1. Jose GONZALEZ PASTOR Economic & Adequacy Analyst - Elia
Dynamic Dimensioning of Balancing Reserves with AI
Jose GONZALEZ PASTOR Economic & Adequacy Analyst - Elia
Data Innovation Summit
27th June 2018
#DISUMMIT

2. Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
Agenda
How to balance power systems with increasing uncertainty driven by renewable generation?
How can machine learning provide solutions to predict and cope with this uncertainty ?
Implementation of a dynamic dimensioning method for reserve capacity to balance the power system
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

3. Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
∑Pload < ∑Pgeneration
Electricity cannot be stored easily
Electricity demand and production must be balanced continuously
Imbalances result in frequency deviations
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

4. Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
∑Pload > ∑Pgeneration
Large frequency deviations can result in protective disconnection of equipment
Power plants
Transmission elements
Industrial equipment
….leading eventually to black-out
As transmission system operator, Elia is responsible for covering the residual system imbalances between injection and off-take in its control zone by using reserve capacity
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

5. Balancing Power Systems - Elia
Belgium’s Transmission System Operator (TSO)
Responsible for covering the residual imbalance by using reserve capacity
Reserve capacity dimensioned to meet defined reliability criteria
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

6. Static Dimensioning - Yearly
Takes into account the potential loss due to outage in power plants
Takes into account the observed system imbalance
Reliability criteria set at 99,9%
Required Volume of Reserve for D+1
Loss of power pdf due to outage in power plant
Imbalance pdf
Probability
Loss of power (MW)
Convolution
Historical imbalance
Imbalance (MW)
P99,9
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
Imbalance (MW)

7. Static Dimensioning – Increasing RES
Renewable Energy Sources (RES) are inherently intermittent
RES introduce additional volatility in grid balance
Increasing share of RES
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

8. Static Dimensioning – Increasing RES
RES are inherently variable
Sensitive to weather conditions
Increase of RES challenges the static dimensioning approach
As 99,9% reliability criteria is sensitive to extreme events
i.e. High winds
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

9. Static Dimensioning – Increasing RES
Static approach yields a volume of reserve to be available all year
Even during lower risk conditions (low wind)
Yearly 99,9%
Weekly 99,9%
Is it possible to forecast the system imbalance risk and dimension reserves accordingly?
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

10. System imbalance risk predictability
System imbalances are driven by
Outage risks in power plants and transmission lines
Prediction risk in wind, photovoltaics and load
System imbalances correlated with predicted system conditions
Higher risks when high winds are predicted
Lower risks when large power plants are not scheduled to run
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

11. Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
Dynamic Outage Risk
Adapt according to schedule of power plants
Probability
Historical Outages
Analysis
Simulation
Loss of power (MW)
Probability
Historical Outages
Analysis
Simulation
Loss of power (MW)
Unit not scheduled
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

12. Dynamic Prediction Risk
Is it possible to predict the imbalance risk according to the forecasted system conditions?
Forecasted System Conditions:
High Wind – Medium PV
Solar (PV) forecast
Wind forecast
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

13. Dynamic Prediction Risk
Classify imbalance risk according to different system conditions
Identify most relevant features driving the imbalance risks
Manual classification
KMeans method
KNN method
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

14. Dynamic Prediction Risk - Training
Optimize classification parameters while meeting reliability criteria with machine learning
Training
Historical Data
Optimal classification
Scenario 1: Reliable
Scenario 2: Reliable
Scenario 3: unreliable
Iterate until all reliable while minimizing cost
Scenario 4: Reliable
Scenario 5: Reliable
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
∑Cost vs Reliability

15. Dynamic Prediction Risk - Prediction
Prediction risk scenario selected based on forecast for D+1
Prediction
Forecasted Data for D+1
Solar
Scenario Selection
n-other features
Wind
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

16. Dynamic Sizing Methodology
Outage Risk
Simulation
Required Volume of Reserve for D+1
Unit schedules on D-1
Prediction Risk
Solar
Imbalance (MW)
P99,9
Forecasted conditions on D-1
Wind
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
n-other features

17. Dynamic Sizing – POC Conclusions
A better reliability management with higher reserves during higher risk period
Financial gains following average reserve reductions
A robust methodology towards the middle and long term system with more uncertainty driven by renewables
With increasing advantages with higher renewable generation shares
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

18. Timeline of Dynamic Sizing Methodology
Take into account increasing RES penetration
Monthly training with most up to date data
Month Ahead
Training
Day Ahead
Sizing
Ex-post
Performance Analysis
Predicted vs Actual
Extreme situations
Update model parameters
Reliability
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

19. Timeline of Dynamic Sizing Methodology
Parallel-run to measure performance and reliability
Transition to operations if parallel-run shows reliability is met with acceptable performance
Month Ahead
Training
Day Ahead
Sizing
Ex-post
Performance Analysis
Predicted vs Actual
Extreme situations
Update model parameters
Reliability
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT

20. Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT
Conclusions
Increasing uncertainty in balancing risk due to higher penetration of Renewable Energy Sources (RES)
Machine learning can help identifying adequate volume of reserve needed
Efficiency : reducing average reserve capacity
Reliability : ensuring adequate reserve capacity during high needs
Sustainability : facilitating the integration of renewable energy
Proof of Concept : > users’group > working group balancing > projects and publications
Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT