- Digital grid technologies are needed to address challenges from increasing distributed renewable energy resources, such as voltage regulation impacts and changes to network reliability and protection. - Solutions include active network management, virtual power plants, adaptive protection schemes, and developing the neural grid of the future through grid digitalization. - A digital twin is a dynamic digital model of a real-world asset that combines mathematical and statistical models with machine learning and real-time data from sensors to enable simulation and forecasting over the asset's lifecycle. - Digital twins, along with technologies like distributed intelligence, two-way communications, and real-time data analytics can help develop more autonomous, self-optimizing neural grids of the future.

1. Digital Grid Technologies for Smooth
Integration of Renewable Energy Resources
By Moustafa Shahin
Manager- Distribution Network Development , DEWA
Head of Shams Dubai (Solar) Integration Working Group, DEWA
PhD Researcher - EGIS, Herriot Watt University

2. Challenges of DER integration
 Demand Management Impact (Duck Curve)
 Power Quality impact (DER inverters)
 Voltage Regulation impact.
 Network Reliability & Protection impacts
Digital Grid Solutions for DER integration
 ANM
 VPP
 Adaptive Protection
 The Neural Grid of the Future
Outline
2

3. DG Impact
Renewable Distributed Generation Impact on Electricity Systems
DER
Penetration
Level
Low
Inertia
Distribution
Transmission
Generation
Balancing

4. Grid Balancing Challenges
4

5. From a centrally controlled, synchronous generator-based grid to a highly distributed, inverter-based system
Low Inertia Challenge of high renewable penetration
5

6. Voltage Profile Impact

7. Total
Demand
Demand Management Impact

8. Projected Data 2025
Total
Demand
Demand Management Impact

9. Over-generation
Significant
ramp-up
need
towards
evening
peak
Demand Management Impact (The Duck Curve)

10. • Demand Management Impact (Duck Curve)
• Power Quality impact (DER inverters)
• Voltage Regulation impact (Intermittent distributed generation).
• Network Reliability (thermal limits) & efficiency impacts
• Complex distribution operations due to reverse Power Flow (Bi-directional flow)
• Protection scheme Impact (Legacy overcurrent schemes are not adequate).
 Increase system flexibility (Adaptive Protection, Dy).
 DER ancillary services.
 Advanced network management through Grid Digitalization
Higher penetration of renewables in the electrical network require solutions to minimize
variability, stabilize the grid and ensure reliability across the network
Digital Grid Solutions to address DER integration challenges

11. DER Management strategy
DER Penetration Level
Low
Medium
High
Passive DER Integration
• Limited Remote monitoring .
• Stringent rules for DER feed-
in grid exports.
Active DER Management
• Advanced DER control with
Real-time data exchange.
• DER grid ancillary services.
Reactive DER Integration
• Remote monitoring with
limited control functionality.
• Lack of regulations for DER
grid ancillary services.
Evolution of Interconnection Grid Codes
Technology

12. DER ancillary services
Increased flexibility through
DER ancillary service
TSO Ancillary Services Fast frequency response
Congestion Management
DSO Ancillary Services
Balancing reserves
Black Start facility
Voltage Regulation support
Dynamic Reactive Power Provision
Demand Management (CVR)
Reactive power Compensation

13. Active DER Management for Eeffective DER Integration
P , Q
t
P , Q
t
Smart
Meter
RTU
+
1. Solar real-time measures
3. SS real-Time measures
2. Weather Forecast
Aggregaterd
Load/Generation data
ADMS 1. Hystorical load
profiles
Secondary
Distribution
Distribution Control Centre
Smart
PV
Inverter
IEC61850 /
Modbus
OLTC
∆ V
Feeder
Demand
=
Autonomous Closed Loop Volt/Var Optimisation
13

14. Adaptive Protection Schemes
Smart substation control & protection Unit
There is a growing need for flexible protection and control The protection and control system needs to become more
flexible and be able to reconfigure faster to adapt the new intermittent renewable energy resources and storage devices.

15. Dynamic Line Ratings provide real-time visibility and access at the operator’s console to the true capacity
for T&D feeders.
15
Dynamic Line Rating (DLR)
Additional Line Capacity
Static Rating (90 MVA)

16. Without DLR, this event will be reported as a violation (exceeding the static limit) & The operator would have been
forced to intervene for curtailments/ load shedding
DLR Improved Grid Reliability & Congestion Relief
True Line Capacity
in Real Time
16
Peak load
 Improved Grid Reliability & Congestion Relief.
 Optimized Asset Utilization & Efficient operations.
 Faster DER Integration & Improved hosting capacity.

17. 18
Past:
Traditional Grid
Emerging:
The Energy Cloud
 Centralized & distributed generation
 IT/OT integration
 2-way communication
 Distributed intelligence (IOE & AI)
 Multi-directional power flow
 IT/OT Convergence
 Operation based on real time data
analytics.
 Centralized power generation
 Uni-directional power flow
 Isolated OT & IT systems
 Operation based on historical
experience
Present:
Smart Grid
From Smart Grids to Neural Energy Cloud

18. Neural Grid & Energy Cloud Platform
Source: Navigant
18
Digitization enables new services & improve operational resiliency throughout the energy value chain.
Smart Grid as a Service (GaaS)
Neural Grid

19. Enabling technologies for Neural Grids
Internet of Energy (IoE)
19
Artificial intelligence & ML
Blockchain
Robotics & Drones
Digital Twins
Enhanced operational models via Machine Learning Cyber Security
Big Data analytics (BDA)
Smart Mobility

20. What is a Digital Twin?
The digital twins are the next big thing in digital grid transformation
A digital twin is a dynamic digital representation of a real-world asset or system.
(Gartner)
20

21. Asset Model Constraints
Machine
Learning
Real-Time
Operational Data
Historical Data
(O&M history, FEMA*,
Fleet Aggregated Data)
*FEMA: Failure Mode and Effect Analysis
Digital Twin = Mathematical Model + Statistical Model + ML
Process
information
Design
information
Real-time data allowing simulation and forecasting over the entire asset life cycle using sensor data,
production data and real-time process information ”
The Digital Twin is not just a blueprint
Analytics
Dynamic Re-calibration
Predictive Learning
21

22. Synchronized Planning and Operations Models
23
Automated synchronization and management of network models from planning, GIS, ADMS, and MDM data
Unified Model Management Concept
Source: Siemens AG 2018
 Synchronized planning
and operations model
 Improved overall
accuracy and consistency
of network models
("single source of truth")
 Improved efficiency and
process optimization in
planning and operations
 Integrated T&D modeling
and analysis
ESB

23. VPP Optimization Thru Digital Twins
Real-Time Optimization and Control of Next-
Generation Distribution Infrastructure
VPP
Engine
Request(s)
Response
Assets
Digital Twins
Grid
Demand
Constraints & Topology
Real-Time Performance
Telemetry
Control

24. Neural Grids of the future
Autonomous T&D grid leveraging digital technologies to support resilient operations, self-healing, seamless
DER integration and self-optimisation.
24
Autonomous control, Real-time optimisation & Automated grid ancillary services.

25. Thank You