1. - 1 - World Gas Model Franziska Holz Joint Work with Ruud Egging, Steve Gabriel, and Jifang Zhuang Work in Progress Presented at the 4 th PhD Seminar of Natural Gas Oxford, November 10, 2006 DIW Berlin University of Maryland

2. - 2 - Outline Model Overview Optimization Problems of Selected Players -Producers -Transmitters -Pipeline operators -Storage operators -Marketers State of the Work and Future Plans

3. - 3 - Model Overview - Static complementarity model of the world natural gas market - Daily flows seasonal patterns over one year - Detailed representation of the players in the natural gas business: -Producers -Transmitters -Liquefiers -Regasifiers -Storage operators -Marketers - Mixed complementarity approach instead of MPEC which would we hard to solve One stage with market power (one player) - Other players assumed to behave competitively, and to be linked via market-clearing conditions

4. - 4 - INDUSTRIAL CITY GATE STATION COMMERCIAL RESIDENTIAL DISTRIBUTION SYSTEM UNDERGROUND STORAGE TRANSMISSION SYSTEM Cleaner Compressor Station GAS PROCESSING PLANT GAS PRODUCTION Gas Well Associated Gas and Oil Well Impurities Gaseous Products Liquid Products ELECTRIC POWER Overview of the Natural Gas Industry International Gas Pipeline National network: local transportation LNG Marketer/Shippers

5. - 5 - Overall picture T1 C1 T2 K1,2,3 S1 M1 T3 C3 K1,2,3 S3 M3 R3 Season 2,3 Season 1 L1 Producer Transmitter Sectors Marketer LNG Liquef Storage LNG Regasif Country 1 Country 3 Country 2 T 1

6. - 6 - Model Overview - Static complementarity model of the world natural gas market - Daily flows seasonal patterns over one year - Detailed representation of the players in the natural gas business: -Producers -Transmitters -Liquefiers -Regasifiers -Storage operators -Marketers - Mixed complementarity approach instead of MPEC which would we hard to solve One stage with market power (one player) - Other players assumed to behave competitively, and to be linked via market-clearing conditions

7. - 7 - Maximize production revenues less production costs s.t. -bounds on daily production rates -bounds on volume of gas produced in time-window of analysis (one year) Decision Variables -How much to produce in season and year (cubic meters/day) Market Clearing -Producers sales MUST EQUAL Transmitters purchases from Producer Producers Problem: Description

8. - 8 - Producers Problem: Formulation

9. - 9 - Complementarity Formulation: KKT Conditions of the Producers Problem

10. - 10 - Maximize selling revenues less purchase costs from its domestic producer s.t. -material balances, including international pipeline losses Decision Variables -How much to sell in season and year (cubic meters/day) -How much to buy from producers and neighboring transmitters (cubic meters/day) Market Clearing: -Sales MUST EQUAL Purchases of (domestic) Marketers, Storage and LNG Liquefaction Transmitters Problem: Description

11. - 11 - Interfaces between producers and end-user markets (marketers) Separate entity Market mechanism vs. dedicated trading companies for each producer Some real world counterparts (e.g., Gazexport) Low/high calorific markets: not that interesting not included for the time being New concept, seems to work, non-conventional Here presented formulation: producer dedicated transmitters Transmitter Characteristics

12. - 12 - Incorporating market power in Transmitters Formulation Market power in Europe: producers Initial transmitter formulation: producers face only one buyer. Exerting market power only relative to this one buyer, no direct means to withhold gas from or bring it to specific markets. So what … ? multiple transmitters, dedicated for one producer integrate transmitter into producer (more conventional) one transmitter per producer = the exporting subsidiary of a producer The transmitter is exerting the market power vis- à -vis its customers, not the producer Decision Criteria: Usefulness Num Variables ~ Solvability, Acceptance Usefulness: there are examples of this type of agent in reality (e.g., Gazexport) Solvability: turns out not to be increased but we stick with this representation

13. - 13 - Transmitters Problem: Formulation

14. - 14 - Maximize congestion revenues s.t. -capacity bounds on flow Decision Variables -How much capacity to sell to Transmitters (in each season and year) Market Clearing -Capacity sold to Transmitters MUST EQUAL Capacity purchased by Transmitters Pipeline Operators Problem: Description

15. - 15 - Pipeline Operators Problem: Formulation

16. - 16 - Storage Reservoir Operators Problem: Description Maximize net revenues from marketers less injection costs, distribution costs, and purchasing costs from transmitter and LNG Regasification s.t. -volumetric bound on working gas -maximum extraction rate bound -maximum injection rate bound -annual injection-extraction balancing Decision Variables -How much gas to buy from Transmitters and LNG Regasifiers -How much gas to sell to Marketers Market Clearing -Storage Operators Sales MUST EQUAL Marketers Purchases from Storage

17. - 17 - Storage Reservoir Operators Problem: Formulation

18. - 18 - Marketer/Shippers Problem: Description Marketer/Shipper 1 3 2 4 Maximize demand sector revenues less local delivered costs from transmitter, storage and LNG Regasification s.t. -Sales to Sectors MUST EQUAL Purchases from Transmitter, Storage, LNG Regasifier Decision Variables -How much to buy from transmitter, storage and LNG -How much to sell to each sector

19. - 19 - Marketer/Shippers Problem: Formulation Marketer/Shipper 1 3 2 4 Marketers dont have a decision variable, but are determined by their demand function to the transmitters Market clearing must be satisfied

20. - 20 - Application Covers Europe and all LNG world-wide, one player each type in each country (when applicable.) 51 countries (also outside of Europe) 28 producers 15 large, 5 LNG only, 13 domestic only 36 consuming countries 3 sectors/countries, 6 LNG only LNG: 10 Liquefiers, 15 Regasifiers, 150 LNG routes 20 Storage Operators 74 pipelines Programmed in GAMS using PATH

21. - 21 - State of the Work and Future Plans Currently: Running simulations for model with market power and for different scenarios The rich data input allows to investigate issues like international LNG flows, substitution effects between Russian, North African pipeline gas and LNG Later: extensions of the model Stochasticity into players problems, for example with stochastic demand realizations Alternative demand functions Scenario Reduction & Decomposition Other strategic behavior/market power for producers, marketers Dynamic model with decisions on investments in transport infrastructure

22. - 22 - Thank you very much for your attention! For any comments and suggestions, please contact: fholz@diw.de