- 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.

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World Gas Model

Franziska Holz Joint Work with Ruud Egging, Steve Gabriel, and Jifang Zhuang

Work in Progress

Presented at the 4th PhD Seminar of Natural Gas

Oxford, November 10, 2006

DIW Berlin

University of Maryland

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Outline

Model Overview

Optimization Problems of Selected Players

- Producers

- Transmitters

- Pipeline operators

- Storage operators

- Marketers

State of the Work and Future Plans

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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

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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

LiquidProducts

ELECTRIC POWER

Overview of the Natural Gas Industry

International Gas Pipeline

National network: local transportation

LNG

Marketer/Shippers

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Overall picture

T1

C1

T2

K1,2,3

S1

M1

T3

C3

K1,2,3

S3

M3

R3

Season 2,3

Season 1L1

Producer

Transmitter

Sectors

Marketer

LNG Liquef

StorageLNG Regasif

Country 1 Country 3

Country 2

T 1 T 1

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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

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Maximize production revenues less production costss.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 Transmitter’s purchases from Producer

Producer’s Problem: Description

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Producer’s Problem: Formulation

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Complementarity Formulation: KKT Conditions of the Producer‘s Problem

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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

Transmitter’s Problem: Description

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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

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Incorporating market power in Transmitter’s 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

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Transmitter’s Problem: Formulation

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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 Operator’s Problem:Description

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Pipeline Operator’s Problem:Formulation

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Storage Reservoir Operator’s Problem: Description

Maximize net revenues from marketers less injection costs, distribution costs, and purchasing costs from transmitter and LNG Regasifications.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

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Storage Reservoir Operator’s Problem: Formulation

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Marketer/Shipper’s Problem: Description

Marketer/Shipper1

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

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Marketer/Shipper’s Problem: Formulation

Marketer/Shipper1

3

2

4

Marketers don‘t have a decision variable, but are determined by their demand function to the transmitters

Market clearing must be satisfied

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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

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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

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Thank you very much for your attention!

For any comments and suggestions, please contact:

[email protected]