Introduction

1. EWEC 2007BB4. Integrated Wind energy into Electricity market5. Spanish Liberalized Electricity Market: Wind Energy Forecasting ExperiencesIgnacio LáinezEnergy Assessment NEO Energía- EDP Group

2. Introduction • Spanish Liberalized Electricity Market: • Wind Energy Forecasting Experiences • 1. Regulation Framework • Spanish Electricity Market • Remuneration encouraged wind Producers to move in to the liberalized market • 2. Technical Issues • Energy Forecasting is mandatory • Measurements Devices in accordance to the Communication Standards • 3. LearnedLessons • Forecasting Performances reality vs theories • Economical efficiency of forecasting activities

3. Regulatory Framework Spanish Liberalized Electricity Market: High Volume allows Wind Energy to participate 360 GWh/Day 130 GWh/Day 35 GWh/Day 225 GWh/Day 35 GWh/Day 525 GWh/Day 3 c€/kWh

4. Regulatory Framework Spanish Liberalized Electricity Market: Pool was formed by all the energy of the system

5. Technical Issues • What was necessary to do to go to the market ? • To be able to Predict the Energy Production: • One Day Ahead (hourly detachment). • Real Time ( 3-hours leap). • To sell the energy in the market every day, every hour. • Technical Requirements (WF and Communications) • Market Representative

6. Technical Issues • Forecasting Activities • Wind Energy programme estimation is a multidisciplinary activity involving , not only the producer, but several private and public companies • Time Series Analysis to correct the 1-day ahead predictions with Real Time data • To transfer these values to local values (WF scale) • To correctt the model values with the WF history • To recover thousand of measurement all around Europe. • To Predict these values for the next day What to do? • Time Series Analysis • Statistical Model How to do it? • Global Forecast Model • Meso-Scale Model • Very High • Global Measurements, Computers • Medium - Communication Resources • Low • Wind Farm Info. Hyst. DB • High • Computer • ECWMF - USA Meteo. Centers • Utilities • Specialised Companies Who can do that? • Specialised Companies • Recently Developed • Development • Depend on WF Data Base • Low Focussed into Wind Energy State of the Art

7. Time Response • Time response moving to the market: • Although regulations encouraged producers to move, time response reached up to 1.5 years 1.5 years for adaptation and application Special Regime to distribution Special Regime to liberalized market

8. Quality Indicators Quality Indicator 67.5 60 52.5 45 Deviation (% production) 37.5 Worse predictions 30 22.5 15 Better predictions 7.5 0 0% 10% 20% 30% 40% 50% 60% 70% 80% Load Factor • Forecasting Quality Indicators: • % Deviation vs %Cf has became as the most useful quality indicator for forecasting activities

9. Learning Period • Forecasting Learning Period: • Once a Forecasting system is put into operation, it takes several months to be fully operational.

10. Portfolio Effect • Portfolio Effect • Portfolio effect is the most important tool to reduce the effect of the forecasting errors

11. Other Issues learned • Forecasting is not affected by the complexity of the terrain: • AEE (Spanish Wind Energy Association) tested several forecasting tools in several wind farms with that result. • Forecasting is affected by location in the globe • Canary island is worse predicted than Iberia due to less accuracy of inputs to the Global Predictions Services • Forecasting is strongly affected by the quality of the SCADA system an Data Storage • Old wind farms, with rough systems and poor communications do not take advantage of Statistical models. • Short term predictions • Short term / hourly forecasting, several statistical methods are necessary to put into operation to ‘catch’ the hourly variability of the wind.