Wholesale markets for electricity : The point of view of a trader Francis HERVÉ (Chief Executive Officer) Philippe GIRARD (Senior Advisor) Vincent MAILLARD (Head of Analytics) EDF TRADING Limited 1
CONTENTS 1- EDF Trading 2- Volatility and electricity 3- The market approach 3-1 The spot market approach 3-2 The forward market approach 4- The physical approach 5- The financial approach 6- Conclusion 2
1- EDF Trading A Brief presentation of EDF Trading EDF Trading results : January 2 June 22 2 21 S1-22 Electricity (TWh) 56 279 264 Natural gas (billions cm) 8 39 42 Coal (millions tons) 12 43 45 Oil products (millions tons) 2 33 11 Turnover ( billions),37 2,95 3,75 EDF Trading top ten counterparts Oil Natural Gas Coal Electricity Banks 7 1 Traders 7 6 8 Others (generators, 3 2 2 2 aggregators) Share of the top ten counterparts 94% 54% 65% 52% 3
2- Volatility and electricity Electricity is the most volatile commodity because of its nonstorability 12% Volatility of power, oil and currency 1% 8% 6% Pow er volatility 4% 2% % Jan- Mar- May- Jul- Sep- Nov- Jan-1 Mar-1 May-1 Jul-1 Sep-1 Nov-1 Jan-2 Mar-2 May-2 Jul-2 Brent MWh $/ Brent: IPE price MWh: Platt s continental price 4
2- Volatility and electricity The most commonly used measurement of electricity prices volatility is the standard deviation of the log of the ratio of prices for one hour and the same hour one week ago 8% 7% 6% 5% 4% 3% 2% 1% % Nordpool market May-92 May-93 May-94 May-95 volatility May-96 May-97 May-98 May-99 May- May-1 May-2 Coefficient variation STD of log(pi/pi-168) Nordpool data + EDFT 5
UK Bilateral Transactions Market Adjustment (NETA)+ Exchange (UKPX, APX 21) 3- The market approach Electricity is traded every where in Europe, mainly in the OTC market, with more than 3 counterparts. The ratio traded volume /consumption reached 2.5 in 21. The Netherlands Bilateral Transactions + One Exchange (APX 1999) Scandinavia Bilateral Transactions + One Exchange (NORDPOOL 93-99) Poland Bilateral Transactions + one Exchange (PPX 2) Germany Bilateral Transactions + one Exchanges (Merger LPX+ EEX 2) France Bilateral Transactions +Exchange ( Powernext 21) Switzerland Bilateral Transactions Spain Pool (OMEL 1998) Obligatory Italy Bilateral Transactions + one Exchange IPX (23?) Austria Bilateral Transactions + Exchange (EXAA 22) 6
3- The market approach The growth of traded volumes is constant as shown in the example of NordPool, the most mature European market TWh Nordpool : traded volumes *22 : 1 months 35 3 25 2 15 1 5 1993 1994 1995 1996 1997 1998 1999 2 21 22* Physical market Financial market Clearing Nordpool data 7
3-1 The spot market approach There is a great dispersion of prices profiles in Norpool due to the level of hydro generation. Norpool spot market 1999-22 4 35 3 Nkr/MWh 25 2 15 1 5 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec 1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1999/2 2/1 21/2 Average 1993-22 Nordpool data 8
3-1 The spot market approach In the NordPool market,high prices do not imply high volatility because of the weight of hydro generation 35 3 Nordpool market 7% 6% Market price /MWh 25 2 15 1 5 5% 4% 3% 2% 1% % May-92 Nov-92 May-93 Nov-93 May-94 Nov-94 May-95 Nov-95 May-96 Nov-96 May-97 Nov-97 May-98 Nov-98 May-99 Nov-99 May- Nov- May-1 Nov-1 May-2 volatility Market price STD of log(pi/pi-168) Nordpool data + EDFT 9
3-1The spot market approach The evolution of peak prices is more difficult to analyse as shown in the example of German exchange EEX due to the multiplicity of events EEX Phelix base index 6 5 /MWh 4 3 2 1 1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/1 1/11 1/12 2 21 22 EEX data 1
3-1 The spot market approach Contrary to the NordPool example, the volatility of German prices is increasing together with the level of prices Volatility of German market volatility 9% 8% 7% 6% 5% 4% 3% 2% 1% % Jul- Sep- Nov- Jan-1 Mar-1 May-1 Jul-1 Sep-1 Nov-1 Jan-2 Mar-2 May-2 Jul-2 EEX data + EDFT 11
3-1 The spot market approach In the example of the Spanish pool of producers, the level of prices is higher than in other countries 12 1 /MWh 8 6 4 2 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec 1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1998/99 1999/2 2/1 21/22 Average OMEL data 12
7 3-1 The spot market approach On The Spanish pool, monthly prices are partially correlated to hydro conditions Spain (6/98-7/2) Correlation between monthly market price and hydro potential market price /MWh 6 5 4 3 2 1 6 7 8 9 1 11 12 13 14 hydro potential GWh (max 18 GWh) Hydraulic potential: REE data Market prices: OMEL data 13
3-1 The spot market approach In Spain, whilst the level of prices is on average higher than in other countries, the volatility of electricity prices is smaller SPAIN : volatility following the market prices 15% 12% 1 8 volatility 9% 6% 3% % Jan-98 OMEL data + EDFT May-98 Sep-98 Jan-99 May-99 Sep-99 Jan- May- Sep- Jan-1 May-1 Sep-1 Jan-2 May-2 Sep-2 volatility of marginal price volatility of final price Average price 6 4 2 price /MWh 14
3-2 The forward market approach Monthly Forward prices can be extrapolated through seasonality effects as shown in the German example Germany Forward curves (october 22) /MWh 3 29 28 27 26 25 24 23 22 21 2 Jan-3 Mar-3 Solid curve : EEX prices Dashed curves : seasonality effect interpolations May-3 Jul-3 Sep-3 Nov-3 Jan-4 Mar-4 May-4 Jul-4 Sep-4 Nov-4 Jan-5 Mar-5 May-5 calendar quarter month Jul-5 Sep-5 Nov-5 15
3-2 The forward market approach In Germany, spot volatility is getting higher while forward volatility is getting lower Germany : comparison of spot and forward volatility 9% 8% 7% 6% 5% 4% 3% 2% 1% % 6/ 8/ 1/ 12/ 2/1 4/1 6/1 8/1 1/1 12/1 2/2 4/2 6/2 Spot FW 23 FW 24 EEX data + EDFT FW : forward for a baseload delivery all the year 16
3-2 The forward market approach In an unpredictable market like Nordpool, forward prices seem to derive directly from moving average of spot prices Nordpool : Correlation spot/forward 23 21 19 NKR/MWh 17 15 13 11 9 7 1/99 3/99 5/99 7/99 9/99 11/99 1/ 3/ 5/ 7/ 9/ 11/ 1/1 3/1 5/1 7/1 9/1 11/1 1/2 3/2 5/2 7/2 9/2 spot moving average FW22 FW 23 FW 24 FW 25 Nordpool data : futures 22, 23, 24, 25 + spot price Moving spot : average on the last 5 working days (2 years) 17
4- The physical approach The margin generation is an important component of spikes formation and is very different from one country to another Switzerland Germany Spain 3% 25% 2% 15% 1% 5% % Portugal Italy UCTE forecasts for January 24 reserve non mobilized capacity margin Netherlands France Belgium UCTE data 18
4- The physical approach On the Spanish pool, from a physical viewpoint, available capacity is directly driving the prices Estimated supply curve for SPAIN for January 21 /MWH 5 45 4 35 3 25 2 15 1 5 Available capacity without hydro and imports 5 1 15 2 25 3 35 4 45 5 GW OMEL hourly prices Installed capacity without imports OMEL data + EDFT 19
5- The financial approach The evolution of volatility has an impact on generators profitability. With different virtual plants, it is interesting to calculate the evolution of profit and loss Nuclear Coal Natural gas Fuel oil Efficiency 33% 35% 55% 33% Fuel Fixed 8 /MWh Spot price Spot price Spot price Transport - 6 /ton.5 /MMBtu 6 /ton O&M cost /kw/y 52 4 18 3 Start-up cost /MW - 5 3 3 Investment /kw 182 15 55 8 Lifetime years 4 4 25 3 Cap costs /kw/y 1 78 35 51 Assumptions based on data provided by international or national entities (IEA for example, French industry ministry, etc.) or generators (press releases) 2
5- The financial approach As shown in the Spanish example, the EBITDA of a plant is highly dependant on electricity market (hourly analysis), fossil fuel prices and volatility Euros/MWh 5 4 3 2 1 Spain 2 : gross profit of a fuel oil plant 1/1/2 1/2/2 3/3/2 3/4/2 5/5/2 5/6/2 6/7/2 6/8/2 7/9/2 8/1/2 8/11/2 9/12/2 2 15 1 5 cumulated Euros/MW 2 Average fuel oil price 164 /t 248 hours in operation 34 start-up Gross profit 185 /MW gross profit cumulated gross profit Euros/MWh 12 1 8 6 4 2 1/1/21 Spain 21 : Gross profit of a fuel oil plant 1/2/21 4/3/21 4/4/21 6/5/21 6/6/21 7/7/21 7/8/21 8/9/21 9/1/21 9/11/21 1/12/21 6 5 4 3 2 1 cumulated Euros/MW 21 Average fuel oil price 133 /t 2551 hours in operation 342 start-up Gross profit 529 /MW gross profit cumulated gross profit OMEL data + Fuel oil price (Platt s delivery Mediterranean) 21
5- The financial approach Euros/MWh 6 5 4 3 2 Comparison between fuel costs and electricity market price Volatility of prices of commodities has a direct impact on profit (weekly analysis) 1 Jan- Mar- May- Jul- Sep- Nov- Jan-1 Mar-1 May-1 Jul-1 Sep-1 Power Gas Coal Fuel Nuclear Nov-1 Power : continental price index (Platt s) Coal : CIF price for delivery ARA (MCIS) Gas : Zeebruge price (Platt s) Fuel oil : CIF price for delivery NWE (Platt s) Nuclear : assumption fixed fuel cost 8 /MWh 2 21 Load factor % Gross profit /kw EBIDTA /kw Load factor % Gross Profit /kw EBIDTA /kw Gas 42% 18.2.2 43% 35.7 17.7 Coal 63% 29.9-1.1 75% 61.1 21.1 Nuclear 87% 19.9 57.9 87% 154.6 12.6 Fuel EDFT % -3 8% 19.9-1 22
6- Conclusion The key problem for all players and analysts is to understand electricity volatility in order to manage the associated risks. Traders use principally forward market prices and the market approach. But according to the activity or the objective (regulators, analyst, energy policy makers, ) one may require different approaches. Any mathematical model, as sophisticated as possible, could not describe the evolution of prices, but mixed approaches are perhaps an efficient solution: The market approach with the evolution of prices and volatility on electricity markets and its different physical or market drivers The physical approach with the balance of supply and demand, and the consequences of the level of reliability on volatility The financial approach with the impact of volatility on profitability and therefore on investments. 23