This document discusses challenges and opportunities for smart grids. It describes smart grids as energy networks that can automatically monitor and adjust energy flows based on supply and demand changes. Key challenges discussed include control and protection, seamlessly integrating renewables, and advanced forecasting of generation, load, and prices. The document also outlines some benefits of smart grids like local reliability and reduced emissions. It provides examples of how technologies like wide area monitoring systems, real-time simulators, and forecasting models can help address challenges in developing smart grid systems.

1. European cooperation Network on Energy Transition in Electricity
Jorge Bruna
CIRCE - Research Centre for Energy Resources and Consumption
SESSION 2:
SMART GRIDS CHALLENGES: THE VISION OF TECHNOLOGICAL CENTRES
WORKSHOP
“DEFINING SMART GRIDS: CONDITIONS FOR SUCCESSFUL IMPLEMENTATION”
Barcelona, 9th February 2017

2. 1. SmartGrid definition
2. Traditional grids vs SmartGrids
3. SmartGrids: PROS and CHALLENGES
4. Challenge: Control and protection
5. Challenge: Renewable seamless integration
6. Challenge: Advanced forecasting
7. Conclusions

3. Smart grids are energy networks that can
automatically monitor energy flows and adjust to
changes in energy supply and demand accordingly.
When coupled with smart metering systems,
smart grids reach consumers and suppliers by
providing information on real-time consumption.
Smart grids can also help to better integrate renewable energy. While the sun
doesn't shine all the time and the wind doesn't always blow, combining
information on energy demand with weather forecasts can allow grid operators to
better plan the integration of renewable energy into the grid and balance their
networks. Smart grids also open up the possibility for consumers who produce
their own energy to respond to prices and sell excess to the grid.

5. PROS
1. Local reliability
2. Reduce emissions
3. Reduce power losses of
distribution networks
CHALLENGES
1. Control and protection
2. Renewable seamless
integration
3. Advanced forecasting

6. Wide Area Monitoring System technology uses a GPS satellite signal to time-
synchronize PMUs or Power Quality Analyzers at important nodes in the power
system to send real-time data to a central node.
Without ubiquitous, accurate, and reliable real-time sensors, the electric grid
will not have the resiliency, reliability, and capacity to manage the
unprecedented number of variable renewable energy sources and millions of
intelligent devices and systems.

8. Power System and PMUs can be modeled in
Real-Time Digital Simulator (RTDS)
Physical Phasor
Data
Concentrator
Physical
PMU
C37.118
PROTOCOL
Emulated
PMUs
SYSTEM
INTEGRITY
PROTECTION
SCHEMES

9. Purpose: To produce heat and power in order to reduce CO2 emission level and
strong dependency from import of gas and oil
Main renewables energy sources:
• Wind energy
• Biomass energy
• Solar thermal energy
• Photovoltaic energy
• Geothermal energy
Types of renewable energy sources:
• Stochastic
• Continuous
Main topic to be addressed:
• Impact on Power Quality

10. Enercon wind turbine
Lagerwey wind turbine
Vestas wind turbine
Solar panels
H2 Building
SERVER

11. 06:00 09:00 12:00 15:00 18:00 21:00 00:00
220
225
230
235
240
400
405
410
415
420
425
430
RMS(V)
TIME (hh:mm)
Enercon Phase R
Vestas Phase R
Solar Phase R
Foundation Phase R
06:00 09:00 12:00 15:00 18:00 21:00 00:00
220
225
230
235
240
400
405
410
415
420
425
430
Enercon Phase S
Vestas Phase S
Solar Phase S
Foundation Phase S
RMS(V)
TIME (hh:mm)
06:00 09:00 12:00 15:00 18:00 21:00 00:00
220
225
230
235
240
245
250
400
405
410
415
420
425
430
Enercon Phase T
Vestas Phase T
Solar Phase T
Foundation Phase T
RMS(V)
TIME (hh:mm)
0
-400
-300
-200
-100
0
100
200
300
400
Voltage(V)
TIME
-150
-125
-100
-75
-50
-25
0
25
50
75
100
125
150
Current(A)

12. Energy forecasting in Smart Grids:
• Load
• Price
• Wind
• Solar
• Demand response
• Congestion
• Flexibility

13. Measurements
Field Devices
(P,Q,SoC from EB)
EMS inputs
External Sources:
AEMET
OMIE
Data Base
24h Forecast Modules
Weather (ARX)
Generation (ARIMAX)
Price (ARIMA)
Optimization Module
(LP, e.g. CPLEX solver)
24-h Forecasts
Set of Restrictions
Working Modes
Defined by ESCO/
Consumer
Set of restrictions
(EB configuration by
ESCO/Consumer)
Updated when needed
Updated when new data is available
Updated every 15min
Minimize energy bill
Maximize system service
(Flexibility services)
Other (future)
Updated
every 15min
Optimization
results updated
every 15min
24-h program
of Control
commands
EMS outputs
Reporting Module
Historical/real-time data
Alerts
Measure & Validation (Energy
Saving)
Graphics
Tables
Reports
Data processing
Demand (ARIMA)

14. • Efficient and reliable energy to final customers
• Limited primary energy sources
• Additional demand supplied by distributed generation
• Energy storage to solve intermittent energy sources (e.g. wind and photovoltaic)
• New algorithm to forecast:
• Generation
• Load
• WAMS and multi-point deployment of PQ analyzers:
• Control and protection
• Propagation of PQ phenomena into the grid
• Interoperability to develop effective Demand Response strategies