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. Enduser 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.
Horarios:

Europe: MonFri, CEST 1619 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Price: 6.900 €
Level: Advanced
Duration: 30 h
Material:

PDF Presentations

Exercises in Excel, R, Python and Jupyterlab

The recorded video of the 30hour course is delivered.
Algunos Clientes
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

UkraineRussia 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

"Fliptheswitch" (FrS)

Spot price products

Usage time product

Seasonal product rate

Fixed invoice product

Spot price products

Realtime 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 valueadded services with basic electricity

Exercise 1: Spot price, equilibrium price, fixed price and timeofuse 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, nonstationary 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 nonlinear portfolios

Volatility Estimation

Value at Risk

Parametric Models


Normal VaR

DeltaNormal VaR

tstudent 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: DeltaNormal, Lognormal VaR and TStudent 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

OrnsteinUhlenbeck process

Simulation with multiple risk factors

Variance Reduction Methods


Multivariate Normal Distribution

Multivariate TStudent Distribution

VaR Monte Carlo based on Gaussian copula

VaR Monte Carlo based on tstudent 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

BlackScholes 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 nonstationary 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

Oneway and twoway 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 quantumclassical algorithms

Quantum annealing, simulation and optimization of algorithms

Quantum machine learning algorithms

Exercise 32: Quantum operations multiexercises
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

BoxCox

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

Portfoliolevel 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 BlackScholes 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