IF YOU NEED ADVANCED ANALYTICS SOLUTIONS FOR ENERGY SECTOR

PRODUCES THE OPTIMIZATION MATH MODELS THAT YOU WANT

is company based on technology, pioneer in Latin America, aimed at specialized consulting and design, implementation and start-up of Decision Support Systems (DSS)

DW integrate state of the art technologies of Mathematical Programing with Advanced Information Systems to work in ADVANCED ANALYTICS AND OPTIMIZATION Actually, DW works as a Mathematical Modeling Factory for many companies. EXPERIENCE

DO ANALYTICS LLC is a spin-off company of DECISIONWARE, dedicates to production and marketing OPTEX MATHEMATICAL MODELING SYSTEM a technology optimization software.

is the optimization technology (a robot) developed by DECISIONWARE, dedicates to production and to solve real world optimization problems for industrial organizations

DecisionWare (DW) is interested in developing Business Alliances to provide services to companies and organizations linked to the electricity sector. DW would brings to the alliance: ▪ More than twenty years of experience working in projects in the electric sector using mathematical programming models. ▪ Mathematical models to be solved using large scale methodologies ▪ Source code in multiples optimization technologies (GAMS, IBM ILOG, FICO, … ) ▪ Transfer technology and technical support agreements to define with the Business Partner (BP). ▪ If the BP is interested, DW can sell the source code and the marketing rights of mathematical models.

OPTIMIZATION & EQUILIBRIUM MODELS ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪

Economical Dispatch of Electrical & Natural Gas Systems ETRM: Energy Trading & Risk Management Simulation of Deregulated Electricity Markets Design and Redesign of Power Networks Design, Redesign and Operation of Smart Grids Optimization of Maintenance of Electrical Assets Integrated Financial Modeling (ALM) and Operations Dispatch Forecast Models for Electricity Demand and for Renewable Energy Sources Oil Supply Chain Optimization Optimum Operation of Pipelines For more information write to [email protected]

▪

OPCHAIN-ESO OPTIMIZING THE VALUE CHAIN

Energy Systems Optimization

▪

▪

▪ ▪

DW would deliver the source code of mathematical models, which are available in multiple optimization technologies, such as: GAMS, IBM CPLEX Optimization Studio (ILOG Concert Technologies, OPL), AIMMS, FICO™ XPRESS Optimization Suite, C linked to CPLEX/GUROBI/XPRESS; the models can be delivered in other optimization technology proposed by the BP. The stochastic multi-stage optimization models can be solved using large scale methodologies, such as: Bender’s Theory and its variations (SDDP, GDDP, SDDiP, GBD), Lagrangian Relaxation and/or Cross Decomposition. The user can selected the optimization methodology and risk control constraints. The agreement can include the computer tool (OPTEX MMS) that DW uses to keep updated the optimization technologies and to facilitate the incorporation, or disincorporation, of constraints in new mathematical models. Transfer technology and technical support agreements to define with the Business Partner (BP). If the BP is interested, DW can sell the source code and the marketing rights of mathematical models.

OPCHAIN OPTIMIZING THE VALUE CHAIN

PROJECTS IN OPTIMIZATION

OPCHAIN

OPTIMIZING THE VALUE CHAIN To capitalize on its expertise in mathematical optimization projects, DW ​created OPCHAIN, the brand through which consistently meets all solutions developed by DW, in different areas of application of mathematical programming methodologies and technologies. In 2017, OPCHAIN ​accumulated experience of more than forty (40) years of engineering problem solving and business analytics using mathematical programming models. In keeping with the standards of modern information technologies, OPCHAIN models are fully programmable​​, easy to customize for each client, and easily integrated with other IT solutions in organizations.

OPCHAIN-SCO

OPCHAIN-TSO

SUPPLY CHAIN OPTIMIZATION

TRANSPORT SYSTEMS OPTIMIZATION

OPCHAIN-DCO

OPCHAIN-BANK

OPCHAIN-ESO

OPCHAIN-RPO

OPCHAIN-MINES

OPCHAIN-EDO

DEMAND CHAIN OPTIMIZATION

ENERGY SYSTEMS OPTIMIZATION

MINES SYSTEMS OPTIMIZATION

BANK SYSTEMS OPTIMIZATION

REGIONAL PLANING OPTIMIZATION

EDUCATIONAL SYSTEMS OPTIMIZATION

ESO

Energy Models & Services

E&G Electricity & Gas Sector/Market Services

ENERGY SERVICES ETRM: Energy Trading & Risk Management

EFO: Industrial Energy Efficiency Optimization EPE: Energy Project Evaluation

SGO: Smarts Grids Optimization MRR: Modeling Regulatory Affairs

OSE: Optimization Services for Electricity Companies OIL: OIL Supply Chain Optimization

HAS OVER TWENTY YEARS OF EXPERIENCE SOLVING PROBLEMS OF COMPANIES IN THE SECTOR ENERGY USING MATHEMATICAL PROGRAMMING MODELS

ETRM Energy Trading & Risk Management

OPCHAIN-ETRM OPTIMIZING THE VALUE CHAIN

Optimal Energy Trading & Risk Management

Peter L. Bernstein Against the Gods The Remarkable Story of Risk

OPCHAIN-ETRM

Optimal Energy Trading & Risk Management OPCHAIN-ETRM corresponds to a set of mathematical models oriented to support the decisions of the different agents involved in energy supply chains. o OPCHAIN-ETRM-OET: Stochastic Optimization model, including risk constraints, oriented to long term energy taxing.

o OPCHAIN-ESO-E&G: Integrated electricity & gas economic dispatch oriented to determining spot prices in the markets, using economic models that simulate the markets and the integrated systems of electricity & gas. o OPCHAIN-ETRM-GARCH-PSPOT: Statistical projection of spot prices using ARMAXGARCH methodologies taking as reference the historical series of the spot price.

D e s v io E s t a nda r ($ )

Curva Eficiente Costo Esperado Vs. Desvio Estandar

500000

375000

RISK MANAGEMENT

250000

125000

▪ Risk Hedging

C o sto E s pe ra do ($ )

0 -241107

▪ Multicriteria Optimization ▪ Value At Risk (VaR) Analysis

R a ngo ($ )

-108007

-37911

-18412

7618

24330

29027

33527

Costo Esperado Vs. Rango Costo Esperado

600000

300000

C o sto E s pe ra do ($ )

0 -241107 -300000

-600000

-900000

-108007

-37911

-18412

7618

24330

29027

33527

EFO Industrial Energy Efficiency Optimization ADDITIONAL INFORMATION

OPCHAIN-EEO OPTIMIZING THE VALUE CHAIN

Industrial Energy Efficiency Optimization

OPCHAIN-EEO

ENERGY EFFICIENCY OPTIMIZATION

OPCHAIN-EEO is integrated by multiple mathematical models which together allow support solutions in different aspects of energy efficiency in heavy industries. The analytic solutions are grouped into two class according to the type of industry: ▪

OPCHAIN-EEO-HIN: heavy industry: chemical, petrochemical, steel, mining, metallurgical, …

▪

OPCHAIN-EEO-OIL: oil sector: production, refining and transportation of crude and refined products.

ENERGY AND POLLUTION MANAGEMENT ELECTRICITY

THERMIC ENERGY EMISSIONS

ELECTRICITY

Fuente: Optimización de la Energía en la Industria del Cemento. Revista AAB.

EPE Energy Projects Evaluation ADDITIONAL INFORMATION

Energy Evaluation Projects ▪ Investment in energy assets-related projects must face an energy market that is changing rapidly: ▪ Smart grid ▪ Distributed Generation ▪ Non-conventional energy sources ▪ Multiple thermal fuel supply (blending) ▪ Smart metering ▪ Intelligent demand response ▪ Energy storages ▪ Large scale of mathematical models ▪

Climatological risk determinant in the electricity market, which is increased if includes exposure to the risk of the price of fuels

▪

Legal and regulatory framework offers new possibilities for self-generation, cogeneration and for your marketing

OPCHAIN-ESO-EPE Energy Evaluation Projects

OPCHAIN-ESO-EPE corresponds to a set of mathematical models oriented to support the decisions of the different stakeholders in investing in energy projects related with the electricity and/or gas market. It is integrated by the following models:

▪ OPCHAIN-ESO-E&G: Integrated electricity & gas economic dispatch oriented to determining spot prices in the markets, using economic models that simulate the markets and the integrated systems of electricity & gas. ▪ OPCHAIN-ETRM-GARCH-PSPOT: Statistical projection of spot prices using ARMAX-GARCH methodologies taking as reference the historical series of the spot price. ▪ OPCHAIN-ESO-FIN: Integration of corporate financial statements with the business of production, transport and distribution of electricity.

ENERGY EVALUATION PROJECTS PROCESS Synthetic Series System Dispatch

OPCHAIN-E&G OPCHAIN-ESO-FIN SISTEMA DE ADDITIONAL

Synthetic Series Spot Price

SISTEMA DE ADDITIONAL

PRECIO SPOT 200 180 160 140

$/KWh

120

Synthetics Series Spot Price

100 80 60

Synthetic Series Revenue and Expenditure

40 20 0 01/07/2014

01/07/2016

01/07/2018

01/07/2020

01/07/2022

RISK MANAGEMENT R a ngo ($ )

FINANCIAL STATEMENTS

Costo Esperado Vs. Rango Costo Esperado

600000

Risk Management VaR - CVaR

300000

C o sto E s pe ra do ($ )

0 -241107

-108007

-37911

-18412

7618

24330

29027

33527

-300000

-600000

-900000

INVESTMENT DECISIONS STOCHASTIC PROJECT EVALUATION

Simulated Financial Statements • Financial Report • Profit and Loss • Cash Flows

SGO

Smart Grids Optimization

ADDITIONAL INFORMATION

OPCHAIN-SGO OPTIMIZING THE VALUE CHAIN

Smart Grids Optimization

Smart Grids Optimization ▪

The "smart grids" are the future of the electricity sector. Its impact is "immeasurable", similar to the impact of the internet.

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"Smart metering" systems are the way of large volumes of information collection (big data) necessary to capture the added value of this new dimension.

▪

The demand side has become an active agent, leaving aside the passive position so far has been

▪

The mathematical models used to optimize large electric networks should be checked having in mind this new environment.

Smart Grids Optimization

OPCHAIN-SGO

Smart Grids Optimization (under development) OPCHAIN-SGO corresponds to a set of mathematical models oriented to support the decisions of agents participating in smart grids. It includes the following models: ▪

OPCHAIN-SGO-DRO (Demand Response Optimization) optimization of the management of electric energy in sets of buildings; as universities, housing developments, shopping centers...

▪

OPCHAIN-SGO-EEO (Energy Efficiency Optimization) optimization of the management of electric energy in energyintensive industrial systems.

▪

OPCHAIN-SGO-FRES (Forecast of Renewable Energy Sources): prediction of short/medium-term of the availability of renewable energy sources, such as: wind, solar radiation and water resources.

▪

OPCHAIN-SGO-OLM (Optimization of Load Management): optimización de la gestión de la carga eléctrica.

▪

OPCHAIN-SGO-DNP (Distribution Network Planning): optimization of the design of smart grids

▪

OPCHAIN-SGO-ETD (Electricity Theft Detection)

▪

OPCHAIN-SGO-RTO (Real Time Optimization): Control systems for optimization of the operation of a "smart grid"

OPCHAIN-SGO

Smart Grids Optimization (model under development) OPTIMAL DESIGN OF RADIAL NETWORKS FOR ELECTRICITY DISTRIBUTION INCLUDING: POWER PLANTS WITH ENERGY RENEWABLE, ENERGY STORAGE AND RELIABILITY CONSTRAINTS

MRA Modeling Regulatory Affairs

MODELING REGULATORY AFFAIRS

DecisionWare has developed competencies in the modeling of the regulatory impact in electricity sector. The models analytical information for: ▪

Regulator: can meet the quantitative impact of regulations intending to impose in the electricity market.

▪

Generators: can know a priori the impact of possible changes in regulations that the regulator intends to perform; or, explaining to agents how a rule/law is working that the regulator has imposed.

Min f(x,y) sujeto a G(x) = bd F(x,y) = bm x Rd y Rm

ECONOMIC DISPATCH MODEL -PHYSIC-

REGULATED MARKET MODEL -ECONOMICS-

x y

dispatch variables market variables

E&G Electricity & Gas Optimal Dispatch Simulation ADDITIONAL INFORMATION

OPCHAIN-E&G OPTIMIZING THE VALUE CHAIN

Electricity & Gas Supply Chain Optimization

OPCHAIN-E&G OPTIMIZING THE VALUE CHAIN

Electricity & Gas Supply Chain Optimization

OPCHAIN-E&G has been developed to support the decision-making processes of the agents involved in the markets of electricity and/or natural gas. OPCHAIN-E&G consists of a set of optimization models that describe the process of supply/demand of electricity and natural gas; from this point of view determines the point of partial equilibrium of the electricity-gas market, under conditions of economic efficiency..

STOCHASTIC OPTIMIZATION ▪ Decision Criteria (type of objective functions): • Mean Value • Mean-Variance • Maximun regret • Value-at-Risk (VaR) & Conditional Value-at-Risk (CVaR) • Mean Value with Constrained CVaR

▪ Type of technologies • Integrated (Full Space) • Large Scale Methodologies (Partition & Decomposition) • Benders Theory (SDDP, GDDP, … ) • Relaxation Lagrangean Relaxation ▪ Distributed Optimization

DIMENSIONS OF UNCERTAINTY CONFIGURABLE BY THE USER

•

Examples: o Demand o Climatologic o Demand – Hydrology o Fuel Prices o Generation Expansion Plan o Cost of Infrastructure …

INCERTIDUMBRE • Demand • Fuel Prices • Climatologic

N21

0.0625

N22

N21

Hidrología 1988

Demanda Alta

Precio Alto

Hidrología 1988

Demanda Alta

Precio Bajo

Hidrología 1988

Demanda Baja

Precio Alto

Hidrología 1988

Demanda Baja

Precio Bajo

Hidrología 1992

Demanda Alta

Precio Alto

Hidrología 1992

Demanda Baja

Precio Bajo

Hidrología 1985

Demanda Alta

Precio Alto

Hidrología 1990

Demanda Alta

Precio Alto

Hidrología 1990

Demanda Baja

Precio Bajo

N22

N1

N21

N22

0.125 N21

ÁRBOL DE DECISIÓN MÚLTI-ETAPAS N22

1

e=1

13

e=2

25

e=3

t

36

MÓDULO HIDRÁULICO

VE

VERTIMIENTO EMBALSE

MÓDULOS DE OPCHAIN-E&G

~ VERTIMIENTO CENTRAL

VC

RIO

CENTRAL HIDRÁULICA

HAF

CONEXIÓN

MÓDULO GAS EMBALSE

~

EMBALSE

~ DRGO

CIRCUITO DEMANDA

BARRA

YACIMIENTO

NODO GAS 1 BARRA 1

NODO GAS 2

CENTRAL HIDRÁULICA

BARRA 2

EMBALSE

NODO DEMANDA GAS-ELECTRICIDAD

MÓDULO ELÉCTRICO

DEMANDA VEGETATIVA

NODO GAS 3

BARRA 4 BARRA 1

BARRA 2

REFINERÍA INDUSTRIA

~ CENTRAL HIDRÁULICA

INTERCONEXIÓN COMERCIAL BARRA 6

TERMOELECTRICA

BARRA 3

TERMOELÉCTRICA

BARRA 5

Multi Tecnología (Gas - Fuel Oil)

PROYECTO INDUSTRIAL

OSE

Optimization Services For Electricity Companies

Mathematical Models for: HID-SIM

Synthetic Hydrology Generation

Historic Hydrology

E&G

National/Regional Economical Dispatch

Long Term Forecast Spot Prices Generation

OPT-PES

Optimal Design Of Networks

Optimal Design Of Electricity Network

OPT-MAN

Maintenance Optimization

Maintenance Scheduling

HID-KAL

KALMAN FILTER State Estimation Models

Hydrology Short Term Forecast

Historic Spot Prices

FSP SARIMA-GARCH Statistical Models

Spot Prices Short Term Forecastr

ETRM

Energy Trading & Risk Management

Energy Trading

OPT-POD

Unit Commitment Optimizatión

Optimal Scheduling

▪

Synthetic hydrology generation

▪

Flow forecast based on a Dual Kalman Filter

▪

ARMAX-GARCH models for the forecast of electricity prices

▪

Optimal dispatch of the Regional Electric System/Market

▪

Investments Optimization

▪

Plant Maintenance Optimization

▪

Power Plants Network Operations Optimization.

OPCHAIN-E&G OPTIMIZING THE VALUE CHAIN

Electricity & Gas Supply Chain Optimization OPTIMIZATION TECHNOLOGIES

OPCHAIN-E&G GAMS PROGRAM GENERATED BY OPTEX

OPCHAIN-E&G IBM ILOG OPL PROGRAM GENERATED BY OPTEX

OPCHAIN-E&G C PROGRAM GENERATED BY OPTEX

O&G Oil & Gas Supply Chain Optimization ADDITIONAL INFORMATION

OPCHAIN-OIL-SCO OPTIMIZING THE VALUE CHAIN

INTEGRATED OIL SUPPLY CHAIN OPTIMIZATION

OPCHAIN-OIL

OPTIMI:ZING THE OIL VALUE CHAIN OPCHAIN-OIL consistently brings together all it solutions developed by DW in different areas of application of mathematical optimization models aimed at the optimization of planning and operations in different business that you integrate the petroleum products supply chain..

OPCHAIN-OIL includes the following models ▪ OPCHAIN-OIL-PRO: oil extraction ▪ OPCHAIN-OIL-PRO-ELE: electricity supply to oil fields ▪ OPCHAIN-OIL-BLEND: transport and blending of oil ▪ OPCHAIN-OIL-REF: oil refining (tactical planning) ▪ OPCHAIN-OIL-REF-ISO: oil refining and industrial services planning ▪ OPCHAIN-OIL-PIPES: optimization of flow in pipelines (tactical and scheduling) ▪ OPCHAIN-OIL-TSO: optimization of multimodal oil transport systems (vessels, barges, trains, trucks, …) ▪ OPCHAIN-OIL-SEA: coordination of scheduling operations of loading/unloading of oil and refined products in refineries and ports ▪ OPCHAIN-OIL-RET: distribution of gasoline to service stations

 OPCHAIN-OIL-SCO (Oil Supply Chain Optimization) integrates aggregate models for each of the links in the oil chain in such a way for the planning of the chain with a holistic vision.

OTHERs DW OPTIMIZATION SERVICES SCO: Supply Chain Optimization ▪ Supply/Distribution Network Design ▪ S&OP Optimization ▪ Inventory Optimization ▪ Sourcing Optimization ▪ Production Scheduling ▪ ATP: Avalaible-to-Promise DCO: Demand Chain Optimization ▪ Demand Characterization ▪ Marketing-Mix Optimization ▪ Optimal Pricing – Revenue Management

TSO: Transport System Optimization ▪ Special Systems (Ports, Airports, …) ▪ Cash Transport Optimization ▪ Massive Routing RPO: Regional System Optimization ▪ Land-Space Use ▪ Social Services Network Design

MATHEMATICAL MODELING EXPERIENCE IN ENERGY SECTOR

In October of 2013, COES-SINAC, (Committee for Economic Operation of the National Electricity Interconnected System of Peru) selected DW to develop the project "NEW SIMULATION MODEL OF THE ECONOMIC DISPATCH OPERATION FOR THE NATIONAL PLAN OF TRANSMISSION", which includes multiple mathematical optimization models to support the expansion of the transmission electricity network of the Republic of Peru. The project includes simulation models of simultaneous optimum dispatch of electricity and natural gas systems and the evaluation of multiple expansion uncertainty scenarios, in order to build paretto curves that support the selection of a robust expansion plan. The model developed is called by COES-SINAC as MODPLAN, and it is the official model used for this purpose in Perú.

In 2017, SAGARPA, Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación" of Mexican Republic, hired Smart Grid Mexico (SGM) to execute the project “STUDY OF CAPACITY FOR THE PRODUCTION AND USE OF BIOGAS IN MEXICO". SGM hire DecisionWare to implement a mathematical model that integrates the bioenergy process with the integrated management of one or multiple farms, in such a way to analyze, and optimize, the economic, social and environmental sustainability of the agricultural production; considering the energy produced by the conversion of energy crops, or animal manure, into biogas and/or bio-fertilizers, through the use of technologies of anaerobic digestion, allocation of crops and animal rearing practices.

In August 2015, CEMENTOS ARGOS selected DW to design and implement a set of mathematical optimization models to support the optimization processes in its cement production plants. ARGOS choses the Yumbo Plant as a pilot project, to implant the following models oriented to:

i)

Planning and scheduling of the production batches, considering as main objective function the reductions of cost and energy consumption, considering the emissions constraints, and

ii)

Real Time Optimization that keeps the process operating adjusted to its "optimal" set-points

The project was started in February 2016

Pacific Rubiales Energy (PRE), a public company listed on the Toronto and Colombian stock exchanges, is the largest independent oil and gas exploration and production company in Colombia. In January 2014, PRE selected a ESEI S.A. (Energía Sostenible Eficiente e Innovadora S.A.) to develop the project “FORMULATION OF THE SCHEME OF MANAGEMENT OF ELECTRIC POWER IN THE OIL FIELDS OF THE LLANOS ORIENTALES ”.

The project includes OPTIMIZATION SOFTWARE FOR THE DEVELOPMENT OF A STRATEGIC MANAGEMENT AND EXPANSION OF ELECTRICITY GENERATION CAPACITY, whose development was assigned to DW.

In 2010 ISI SOLUTIONS selected DW to supply to ECOPETROL, to design and to implement models for real-time optimization operations in its pipeline systems.

Those models include the use of pumps and optimization of operating conditions, based on detailed models of the hydraulic behavior of the system components.

ECOPETROL, in 2003 hired DW to the design mathematical models for planning and scheduling of its pipeline system, to transport oil and refined products. Models were assembled by ECOPETROL and operate until today. CRUDE OIL PIPELINE NETWORK

113.3

134.0.

91.0

>2002

Current Capacity Current Utilization

CAR

( 18” )

Future Capacity Coverage Period

( 12” ) ( 18” + 20” + 24” )

36.0

36.0

29.2

>2002

Ayacucho

( 16” )

Coveñas

215

215

141.4

>2002

Caño Limón

( 8” )

55.6

55.6

14.8

14.8

33.0

>2002

14.6

>2002

( 18” ) 68.1

68.1

58.0

>2002

OCENSA 30” 290 290 290

>2002

CIB Araguaney Sebastopol ODC 24” 212 212 179

Cusiana

>2002

( 14” + 12” )

( 20” ) 170 225.0 149.7

Vasconia ( 20” )

*

94.8

94.8

74.8

>2002

>2002

41.5

41.5

37.8

>2002

Porvenir ( 36” + 30” ) 615.0 615.0 544.0

>2002

(16”) 57.6

57.6

62.8

>2002

*

Requires DRA

Apiay T enay

PAD 1999 - 2010 PLANEACION - VIT Revisión January 99

In 2009 ECOPETROL hired DW to the design and the implementation of decision support system for optimization of energy resources. The project involved the design and implementation of an integrated model that links all steps of the supply chain of petroleum products, with emphasis on the consumption and marketing of the energy used by the network. Models were included: i) production of crude oil, ii) transportation of crude oil, iii) oil refining and bio-mass and iv) transport of refined.

In 1998, BP hired DW to develop a model that will support decision making regarding the exploitation of natural gas in the CUSUIANA field in Colombia. DW developed a model to simulate the operation of integrated electricity and gas supply chain. Based on the model BP took the decisions about the their natural gas.

MM3

NIVEL FINAL DEL EMBALSE PEÑOL

1200

Penalizando Embalses Penalizando Generación

1100

Mínimo Operativo 1000

Simulación Binaria

900 800 700 600 500 400 Ene-00

Jul-00

Ene-01

FECHA

Jul-01

In December 2013, Duke Energy International Group Ltd., selected DW to advise him in the bidding process opened by the Colombian Government for the sale of its majority stake in ISAGEN S.A. E.S.P.

ISAGEN owns and operates six plants for electricity generation, with a total installed capacity of 2.212 MW and an annual average generation of 9500 GWh-year, distributed in 1912 MW hydraulic and 300 MW thermal. Its total generation during 2012 accounted for 16.45% demand for electricity in Colombia, placing it as the third largest national generating company and as a fundamental agent in the development of the Colombian energy industry.

A seventh hydraulic plant, Sogamoso, 820 MW, was included in the valuation process, considering that it started generation at the end of 2013.

In December 2011, the National Authority of Public Services from Panama hired the alliance MERCADOS ENERGETICOS-DW for the consulting project OPERATION ASSESSMENT OF PANAMA NATIONAL GRID AND THE OPTIMIZATION OF HYDROTHERMAL RESOURCES.

As a result of the opening of the electricity market in Colombia (July of 1995), as part of the process of creation of ISAGEN S.A. ESP (from the assets of electricity generation of ISA), in June 1995, ISA hired DW to undertake a process of technology transfer to ISAGEN in subjects related with the mathematical models required to operate in the new environment of the Colombian electricity sector, whose outcome should be the design of the decision support system that should have ISAGEN as a generator agent.

In 1999, Teknecon Energy Risk Advisors LLC (“TERA”) contracted DecisionWare to provide consulting services under a contract with the Ministry of Energy and Mines ("MEM") of the Republic of Colombia, as part of a project concerning the reorganization of the entity owning the electricity transmission system and development of a forwards market for electric power, financed by The World Bank.

In 2002, the World Bank selected the Consortium Mercados Energéticos (Argentina), Power Systems Research, Inc. (Brazil), and Risk Capital Management Partners (USA) to realize the study COMPUTER-BASED SIMULATION OF AUCTIONS OF OPTION CONTRACTS AND OF FUTURES CONTRACTS IN THE COLOMBIAN WHOLESALE ELECTRICITY MARKET. DecisionWare participated in the study as a subcontractor.

Since 1998, on multiple occasions, AES CHIVOR has hired DW to give technical support in various aspects of the Colombian electricity sector regulation that deal with the use and application of mathematical models as a way to establish remuneration of agents. 0010 - 17/12/93CONDICIONES DE SUMINISTRO DE ENERGÍA Y POTENCIA A GRANDES CONSUMIDORES A1 Definiciones fundamentales:- Gran consumidor: es aquel que estando conectado a niveles superiores a 1 KV y cuya demanda máxima mensual, medida en el sitio individual de entrega, excede un determinado nivel mínimo de consumo de energía, que inicialmente se define igual a 2MW (Articulo 2)- Comercializador: compra energía para venderla a los grandes consumidores, a las empresas de distribución y/o a los grupos de consumidores con tarifa reguladaGenerador: es un productor de energía y potencia que la vende a terceros (comercializadores) en el mercado de corto y largo plazo. Se definen tres tipos de generadores. Actuales: los que pertenecen al sistema Interconectado. Independientes: que utilizan el sistema Interconectado para su generación propia y para comercializar energía. cogeneradores: que producen en forma combinada electricidad y calor para uso industrial y venden energía y potencia a terceros-Mercado de corto plazo: sistema de intercambios hora a hora valorados al costo marginal de corto plazo- Mercado de largo plazo: sistema de contratos bilaterales a plazos superiores a un (1) mes. A2 Caracteriza detalladamente a los grandes consumidores industriales y comerciales. A3 Limita los contratos bilaterales a una duración no mayor de dos (2) años. A7 Define el tratamiento tarifario para los usuarios que no son grandes consumidores, el cual se regirá por la junta nacional de tarifas

Min

 t  j  h CTt(GTjth) sujeto a:

GDzth -  uTN(z) LDuzth = 0 GDzth + GHAzth + DEFzth = DEMzth

ENuth -  jL1(u) GTEjuth -  vL2(u) LLvuth = 0

In 2002, URRA S.A. E.S.P., that generates and sells the energy that produces the Central Hidroeléctrica URRÁ I (located in the Department of Córdoba, Colombia), hired DW to give technical support in several aspects related to the regulation of the Colombian electricity sector, specifically related to the “capacity charge” and its impact in the definition of the guide curve that should declared to the system operator, having in the mind that the central must provide flood control services.

In 2007, THE COLOMBIAN SUPERINTENDENCE OF DOMESTIC PUBLIC SERVICES hired DW for the implementation of a system of models in order to track electricity market in Colombia. The main model is based on the optimal dispatch of the interconnected system at minimum cost. Caribe

2297 MW

Antioquia Chocó

340 MW 1550 MW

Nordeste 920.2 MW

1285 MW

700 MW

101.5 MW

1270 MW 2546 MW 806 MW

San Carlos

181 MW 26.2 MW

1431 MW

561 MW

CHEC

3073 MW

230 MW 1950 MW

1250 MW

Sur Occidente

Centro Oriente

The SUPERINTENDENCIA DE SERVICIOS PÚBLICOS DOMICILIARIOS DE COLOMBIA hired DW in 2010 to implement a system of models to simulate joint operation of the electricity market and the gas market in Colombia.

The models include the gas transport system, the production and handling of liquefied gas by liquefaction and regasification.

In 2010, THE COLOMBIAN SUPERINTENDENCY OF DOMESTIC PUBLIC SERVICES hired DW to implement a model system to track electricity market in Colombia based on a Nash- Cournout equilibrium model that simulates dominant positions of generators that maximize its revenue. EQUILIBRIO MERCADOS IMPERFECTOS PRECIO ($/Q)

Función de oferta D-1(s) VARIACIÓN EN EL PRECIO QUE FAVORECE AL PODER DE MERCADO

Función de demanda S-1(s) DISMINUCIÓN EN LA CANTIDAD

12

Q

CANTIDAD (Q)

CURVA DE OFERTA ÓPTIMA DE ENERGIA A LARGO PLAZO FUNCIÓN DE UTILIDAD: MAXIMIZAR MEAN-VARIANCE (a)

In 1996 the company EMPRESA DE ENERGÍA ELÉCTRICA DE BOGOTÁ (EEEB) hired DW to develop a model that will support decision-making relating to the market of electricity in the long run, to the nascent market for wholesale electricity in Colombia. The ETRM (Energy Trading & Risk Management) model was used in the evaluation of bids for purchase of energy by the EEEB and others power companies in Colombia.

Ventas Largo Plazo QC (MWh)

PSPmin

PSPmax INCERTIDUMBRE PRECIO DEL SPOT

QC(PC|a)

“ Energía Firme” (EF)

VENDER TODO EN EL SPOT

0

REPARTIR LOS RIESGOS ENTRE EL SPOT Y LARGO PLAZO

EPS E [Precio Spot]

VENDER TODO EN EL LARGO PLAZO

Precio Largo Plazo PC – ($/MWh)

In 2008, CENTRAL HIDROELECTRICA DE CALDAS (CHEC) hired DW for the development a decision support system, to optimize the planning and scheduling of the power generation and long term energy marketing.

HID-SIM

Generación Sintética MATALAS

Hidrologí a Histórica

HID-KAL

MODSEI

Despacho Óptimo SIN

Predicción Aportes Horas-Días

OPT-MAN

Optimización Diseño Red Generación

Optimización Mantenimiento

Capacidad Optima Infraestructura

Planes Mantenimie nto

PBO

Modelos Estadísticos ARIMA-GARCH

Proyección Precio Spot Mensual x Bloques

OPT-PES

Precios Spot Históricos

Estimación de Estado KALMAN FILTER

Predicción Precio Spot Horas-Días

OPT-MER

Optimización Compra/Venta Electricidad

Políticas Comerciales

OPT-POD

Optimización Operación Diaria

Políticas Operativas

Models were provided for: ▪ Synthetic hydrology generation ▪ Flow forecast based on a Dual Kalman Filter ▪ ARMAX-GARCH models for the forecast of electricity prices ▪ Optimal dispatch of the Colombian electric system/market ▪ Investments optimization ▪ Plant maintenance optimization ▪ ETRM: Electricity Trading and Risk Mangement ▪ Power plants network operations optimization.

In August 1998, ELECTROCOSTA and ELECTRICARIBE born, two electricity distributors companies on the Atlantic Colombian Coast, that were capitalized by 65% by the consortium formed between HOUSTON INDUSTRIES and ELECTRICIDAD DE CARACAS, he remaining 35% was in the hands of the previous distributiors and CORELCA. All the assets and some liabilities of the original companies was transferred. ELECTRICIDAD DE CARACAS hired DecisionWare services to perform the due diligence of the asset valuation that sold the nation.

The Río Piedras Hydropower Plant, located in the Department of Antioquia (Colombia), 90 km southwest of Medellin, has an installed capacity of 19.9 MW. In 1996, the Alliance INTEGRAL S.A. INGENIEROS CONSULTORES and GENERADORA UNION hired HydroEnergy Consulting Ltda. (company operated by DecisionWare Ltda.) the economic evaluation of the project Rio Piedras. The project includes the hydrological study, modelling of optimal design of the generating capacity and the storage capacity of a possible “reservoir” and the financial evaluation of the project. Everything in the light of the new regulation of the Colombian Wholesale Electricity Market, created in 1995.

DecisionWare has conducted the financial evaluation and/or optimization of the sizing of the infrastructure of investment projects (expansion/purchase/redesign) on electricity generation capacity for multiple companies.

C.A. La Electricidad de Caracas, SACA

CEA AMÉRICAS (hoy )

QUORUM INGENIERÍA S.A.

GENERAR S.A. E.S.P. (Comprada por CELSIA)

In December of 2013, EPM (Empresas Publicas de Medellin S.A. E.S.P.) selected AIMMS as one of its optimization platform. DW was the dealer of the software licenses.

Since 2010, in partnership with Nexsys DW, has supplied, to various companies in the Peruvian electricity sector, IBM-ILOG modeling tools to be incorporated into simulation models of interconnected electric system dispatch of Peru.

In the energy sector, DW has completed consulting projects and / or training based on mathematical modeling of aspects, for the following companies:

AES CHIVOR. S.A. E.S.P. AMBIOTEC S.A. ASEP (Panama) BP British Petroleum Exploration Company (Colombia) Ltd. CEA AMÉRICAS (hoy PSEG) Central Hidroeléctrica de Caldas E.S.P. (CHEC) Corporación Electricidad de Caracas S.A.C.A. ELECTROLIMA E.S.P. EMCALI E.I.C.E. EMGESA S.A. E.S.P. Empresa Colombiana de Petróleos S.A. (ECOPETROL) Empresa de Energía de Bogotá S.A. (EEB) Empresa de Energía del Pacífico S.A. E.S.P. (EPSA) Empresa Públicas de Medellín S.A. E.S.P. (EEPPM) Generadora Unión S.A. E.S.P. GENERAR S.A. E.S.P. GLOBELEQ Generation Limited GÓMEZ, CAJIAO & Asociados Ingenieros Consultores ISAGEN S.A. E.S.P. Mercados Energéticos (Argentina) PetroTiger Ltd. Quorum Ingeniería S.A. San Gabán S.A. Superintendencia de Servicios Públicos Domiciliarios (SSPD) Teknecon Energy Risk Advisors (TERA) URRA S.A. E.S.P.

GENERAR S.A. E.S.P.

QUORUM INGENIERÍA S.A.

CEA AMÉRICAS (hoy )

INGENIERIA JJN C.A

TECHNOLOGY TRANSFER COURSE

ADVANCED OPTIMIZATION APPLIED TO ELECTRIC SECTOR

ADDITIONAL INFORMATION

OPTIMIZATION SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY Ing. Jesús Velásquez Bermúdez, Ph. D. Chief Scientist, DecisionWare [email protected]

A Knowledge Company Supporting

Your Smarter Decision

[email protected]

Bogotá D.C., Lima, Madrid, México D.F.