Production Synergies and Cost Shocks: Hydropower Generation in - PowerPoint PPT Presentation

Production Synergies and Cost Shocks: Hydropower Generation in Colombia Michele Fioretti 1 Jorge A. Tamayo 2 1 Sciences Po 2 Harvard Business School May, 2020 CEPR/JIE School on Applied Industrial Organisation

Motivation ◮ Shocks can result in considerable disruptions to production technologies 2 / 10

Motivation ◮ Shocks can result in considerable disruptions to production technologies ◮ Hedging is not always possible 2 / 10

Motivation ◮ Shocks can result in considerable disruptions to production technologies ◮ Hedging is not always possible ◮ This paper: production technologies, synergies, and market power 2 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q , ε ) 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q , ε ) ◮ A necessary optimal condition states p = mc dDR dq + η 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q , ε ) ◮ A necessary optimal condition states p = mc dDR dq + η ◮ How to hedge production shocks ε ? 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q , ε ) + QC · ( PC − p ) ◮ A necessary optimal condition states p = mc dDR dq + η ◮ How to hedge production shocks ε ? 1. Forward contracts 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q , ε ) + QC · ( PC − p ) ◮ A necessary optimal condition states p = mc dDR dq + η ◮ How to hedge production shocks ε ? 1. Forward contracts mc ◮ p = dDR dq + η · ( 1 − QC q ) 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q , ε ) + QC · ( PC − p ) ◮ A necessary optimal condition states p = mc dDR dq + η ◮ How to hedge production shocks ε ? 1. Forward contracts mc ◮ p = dDR dq + η · ( 1 − QC q ) ◮ In oligopolistic mkt: spot prices → forward prices (Ausubel and Cramton, 2010; de Bragança and Daglish, 2016) 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q 1 , q 2 , ε ) + QC · ( PC − p ) ◮ A necessary optimal condition states p = mc dDR dq + η ◮ How to hedge production shocks ε ? 1. Forward contracts mc ◮ p = dDR dq + η · ( 1 − QC q ) ◮ In oligopolistic mkt: spot prices → forward prices (Ausubel and Cramton, 2010; de Bragança and Daglish, 2016) 2. Production synergies 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q 1 , q 2 , ε 1 ) + QC · ( PC − p ) ◮ A necessary optimal condition states p = mc dDR dq + η ◮ How to hedge production shocks ε ? 1. Forward contracts mc ◮ p = dDR dq + η · ( 1 − QC q ) ◮ In oligopolistic mkt: spot prices → forward prices (Ausubel and Cramton, 2010; de Bragança and Daglish, 2016) 2. Production synergies ∂ qj mc i + mc j · ∂ qi ◮ p = dDR dqi + η i · ( 1 − QC q ) 3 / 10

Motivation ◮ Consider a simple oligopoly for homogeneous goods Π = p · D R ( p ) − C ( q 1 , q 2 , ε 1 ) + QC · ( PC − p ) ◮ A necessary optimal condition states p = mc dDR dq + η ◮ How to hedge production shocks ε ? 1. Forward contracts mc ◮ p = dDR dq + η · ( 1 − QC q ) ◮ In oligopolistic mkt: spot prices → forward prices (Ausubel and Cramton, 2010; de Bragança and Daglish, 2016) 2. Production synergies ∂ qj mc i + mc j · ∂ qi ◮ p = dDR dqi + η i · ( 1 − QC q ) ◮ This paper: the price-impact of production synergies 3 / 10

Empirical Set-up ◮ We focus on the Colombian energy market 1. Firms own multiple and technologically diversified generators 2. Each generator submits price- and quantity-bids 4 / 10

Empirical Set-up ◮ We focus on the Colombian energy market 1. Firms own multiple and technologically diversified generators 2. Each generator submits price- and quantity-bids ◮ Hydropower generation constitutes 80% of total energy production 1. Cost to hydro generators depend on forecasted water inflows 2. Weather changes create cost shocks 0.85 Hydropower Share of Total Production (%) 0.835 0.824 0.819 0.8 0.793 0.791 0.781 0.781 0.776 0.773 0.769 0.75 0.750 0.725 0.7 0.696 0.65 Dry season 0.6 1-Jun-17 1-Aug-17 1-Oct-17 1-Dec-17 1-Feb-18 1-Apr-18 1-Jun-18 4 / 10

Empirical Framework 4 / 10

A Dynamic Problem For Hydropower Plants ◮ When water abounds, hydropower plants produce more at lower prices, and vice-versa 5 / 10

A Dynamic Problem For Hydropower Plants ◮ When water abounds, hydropower plants produce more at lower prices, and vice-versa ◮ We plot this with respect to the future expected inflow to a hydropower plant below Quantity Bids Price Bids 600 2 400 0 200 $/kWh kWh -2 0 -4 -200 -400 -6 1 2 3 4 1 2 3 4 Quarter Quarter 5 / 10

Dynamic Spillover to Thermal Siblings ◮ When water abounds at sibling hydropower plant, a thermal plant demands more $ to produce energy, and vice-versa 6 / 10

Dynamic Spillover to Thermal Siblings ◮ When water abounds at sibling hydropower plant, a thermal plant demands more $ to produce energy, and vice-versa ◮ We plot this with respect to the future expected inflow to a hydropower plant below Quantity Bids Price Bids 0 1 -100 .5 0 $/kWh kWh -200 -.5 -300 -1 -400 -1.5 1 2 3 4 1 2 3 4 Quarter Quarter 6 / 10

How Responses to Dynamic Shocks Affect Prices 1. Spot prices decrease with total water stock ◮ Going from the 90 th to the 10 th quant = 62% ∆ average prices 7 / 10

How Responses to Dynamic Shocks Affect Prices 1. Spot prices decrease with total water stock ◮ Going from the 90 th to the 10 th quant = 62% ∆ average prices 2. During droughts ◮ Siblings thermal plants increase production ◮ Synergies account for ∼ 28% of average price during droughts 7 / 10

How Responses to Dynamic Shocks Affect Prices 1. Spot prices decrease with total water stock ◮ Going from the 90 th to the 10 th quant = 62% ∆ average prices 2. During droughts ◮ Siblings thermal plants increase production ◮ Synergies account for ∼ 28% of average price during droughts 3. However, the impact is asymmetric ◮ Siblings thermal plants do not increase spot prices in wet periods 7 / 10

Measuring the Impact on Prices 7 / 10

A Quantitative Model ◮ For each plant j , hour h and time t , firm i chooses 1. a daily price-bid b ijt 2. a hourly quantity-bid q ijht , to maximize (e.g., Wolak, 2007) � 23 J � � � � D R V i ( w it ) = E ǫ iht p ht − C j ( q ijht ) + β V i ( u ) f ( u | w it ) d u W h = 0 j = 1 where 8 / 10

A Quantitative Model ◮ For each plant j , hour h and time t , firm i chooses 1. a daily price-bid b ijt 2. a hourly quantity-bid q ijht , to maximize (e.g., Wolak, 2007) � 23 J � � � � D R V i ( w it ) = E ǫ iht p ht − C j ( q ijht ) + β V i ( u ) f ( u | w it ) d u W h = 0 j = 1 where 1. the per-period payoff 8 / 10

A Quantitative Model ◮ For each plant j , hour h and time t , firm i chooses 1. a daily price-bid b ijt 2. a hourly quantity-bid q ijht , to maximize (e.g., Wolak, 2007) � 23 J � � � � D R V i ( w it ) = E ǫ iht p ht − C j ( q ijht ) + β V i ( u ) f ( u | w it ) d u W h = 0 j = 1 where 1. the per-period payoff 2. the continuation payoff [transition matrix f ( ·| w it ) ] 8 / 10

A Quantitative Model ◮ For each plant j , hour h and time t , firm i chooses 1. a daily price-bid b ijt 2. a hourly quantity-bid q ijht , to maximize (e.g., Wolak, 2007) � 23 J � � � � D R V i ( w it ) = E ǫ iht p ht − C j ( q ijht ) + β V i ( u ) f ( u | w it ) d u W h = 0 j = 1 where 1. the per-period payoff 2. the continuation payoff [transition matrix f ( ·| w it ) ] ◮ We proceed by 8 / 10

A Quantitative Model ◮ For each plant j , hour h and time t , firm i chooses 1. a daily price-bid b ijt 2. a hourly quantity-bid q ijht , to maximize (e.g., Wolak, 2007) � 23 J � � � � D R V i ( w it ) = E ǫ iht p ht − C j ( q ijht ) + β V i ( u ) f ( u | w it ) d u W h = 0 j = 1 where 1. the per-period payoff 2. the continuation payoff [transition matrix f ( ·| w it ) ] ◮ We proceed by 1. Performing identification in multi-unit dynamic auctions 8 / 10

A Quantitative Model ◮ For each plant j , hour h and time t , firm i chooses 1. a daily price-bid b ijt 2. a hourly quantity-bid q ijht , to maximize (e.g., Wolak, 2007) � 23 J � � � � D R V i ( w it ) = E ǫ iht p ht − C j ( q ijht ) + β V i ( u ) f ( u | w it ) d u W h = 0 j = 1 where 1. the per-period payoff 2. the continuation payoff [transition matrix f ( ·| w it ) ] ◮ We proceed by 1. Performing identification in multi-unit dynamic auctions 2. Estimating marginal costs and the value function from F.O.C.s ◮ Polynomial expansion of the value function ◮ Instrumental variables exploiting rich dataset 8 / 10