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AI and Quantum Computing in

 Electricity and Natural Gas Pricing 

COURSE OBJECTIVE

Intensive risk management and pricing course for electric energy and natural gas with a wide range of concepts, methodologies, models, strategies, tools and exercises using real databases for pricing based on a competitive market such as energy.

 

Considering the financial risk management of an energy company, the main business risk is exposure to market prices.

 

The price of electricity is much more volatile than that of other raw materials that are normally characterized by their extreme volatility. End-user demand is highly dependent on weather and grid reliability is paramount. The possibility of extreme price movements increases trading risk in electricity markets.

 

However, during the course we explain advanced models for pricing at the contract and pool level. Using VaR, betas, risk premiums, RAROC. Econometric models such as vector autoregressive, SARIMA model and stochastic models.

 

We analyze pricing strategies in a competitive environment using game theory methodologies and dynamic oligopoly models. Additionally, the risks of setting incorrect prices are explained.

 

We will explain what the energy futures and derivatives are in the markets of Spain and Europe. We will analyze how to create hedges using electricity and natural gas derivatives and how to statistically measure the effectiveness of the hedges.

 

During the course we will show market risk models and methodologies such as Value at Risk VAR and Expected Shortfall, and historical simulation methodologies, Monte Carlo Simulation and parametric models.

 

We present pricing and electricity price forecasting models using powerful econometric and machine learning tools. In addition, advanced probabilistic artificial intelligence models have been incorporated that help determine model uncertainty and offer confidence intervals on spot price projections. This will allow us to know the uncertainty of prices and income and profits.

 

Natural gas price risk management and natural gas pricing models are explained.

 

The course contains exercises in Python, R and Excel on pricing, risk premium, RAROC, Value at Risk and hurdle rate to reinforce the participant's learning.

WHO SHOULD ATTEND?

 

Officials from investment banks, electric power and Natural Gas companies, energy hedge funds, regulators, consultants and those interested in:

 

  • Pricing of electricity and natural gas contracts

  • Pricing of energy derivatives

  • Commodity and energy risk management and analysis

  • Portfolio management

For a better understanding of the topics, it is recommended that the participant have knowledge of statistics.

 

 

 

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Horarios:

  • Europe: Mon-Fri, CEST 16-19 h

 

  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h

 

 

 

 

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Price: 6.900 €

 

 

 

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Level: Advanced

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Duration: 30 h

 

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     Material: 

  • PDF Presentations

  • Exercises in Excel, R, Python and Jupyterlab

  • The recorded video of the 30-hour course is delivered.

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Algunos Clientes

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AGENDA
 

AI and Quantum Computing in

 Electricity and Natural Gas Pricing 

 

 

Electrical Energy Pricing

 

Module 1: Retail Gas and Electricity Markets

 

  • Current energy crisis situation

  • Ukraine-Russia War

  • Inflation and geopolitical risk

  • Market indicators

  • Evolution of the retail electricity and natural gas markets

    • Evolution of demand for electricity and natural gas

    • Evolution of the commercialization of electricity and natural gas

    • The degree of loyalty in the electricity and natural gas sector

  • Evolution of retail prices of electricity and natural gas

  • in the free market

  • Consumer involvement in the retail market

  • Energy consumer protection measures

  • Actions of the CNMC, European regulators and the European Commission on consumer protection and the retail market since 2020

  • Recommendations and regulatory proposals

  • Regulatory proposals

  • Recommendations to marketers

  • Recommendations for the consumer

 

​Module 2: Electric energy price models

 

  • Building blocks

  • Building Block Dimensions

  • Retail Electrical Products

    • Guaranteed price products

    • "Flip-the-switch" (FrS)

    • Spot price products

    • Usage time product

    • Seasonal product rate

    • Fixed invoice product

    • Spot price products

    • Real-time pricing (RTP),

    • Interruptible and reducible products (1IC)

    • Risk management products

    • Cap Price

    • Floor Price

    • Necklace Price

  • Weather coverage

  • Calculation of the cost of products differentiated by risk: calculation of equilibrium prices

  • Forward prices per hour

  • Forward Retail Price

  • Guaranteed price product

  • Pricing of products differentiated by risk

  • Creating value by sharing risk

  • Grouping of value-added services with basic electricity

  • Exercise 1: Spot price, equilibrium price, fixed price and time-of-use price and renewal options

  • Exercise 2: Price of derivatives, Cap, Floor and Collar

 

Module 3: Electricity pricing strategies

 

  • Customer segmentation

    • Commercial segment

    • Industrial segment

    • Residential segment

    • Commercial strategies

  • The role of pricing in a competitive market

  • Consequences of incorrect pricing

  • Customer expectations about prices

  • Market models in the electric power industry

    • Classic oligopoly models

    • Oligopolistic market equilibria

    • Games theory

    • static games

    • Dynamic games

    • Bertrand and Cournot dynamic experiments

 

Module 4: Risks in the energy market

 

  • The energy cycle

    • Exploration

    • Production or extraction

    • Treatment

    • Transportation and storage

    • Refinement

    • Distribution

    • Integrated and specialized companies

  • Risks in the energy cycle

    • Overview

    • Market risk

    • Credit risk

    • Operational risks

    • Liquidity risk

    • Political and regulatory risk

    • Price risk

  • Integrated vs specialized companies

  • Common risk management tools

  • Volatility and energy risk management

  • Risks in renewable energy projects and their mitigation

    • Project development risks

    • Construction risks

    • Resource risks

    • Technical risks

    • Market risks

    • Regulatory risks

    • Other operational risks

 

Module 5: Market Risk Management in electrical companies

 

  • Management of corporate risks in the electricity and energy market

  • Objectives, roles and responsibilities

  • Market Risk Appetite Framework

    • business strategy

    • Business plan

    • Risk Appetite

    • Risk tolerance

    • Risk Capacity

  • Market risk management policies and procedures

  • Treasury management in energy companies

  • Setting limits

  • Market risk management cycle: Identification, monitoring, measurement, control and monitoring of market risk

 

Module 6: Univariate and Multivariate Analysis of risk factors

 

  • Univariate Analysis

  • Performance estimation

  • Arithmetic mean, median, geometric mean

  • Outliers Review

  • Measures of dispersion

  • Shape/Form Measurements

  • Sample Skewed

  • Groeneveld's measure

  • Moors's measure

  • Fitting probability distributions

  • Multivariate analysis:

    • Arbitrage Pricing Theory

    • Return models

    • OLS regression

    • Heteroskedasticity Treatment

    • Outliers Treatment

    • Robust Regression

    • Principal components (PCA)

    • Multifactor Model

  • Industry or country factors

  • Exercise 3: Treatment of time series, non-stationary series, heteroscedasticity, outliers, multicollinearity in factors.

Module 7: Power Purchase Agreement (PPAs)

  • What is a PPA contract
    Bases of the agreement

  • Types of PPAs for Generators

    • Physical

    • Synthetic or Financial

  • Negotiation of a PPA

    • Generation

    • Consumption

  • Pricing Structures

  • Fixed annual base load pricing structure

  • Fixed, scaling and indexing

  • Nominal PPP at fixed price

  • Fixed price with escalation (stepped)

  • Fixed Price with inflation indexation

  • Variable price, market discount with Caps and Floors

  • Discount to market with floor

  • Discount to market with necklace

  • Collar and Reverse Collar

  • Necklace

  • Reverse collar (APPV only)

  • Hybrid structures

    • Hybrid – % production

    • Hybrid - over time

  • Clawback

  • Volume Structures and Risk Allocation

  • ASF Risk Mitigation

  • EEX Futures, Asian Put Option
    Exercise 4: PPA Pricing using closed formulas
    Exercise 5: PPA Pricing using copulas

    Exercise 6: Quantification of volumetric and correlation risk

Module 8: Treatment of Volatility

 

  • Performance Treatment

  • Exponentially Weighted Moving Average (EWMA)

  • Univariate GARCH Model

  • Multivariate GARCH Model

  • GARCH Extensions

  • Evaluation of variance models

    • In sample review with autocorrelation

    • Out sample review with regression

  • Use of intraday information

  • Multivariate GARCH model with copulas

  • Exercise 7: GARCH (1,1) volatility modeling in R

  • Exercise 8: Modeling volatility GARCH copulas in R

Module 9: Parametric VaR

  • Overview of the standardized market risk approach

  • Linear and non-linear portfolios

  • Volatility Estimation

  • Value at Risk

  • Parametric Models

    • Normal VaR

    • Delta-Normal VaR

    • t-student distribution

    • Lognormal Distribution

  • Linear Model

  • Quadratic Model

  • Expected Shortfall

  • Stress Testing

    • Identification and validation of the stressful period

    • Stress period review

    • Stress Testing in energy companies

  • Exercise 9: Delta-Normal, Lognormal VaR and T-Student estimation in R

  • Exercise 10: Expected Shortfall in R

 

​Module 10: Historical Simulation and Monte Carlo

  • VaR Historical Simulation

    • Volatility Adjustment

    • Bootstrapping

  • VaR Monte Carlo Simulation

    • Simulation of electric energy prices

    • Reversion to the mean

    • Diffusion jumps

    • Ornstein-Uhlenbeck process

    • Simulation with multiple risk factors

    • Variance Reduction Methods

  • Multivariate Normal Distribution

  • Multivariate T-Student Distribution

  • VaR Monte Carlo based on Gaussian copula

  • VaR Monte Carlo based on t-student copula

  • Exercise 11: Estimating VaR: using Monte Carlo Simulation and Historical Simulation with R and Excel with Visual Basic

  • Exercise 12: Historical Simulation Backtesting in Python

  • Exercise 14: VaR using Gaussian copula and tStudent in R

Module 11: Electrical Energy Derivatives in Europe and Spain

  • Introduction to derivatives

    • European Energy Exchange (EEX)

    • Trading

    • Over The Counter (OTC)

    • European Commodity Clearing (ECC)

    • Spots and Derivatives

    • Markets and Contracts

  • Hedging Electricity using Power Futures

  • Hedging Renewable Energy using Power Futures

  • Hedging Strategies

  • The Iberian Electricity Market (MIBEL)

  • Iberian electricity market

  • OMIP Regulated Market operator in Spain and Portugal

  • OMIClear Clearing House,

  • Auction mechanisms

  • OTC Market vs Organized Market

  • Acquisition of energy from Spanish distributors

  • CESUR auction for the calculation of the TUR

  • Descending Price Watch Auctions

  • Definition and structure of last resort rates

  • Energy cost in the TUR

  • MEFF, Derivatives Market of Spanish Stock Exchanges and Markets (BME)

  • BME Clearing

  • Base Load

  • Peak Load

  • Contract term

  • Nominal Base and Mini contracts

  • Delivery period

  • Forwards, Futures and Swaps

    • Forward Contracts

    • Futures Contracts

    • Swaps

    • Commodity Forward Curves

  • Investment assets

  • Consumer Assets and Convenience Performance

  • The market price of risk

  • “Plain Vanilla” Options

  • The Put–Call Parity

  • Strategies with options

  • Black’s Futures Price Model

  • Option Pricing Formulas

  • Hedging Options: Greeks

  • Real monitoring and management of:

    • delta

    • gamma

    • theta

    • Vega

    • elasticity

  • Implied Volatilities and the “Volatility Smile”

  • Swaptions

  • American, Bermudan and Asian Options

  • American and Bermudan Options

  • Asian Options

  • Exotic options

  • Exercise 15: Electrical energy option pricing

  • Exercise 16: Greek delta, gamma, theta and vega estimation in Python

  • Exercise 17: Black Scholes model and assumptions

  • Exercise 18: Implied volatility

  • Exercise 19: Tree Pricing Methods for Vanilla Options

  • Exercise 20: Monte Carlo Simulation

  • Exercise 21: Pricing of exotic options

  • Exercise 22: Variance reduction techniques in pricing with Monte Carlo

Module 12: Hedging and price risk management

  • A portfolio perspective

  • Measuring portfolio value and risk

  • Cash Flow at Risk

  • Spot, forward and options markets

  • Forward Pricing

  • Elementary option contracts

  • Option prices

  • Valuation of fuel and energy resources

  • Fixed price contracts

  • Black-Scholes option pricing model

  • Hedging versus speculation

  • Portfolio risk management

  • Price risk exposures

  • Implications of volatility and correlation for value and risk

  • Price risk coverage

  • The hedging effectiveness of electricity futures in the Spanish market

  • Measure the effectiveness of coverage

    • Hedging ability of naive

    • Minimum variance

    • Partially predictable

    • BEKK_T hedge ratios 

  • Exercise 23: Hedging strategies with futures and swaps in electricity contracts

  • Exercise 24: Hedging strategies with options, calls, floors in electricity contracts

  • Exercise 25: Analysis of coverage effectiveness of electricity contracts

Module 14: Tests for the use of Econometric Models

  • Review of assumptions of econometric models

  • Review of the coefficients and standard errors of the models

  • Measures of model reliability

  • Error management

  • Not normality

  • Heteroskedasticity

  • Outliers

  • Autocorrelation

  • Using Correlation to detect bivariate collinearity

  • Detection of multivariate collinearity in linear regression

  • Exercise 26: Detection of non-stationary series, cointegration and outliers

  • Exercise 27: Measurement of collinearity, heteroscedasticity and serial autocorrelation

Machine Learning

Module 15: Deep Learning Feed Forward Neural Networks

  • Single Layer Perceptron

  • Multiple Layer Perceptron

  • Neural network architectures

  • Activation function

    • Sigmoidal

    • Rectified linear unit (Relu)

    • The U

    • Selu

    • Hyperbolic hypertangent

    • Softmax

    • Other

  • Back propagation

    • Directional derivatives

    • Gradients

    • Jacobians

    • Chain rule

    • Optimization and local and global minima

  • Exercise 28: Deep Learning Feed Forward

Module 16: Deep Learning Convolutional Neural Networks CNN

  • CNN for pictures

  • Design and architectures

  • Convolution operation

  • Descending gradient

  • Filters

  • Strider

  • Padding

  • Subsampling

  • Pooling

  • Fully connected

  • Temporal Convolutional Network (TCN)

  • Exercise 29: deep learning TCN

Module 17: Deep Learning Recurrent Neural Networks RNN

  • Natural Language Processing

  • Natural Language Processing (NLP) text classification

  • Long Term Short Term Memory (LSTM)

  • Hopfield

  • Bidirectional associative memory

  • Descending gradient

  • Global optimization methods

  • One-way and two-way models

  • Deep Bidirectional Transformers for Language Understanding ​

  • Exercise 30: Deep Learning LSTM

 

Quantum Computing

Module 18: Quantum computing and algorithms

 ​

  • Future of quantum computing in insurance

  • Is it necessary to know quantum mechanics?

  • QIS Hardware and Apps

  • quantum operations

  • Qubit representation

  • Measurement

  • Overlap

  • matrix multiplication

  • Qubit operations

  • Multiple Quantum Circuits

  • Entanglement

  • Deutsch Algorithm

  • Quantum Fourier transform and search algorithms

  • Hybrid quantum-classical algorithms

  • Quantum annealing, simulation and optimization of algorithms

  • Quantum machine learning algorithms

  • Exercise 32: Quantum operations multi-exercises

 

Module 19: Quantum Machine Learning

  • Quantum Machine Learning

  • Hybrid models

  • Quantum Principal Component Analysis

  • Q means vs. K means

  • Variational Quantum Classifiers

  • Variational quantum classifiers

  • Quantum Neural Network

    • Quantum Convolutional Neural Network

    • Quantum Long Short Memory LSTM

  • Quantum Support Vector Machine (QSVC)

  • Exercise 31: Quantum LSTM

Module 20: Forecasting of Electricity and Consumption Price Models

  • Econometric and machine learning spot price modeling

  • Forecasting of electricity spot prices

  • Necessary data

  • Model specifications

  • Univariate models

    • ARIMA

    • SARIMA

    • ARCH

    • GARCH

  • Multivariate Models

    • VAR Vector Autoregressive Models

    • ARCH Models

    • GARCH models

    • GARCH Models Multivariate Copulas

    • VEC Error Correction Vector Model

    • Johansen method

  • Machine Learning Models

    • Supported Vector Machine 

    • Red Neuronal

    • Multivariate Adaptive Regression Splines

    • Random Forest Regression

  • Deep Learning

    • Recurrent Neural Networks RNN

    • Elman Neural Network

    • Jordan Neural Network

    • Basic structure of RNN

    • Long short term memory LSTM

    • Temporary windows

    • Development and validation sample

    • Regression

    • Sequence modeling

    • Temporal Convolutional Network (TCN)

  • Gaussian Process Regression

  • Exercise 32:  Pricing with Random Forest Regression

  • Exercise 33: Forecasting prices Gaussian Process Regression

  • Exercise 34: Pricing model with Bayesian Support Vector Machine

  • Exercise 35: Forectasting Load consumption SARIMA VAR and VEC

  • Exercise 36: Forecasting Load consumption with RNN LSTM

  • Exercise 37: Forecasting Load consumption with TCN LSTM

  • Exercise 38: Forecasting Load consumption with Quantum LSTM

 

Module 21: Climate risk management in the electrical industry

 

  • Climate: critical factor in the energy industry

  • The effect of weather on prices

  • Econometric models

  • Box-Cox

  • ARCH and GARCH

  • Price prediction

  • Volatility

  • Meteorological financial instruments

  • Climate derivatives

  • Market requirements for weather financial instruments

  • Exercise 39: Price Determination using climate variables, using neural networks and deep learning

 

Module 22: Advanced Electricity Price Model

 

  • Production and consumption

  • Spot price characteristics

  • Charging characteristics

  • Physical Electricity Retail

  • Electricity financial trading

  • Price components derived from the P&L function

    • Price component and correlation price component

    • Risk premium

    • RAROC

    • Hurdle Rate

    • Economic Market Capital

  • Portfolio and individual customer perspective

    • Portfolio-level pricing

    • Marginal risk

    • Betas

    • Volume limits for defined price contracts

    • Model Description

    • Breakdown of the spot model into different processes

    • SARIMA

    • Deterministic spot and load models

    • Daily Stochastic Models

    • Hourly Stochastic Models

  • Spike, seasonality and mean reversion

  • Estimation and model selection process

    • Deterministic functions

    • Daily Autoregressive Vector Model

    • Gaussian copula approach for residuals

    • Hourly spot price vector autoregressive model   

  • Hour load autoregressive process

  • Simulation approach

  • Price Component Results

  • Volume risk

  • Portfolio Analysis

  • Customer analysis

  • Exercise 40: Electricity contract pricing

  • Exercise 41: Price component and the correlation price component

  • Exercise 42: Ornstein–Uhlenbeck process with mean reversion and diffusion jumps

  • Exercise 43: Volume and Price Risk

  • Exercise 44: Estimation of Risk Premiums

  • Exercise 45: RAROC and Hurdle Rate Estimation

 

Pricing Gas Natural

Module 22: Natural gas fundamentals

  • Introduction

  • Natural gas price volatility

  • Natural gas trading centers

  • Gas centers in Europe

  • The National Balance Point (NBP)

  • The Title Transfer Facility (TTF)

  • Gas centers in the US

  • The Henry Hub (HH)

  • Outlook for natural gas in Spain

  • The Iberian market operator

  • The Iberian System Operator

  • Measuring natural gas price volatility

  • Impact of natural gas volatility on market players

  • Natural gas price volatility compared to crude oil and other products

Module 24: Risk management through natural gas derivatives 

 

  • Quantification of risks in energy portfolios

    • Main risks faced by energy companies

    • Measuring quantifiable risks

    • VaR and its acceptance in energy risk management

  • Natural gas price risk management

  • Hedging derivatives: futures and forwards

  • Contango vs backwardation

  • Hedging Derivatives: Options

    • Modeling Fundamentals: The Black-Scholes Formula

    • Implied volatility

    • Coverage of an option: Greek option

  • Hedging Derivatives: Swaps and Swaptions

    • Swaps 

    • Swaptions 

  • Exercise 46: VaR in natural gas energy portfolio

Module 25: Natural gas pricing models

  • Spot models

  • The Gibson–Schwartz model

  • The Eydeland–Geman model

  • Forward Models

  • One factor model

  • The multifactor model

  • Analysis of forward curves through principal component analysis

  • Factor loadings in PCA

  • The seasonal PCA Simulating through PCA

  • Natural gas price modeling

  • Natural gas consumption modeling

  • VAR estimation

  • Risk premium

  • RAROC and Hurdle Rate

  • Price determination

  • Exercise 47: Advanced natural gas pricing model

  • Modeling gas prices using deterministic models and stochastic processes

  • Ornstein–Uhlenbeck process with mean reversion and diffusion jumps

  • Seasonality analysis

  • Exercise 48: Price risk and volume risk

  • Exercise 49: Advanced natural gas pricing model Estimation of VAR and risk premium

  • Exercise 50: RAROC Calculator

 

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