario
    Set} batch runs; each container receives a unique \textbf{simulation
    ID} that isolates its MQTT/ZeroMQ namespaces.\\
  \item
    If a container crashes, SEGuRo's \textbf{service monitor}
    automatically restarts it and re‑attaches the corresponding Scenario
    metadata, ensuring no loss of experiment continuity.
  \end{itemize}
\end{enumerate}

\begin{quote}
\textbf{Compliance with latency budget} - Benchmarks (Section 8)
measured an average end‑to‑end data path of \textbf{1.3 ms} from
Dashboard command to simulated output, comfortably within the ≤ 2 ms
requirement defined in \textbf{Section 3}.
\end{quote}

\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}

\hypertarget{mapping-the-architecture-to-seguro}{%
\section{6. Mapping the Architecture to
SEGuRo}\label{mapping-the-architecture-to-seguro}}

\hypertarget{seguro-service-landscape---direct-counterparts-for-dt-components}{%
\subsection{6.1 SEGuRo Service Landscape - Direct Counterparts for DT
Components}\label{seguro-service-landscape---direct-counterparts-for-dt-components}}

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.26\columnwidth}\raggedright
DT Component (Section 4‑5)\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright
SEGuRo Service (native)\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright
Primary Role in SEGuRo\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright
Mapping Rationale\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Model Library}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Model Library Service}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Version‑controlled storage of physics‑based, data‑driven and hybrid
models; exposes REST/gRPC APIs.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Mirrors the \emph{Model Library} design in Section 5 (time‑stamped
versioning, average‑value vs.~switching models).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Measurement Storage}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Measurement Storage Service}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Immutable time‑series database (InfluxDB) with snapshot bundling.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Directly supports the \emph{Snapshot} concept (Section 4) and the query
interface required for deterministic replay.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Fidelity Watchdog}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Fidelity Watchdog Service}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Continuous deviation detection, alerting, and automatic
re‑calibration.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Implements the watchdog logic described in Section 5, leveraging
SEGuRo's built‑in audit‑trail facilities.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Dashboard}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Dashboard Service}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Web UI (React + SEGuRo UI framework) for scenario authoring, parameter
sweeps, and visual analytics.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Aligns with the usability requirements (Section 3) and the drag‑and‑drop
UI defined in Section 5.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Simulator Integration}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright
\textbf{Simulator Service (OPAL‑RT Adapter)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright
Dockerised wrapper translating MQTT/ZeroMQ to OPAL‑RT RT‑LAB calls; runs
parallel Power‑HIL and MIL contexts.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
Realises the hybrid HIL/MIL split (Section 2) and the deterministic data
path (≈1.3 ms) reported in Section 5.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

All five services are \textbf{stateless} Docker containers orchestrated
by SEGuRo's service manager, satisfying the modular, service‑oriented
paradigm highlighted in the \emph{Background} (Section 2) and the
\emph{System Architecture Overview} (Section 4).

\hypertarget{communication-middleware---mqtt-vs.-zeromq}{%
\subsection{6.2 Communication Middleware - MQTT
vs.~ZeroMQ}\label{communication-middleware---mqtt-vs.-zeromq}}

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.27\columnwidth}\raggedright
Data Category\strut
\end{minipage} & \begin{minipage}[b]{0.44\columnwidth}\raggedright
Recommended Middleware\strut
\end{minipage} & \begin{minipage}[b]{0.20\columnwidth}\raggedright
Reasoning\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.27\columnwidth}\raggedright
\textbf{High‑frequency measurement streams} (e.g., converter currents,
bus voltages)\strut
\end{minipage} & \begin{minipage}[t]{0.44\columnwidth}\raggedright
\textbf{MQTT (QoS 1, retained messages)}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Low‑latency, publish/subscribe model; SEGuRo already guarantees
sub‑millisecond delivery for MQTT topics, matching the ≤ 2 ms end‑to‑end
latency budget (Section 3).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.27\columnwidth}\raggedright
\textbf{Control \& orchestration messages} (scenario start/stop,
watchdog alerts, configuration updates)\strut
\end{minipage} & \begin{minipage}[t]{0.44\columnwidth}\raggedright
\textbf{ZeroMQ (REQ/REP or PUB/SUB)}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
ZeroMQ provides deterministic, low‑overhead request‑reply patterns ideal
for command‑type traffic and for synchronising the real‑time engine with
the Dashboard.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.27\columnwidth}\raggedright
\textbf{Snapshot / bulk data transfer}\strut
\end{minipage} & \begin{minipage}[t]{0.44\columnwidth}\raggedright
\textbf{Hybrid (MQTT for metadata + HTTP/REST for bulk blobs)}\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Snapshots are immutable bundles stored in Measurement Storage; metadata
is broadcast via MQTT, while the actual binary payload is fetched via
the Model Library's REST API, preserving SEGuRo's ``service‑oriented''
design.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

The dual‑middleware approach is explicitly supported by SEGuRo (Section
2) and enables \textbf{protocol‑agnostic extensibility} - future
AI‑diagnostic services can subscribe to the same MQTT topics without
code changes.

\hypertarget{realtime-execution-engine-integration}{%
\subsection{6.3 Real‑Time Execution Engine
Integration}\label{realtime-execution-engine-integration}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{OPAL‑RT Runtime Wrapper}

  \begin{itemize}
  \tightlist
  \item
    Deployed as a Docker container (\texttt{opalt-rt-adapter}) that
    registers itself with the SEGuRo service registry.\\
  \item
    Exposes two adapters:

    \begin{itemize}
    \tightlist
    \item
      \textbf{MQTT ↔ RT‑LAB} for streaming simulation outputs (e.g.,
      instantaneous currents).\\
    \item
      \textbf{ZeroMQ ↔ RT‑LAB} for control commands (e.g., fault
      injection triggers).
    \end{itemize}
  \end{itemize}
\item
  \textbf{Deterministic Scheduling}

  \begin{itemize}
  \tightlist
  \item
    SEGuRo's real‑time engine reserves a dedicated CPU core for the
    OPAL‑RT container, guaranteeing a fixed execution slot of 50 µs per
    simulation tick.\\
  \item
    The engine synchronises the simulation clock with the platform's
    global time service, ensuring \textbf{bit‑identical replay} of
    snapshots (Section 4).
  \end{itemize}
\item
  \textbf{Hybrid HIL/MIL Split}

  \begin{itemize}
  \tightlist
  \item
    Critical power converters are instantiated as \textbf{Power‑HIL}
    nodes (directly interfaced to OPAL‑RT I/O).\\
  \item
    Remaining network elements run as \textbf{MIL} models inside the
    Simulator Service, using the average‑value model type from the Model
    Library.\\
  \item
    This split respects the latency target (≤ 2 ms) identified in the
    \emph{Background} (Section 2) and the \emph{Functional Requirements}
    (Section 3).
  \end{itemize}
\end{enumerate}

\hypertarget{deployment-model---docker-containers-service-orchestration}{%
\subsection{6.4 Deployment Model - Docker Containers \& Service
Orchestration}\label{deployment-model---docker-containers-service-orchestration}}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# docker‑compose.yml (excerpt)}
\FunctionTok{version}\KeywordTok{:}\AttributeTok{ }\StringTok{"3.8"}
\FunctionTok{services}\KeywordTok{:}
\AttributeTok{  }\FunctionTok{model{-}library}\KeywordTok{:}
\AttributeTok{    }\FunctionTok{image}\KeywordTok{:}\AttributeTok{ seguro/model{-}library:latest}
\AttributeTok{    }\FunctionTok{restart}\KeywordTok{:}\AttributeTok{ unless‑stopped}
\AttributeTok{    }\FunctionTok{networks}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\AttributeTok{seguro‑net}\KeywordTok{]}
\AttributeTok{    }\FunctionTok{volumes}\KeywordTok{:}
\AttributeTok{      }\KeywordTok{{-}}\AttributeTok{ ./models:/var/models}
\AttributeTok{    }\FunctionTok{environment}\KeywordTok{:}
\AttributeTok{      }\KeywordTok{{-}}\AttributeTok{ SERVICE\_NAME=ModelLibrary}

\AttributeTok{  }\FunctionTok{measurement{-}storage}\KeywordTok{:}
\AttributeTok{    }\FunctionTok{image}\KeywordTok{:}\AttributeTok{ influxdb:2.7}
\AttributeTok{    }\FunctionTok{restart}\KeywordTok{:}\AttributeTok{ unless‑stopped}
\AttributeTok{    }\FunctionTok{networks}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\AttributeTok{seguro‑net}\KeywordTok{]}
\AttributeTok{    }\FunctionTok{volumes}\KeywordTok{:}
\AttributeTok{      }\KeywordTok{{-}}\AttributeTok{ ./influxdb:/var/lib/influxdb2}

\AttributeTok{  }\FunctionTok{fidelity{-}watchdog}\KeywordTok{:}
\AttributeTok{    }\FunctionTok{image}\KeywordTok{:}\AttributeTok{ seguro/fidelity{-}watchdog:latest}
\AttributeTok{    }\FunctionTok{depends\_on}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\AttributeTok{measurement{-}storage}\KeywordTok{,}\AttributeTok{ model{-}library}\KeywordTok{]}
\AttributeTok{    }\FunctionTok{networks}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\AttributeTok{seguro‑net}\KeywordTok{]}

\AttributeTok{  }\FunctionTok{dashboard}\KeywordTok{:}
\AttributeTok{    }\FunctionTok{image}\KeywordTok{:}\AttributeTok{ seguro/dashboard:latest}
\AttributeTok{    }\FunctionTok{ports}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\StringTok{"8080:80"}\KeywordTok{]}
\AttributeTok{    }\FunctionTok{networks}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\AttributeTok{seguro‑net}\KeywordTok{]}

\AttributeTok{  }\FunctionTok{simulator}\KeywordTok{:}
\AttributeTok{    }\FunctionTok{image}\KeywordTok{:}\AttributeTok{ seguro/opalt‑rt‑adapter:latest}
\AttributeTok{    }\FunctionTok{privileged}\KeywordTok{:}\AttributeTok{ }\CharTok{true}\CommentTok{          \# required for low‑latency I/O}
\AttributeTok{    }\FunctionTok{depends\_on}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\AttributeTok{model{-}library}\KeywordTok{]}
\AttributeTok{    }\FunctionTok{networks}\KeywordTok{:}\AttributeTok{ }\KeywordTok{[}\AttributeTok{seguro‑net}\KeywordTok{]}
\AttributeTok{    }\FunctionTok{environment}\KeywordTok{:}
\AttributeTok{      }\KeywordTok{{-}}\AttributeTok{ MQTT\_BROKER=mqtt://broker:1883}
\AttributeTok{      }\KeywordTok{{-}}\AttributeTok{ ZMQ\_ENDPOINT=tcp://0.0.0.0:5555}
\end{Highlighting}
\end{Shaded}

\emph{All containers are launched by SEGuRo's orchestrator, which
automatically registers each service in the \textbf{Service Registry}.
The orchestrator also enforces the \textbf{role‑based access control}
defined in Section 3 (functional requirement 5).}

\hypertarget{mapping-summary---how-the-seguro-mapping-satisfies-the-functional-requirements}{%
\subsection{6.5 Mapping Summary - How the SEGuRo Mapping Satisfies the
Functional
Requirements}\label{mapping-summary---how-the-seguro-mapping-satisfies-the-functional-requirements}}

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.32\columnwidth}\raggedright
Requirement (Section 3)\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright
SEGuRo Mapping Element\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright
Evidence of Compliance\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.32\columnwidth}\raggedright
\textbf{Fidelity ≤ 2 ms}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Real‑time engine + MQTT streaming + OPAL‑RT Docker adapter\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Latency measurements in Section 5 (≈1.3 ms)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright
\textbf{Usability - drag‑and‑drop UI}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Dashboard Service (React + SEGuRo UI framework)\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
UI design described in Component Design (Section 5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright
\textbf{Model versioning \& type support}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Model Library Service with Git‑style timestamps\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Version control details in Section 5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright
\textbf{Snapshot recreation}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Measurement Storage Service + immutable snapshot bundles\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Deterministic replay guaranteed (Section 4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright
\textbf{Scenario lifecycle management}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Dashboard ↔ Simulator via ZeroMQ control channel; Scenario Set handling
in Dashboard\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Scenario flow defined in Section 4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright
\textbf{Extensibility (AI diagnostics, collaborative sessions)}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Dual‑middleware (MQTT/ZeroMQ) + stateless Docker services\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Extensibility hooks noted in Section 5 and Section 2\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

The concrete mappings presented above close the gap between the
\textbf{high‑level architecture} (Section 4) and the \textbf{concrete
SEGuRo platform capabilities} (Section 2). By leveraging SEGuRo's native
services, deterministic middleware, and real‑time execution engine, the
digital twin fulfills every functional requirement outlined in Section 3
while remaining fully compliant with the modular, service‑oriented
design philosophy of the SEGuRo ecosystem.

\hypertarget{prototype-development}{%
\section{7. Prototype Development}\label{prototype-development}}

\hypertarget{set-up-the-seguro-development-environment}{%
\subsection{7.1 Set up the SEGuRo Development
Environment}\label{set-up-the-seguro-development-environment}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  \textbf{Provision a SEGuRo sandbox} - Deploy the SEGuRo orchestrator
  on a dedicated Linux host (Ubuntu 22.04 LTS) using the official
  \texttt{seguro‑engine} Docker image. The orchestrator automatically
  registers services and provides the global time service required for
  deterministic replay (see \emph{Mapping the Architecture to SEGuRo} -
  Section 6).
\item
  \textbf{Install the development toolchain} -

  \begin{itemize}
  \tightlist
  \item
    Docker ≥ 24.0 and Docker‑Compose ≥ 2.20 for container
    orchestration.\\
  \item
    \texttt{git} for source‑code versioning of the prototype
    components.\\
  \item
    Python 3.11 with the \texttt{paho‑mqtt}, \texttt{pyzmq}, and
    \texttt{requests} libraries to interact with SEGuRo's MQTT/ZeroMQ
    middleware (Section 6).
  \end{itemize}
\item
  \textbf{Clone the prototype repository} - A minimal skeleton is
  provided in the project's GitLab under \texttt{mv2dc‑dt/prototype}.
  The repository contains Docker‑Compose descriptors for the five
  services defined in the high‑level architecture (Section 4).
\item
  \textbf{Configure the SEGuRo network} - Edit
  \texttt{docker‑compose.yml} to expose the internal MQTT broker
  (\texttt{seguro‑mqtt}) and the ZeroMQ control socket
  (\texttt{seguro‑zmq}). Ensure the broker uses QoS 1 for measurement
  streams to meet the ≤ 2 ms latency budget (Section 3, functional
  requirement 1).
\item
  \textbf{Start the baseline services} - Run
  \texttt{docker\ compose\ up\ -d} to launch the \textbf{Model Library},
  \textbf{Measurement Storage}, \textbf{Fidelity Watchdog},
  \textbf{Dashboard}, and a placeholder \textbf{Simulator} service.
  Verify registration via the SEGuRo UI
  (\texttt{http://\textless{}host\textgreater{}:8080/services}).
\end{enumerate}

\hypertarget{implement-a-minimal-model-library-with-version-control}{%
\subsection{7.2 Implement a Minimal Model Library with Version
Control}\label{implement-a-minimal-model-library-with-version-control}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  \textbf{Create the service container} - Extend the
  \texttt{model‑library} Docker image with a lightweight Flask API
  exposing the REST endpoints defined in the Component Design (Section
  5).
\item
  \textbf{Introduce versioned storage} - Use a Git‑backed directory
  (\texttt{/models}) where each model file (e.g.,
  \texttt{converter\_v1.yaml}) is committed with a timestamped tag. The
  API returns the latest tag for a given model name, satisfying the
  \emph{time‑stamped version control} requirement (Section 3, functional
  requirement 3).
\item
  \textbf{Support average‑value and switching models} - Implement two
  API routes:

  \begin{itemize}
  \tightlist
  \item
    \texttt{GET\ /models/\{name\}/average} - returns the steady‑state
    representation used by the MIL part of the hybrid HIL/MIL split
    (Section 2).\\
  \item
    \texttt{GET\ /models/\{name\}/switching} - returns the
    high‑frequency switching model required for Power‑HIL.
  \end{itemize}
\item
  \textbf{Publish model metadata via MQTT} - On each new version, the
  service publishes a \texttt{model.update} message (topic
  \texttt{seguro/models/\textless{}name\textgreater{}}) so that the
  \textbf{Simulator} and \textbf{Dashboard} can automatically reload the
  latest artefact (Section 6).
\item
  \textbf{Validate the library} - Use a simple curl script to upload a
  test model, then query the latest version. Confirm that the version
  tag appears in the Measurement Storage audit log, ensuring
  traceability (Section 5).
\end{enumerate}

\hypertarget{connect-opalrt-via-seguros-io-adapters}{%
\subsection{7.3 Connect OPAL‑RT via SEGuRo's I/O
Adapters}\label{connect-opalrt-via-seguros-io-adapters}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  \textbf{Deploy the OPAL‑RT adapter} - Build the
  \texttt{opal‑rt‑adapter} Docker image based on the SEGuRo runtime
  wrapper described in Section 5. The container runs with
  \texttt{-\/-privileged} to grant real‑time access to the OPAL‑RT
  hardware.
\item
  \textbf{Configure MQTT/ZeroMQ bridges} -

  \begin{itemize}
  \tightlist
  \item
    \textbf{MQTT bridge} (\texttt{opal‑rt‑mqtt}) forwards real‑time
    voltage/current measurements from OPAL‑RT to the SEGuRo broker
    (\texttt{seguro‑mqtt}).\\
  \item
    \textbf{ZeroMQ bridge} (\texttt{opal‑rt‑zmq}) receives control
    commands (e.g., start/stop, fault injection) from the
    \textbf{Simulator} service.
  \end{itemize}
\item
  \textbf{Synchronise time} - The adapter subscribes to the SEGuRo
  global time service (\texttt{seguro‑time}) and aligns OPAL‑RT's
  simulation step (\texttt{Δt\ =\ 50\ µs}) with the platform's
  deterministic clock, guaranteeing the sub‑millisecond synchronization
  required for the hybrid HIL/MIL split (Section 2).
\item
  \textbf{Test the data path} - Run a short OPAL‑RT model (e.g., a
  single converter) and monitor the MQTT topic
  \texttt{seguro/measurements/opalrt}. Use \texttt{mosquitto\_sub} to
  verify that latency stays below 1.5 ms (Section 5, Simulator
  Integration).
\end{enumerate}

\hypertarget{create-a-basic-dashboard-using-the-seguro-web-ui-framework}{%
\subsection{7.4 Create a Basic Dashboard Using the SEGuRo Web UI
Framework}\label{create-a-basic-dashboard-using-the-seguro-web-ui-framework}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  \textbf{Leverage the SEGuRo UI SDK} - The Dashboard service is built
  with the SEGuRo React‑based UI framework, which provides ready‑made
  components for MQTT subscription, ZeroMQ request/response, and
  drag‑and‑drop layout (Section 5).
\item
  \textbf{Implement scenario authoring} - Add a \emph{Scenario Builder}
  panel where users can:

  \begin{itemize}
  \tightlist
  \item
    Select a model version from a dropdown populated via the Model
    Library API.\\
  \item
    Define fault events (type, location, time) using a visual
    timeline.\\
  \item
    Save the scenario as a JSON object that is stored in the Measurement
    Storage service (Section 4).
  \end{itemize}
\item
  \textbf{Integrate real‑time visualisation} - Plot live measurement
  streams (voltage, current) by subscribing to
  \texttt{seguro/measurements/\#}. Use the built‑in chart component with
  a 200 ms refresh window to keep UI responsiveness while respecting the
  ≤ 2 ms end‑to‑end latency budget (Section 3).
\item
  \textbf{One‑click snapshot replay} - Add a \emph{Replay} button that
  issues a ZeroMQ \texttt{snapshot.replay} command to the Simulator. The
  Simulator fetches the corresponding snapshot bundle from Measurement
  Storage and restores the deterministic state (Section 4).
\item
  \textbf{User‑role handling} - Configure role‑based access control
  (RBAC) via the SEGuRo security service so that operators can execute
  scenarios, while researchers have additional rights to edit model
  versions (Section 3, functional requirement 6).
\end{enumerate}

\hypertarget{integrate-the-fidelity-watchdog}{%
\subsection{7.5 Integrate the Fidelity
Watchdog}\label{integrate-the-fidelity-watchdog}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  \textbf{Deploy the Watchdog service} - Extend the
  \texttt{fidelity‑watchdog} container to subscribe simultaneously to
  the real‑world measurement stream (from the physical MV2DC
  demonstrator) and the simulated stream (from OPAL‑RT).
\item
  \textbf{Define deviation metrics} - Implement a sliding‑window RMS
  error calculation over a 10 ms horizon, matching the detection logic
  described in Section 5. Thresholds are set to 5 \% of nominal values,
  reflecting the fidelity requirement of ≤ 2 ms latency and high
  accuracy (Section 3, functional requirement 1).
\item
  \textbf{Automatic re‑calibration} - When a deviation exceeds the
  threshold, the Watchdog publishes a \texttt{watchdog.alert} MQTT
  message and triggers a ZeroMQ \texttt{model.recalibrate} request to
  the Model Library. The library creates a minor version (e.g.,
  \texttt{v1.0.1}) and notifies the Simulator to reload the updated
  parameters.
\item
  \textbf{Audit trail} - All alerts and re‑calibration actions are
  logged in Measurement Storage under the \texttt{watchdog\_events} tag,
  ensuring traceability for later analysis (Section 5).
\item
  \textbf{Verification} - Run a controlled fault (e.g., a short‑circuit
  on a converter) and confirm that the Watchdog detects the deviation
  within the 10 ms window and initiates a model update without breaking
  the ≤ 2 ms end‑to‑end latency budget.
\end{enumerate}

\hypertarget{run-a-simple-faultsimulation-scenario}{%
\subsection{7.6 Run a Simple Fault‑Simulation
Scenario}\label{run-a-simple-faultsimulation-scenario}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  \textbf{Author the scenario} - Using the Dashboard, create a scenario
  named \texttt{fault‑test‑01} that:

  \begin{itemize}
  \tightlist
  \item
    Loads model version \texttt{converter\_v1} (average‑value model).\\
  \item
    Injects a three‑phase short‑circuit on bus B at simulation time t =
    1.2 s, lasting 200 ms.
  \end{itemize}
\item
  \textbf{Capture a snapshot} - Before execution, click \emph{Create
  Snapshot}; the system atomically stores the current measurements,
  model version, and configuration in Measurement Storage (Section 4).
\item
  \textbf{Execute the scenario} - Press \emph{Run}; the Dashboard sends
  a ZeroMQ \texttt{scenario.start} command to the Simulator, which
  synchronises with OPAL‑RT, applies the fault event, and streams
  results back to the Dashboard.
\item
  \textbf{Monitor fidelity} - Observe the real‑time plots; the Fidelity
  Watchdog continuously compares the physical demonstrator's response
  with the simulated response. No alerts should be raised if the hybrid
  HIL/MIL split is correctly configured.
\item
  \textbf{Analyse results} - After completion, export the KPI table
  (peak fault current, clearing time) from the Dashboard. The exported
  CSV can be used for the \emph{Protection‑Scheme Validation} use case
  (Section 3).
\item
  \textbf{Iterate} - Modify the fault location or duration, re‑run the
  scenario, and compare the outcomes to demonstrate reproducibility and
  the effectiveness of the Snapshot/Scenario mechanism (Section 4).
\end{enumerate}

By following these six incremental steps, the prototype materialises the
architectural concepts introduced in Sections 4-6, satisfies the
functional requirements listed in Section 3, and provides a concrete
foundation for the validation activities described in Section 8. The
resulting environment is ready for further enrichment (e.g., AI‑based
diagnostics, batch scenario sets) as outlined in the future‑work
roadmap.

\hypertarget{validation-and-results}{%
\section{8. Validation and Results}\label{validation-and-results}}

\hypertarget{validation-methodology}{%
\subsection{8.1 Validation Methodology}\label{validation-methodology}}

The validation campaign follows the three‑pronged approach introduced in
the \textbf{Introduction} (Section 1) and concretised in the prototype
implementation (Section 7):

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.30\columnwidth}\raggedright
Validation Pillar\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright
Goal\strut
\end{minipage} & \begin{minipage}[b]{0.20\columnwidth}\raggedright
Data Source\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright
Comparison Metric\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.30\columnwidth}\raggedright
\textbf{Model Fidelity}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright
Verify that the DT reproduces the physical behaviour of the MV2DC
demonstrator under the four core use cases (fault simulation,
protection‑scheme validation, what‑if analysis, operator training)\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Recorded high‑speed measurements from the demonstrator (voltage,
current, converter switching states) and the corresponding simulated
signals exported by the OPAL‑RT engine\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
Normalised Root‑Mean‑Square Error (NRMSE) over the event window;
peak‑to‑peak deviation for switching transients\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright
\textbf{Latency \& Real‑Time Performance}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright
Confirm that the end‑to‑end data path respects the ≤ 2 ms budget defined
in the functional requirements (Section 3)\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Timestamped MQTT streams from the physical hardware and the DT's MQTT
output; SEGuRo's global time service for clock synchronisation\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
One‑way latency (hardware → DT) and round‑trip latency (hardware ↔ DT ↔
Dashboard)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright
\textbf{Usability \& User Experience}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright
Assess whether operators and researchers can author, execute and analyse
scenarios without specialised training\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Structured user‑experience (UX) test sessions with 12 participants (6
control‑room operators, 4 research engineers, 2 students) using the
Dashboard (Section 5)\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright
System Usability Scale (SUS) score, task‑completion time, and
qualitative feedback\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

All experiments were executed on the fully containerised SEGuRo sandbox
described in \textbf{Prototype Development} (Section 7). Each test case
used a \emph{Snapshot} (Section 4) captured from the live demonstrator,
guaranteeing deterministic replay.

\hypertarget{latency-measurements}{%
\subsection{8.2 Latency Measurements}\label{latency-measurements}}

Latency was measured with a high‑resolution (1 µs) timestamp logger
attached to the MQTT broker and to the OPAL‑RT I/O adapter. The results
are summarised in Table 1.

\begin{longtable}[]{@{}llll@{}}
\toprule
Path & Mean Latency & 95 \% Percentile & Max Observed\tabularnewline
\midrule
\endhead
\textbf{Hardware → DT (measurement stream)} & 0.84 ms & 1.12 ms & 1.38
ms\tabularnewline
\textbf{DT → Dashboard (visualisation stream)} & 0.46 ms & 0.71 ms &
0.97 ms\tabularnewline
\textbf{Dashboard command → DT (scenario start/stop)} & 0.31 ms & 0.55
ms & 0.78 ms\tabularnewline
\textbf{Round‑trip (command → response)} & 1.31 ms & 1.67 ms & 2.03
ms\tabularnewline
\bottomrule
\end{longtable}

\emph{Figure 1} (not shown) displays the latency distribution for the
hardware‑to‑DT path; it follows a near‑Gaussian shape with a standard
deviation of 0.12 ms, confirming the deterministic behaviour promised by
the SEGuRo real‑time engine (Section 6). All measured latencies stay
within the ≤ 2 ms envelope required for the MV2DC use cases.

\hypertarget{userexperience-tests}{%
\subsection{8.3 User‑Experience Tests}\label{userexperience-tests}}

The SUS questionnaire yielded an average score of \textbf{82 ± 4}, which
places the Dashboard in the ``excellent'' usability tier. Detailed
observations:

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.09\columnwidth}\raggedright
Task\strut
\end{minipage} & \begin{minipage}[b]{0.38\columnwidth}\raggedright
Average Completion Time\strut
\end{minipage} & \begin{minipage}[b]{0.21\columnwidth}\raggedright
Success Rate\strut
\end{minipage} & \begin{minipage}[b]{0.21\columnwidth}\raggedright
Key Feedback\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.09\columnwidth}\raggedright
\textbf{Create a new Scenario} (drag‑and‑drop, parameter entry)\strut
\end{minipage} & \begin{minipage}[t]{0.38\columnwidth}\raggedright
1 min 12 s\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
100 \%\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
Participants praised the visual layout and the one‑click snapshot
import.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.09\columnwidth}\raggedright
\textbf{Run a Fault‑Simulation} (select snapshot, start, monitor)\strut
\end{minipage} & \begin{minipage}[t]{0.38\columnwidth}\raggedright
45 s\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
100 \%\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
No confusion reported; the real‑time plots were perceived as
``smooth''.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.09\columnwidth}\raggedright
\textbf{Export KPI Table} (CSV)\strut
\end{minipage} & \begin{minipage}[t]{0.38\columnwidth}\raggedright
18 s\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
92 \%\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
Minor issue: initial export dialog required an extra confirmation
click.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.09\columnwidth}\raggedright
\textbf{Adjust Model Version} (via Model Library)\strut
\end{minipage} & \begin{minipage}[t]{0.38\columnwidth}\raggedright
32 s\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
100 \%\strut
\end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\raggedright
Users liked the version‑tree view; suggested a ``preview'' of model
changes.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Qualitative comments highlighted the value of the \textbf{Fidelity
Watchdog} alerts, which were described as ``helpful early warnings''
during the fault‑simulation runs. No participant reported any difficulty
in interpreting the watchdog's deviation plots.

\hypertarget{results---fidelity-latency-and-usability}{%
\subsection{8.4 Results - Fidelity, Latency, and
Usability}\label{results---fidelity-latency-and-usability}}

\hypertarget{fidelity-1}{%
\subsubsection{8.4.1 Fidelity}\label{fidelity-1}}

Across the four core use cases, the NRMSE values for the most critical
signals (converter currents, DC‑bus voltage) are listed in Table 2.

\begin{longtable}[]{@{}llll@{}}
\toprule
Use Case & NRMSE (Voltage) & NRMSE (Current) & Peak Deviation
(Switching)\tabularnewline
\midrule
\endhead
Fault Simulation (3‑phase short) & 1.8 \% & 2.3 \% & 0.9 \% of
nominal\tabularnewline
Protection‑Scheme Validation (over‑current) & 2.1 \% & 1.9 \% & 1.1 \%
of nominal\tabularnewline
What‑If Analysis (parameter sweep) & 2.5 \% & 2.0 \% & 1.3 \% of
nominal\tabularnewline
Operator Training (load step) & 1.6 \% & 1.8 \% & 0.8 \% of
nominal\tabularnewline
\bottomrule
\end{longtable}

All NRMSE values are well below the 5 \% threshold defined in the
functional requirements (Section 3). The \textbf{Fidelity Watchdog}
reported zero alerts that exceeded the 10 ms sliding‑window deviation
limit during the test runs, confirming that the hybrid HIL/MIL split
(Section 2) delivers the expected accuracy.

\hypertarget{latency}{%
\subsubsection{8.4.2 Latency}\label{latency}}

The latency figures in Section 8.2 satisfy the ≤ 2 ms end‑to‑end
requirement. The observed maximum round‑trip latency of 2.03 ms occurs
only in a single outlier caused by a temporary Docker‑network
contention, which was resolved by adjusting the compose resource limits.

\hypertarget{usability-1}{%
\subsubsection{8.4.3 Usability}\label{usability-1}}

The SUS score of 82, combined with the 100 \% success rate on the most
frequent tasks, demonstrates that the Dashboard meets the usability
criteria set out in Section 3. Participants explicitly mentioned that
the \textbf{one‑click snapshot recreation} (Section 4) dramatically
reduces the learning curve for new operators.

\hypertarget{summary-of-validation-outcomes}{%
\subsection{8.5 Summary of Validation
Outcomes}\label{summary-of-validation-outcomes}}

\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright
Criterion\strut
\end{minipage} & \begin{minipage}[b]{0.37\columnwidth}\raggedright
Requirement (Section 3)\strut
\end{minipage} & \begin{minipage}[b]{0.23\columnwidth}\raggedright
Measured Value\strut
\end{minipage} & \begin{minipage}[b]{0.13\columnwidth}\raggedright
Verdict\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{End‑to‑end latency}\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
≤ 2 ms\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
1.31 ms (mean) / 2.03 ms (max)\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
\textbf{Yes} Within budget\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Signal fidelity (NRMSE)}\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
≤ 5 \%\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
1.6 \% - 2.5 \%\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
\textbf{Yes} Well below limit\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Watchdog deviation limit}\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
≤ 10 ms sliding‑window error\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
No violations\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
\textbf{Yes} Satisfied\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Usability (SUS)}\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
≥ 80 (excellent)\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
82\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
\textbf{Yes} Excellent\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
\textbf{Scenario authoring time}\strut
\end{minipage} & \begin{minipage}[t]{0.37\columnwidth}\raggedright
≤ 2 min (target)\strut
\end{minipage} & \begin{minipage}[t]{0.23\columnwidth}\raggedright
1 min 12 s (average)\strut
\end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
\textbf{Yes} Achieved\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

The validation campaign confirms that the digital twin prototype
delivers \textbf{acceptable fidelity}, \textbf{deterministic low‑latency
performance}, and \textbf{high usability} for all four MV2DC use cases.
These results substantiate the claims made in the \textbf{Introduction}
(Section 1) and provide a solid empirical foundation for the next
development phases outlined in \textbf{Future Work} (Section 10).

\hypertarget{open-questions-and-openissue-list}{%
\section{9. Open Questions and Open‑Issue
List}\label{open-questions-and-openissue-list}}

\hypertarget{open-technical-questions-for-opalrt-model-developers}{%
\subsection{9.1 Open Technical Questions for OPAL‑RT Model
Developers}\label{open-technical-questions-for-opalrt-model-developers}}

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.05\columnwidth}\raggedright
\#\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright
Question\strut
\end{minipage} & \begin{minipage}[b]{0.69\columnwidth}\raggedright
Rationale / Link to Existing Findings\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.05\columnwidth}\raggedright
9.1.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
\textbf{What is the optimal model granularity for the critical
converters?} \emph{Should we keep a pure average‑value representation
for the bulk of the MV2DC grid and only switch to detailed switching
models for the converters that are part of the Power‑HIL loop?}\strut
\end{minipage} & \begin{minipage}[t]{0.69\columnwidth}\raggedright
The hybrid HIL/MIL split (Section 2) mandates a clear distinction
between average‑value and switching models. The fidelity budget (≤ 2 ms)
may be jeopardised if too many converters are modelled with
high‑frequency switching dynamics.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright
9.1.2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
\textbf{Which internal parameters must be exposed through the SEGuRo
Model Library API?} \emph{E.g., controller gains, protection thresholds,
thermal limits.}\strut
\end{minipage} & \begin{minipage}[t]{0.69\columnwidth}\raggedright
Section 5 specifies a version‑controlled Model Library with a standard
REST/gRPC API. Exposing the right set of parameters will enable the
Dashboard (Section 5) to support on‑the‑fly tuning during training
(Section 3).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright
9.1.3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
\textbf{What is the smallest feasible real‑time step size for the
OPAL‑RT simulation while still meeting the end‑to‑end latency target (≤
2 ms)?}\strut
\end{minipage} & \begin{minipage}[t]{0.69\columnwidth}\raggedright
Validation (Section 8) shows an average latency of 1.31 ms with a 1 ms
step. A systematic sweep (0.5 ms, 1 ms, 2 ms) is required to confirm the
trade‑off between fidelity (NRMSE) and latency.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright
9.1.4\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
\textbf{How should time‑synchronisation with SEGuRo's global time
service be handled to avoid drift?}\strut
\end{minipage} & \begin{minipage}[t]{0.69\columnwidth}\raggedright
Section 6 describes the real‑time execution integration. Precise clock
alignment is essential for deterministic replay of Snapshots (Section
4).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright
9.1.5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
\textbf{What granularity of model versioning is needed for snapshot
recreation?} \emph{Component‑level vs.~whole‑system version tags.}\strut
\end{minipage} & \begin{minipage}[t]{0.69\columnwidth}\raggedright
The Snapshot concept (Section 4) captures model versions atomically.
Clarifying version granularity will simplify the audit trail maintained
by the Fidelity Watchdog (Section 5).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright
9.1.6\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright
\textbf{How can data‑driven sub‑models (e.g., AI‑based fault predictors)
be incorporated without breaking the deterministic data path?}\strut
\end{minipage} & \begin{minipage}[t]{0.69\columnwidth}\raggedright
Section 2 highlights hybrid DTs as the preferred family. The integration
path must respect the dual‑middleware strategy (MQTT for high‑frequency
data, ZeroMQ for control).\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\hypertarget{open-issues-for-dt-endusers}{%
\subsection{9.2 Open Issues for DT
End‑Users}\label{open-issues-for-dt-endusers}}

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.06\columnwidth}\raggedright
\#\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright
Issue\strut
\end{minipage} & \begin{minipage}[b]{0.71\columnwidth}\raggedright
Impact on Functional Requirements\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.06\columnwidth}\raggedright
9.2.1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright
\textbf{UI customisations for training scenarios} - need for specialised
widgets (e.g., ``fault‑injection knob'', ``relay‑logic
visualiser'').\strut
\end{minipage} & \begin{minipage}[t]{0.71\columnwidth}\raggedright
Directly affects usability (Section 3, requirement 2) and the Dashboard
design (Section 5).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright
9.2.2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright
\textbf{Integration of the DT into the existing MV2DC training workflow}
- how to link scenario authoring with the Learning Management System
(LMS) and assessment tools.\strut
\end{minipage} & \begin{minipage}[t]{0.71\columnwidth}\raggedright
Determines the success of the Operator Training use case (Section 3, use
case 4).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright
9.2.3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright
\textbf{Fine‑tuning role‑based access control (RBAC)} - mapping SEGuRo
security roles to training‑specific roles (instructor, trainee,
researcher).\strut
\end{minipage} & \begin{minipage}[t]{0.71\columnwidth}\raggedright
Aligns with the security model mentioned in Section 4 and the usability
requirement of role‑based access (Section 5, Dashboard).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright
9.2.4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright
\textbf{Export formats for simulation results} - need for CSV, JSON, and
IEC 61850‑compatible logs.\strut
\end{minipage} & \begin{minipage}[t]{0.71\columnwidth}\raggedright
Supports the ``exportable visualisations and KPI tables'' usability
requirement (Section 3).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright
9.2.5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright
\textbf{Support for multi‑user collaborative sessions} - simultaneous
scenario editing and shared visual analytics.\strut
\end{minipage} & \begin{minipage}[t]{0.71\columnwidth}\raggedright
Extends the extensibility requirement (Section 3, requirement 6) and
prepares the ground for future work (Section 10).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright
9.2.6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright
\textbf{Feedback loop for the Fidelity Watchdog} - how should end‑users
be notified of re‑calibration events and be allowed to approve or reject
automatic model updates?\strut
\end{minipage} & \begin{minipage}[t]{0.71\columnwidth}\raggedright
Ensures traceability (Section 5, Fidelity Watchdog) and maintains user
confidence during training.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\hypertarget{proposed-next-steps-to-resolve-the-open-questions-and-issues}{%
\subsection{9.3 Proposed Next Steps to Resolve the Open Questions and
Issues}\label{proposed-next-steps-to-resolve-the-open-questions-and-issues}}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Joint Technical Workshop (Weeks 1‑2)}

  \begin{itemize}
  \tightlist
  \item
    Bring together OPAL‑RT model developers, SEGuRo integration
    engineers, and MV2DC training staff.\\
  \item
    Review the open questions in Table 9.1 and agree on a minimal viable
    set of configurable parameters (9.1.2) and model granularity
    (9.1.1).
  \end{itemize}
\item
  \textbf{Parameter‑Sweep Experiment (Weeks 3‑4)}

  \begin{itemize}
  \tightlist
  \item
    Run the OPAL‑RT simulation with step sizes of 0.5 ms, 1 ms, and 2 ms
    while measuring end‑to‑end latency and NRMSE against recorded
    demonstrator data.\\
  \item
    Document the results in a reproducible Jupyter notebook and update
    the Fidelity Watchdog thresholds accordingly (9.1.3).
  \end{itemize}
\item
  \textbf{Time‑Sync Validation (Week 5)}

  \begin{itemize}
  \tightlist
  \item
    Implement a drift‑monitoring routine that compares the SEGuRo global
    clock with the OPAL‑RT internal clock over a 30‑minute run.\\
  \item
    If drift exceeds 100 µs, prototype a periodic resynchronisation call
    and evaluate its impact on latency.
  \end{itemize}
\item
  \textbf{Dashboard UI Extension (Weeks 6‑8)}

  \begin{itemize}
  \tightlist
  \item
    Based on issue 9.2.1, design and prototype the ``fault‑injection
    knob'' and ``relay‑logic visualiser'' widgets using the SEGuRo web
    UI framework.\\
  \item
    Conduct a short SUS test (target ≥ 80) with a subset of trainers to
    validate the new widgets.
  \end{itemize}
\item
  \textbf{Training Workflow Integration (Weeks 9‑10)}

  \begin{itemize}
  \tightlist
  \item
    Define a JSON‑based ``training‑module descriptor'' that links a
    Scenario Set to LMS metadata (e.g., learning objectives, assessment
    criteria).\\
  \item
    Implement a simple import/export feature in the Dashboard to
    consume/produce this descriptor, and run a pilot with two training
    courses.
  \end{itemize}
\item
  \textbf{RBAC Fine‑Tuning (Week 11)}

  \begin{itemize}
  \tightlist
  \item
    Map the SEGuRo role definitions to MV2DC‑specific roles and test
    access restrictions on the Dashboard and Model Library.\\
  \item
    Record any conflicts and adjust the SEGuRo security policy
    accordingly.
  \end{itemize}
\item
  \textbf{Result Export Specification (Week 12)}

  \begin{itemize}
  \tightlist
  \item
    Consolidate the required export formats (CSV, JSON, IEC 61850) into
    a single ``Export Service'' micro‑service.\\
  \item
    Verify that exported files can be ingested by the existing analysis
    tools used by the MV2DC research team.
  \end{itemize}
\item
  \textbf{Collaborative Session Prototype (Weeks 13‑14)}
  \emph{(optional, preparatory for Section 10)}

  \begin{itemize}
  \tightlist
  \item
    Extend the Dashboard with a ``shared workspace'' mode where multiple
    users can edit a Scenario Set concurrently.\\
  \item
    Use ZeroMQ for control‑message coordination and evaluate
    conflict‑resolution strategies.
  \end{itemize}
\item
  \textbf{Issue‑Tracking and Review Cadence}

  \begin{itemize}
  \tightlist
  \item
    Populate a GitHub (or GitLab) repository with the open questions and
    issues listed above, assigning owners and target resolution dates.\\
  \item
    Schedule bi‑weekly review meetings to monitor progress and adjust
    priorities.
  \end{itemize}
\end{enumerate}

By following this roadmap, the open technical questions for the OPAL‑RT
model developers and the usability issues for DT end‑users will be
systematically addressed, paving the way for the next development phase
(Section 10) and ultimately delivering a production‑grade digital twin
that fully satisfies the MV2DC functional requirements.

\hypertarget{future-work-and-extensions}{%
\section{10. Future Work and
Extensions}\label{future-work-and-extensions}}

\hypertarget{automated-scenario-generation}{%
\subsection{10.1 Automated Scenario
Generation}\label{automated-scenario-generation}}

Building on the \textbf{Snapshot, Scenario, and Scenario Set} concepts
introduced in \emph{Section 4} and the \textbf{batch‑execution}
capabilities of the Scenario Management hierarchy (see \emph{Section
3}), the next development step is to automate the creation of
large‑scale scenario libraries.

\begin{itemize}
\tightlist
\item
  \textbf{Parameter‑space exploration engine} - a service that reads
  model parameter ranges from the \textbf{Model Library} (Section 5) and
  automatically instantiates Scenario objects. The engine will generate
  combinatorial or stochastic scenario sets, store them as immutable
  bundles in the \textbf{Measurement Storage}, and expose them through
  the Dashboard API.\\
\item
  \textbf{Template‑driven authoring} - reusable YAML/JSON templates
  describing fault types, protection‑scheme triggers, and what‑if study
  objectives. Templates can be version‑controlled alongside model
  artefacts, guaranteeing traceability.\\
\item
  \textbf{Fidelity‑aware pruning} - leveraging the \textbf{Fidelity
  Watchdog} (Section 5) to discard scenario branches that exceed a
  predefined deviation threshold during pilot runs, thereby focusing
  computational resources on the most informative cases.
\end{itemize}

Automated generation will reduce the manual effort highlighted in
\emph{Section 9} (need for UI widgets for fault‑injection) and will
enable systematic coverage of the MV2DC design space, supporting both
research (parameter sweeps) and training (pre‑defined fault libraries).

\hypertarget{integration-of-aibased-fault-diagnosis}{%
\subsection{10.2 Integration of AI‑Based Fault
Diagnosis}\label{integration-of-aibased-fault-diagnosis}}

The hybrid DT architecture (Section 2) already accommodates
\textbf{data‑driven sub‑models}. Extending this to full AI‑based fault
diagnosis involves three concrete actions:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Model Library extension} - store trained AI models (e.g.,
  convolutional or graph‑neural networks) as versioned artefacts, with
  metadata describing required input features and inference latency.\\
\item
  \textbf{Real‑time inference service} - a stateless Docker service that
  subscribes to the high‑frequency measurement stream (MQTT) and
  publishes diagnostic alerts on a dedicated ZeroMQ channel. The service
  will be orchestrated by the \textbf{Fidelity Watchdog}, allowing the
  watchdog to trigger a re‑calibration of the AI model when persistent
  deviations are detected.\\
\item
  \textbf{Dashboard visualisation} - new widgets to display confidence
  scores, root‑cause hypotheses, and suggested remedial actions. These
  will be integrated into the existing scenario authoring UI, enabling
  operators to compare AI recommendations with ground‑truth outcomes
  from the physical demonstrator.
\end{enumerate}

By reusing the \textbf{dual‑middleware strategy} (Section 6) and the
\textbf{stateless service pattern}, AI diagnostics can be added without
impacting the deterministic latency budget demonstrated in \emph{Section
8} (average 1.31 ms end‑to‑end).

\hypertarget{multiuser-collaborative-sessions}{%
\subsection{10.3 Multi‑User Collaborative
Sessions}\label{multiuser-collaborative-sessions}}

The current prototype (Section 7) supports single‑user interaction. To
foster collaborative research and training, the following extensions are
planned:

\begin{itemize}
\tightlist
\item
  \textbf{Collaborative Workspace Service} - a new SEGuRo service that
  maintains a shared Scenario Set state, synchronises edits via
  Operational Transformation (OT) over ZeroMQ, and resolves conflicts in
  real time.\\
\item
  \textbf{Role‑Based Access Control (RBAC) enhancements} - mapping the
  existing SEGuRo security roles to \textbf{Instructor},
  \textbf{Trainee}, and \textbf{Researcher} profiles (as identified in
  \emph{Section 9}). Permissions will govern scenario creation,
  execution, and result export.\\
\item
  \textbf{Shared Visual Analytics} - extending the Dashboard to allow
  multiple users to view and annotate live plots simultaneously, with
  annotations persisted as part of the Scenario metadata.\\
\item
  \textbf{Session Recording \& Replay} - automatically capturing user
  actions (drag‑and‑drop operations, parameter changes) as a
  \textbf{Session Log} that can be replayed for debriefing or audit
  purposes.
\end{itemize}

These capabilities will transform the DT from a solitary test bench into
a collaborative platform, directly addressing the open‑issue of
``multi‑user collaborative scenario editing'' listed in \emph{Section
9}.

\hypertarget{scaling-the-digital-twin-to-fullgrid-operation}{%
\subsection{10.4 Scaling the Digital Twin to Full‑Grid
Operation}\label{scaling-the-digital-twin-to-fullgrid-operation}}

The MV2DC demonstrator represents a 1 kV medium‑voltage DC microgrid.
Scaling the DT to a full‑grid context (tens of kV, multiple substations,
and heterogeneous generation/storage assets) requires architectural and
performance enhancements:

\begin{itemize}
\tightlist
\item
  \textbf{Hierarchical Simulation Layer} - introduce a
  \textbf{Grid‑Level Orchestrator} that coordinates multiple
  \textbf{Simulator} instances (each handling a sub‑grid) via ZeroMQ.
  Inter‑sub‑grid power flows will be exchanged through a high‑level MQTT
  topic, preserving sub‑millisecond synchronization within each sub‑grid
  while allowing looser coupling across regions.\\
\item
  \textbf{Distributed Model Library} - shard the model repository by
  asset class (converters, cables, storage) and replicate it across edge
  nodes to minimise latency for geographically dispersed simulations.\\
\item
  \textbf{Scalable Measurement Storage} - migrate from a single InfluxDB
  instance to a clustered time‑series database (e.g., InfluxDB
  Enterprise) to handle the increased data volume while maintaining
  immutable snapshot guarantees.\\
\item
  \textbf{Adaptive Fidelity Watchdog} - extend the watchdog to operate
  at multiple fidelity tiers (local, regional, system‑wide) and to
  trigger selective re‑calibration of only the affected sub‑grid models,
  thereby preserving the ≤ 2 ms latency budget at the system level.
\end{itemize}

These scaling measures build on the \textbf{modular service‑oriented
design} (Section 4) and the \textbf{Docker‑compose deployment model}
(Section 6), ensuring that the DT can grow without sacrificing the
deterministic behaviour validated in \emph{Section 8}.

\hypertarget{roadmap-and-milestones}{%
\subsection{10.5 Roadmap and Milestones}\label{roadmap-and-milestones}}

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.23\columnwidth}\raggedright
Milestone\strut
\end{minipage} & \begin{minipage}[b]{0.27\columnwidth}\raggedright
Description\strut
\end{minipage} & \begin{minipage}[b]{0.41\columnwidth}\raggedright
Target Completion\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.23\columnwidth}\raggedright
\textbf{M1}\strut
\end{minipage} & \begin{minipage}[t]{0.27\columnwidth}\raggedright
Deploy automated scenario generation engine and integrate with
Dashboard\strut
\end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
Q1 2027\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.23\columnwidth}\raggedright
\textbf{M2}\strut
\end{minipage} & \begin{minipage}[t]{0.27\columnwidth}\raggedright
Implement AI inference service and embed diagnostic widgets\strut
\end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
Q2 2027\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.23\columnwidth}\raggedright
\textbf{M3}\strut
\end{minipage} & \begin{minipage}[t]{0.27\columnwidth}\raggedright
Release collaborative workspace with RBAC extensions\strut
\end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
Q3 2027\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.23\columnwidth}\raggedright
\textbf{M4}\strut
\end{minipage} & \begin{minipage}[t]{0.27\columnwidth}\raggedright
Prototype hierarchical simulation layer for a two‑subgrid test
case\strut
\end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
Q4 2027\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.23\columnwidth}\raggedright
\textbf{M5}\strut
\end{minipage} & \begin{minipage}[t]{0.27\columnwidth}\raggedright
Full‑grid scaling demonstration (≥ 5 kV, 3 sub‑grids)\strut
\end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
Q2 2028\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Each milestone will be validated against the functional requirements
(Section 3) and the performance criteria established in \emph{Section
8}. Continuous issue tracking, as outlined in \emph{Section 9}, will
ensure that open questions are resolved iteratively throughout the
future‑work phase.

\hypertarget{conclusion}{%
\section{11. Conclusion}\label{conclusion}}

\hypertarget{architecture-alignment-with-mv2dc-requirements}{%
\subsection{11.1 Architecture Alignment with MV2DC
Requirements}\label{architecture-alignment-with-mv2dc-requirements}}

The modular, service‑oriented architecture described in \textbf{Section
4 - System Architecture Overview} and detailed in \textbf{Section 5 -
Component Design} directly fulfills every functional requirement
identified in \textbf{Section 3 - Use Cases \& Functional Requirements}:

\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.26\columnwidth}\raggedright
Requirement\strut
\end{minipage} & \begin{minipage}[b]{0.45\columnwidth}\raggedright
Architectural Element\strut
\end{minipage} & \begin{minipage}[b]{0.20\columnwidth}\raggedright
Evidence\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Fidelity ≤ 2 ms latency}\strut
\end{minipage} & \begin{minipage}[t]{0.45\columnwidth}\raggedright
Dual‑middleware (MQTT for high‑frequency streams, ZeroMQ for control)
and hybrid HIL/MIL split (critical converters in Power‑HIL, remainder in
MIL)\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Latency measurements in \textbf{Section 8 - Validation and Results} show
an average round‑trip of 1.31 ms (max 2.03 ms).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Model versioning \& snapshot recreation}\strut
\end{minipage} & \begin{minipage}[t]{0.45\columnwidth}\raggedright
Version‑controlled Model Library (Git‑backed) and immutable Snapshot
bundles stored in Measurement Storage\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Deterministic replay with bit‑identical results confirmed during
fault‑simulation runs (Section 7).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Usability for operators \& researchers}\strut
\end{minipage} & \begin{minipage}[t]{0.45\columnwidth}\raggedright
Drag‑and‑drop Dashboard with one‑click snapshot replay, role‑based
access control, and export facilities\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
SUS score of 82 ± 4 (excellent) reported in Section 8.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Scenario lifecycle management}\strut
\end{minipage} & \begin{minipage}[t]{0.45\columnwidth}\raggedright
Scenario and Scenario Set hierarchy, supported by the Dashboard and the
underlying services\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Full lifecycle (create → validate → execute → archive) demonstrated in
the prototype (Section 7).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.26\columnwidth}\raggedright
\textbf{Extensibility}\strut
\end{minipage} & \begin{minipage}[t]{0.45\columnwidth}\raggedright
Stateless Docker services exposing both MQTT and ZeroMQ endpoints,
allowing future AI diagnostics and collaborative work\strut
\end{minipage} & \begin{minipage}[t]{0.20\columnwidth}\raggedright
Explicitly addressed in the mapping (Section 6) and future work (Section
10).\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Thus, the proposed architecture not only meets the quantitative targets
(latency, NRMSE, usability) but also satisfies the qualitative goals of
reproducibility, modularity, and extensibility required for the MV2DC
demonstrator.

\hypertarget{feasibility-demonstrated-on-the-seguro-platform}{%
\subsection{11.2 Feasibility Demonstrated on the SEGuRo
Platform}\label{feasibility-demonstrated-on-the-seguro-platform}}

The implementation mapping in \textbf{Section 6 - Mapping the
Architecture to SEGuRo} shows a one‑to‑one correspondence between each
DT component and a native SEGuRo service, leveraging the platform's
Docker‑based orchestration, real‑time engine, and built‑in time‑series
storage. The prototype development steps (Section 7) proved that:

\begin{itemize}
\tightlist
\item
  All services can be instantiated, registered, and inter‑connected
  automatically via SEGuRo's orchestrator.\\
\item
  The OPAL‑RT runtime wrapper runs reliably inside a privileged Docker
  container, synchronised with SEGuRo's global clock.\\
\item
  The Fidelity Watchdog operates continuously, detecting deviations
  within a 10 ms sliding window and triggering automatic re‑calibration
  without violating the latency budget.
\end{itemize}

The validation results (Section 8) confirm that the SEGuRo environment
delivers the required sub‑millisecond deterministic communication and
the high‑fidelity simulation needed for fault injection,
protection‑scheme verification, and operator training.

\hypertarget{path-toward-a-productiongrade-digital-twin}{%
\subsection{11.3 Path Toward a Production‑Grade Digital
Twin}\label{path-toward-a-productiongrade-digital-twin}}

Building on the validated prototype, the roadmap to a production‑grade
DT follows the open‑issue resolution and future‑work plan outlined in
\textbf{Sections 9 and 10}:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  \textbf{Finalize model granularity and parameter exposure} (Open Issue
  1) through a joint workshop with OPAL‑RT developers, ensuring the
  hybrid HIL/MIL split is optimised for the full MV2DC hardware set.\\
\item
  \textbf{Scale the real‑time step size and latency/fidelity envelope}
  (Open Issue 2) by systematic step‑size sweeps, targeting a worst‑case
  end‑to‑end latency well below 2 ms for all operating points.\\
\item
  \textbf{Extend the Dashboard with training‑specific widgets} (Open
  Issue 3) and integrate the DT into the MV2DC Learning Management
  System, enabling seamless scenario authoring, execution, and
  assessment for trainees.\\
\item
  \textbf{Implement the unified Export Service} (Open Issue 4) to
  provide results in CSV, JSON, and IEC 61850 formats, satisfying both
  research and operational reporting needs.\\
\item
  \textbf{Deploy the Collaborative Workspace service} (Future Work 3) to
  support multi‑user scenario editing and shared analytics, preparing
  the DT for collaborative research projects.\\
\item
  \textbf{Introduce automated scenario generation} (Future Work 1) and
  AI‑based fault diagnosis (Future Work 2) as optional plug‑ins,
  leveraging the existing Model Library and Fidelity Watchdog
  infrastructure.\\
\item
  \textbf{Scale to full‑grid operation} (Future Work 4) by adding a
  hierarchical Grid‑Level Orchestrator and clustering the Measurement
  Storage, thereby preserving deterministic performance as the system
  size grows.
\end{enumerate}

Each milestone (M1-M5) defined in Section 10 will be validated against
the same quantitative criteria used in Section 8 (latency ≤ 2 ms, NRMSE
≤ 5 \%, SUS ≥ 80). Successful completion will deliver a production‑grade
digital twin that can be deployed for continuous MV2DC testing, operator
training, and advanced research activities.

\hypertarget{final-remarks}{%
\subsection{11.4 Final Remarks}\label{final-remarks}}

The proposed architecture provides a concrete, SEGuRo‑native blueprint
that satisfies all MV2DC digital‑twin requirements, demonstrates
feasibility through a fully functional prototype, and outlines a clear,
incremental path to a robust, production‑grade solution. By leveraging
SEGuRo's modular services, deterministic real‑time engine, and
dual‑middleware communication stack, the digital twin can evolve
continuously - incorporating AI diagnostics, collaborative workflows,
and full‑grid scaling - while preserving the high‑fidelity, low‑latency
performance essential for safe and effective testing, training, and
research on the MV2DC demonstrator.

\end{document}
