Hedging the Risk of Renewable Energy Sources in Electricity - - PowerPoint PPT Presentation

Outline RES penetration Case study Modeling Results Conclusions

Hedging the Risk of Renewable Energy Sources in Electricity Production

Giorgia Oggioni1 Cristian Pelizzari2 Mercati energetici e metodi quantitativi: un ponte tra Universit` a e Aziende Padova October 8th, 2015

1University of Brescia, Department of Economics and Management, 25122 Brescia, Italy. E-mail: giorgia.oggioni@unibs.it. 2University of Brescia, Department of Economics and Management, 25122 Brescia, Italy. E-mail: cristian.pelizzari@unibs.it. 1/ 38

Outline RES penetration Case study Modeling Results Conclusions

Outline

1 Effects of renewable energy sources penetration 2 Wind strategies 3 Modeling wind penetration in a risk neutral world

Reference equilibrium model: no wind energy production Modeling wind energy production

4 Results 5 Conclusions 2/ 38

Outline RES penetration Case study Modeling Results Conclusions

The 20-20-20 European targets

Europe 2020, the 2020 climate and energy package, sets demanding climate and energy targets to be met by 2020, known as the “20-20-20” targets: 20% reduction of GHG emissions by 2020 compared to 1990 through the EU Emissions Trading System (Directives 2003/87/EC and 2009/29/EC); 20% share of renewable energy sources (RES) based energy in final energy consumption by 2020 (Directive 2009/28/EC); 20% reduction in EU primary energy consumption by 2020, compared with projected levels, to be achieved by improving energy efficiency. In addition, Europe 2030, 2030 framework for climate and energy policies, and Europe 2050, Roadmap for moving to a low-carbon economy in 2050, set more ambitious objectives, to the aim of a full decarbonization of the energy sector.

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Effects of RES penetration

BUT RES penetration implies:

1 Intermittence of energy production; 2 Reduction of electricity prices that implies a significant revenue drop and

thus:

reduction of incentives to invest in conventional power plants; mothballing and/or dismantling of existing power plants,

with the result that the security of supply becomes riskier and riskier. RES penetration has some side effects that need to be quantified in relation to the relevant market design!

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Wind installed capacity in Europe

MALTA CYPRUS 146.7

* Provisional data ** Former Yugoslav Republic of Macedonia Note: due to previous year adjustments, 423.5 MW of project decommissioning, repowering and rounding of fjgures, the total 2014 end-of-year cumulative capacity is not exactly equivalent to the sum of the 2013 end-of-year total plus the 2014 additions.

Installed 2013 End 2013 Installed 2014 End 2014 Candidate Countries (MW) FYROM

• 37

37 Serbia

• Turkey

646.3 2,958.5 804 3,762.5 Total 646.3 2,958.5 841 3,799.5 EFTA (MW) Iceland 1.8 1.8 1.2 3 Liechtenstein

• Norway

110 771.3 48 819.3 Switzerland 13.3 60.3

• 60.3

Total 125.1 833.4 49.2 882.6 Other (MW) Belarus

• 3.4
• 3.4

Faroe Islands 4.5 6.6 11.7 18.3 Russia

• 15.4
• 15.4

Ukraine 95.3 371.2 126.3 497.5 Total 99.8 396.7 138.0 534.7 Total Europe 12,228.5 121,572.2 12,819.6 133,968.2 Installed 2013 End 2013 Installed 2014 End 2014 EU Capacity (MW) Austria 308.4 1,683.8 411.2 2,095 Belgium 275.6 1,665.5 293.5 1,959 Bulgaria 7.1 681,1 9.4 690.5 Croatia 81.2 260.8 85.7 346.5 Cyprus

• 146.7
• 146.7

Czech Republic 8 268.1 14 281.5 Denmark* 694.5 4,807 67 4,845 Estonia 10.5 279.9 22.8 302.7 Finland 163.3 449 184 627 France 630 8,243 1,042 9,285 Germany 3,238,4 34,250.2 5,279,2 39,165 Greece 116.2 1,865,9 113.9 1,979.8 Hungary

• 329.2
• 329,2

Ireland 343.6 2,049.3 222.4 2,271.7 Italy 437.7 8,557.9 107.5 8,662.9 Latvia 2.2 61.8

• 61.8

Lithuania 16.2 278.8 0.5 279.3 Luxembourg

• 58.3
• 58.3

Malta

• Netherlands

295 2,671 141 2,805 Poland 893.5 3,389.5 444.3 3,833.8 Portugal* 200 4,730.4 184 4,914.4 Romania 694.6 2,599.6 354 2,953.6 Slovakia

• 3.1
• 3.1

Slovenia 2.3 2.3 0.9 3.2 Spain 175.1 22,959.1 27.5 22,986.5 Sweden 689 4,381.6 1,050.2 5,424.8 UK 2,075 10,710.9 1,736.4 12,440.3 Total EU-28 11,357.3 117,383.6 11,791.4

128,751.4

European Union: 128,751.4 MW Candidate Countries: 3,799.5 MW EFTA: 882.6 MW Total Europe: 133,968.2 MW

EWEA (2015). Wind in Power - 2014 European Statistics. Available at http://www.ewea.

• rg/fileadmin/files/library/publications/statistics/EWEA-Annual-Statistics-2014.pdf.

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Wind strategies

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Wind policies and assumptions

Wind penetration levels

1 No wind penetration (reference case without wind production); 2 Wind penetration (priority dispatch).

Load and wind electricity production uncertainty

1 Load and wind-power scenarios.

Wind derivatives (in a risk neutral world)

1 Call option (hedge of “too strong” wind); 2 Put option (hedge of “too weak” wind); 3 Monte Carlo pricing based on wind speed scenarios.

Link between wind speed scenarios and load/wind-power scenarios

1 closeness of simulated wind-power duration curves to observed wind-power

duration curve;

2 probability distribution of observed distances; 3 probabilistic assignment of wind speed scenarios to wind-power scenarios. 7/ 38

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Reference equilibrium model: no wind energy production

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Notation

Sets m ∈ M: Set of plant types. M = res ∪ conv, where res and conv respectively indicate wind and conventional power plants; b ∈ B: Set of demand blocks. Parameters Gm: Capacity of plant type m (MW); db: Power consumed in block b (MWh); cm: Variable costs of plant type m (e/MWh); em: Emission factor associated to plant type m (ton/MWh); pCO2: Allowance price (e/ton); pc: Price cap (e/MWh); Hb: Duration in hours of each block b. Variables gb,m: Power generated in block b by plant type m (MWh); gsb: Power sold in block b (MWh); nb: Shortage in block b (MWh); pb: Electricity price in block b (e/MWh).

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Reference equilibrium model

Generator’s profit maximization problem Max

• b

Hb ·

• pb · gsb −
• m∈conv

cm · gb,m − pCO2 ·

• m∈conv

em · gb,m

• subject to:

Gm − gb,m ≥ 0 (ϕb,m) ∀b ∀m ∈ conv

• m∈conv

gb,m = gsb (ηb) ∀b gb,m ≥ 0 ∀b ∀m ∈ conv gsb ≥ 0 ∀b Clearing of the energy market Min

• b

Hb · pc · nb subject to: gsb + nb − db = 0 (pb) ∀b nb ≥ 0 ∀b

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Complementarity formulation of the reference equilibrium model

0 ≤ cm + em · pCO2 + ϕb,m − ηb⊥gb,m ≥ 0 ∀ b ∀m ∈ conv 0 ≤ −pb + ηb⊥ gsb ≥ 0 ∀ b 0 ≤ Gm − gpb,m⊥ ϕb,m ≥ 0 ∀ b ∀m ∈ conv

• m∈conv

gb,m − gsb = 0 (ηb free) ∀ b gsb + nb − db = 0 (pb free) ∀b 0 ≤ pc − pb⊥ nb ≥ 0 ∀ b

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Modeling wind energy production

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Notation

Additional Sets s ∈ S: Set of scenarios considered in each block b. Additional Parameters θs,b: Wind power capacity factor in scenario s and block b (%); ds,b: Power consumed in scenario s and block b (MWh); τs,b: Probability of scenario s associated to block b; α: Wind derivative (call/put option) price (e/MWh); βs,b: Wind derivative (call/put option) payoff in scenario s and block b (e/MWh). Variables gs,b,m: Power generated in scenario s and block b by existing plant of type m (MWh); gss,b: Power sold in scenario s and block b (MWh); ns,b: Shortage in scenario s and block b (MWh); ps,b: Electricity price in scenario s and block b (e/MWh).

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Generator’s profit maximization problem

Max

• s,b

τs,b · Hb · pb · gss,b −

• s,b

τs,b · Hb ·

• m

cm · gs,b,m −

• s,b

τs,b · Hb · pCO2 ·

• m∈conv

em · gs,b,m +

• s,b

τs,b · Hb · (βs,b − α) ·

• m∈res

gs,b,m subject to: Gm − gs,b,m ≥ 0 (ϕs,b,m) ∀s, b ∀m ∈ conv Gm · θs,b − gs,b,m ≥ 0 (ϕs,b,m) ∀s, b ∀m ∈ res

• m

gs,b,m = gss,b (ηs,b) ∀s, b gs,b,m ≥ 0 ∀s, b, m gss,b ≥ 0 ∀s, b

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Clearing of the energy market

Min

• s,b

τs,b · Hb · pc · ns,b subject to: gss,b + ns,b − ds,b = 0 (ps,b) ∀s, b ns,b ≥ 0 ∀s, b

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Complementarity formulation of the wind equilibrium model

0 ≤ cm + em · pCO2 + ϕs,b,m − ηs,b⊥gs,b,m ≥ 0 ∀ s, b ∀m ∈ conv 0 ≤ cm + ϕs,b,m − ηs,b + α − βs,b⊥gs,b,m ≥ 0 ∀ s, b ∀m ∈ res 0 ≤ −ps,b + ηs,b⊥ gss,b ≥ 0 ∀ s, b 0 ≤ Gm − gs,b,m⊥ ϕs,b,m ≥ 0 ∀ s, b ∀m ∈ conv 0 ≤ Gm · θs,b − gs,b,m⊥ ϕs,b,m ≥ 0 ∀ s, b ∀m ∈ res

• m

gs,b,m − gss,b = 0 (ηs,b free) ∀ s, b gss,b + ns,b − ds,b = 0 (ps,b free) ∀s, b 0 ≤ pc − ps,b⊥ ns,b ≥ 0 ∀ s, b

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Wind derivatives

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Wind derivatives: call and put option payoffs

t: hour of the reference year, t ∈ {1, ..., 8760} Wt: wind speed at hour t, measured in m/s Wind call option

designed to hedge “too strong” wind: it pays for hours with wind speed higher than a “strike price” strike price x × ¯ wt,Y : for each hour t, it is a factor x of the average wind speed ¯ wt,Y of the previous Y years χ: conversion coefficient from m/s to e/MWh expiration: end of the reference year Asian style option: the option sums the excess wind speeds of each hour of the reference year payoff: payoffc =

8760

• t=1

χ max(0, Wt − x × ¯ wt,Y )

Wind put option

hedging of “too weak” wind payoff: payoffp =

8760

• t=1

χ max(0, x × ¯ wt,Y − Wt)

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Wind derivatives: call and put option prices

Pricing of wind options

Call option fair price: αc = e−rEQ(payoffc) assumptions: r (continuously compounded interest rate on an annual basis), Q ≡ P (risk neutral world) Monte Carlo method:

generate N wind speed scenarios evaluate the option payoff for each wind speed scenario average the N option payoffs take the previous average as an approximation of EQ(payoffc) EQ(payoffc) ≈ N

i=1 payoffc,i

N , where payoffc,i is the call payoff (in e/MWh) associated to wind speed scenario i

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Wind derivatives: wind speed scenarios

Input data: Hourly wind speeds of Germany in 2014

Data from NCAR are u-components and v-components of wind collected every 6 hours at 10 metre heights on a grid of 48 intersection points between parallels and meridians, transformed into wind speeds and then made hourly

N = 100 Scenarios are based on Weibull distributions fitted on the 6-hour 48-point grid data, then made hourly

Dependence of data is indirectly taken into account because scenarios are composed of wind speeds with the same time order of the input data

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Wind derivatives: other settings

Strike price:

¯ wt,Y : hourly average of German wind speeds in the last 12 years (2002 − 2013) call option: x is set equal to 1.01, 1.03, 1.05, 1.10, 1.15, and 1.20 put option: x is set equal to 0.99, 0.97, 0.95, 0.90, 0.85, and 0.80

r = 0.05% Conversion coefficient χ based on the slope coefficient of a linear regression model fitted to the 2014 data of wind speeds and wind electricity productions in Germany 100 wind speed scenarios (and corresponding option payoffs) assigned probabilistically to the 36 load/wind-power scenarios based on their closeness

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Link between wind speed scenarios and load/wind-power scenarios

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Load/Wind-power scenarios

0.40 0.50 0.60 0.70 0.80 0.90 1.00 Demand factor 0.00 0.10 0.20 0.30 0.40 1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 8001 8501 Hour 0.40 0.50 0.60 0.70 0.80 Wind factor 0.00 0.10 0.20 0.30 1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 8001 8501 Hour

Figure: Load and wind-power capacity factor duration curves of Germany in 2014 - 3 levels for each of the 4 blocks of the load and wind-power duration curves.

Scenarios based on the load and wind-power capacity factor duration curves

• f Germany in 2014.

A total of 36 scenarios, 3 levels of wind-power capacity factors times 3 levels

• f load capacity factors times 4 load blocks.

Baringo, L., A.J., Conejo (2013). Correlated wind-power production and electric load scenarios for investment decisions, Applied Energy, 101, 475–482.

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Link between wind speed scenarios and load/wind-power scenarios (1)

IDEA assess the similarity of each wind speed scenario generated for the wind derivatives to the wind-power scenarios of the equilibrium model assign wind speed scenarios to wind-power scenarios in a probabilistic way, in particular option payoffs payoffc,i and payoffp,i, with i = 1, ..., 100 do this for each of the 4 load blocks calculate the option payoffs, βs,b,c and βs,b,p for the call and the put respectively, for each wind-power scenario and load block as the average of the option payoffs assignments

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Link between wind speed scenarios and load/wind-power scenarios (2)

15000 20000 25000 30000 35000 nd electricity production (MWh) 5000 10000 1 2 3 4 5 6 7 8 9 Wind Wind speed (m/s)

Figure: Wind electricity production against wind speed in Germany in 2014.

Lydia, M., Kumar, S.S., Selvakumar, A.I., and G.E.P., Kumar (2015). Wind resource estimation using wind speed and power curve models, Renewable Energy, 83, 425–434.

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Link between wind speed scenarios and load/wind-power scenarios (3)

Sigmoid regression model fitted to the 2014 data of wind speeds and wind electricity productions in Germany Each wind speed scenario transformed into a wind electricity production scenario

Sigmoid function value altered by a normally distributed number accounting for variability of production at different speeds

Wind-power capacity factors calculated for each wind electricity production scenario Assessment of closeness of capacity factors of wind electricity production scenarios to capacity factors of wind-power scenarios (capacity factors adjusted by their standard deviation) Probability distribution estimation with higher probability to lower absolute differences Probabilistic assignment of wind electricity production scenarios to wind-power scenarios The last 3 steps repeated for each load block

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Model additional assumptions

Input data of the equilibrium model

Electricity market: Germany Reference year: 2014

EU-ETS

No EU-ETS: CO2 price 0 e/ton EU-ETS: CO2 price 40 and 50 e/ton

Available technologies

RES based plants: wind Conventional plants: nuclear, lignite, coal, CCGT, oil

Conventional plant dismantling/mothballing

Dismantling of 30% of the available nuclear capacity Mothballing of 30% of the available CCGT capacity

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Results

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No wind penetration

Production per block and technology in MWh (reference capacity)

0 ¡ 10,000 ¡ 20,000 ¡ 30,000 ¡ 40,000 ¡ 50,000 ¡ 60,000 ¡ 70,000 ¡ 80,000 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ Block ¡1 ¡ Block ¡2 ¡ Block ¡3 ¡ Block ¡4 ¡ Electricity ¡produc.on ¡(MWh) ¡ Oil ¡ Gas ¡ Coal ¡ Lignite ¡ Nuclear ¡ Wind ¡

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No wind penetration

Profits (Ke)

No EU-ETS Reference capacity 70% nuclear 70% CCGT 70%-70% Revenues 21,525,027 169,904,105 180,108,829 831,852,306 Costs 8,928,503 9,578,034 8,978,623 10,000,624 Emissions

• Profits

12,596,524 160,326,071 171,130,206 821,851,683 EU-ETS CO2 40 e/ton Revenues 33,501,692 180,059,147 193,309,454 837,123,275 Costs 8,957,224 9,594,775 9,007,344 10,017,365 Emissions 14,519,622 15,054,114 14,450,594 14,982,118 Profits 10,024,845 155,410,259 169,851,516 812,123,792 EU-ETS CO2 50 e/ton Revenues 37,172,267 183,274,317 196,989,411 838,820,818 Costs 11,480,140 10,407,480 11,748,578 11,219,308 Emissions 15,393,016 16,458,072 16,528,039 17,408,326 Profits 10,299,110 155,067,667 170,053,892 810,193,183 30/ 38

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No Wind vs. Wind penetration

Prices e/MWh, no EU-ETS

600.00 800.00 1000.00 1200.00 1400.00 1600.00 Wind No wind 0.00 200.00 400.00 Initial capacity 70% nuclear 70% CCGT 70%-70%

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Wind penetration

Production per block and technology in MWh (reference capacity)

0 ¡ 10,000 ¡ 20,000 ¡ 30,000 ¡ 40,000 ¡ 50,000 ¡ 60,000 ¡ 70,000 ¡ 80,000 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ No ¡EU-­‑ETS ¡ EU-­‑ETS ¡40 ¡ EU-­‑ETS ¡50 ¡ Block ¡1 ¡ Block ¡2 ¡ Block ¡3 ¡ Block ¡4 ¡ Electricity ¡produc.on ¡(MWh) ¡ Oil ¡ Gas ¡ Coal ¡ Lignite ¡ Nuclear ¡ Wind ¡

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Wind penetration

Profits (Ke)

No EU-ETS Reference capacity 70% nuclear 70% CCGT 70%-70% Revenues 21,135,150 38,868,742 105,009,753 327,265,260 Wind revenues 1,640,170 2,184,352 4,932,441 20,060,014 Costs 7,080,919 7,661,617 7,170,246 7,825,957 Emissions

• Profits

15,694,402 33,391,476 102,771,947 339,499,317 EU-ETS CO2 40 e/ton Revenues 32,857,787 49,625,557 117,120,313 335,587,664 Wind revenues 3,535,022 3,892,320 6,873,776 21,629,047 Costs 7,211,097 7,753,520 7,300,424 7,917,860 Emissions 12,694,553 13,403,830 12,684,668 13,370,863 Profits 16,487,159 32,360,527 104,008,997 335,927,989 EU-ETS CO2 50 e/ton Revenues 36,272,524 52,866,220 120,522,051 338,189,507 Wind revenues 4,030,516 4,368,912 7,396,694 22,083,426 Costs 10,282,913 10,615,683 9,263,817 9,715,466 Emissions 12,530,962 13,639,757 13,715,935 14,750,785 Profits 17,489,166 32,979,692 104,938,993 335,806,682 33/ 38

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Introducing call options

Wind electricity production (MWh)

26,000 27,000 28,000 29,000 30,000 No EU-ETS EU-ETS 23,000 24,000 25,000 No Call Call 1% Call 3% Call 5% Call 10% Call 15% Call 20%

Call payoff net of call price (e)

• 50,000,000
• 50,000,000

100,000,000 Call 1% Call 3% Call 5% Call 10% Call 15% Call 20% No EU-ETS EU-ETS

• 200,000,000
• 150,000,000
• 100,000,000

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Introducing put options

Wind electricity production (MWh)

15,000 20,000 25,000 30,000 35,000 No EU-ETS EU-ETS

• 5,000

10,000 No Put Put 1% Put 3% Put 5% Put 10% Put 15% Put 20%

Put payoff net of put price (e)

150,000,000 200,000,000 250,000,000 300,000,000 No EU-ETS EU-ETS

• 50,000,000

100,000,000 Put 1% Put 3% Put 5% Put 10% Put 15% Put 20%

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Both call and put options

Wind electricity production (MWh)

28,600 28,800 29,000 29,200 No EU-ETS EU-ETS 28,000 28,200 28,400 No Call-Put Call-Put 1% Call-Put 3% Call-Put 5% Call-Put 10% Call-Put 15% Call-Put 20%

Call and put payoffs net of corresponding call and put prices (e)

80,000,000 100,000,000 120,000,000 140,000,000 160,000,000 180,000,000 No EU-ETS EU-ETS

• 20,000,000

40,000,000 60,000,000 Call-Put 1% Call-Put 3% Call-Put 5% Call-Put 10% Call-Put 15% Call-Put 20%

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Conclusions and further steps

Options can be beneficial, with exceptions (that could be the reason of a small (and OTC) market of wind derivatives) Design of scenarios to favor an integration between a financial approach and an (economic) equilibrium approach Consider a risk averse world by introducing a Value-at-Risk-based objective function on the side of the electricity producer in the equilibrium model and a market price of risk for the underlying asset (wind speed) of the wind derivatives

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Thank you for your attention!

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