Day-Ahead and Real Time prices of delivery of electricity. 7 août Arbitrage Strategy of the spread between the Day-Ahead and Re

Transcription

1 Arbitrage Strategy of the spread between the Day-Ahead and Real Time prices of delivery of electricity 7 août 2017

2 CWP Energy CWP Energy is a private company involved in the physical and financial electricity markets in North America Exports/Imports activities between markets Speculative trading inside each market using financial instruments : Virtual Bidding, FTR, financial Contracts. Markets currently covered : AESO, NYISO, NEISO, MISO, ERCOT, SWPP, IESO, CAISO.

3 North American Electricity Markets

4 The CAISO Market California Independent System Operator (CAISO) is an independent, non-profit Independent System Operator (ISO), serving California. In 1998, the process of electricity deregulation started with two main steps : while utilities kept owning the transmissions assets, the ISO controls the routing of electrons, maximizing the transmission efficiency and the generation ressources, and supervizing maintenance of the lines. This deregulation led to the creation of a market where any participant can buy/sell MW of electricity everyday. Mostly four types of market participants : Generation plants sell MW of electricity. Utility companies buy MW of electricity Exporter and importers buy/sell to the adjacents markets of California (NEVP (Nevada Power), APS, PAC-West, and BPAT) Speculators that only beat the markets using financial instruments, exploiting the existence of arbitrage opportunities. Their presence is crucial for maintaining healthy competition between market participants and reduce the opportunities and market manipulations by former Historical Utilities or Generators.

5 Virtual Contracts In this project we propose to design a trading strategy that would trade on Virtual Bidding Contracts. VB contracts allow for a day j at a given location on the grid (node) for a given hour h {1,..., 24} of day j + 1 to sell/buy a given quantity q t+1,h of Mega Watts at given price d at the Day Ahead Price of day j+1,h (j + 1) : d j+1,h and buy/sell back this quantity at the Real Time price of day (j + 1) : r j+1,h if d j+1,h < d j+1,h /d j+1,h > d j+1,h. In this case the MP has been selected. Otherwise no contract is selected. no physical delivery is expected. The electricity is virtually sold/bought in the DA. when selling Two possible positions Short q t+1,h sell DA/buy RT. Payoff : q j+1,h (d j+1,h r j+1,h ) 1 d j+1,h <d j+1,h Long q t+1,h buy DA/sell RT. Payoff : q j+1,h (r j+1,h d j+1,h ) 1 d j+1,h >d j+1,h

6 RT and DA behaviour Electricity is a non storable good. At all time, the electricity production must equal the electricty demand. r j,h is determined by the ISO in order to match the Supply of electricity to the real Demand at hour h of day j. it is ensured via a Market Clearing Mechanism realized at hour h of day j. Bidding Step MP proposes their curves (Price, Quantity) of buying/selling decreasing curve/increasing curve. Fixing Step all the market participants supply and demand curves are aggregated to provide two main curves : the total supply curve and the total aggregated curve (Price, Quantity). These curves will typically cross at the point (r j,h, Demand j,h ). This matching mechanism is done at all nodes, accounting for transmissions constraints and security constraints. All sell or bought electricity quantity is exchanged at this price r j,h. d j,h is determined by the ISO through the same process but at day j 1 at 10 :30 LT for all h {1,..., 24} based on forecast of demand, and renewable production made by the CAISO and bids curves of MP.

7 Stylized facts of Electricity Demand 1 In practice, the electricity Demand is close to be inelastic (when neglecting export or import effects). So the Supply has to adjust to a varying Demand. 2 More over, the electricity Demand is subject to many factors. Yearly Seasonal higher during the winter and the summer, because of heat and cooling effects Weekly seasonal because of economic activities Daily seasonal day and night effects Special days holidays Volatile mostly depends on climates variables such as temperature, nebulosity, so the variation from one day to the other one can be significant. Trends due to the busyness cycle.

8 Generator Plants in CAISO The cheapest available Generator Plant type is currently the nuclear one. But problem : its time of reaction is very slow. Nuclear power plants unit solely cannot adjust sufficiently quickly to strongly ramping up/down Demand. To get a sufficiently reactive electric park, any country develop "a Generation Mix", not only composed of nuclear, but also hydro, coal, fuel and gas power plants, which are more flexible. Ramp rate : number of MW in a unit of time for a plant to increase or decrease its production. The higher it is for a unit the most flexible, the unit is. Hydro power plants with reservoir possess the highest ramp rate, while gas units have a lowest, but significantly ramp rate. The nuclear ramp rate is very low. Californian Generation Mix is mostly composed of Gas, Renewable (Solar and Wind) and Nuclear Power Plants and so is very flexible. These plants are located inside the Market with mostly Hydro Production in the North, Wind and Solar in the South. Figure CAISO Generation Mix (June 2016). Total installed generation capacity is 71,417 MW (Source : CAISO Outlook)

9 Factors impacting RT Prices The generation Mix and load behaviour impacts a lot the fixing of RT prices. when load is low ( for example during night) only cheap generators are needed (solar, wind, nuclear, hydro) Factors impacting the level of prices : when load is high ( for example during the afternoon ), more expensive Generators are needed r j (.) will strongly be driven by the load. d j (.) will strongly be driven by the forecast of the load. Seasonalities the price is thus sensible to the three main seasonalities of the load. It may vary : across the day across the year across day of week (week end effect) Price Takers the level of price takers generation (mostly Solar, Wind and Hydro in CAISO) has a significant impact. These variables are mostly exogenous. Congestion in some load nodes, the prices may rise dramatically when only peakers ( 100$) can meet their demands Outages when some importants generators become offline suddenly, it can creates congestion, or at least significant rise of prices. Volatility the previous effects may impact the level and the volatility. A sudden rise/drop in the congestion and in the Price takers production leads often to sudden spikes.

10 RT quantiles on node SP15 Winter 200 Spring 100 factor(measure) mean 150 factor(measure) mean Q10 Q10 Q20 Q20 Q30 Q30 spread 50 Q40 Q50 Q60 spread 100 Q40 Q50 Q60 Q70 Q80 50 Q70 Q80 Q90 Q90 Q95 Q he he 250 Summer Fall factor(measure) mean factor(measure) mean Q Q10 Q20 Q Q30 Q30 spread Q40 Q50 spread 100 Q40 Q Q60 Q60 Q70 Q70 50 Q80 Q90 Q95 50 Q80 Q90 Q he he

11 First Problem Proposed CWP Energy propose to design an automated trading strategy for trading virtual portfolio in the CAISO. for all days j 1,..., T on one of the nodes : SP15, NP15 the algorithm must decide at day j for all hours h {1,..., 24} the quantity q j+1,h to bid in the DA Market for day j + 1 (q > 0 for short and q < 0 for long position) and a price of bidding d j+1,h. to maximize the value of the portfolio V V (1, T ) = T j=1 24 p j,h where p j,h = q j,h (d j,h r j,h ) 1 sgn(qj,h )d j,h <sgn(q j,h )d j,h h=1 24 under a constraint of maximum loss h=1 p j,h p j 1,..., T. it will base its decision on datas available up to day j 1 of : 1 day ahead forecast of wind and solar generation 2 past actual generation of wind and solar 3 day ahead forecast of outages 4 past actual generation of outages 5 day ahead forecast of load 6 past actual generation of load 7 past DA RT prices 8 past Congestion Components 9 for each of these nodes 10 all these forecasts are 24 values. data are provided from to

12 Challenges of First Problem Building RT DA Forecast model q j+1,h and d can be based on forecasts of RT or DA or of DART Spread. j+1,h Need of a Bidding Strategy Building a model to forecast RT and/or DA ahead prices is not sufficient for assessing a strategy. Need to build decision rules based on forecasts that limits the downside risk of the strategy. Hours Selection different hours have very different returns and risk profile Spikes the spikes impacts the most the PNL of a VB strategy. Fundamental variables the fundamental variables may help to find the good days where to short/long more MW and those where to not trade. Overfitting but a lot of fundamental variable : 24 values of all of them!!! how to manage overfitting? Out of sample validation need to backtest out of sample the trading strategy Robustness the PNL profile V (t 1, t 2 ) should be as much as possible profitable in various subsample (t 1, t 2 ) of the backtest Proposed approachs : 1 Forecasting the quantiles of RT using linear quantile regression or quantregforrest design of a bidding strategy based on these quantiles. 2 Price takers approach : forecast of probability that the spread move above/under a certain threshold and determination of a threshold of probability to which short or long price taker. (Logistic Regression, Logistic Decision tree)

13 Simple Short Strategies for the Summer and impact of the bidding strategy cumsum(pricesbis$value[cond]) cumsum(pricesbis$value[cond]) Index Index cumsum(pricesbis$value[cond]) cumsum(pricesbis$value[cond]) Index Index Figure cumulative Profit and Loss over summers 2014, 2015, 2016 and 2017 of a short position 1MW at hour 19 on SP15. Top left : price taker strategy. Top right : short at 30$. Bottom left : short at 40$. Bottom right : short at 50$.

14 Second Problem Proposed CWP Energy propose to study on these nodes to evaluate. the impact on the DART spread (DA-RT) of daily forecast error of 1 of wind and solar generation 2 of load data are provided from to and study the persistence and the forecasting of these error forecast and the design of a strategy to beat the market using this persistence. in this approach the modeling seeks to explain the spread by error of forecast.