How to Build an Energy Market Modeling System Presented at the 40th International IAEE Conference June 18-21, 2017 Singapore Robert Brooks, PhD, RBAC, Inc.

Presentation Overview Scope Approach Application Presentation Overview

Scope of Presentation There are many types of energy models Focus today is on those models which tie together energy suppliers with energy consumers using a transportation network Usually described as an energy market model Also known as a partial equilibrium model of a single energy commodity such as crude oil, natural gas, or electricity Sometimes multiple energy commodities can be included in the same model But also focusing on building a modeling system not simply a “model”

Presentation Overview Scope Approach Application Presentation Overview

Modeling System Design Approach Construct the conceptual design Node-arc network diagrams, input-output drawings, etc. Write the model using a modeling language such as AMPL Programmatically create the application Test and iterate

Global Gas Geo-Political Unit (Gpu) Model Consumption Pipeline Exports LNG Exports  Injection  Re-gas Gas Storage LNG Storage Withdrawal  Liquefaction  Pipeline Imports LNG Imports Gas Production

Global Gas Pipeline Model Pipe A deliveries to Gpu 3 Pipe A deliveries to Gpu 2 N Pipe Intx Segment N (Pipe B) 3 Segment 1 (Pipe A) Pipe Link 2-3 Segment 3 (Pipe A) 1 2 Pipe Link 1-2 Pipe A deliveries to Gpu 4 Segment 2 (Pipe A) Pipe Link 2-4 4 Pipe A receipts from Gpu 1 Segment 4 (Pipe A)

Global Gas LNG Flow Model LNG Tanker Shipment from Gpu 1 to Gpu 2 2 Import Region (Gpu 2) 1 LNG Tanker diversion from Gpu 3 to Gpu 2 Liquefaction / Export Region (Gpu 1) LNG Tanker Shipment from Gpu 1 to Gpu 3 3 Import Region (Gpu 3)

LNG Transport Cost Model

Modeling System Design Approach Construct the conceptual design Write the model using a modeling language such as AMPL Entities: the model’s geographical areas or locations, commodities, facilities, transport modes, periods, etc. Level and flow variables: the quantities forecast to be made, stored, moved and consumed Entity data: capacities, costs, yields, loss factors, conversion rates, etc. Constraints or limitations: production or flow capacities, mass balance at storage facilities and terminals, etc. Economic objective: market clearing, supply cost minimization, profit maximization, etc. Programmatically create the application Test and iterate

AMPL Objects Sets Parameters Variables Constraints Objective Define the basic entities in the model Parameters The numerical data representing supply and demand curves, processing, transportation, and storage costs, capacities, yields & losses Variables The “decision variables” of the model: levels and flows for production, transport, storage, and consumption of the energy commodities of the model Constraints Equations or inequalities describing the limitations on commodity levels and flows, activity matrices defining operations on those commodities such as refining, chemical conversion, etc. Objective What is the criterion (or criteria) operating in this market which determine the “best” or most likely economic outcome?

AMPL Model Code: Sets # Sets == indices include geo-political units (GPU) such as countries, periods (months), pipelines, and multi-dimensional combinations of these set GPU; # countries or sub-country areas set ORIGIN within {GPU}; # origin gpu for LNG or pipeline set DEST within {GPU}; # destination gpu for LNG or pipeline set PIPELINE; # inter-regional pipes set PERIOD; # this will be integers from 1 to N where N is the number of periods in the scenario set LNGLINKPER within {ORIGIN,DEST,PERIOD}; # compound set of origin gpu to destination gpu in each time period

AMPL Model Code: Parameters # Parameters: costs, capacities, loss factors, distances associated with flows of gas and LNG; conversion factors for gas to LNG, and LNG to gas; and other numeric values used in constraints # for example, the number of days in the month for each forecast period param dpm{PERIOD} >=0; # days per month # and parameters used in conversions of units of measure param LNGEXPRATIO default 585; # cubic meters gas per cubic meter liquid (LNG) param F3perM3 default 35.31445; # cubic feet per cubic meter param LNG2Gas = LNGEXPRATIO * F3perM3 / 1000000; # mmcf gas per cubic meter of LNG # and parameters used in computing LNG travel time param lnglink_distance {LNGLINKPER} >=0; # distance from origin to destination by tanker param lnglink_canaltime {LNGLINKPER} >=0; # uom=days, number of days spent transiting either Suez or Panama canal (zero if no canal)

AMPL Model Code: Variables Variables: the quantities for which optimal values are to be determined, which typically represent flows of natural gas and LNG throughout the global network var pipesegmentuse_qty{PIPESEGMENT}>=0; var pipelinkuse_qty{PIPELINK}>=0; var pipercpts_qty{PIPESUP}>=0; var pipedelivs_qty{PIPEDEM}>=0;

AMPL Model Code: Constraints limitations on flow variables such as upper bounds (capacities) balance equations for each node (what comes in is what goes out) conversion equations (gas to LNG, LNG to gas) useful definitional equations The name of the constraint represents its dual variable; e.g. the marginal value of capacity increase Examples: pipesegmentuse_mvcap{(pipeline,gpu,t) in PIPESEGMENT}: pipesegmentuse_qty[pipeline,gpu,t] <= pipesegment_cap[pipeline,gpu,t] * dpm[t] ; gasstorage_price{(gpu,t) in GASSTOR}: gasstorage_endstor[gpu,t] = gasstorage_endstor[gpu,t-1] + gasstorage_injqty[gpu,t] - gasstorage_wdlqty[gpu,t] ;

AMPL Model Code: Objective Function maximize EconValue: sum{(gpu,sector,t,seg) in DEMSEG,s in DEMDSTEP} (demand_price1[gpu,sector,t,seg]+((s-1)/nsteps) * (demand_price2[gpu,sector,t,seg]-demand_price1[gpu,sector,t,seg])) * gasdelivsstep_qty[gpu,sector,t,seg,s] - ( sum{(gpu,gtype,t,seg) in SUPSEG, s in PRODSTEP} (supply_price1[gpu,gtype,t,seg]+((s-1) / nsteps) * (supply_price2[gpu,gtype,t,seg]-supply_price1[gpu,gtype,t,seg])) * gasprodstep_qty[gpu,gtype,t,seg,s] + sum{(gpu,t) in GPUPERIOD} gasflowdata_cost[gpu,t]*gasflow_qty[gpu,t] + sum{(pipeline,gpu,t) in PIPESEGMENT} pipesegment_cost[pipeline,gpu,t] * pipesegmentuse_qty[pipeline,gpu,t] + … )

Modeling System Design Approach Construct the conceptual design Write the model using a modeling language such as AMPL Programmatically create the application Create the required I/O tables in a relational database Create the user interface for the application (based on “empty” template) Create test problems to debug the model Test and iterate

Programmatically Create the New App Function CreateAll() Call CreateAMPLModelTable Call CreateDatabaseFromAMPLModelFile Call WriteInputScript Call WriteOutputScript Call PopulateTablelist Call PopulateRelations Call RestoreRelations Call CreateBalanceTables Call CreateAggregationFields Call MakeFormQueries Call BuildForms Call LoadTestScenario End Function

New App Database Tables and Relations

New App User Interface

Generated Test Problems Geographical entities (gpu’s) Gas supply curves Gas demand curves Pipeline segments, supply, demand & interconnect links LNG production capacities and costs LNG re-gas capacities and costs Tanker classes and inventories Tanker day rates, fuel use, fuel cost, etc. Maritime distances between LNG origins & destinations

Modeling System Design Approach Construct the conceptual design Write the model using a modeling language such as AMPL Programmatically create the application Test and iterate Run test problems and examine the results Modify AMPL code, re-create application, run tests, and analyze results until the model is working exactly as desired

Tabular Reports

Graphical Reports

Presentation Overview Scope Approach Application Presentation Overview

Application: G2M2 Global Gas Market Model Monthly time frame: permits modeling of seasonality Scenario forecast horizon: from now to 2050 Can “back-cast” from 2006 to now Coverage: more than 100 countries represented All natural gas producing and /or consuming countries All LNG exporting and / or importing countries Large markets divided into sub-country areas USA -> Census Regions Canada, Mexico, Russia -> East and West Nearly 400 gas pipelines: existing and proposed Multiple LNG tankers classes Q-Max, Q-Flex, conventional grouped by size

G2M2 Data Sources International Group of Liquefied Natural Gas Imports (GIIGNL) International Gas Union (IGU) Gas Infrastructure Europe (GIE) U.S. Energy Information Agency (EIA) U.S. Federal Energy Regulatory Commission (FERC) U.S. Department of Energy (DOE) Canada National Energy Board (NEB) LNG Journal LNG Company websites Panama Canal Authority (ACP) Suez Canal Authority (SCA) Timera Energy BP Statistical Review 2007-2014 International Energy Agency (IEA) Joint Organizations Data Initiative (JODI) European Network of Transmission System Operators for Gas (ENTSOG) East European Gas Analysis (EEGA) Gazprom and other company websites

How is G2M2 used? Scenario Design: Forecasting potential impacts of various conditions and events on gas and LNG production, transportation, pricing and demand Growing shale gas production in new plays and countries Proposed new gas pipelines or expansions of existing ones New or expanded LNG import & export terminals Demand sensitivity to weather and gas price Effect of economic growth changes on demand Effect of governmental policies Scenario Evaluation Built-in reports Portal to Excel

What issues can G2M2 address? Natural gas production: How fast & where will it grow? World gas prices: Will they converge? if so, when? LNG market share: How will LNG fare against local production and pipeline imports? North American LNG: Can the US and Canada compete against Qatar, Australia, Russia? New capacity: LNG liquefaction, re-gas, and tankers; new gas pipelines: how much is needed to satisfy future market growth?

Where will gas production grow?

Will global prices converge?

How much market share for LNG?

Can North America compete?

Can North America compete?

Will new LNG tanker capacity be needed?

Thank you! Questions?

Contact Information Robert Brooks, Ph.D. Founder rebrooks@rbac.com Liam Leahy, Chief Executive Officer leahy@rbac.com James Brooks, Director Business Development james.brooks@rbac.com Bethel King, Senior Director, Market Analysis bking@rbac.com Jill Quick, Senior Analyst jill.quick@rbac.com Contact Numbers Administration (281) 506-0588 Contracts and Sales (281) 506-0588 ext. 126 More information: http://www.rbac.com

About RBAC RBAC Inc. licenses economic forecasting tools the energy industry, as well as State and Federal government agencies involved with Energy, Transportation and the Environment. RBAC’s principal products include the industry standard GPCM® Natural Gas Market Forecasting System™, the GPCM® Base Case Database for North America, and GPCM Viewpoints® on Natural Gas. We continuously advance our modeling tools through technology, feature development and regularly released updates based on client requests and energy industry needs. Those needs resulted in our development of RBAC’s North American Natural Gas Liquids Model (NGL-NA™) and GPCM Daily™. Additional forecasting tools scheduled for release include: G2M2® Global Gas Market Modeling System .Net Ver. 3.5 GPCM® for MS-SQL Licensees of RBAC’s systems are involved in natural gas exploration and production, LNG infrastructure development and marketing, natural gas marketing and transportation, electric power generation, natural gas distribution, and commodities trading, as well as most of the major consulting firms that service business and planning needs of the energy industry and its bankers. Dr. Robert Brooks founded RBAC in 1987 based on experience developing several well respected predictive models since his first work on his doctoral research in natural gas transportation economics at MIT in the 70’s. Designing forecasting tools for global energy market prices, basis, and flows is RBAC’s core business. RBAC’s staff includes experts in natural gas supply, demand, marketing and transportation as well as the dynamic global pipeline and LNG markets. Our team applies its world class expertise in mathematical modeling, statistical analysis, mathematical algorithm development, software engineering, and database design to current and future challenges, risks and opportunities in energy.