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

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

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

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

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

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)

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

Static Dimensioning – Increasing RES Http://www.elia.be/~/media/files/Elia/users-group/Working-Group-Balancing/20171030_dynamic-dimension-of-the-frr-needs.pdf 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

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

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

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

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

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

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

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

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

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

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

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

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 : www.elia.be > users’group > working group balancing > projects and publications Balancing with AI - Jose GONZALEZ PASTOR - #DISUMMIT