Electricity Spot Price Forecast by Modelling Supply and Demand Curve

Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This research received no external fundingElectricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting.publishersversionpublishe

Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting

Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting. In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices

Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting

Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting. In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices.Dynamic factor analysis, Bootstrap, Forecasting, Confidence intervals

Iberian Energy Market: Spot Price Forecast by Modelling Market Offers

Electricity is a very special commodity since it is economically non-storable, and thus requiring a constant balance between production and consumption. At the corporate level, electricity price forecasts have become a fundamental input to energy companies’ decision making mechanisms [22, 45]. Electric utilities are higly vulnerable to economical crisis, since they generally cannot pass their excess costs on the wholesale market to the retail consumers [77] and, since the price depends on variables like weather (temperature, wind speed, precipitation, etc.) and the intensity of business and everyday activities (on-peak vs. off-peak hours, weekdays vs. weekends, holidays and near-holidays, etc.) it shows specific dynamics not observed in any other market, exhibiting seasonality at the daily, weekly and annual levels, and abrupt, short-lived and generally unanticipated price spikes. These extreme price volatility make price forecasts from a few hours to a few months ahead to become of particular interest to power portfolio managers. An utility company or large industrial consumer who is able to accurately forecast the wholesale prices and it’s volatility, can adjust its bidding strategy and its own production/consumption schedule in order to reduce the risk or maximize the profits in day-ahead trading. In this work I discuss the dynamics of the Iberian electricity day-ahead market (OMIE), review the state-of-the-art forecasting techniques and introduce a new approach to Electricity Price Forecasting, by forecasting the underlying dynamics, the market demand/supply curves. With this method it is possible to predict not only the electricity prices for the next hours, but also the market curves, which can then be used for risk management and a more accurate schedule of generation units. I analyze the model results and benchmark them against other models in the industry.A eletricidade é uma commodity muito especial, uma vez que não é possível armazená-la, e por isso, requer um constante equilíbrio entre a produção e consumo. ao nível empresarial, a previsão de preços de eletricidade tornou-se um input fundamental para os mecanismos de tomada de decisão das companhias [22, 45]. As empresas de eletricidade são altamente vulneráveis a crises económicas, uma vez que, em geral, não conseguem passar os seus custos excessivos para o mercado retalhista [77] e, uma vez que o preço depende de variáveis como meteorologia (temperatura, velocidade do vento, precipitação, etc.) e da intensidade de negócio e das atividades do dia-a-dia (pico vs vazio, dias da semana vs fim-de-semana, feriados e pontes, etc.) apresenta uma dinâmica que não é observada em mais nenhum mercado, com sazonalidade diária, semanal e anual, e com picos de preço abruptos de pouca duração e, em termos gerais, impossíveis de antecipar. Esta volatilidade de preços torna a previsão de preços particularmente interessante para gestores de portfólio, seja a curto ou a longo prazo. Uma companhia de eletricidade ou grande consumidor industrial que seja capaz de prever corretamente os preços do mercado grossista e a sua volatilidade, pode ajustar a estratégia de oferta da sua produção/seu consumo de maneira a reduzir o risco ou maximizar os ganhos no mercado à vista. Neste trabalho abordo a dinâmica do mercado de eletricidade ibérico (Operador de Mercado Iberico - Polo Español (OMIE)), revendo o estado da arte dos métodos de previsão de preços de eletricidade, e introduzo uma nova técnica de previsão de preços de eletricidade, através da previsão da sua dinâmica subjacente, as curvas de mercado da procura e oferta. Com este método é possível prever, não só o preço de eletricidade para as próximas horas, mas também as próprias curvas de oferta, o que pode ser utilizado na gestão de risco ao melhor a capacidade de programar as suas unidades de geração.Os resultados do modelo são analisados e comparados com outros modelos já utilizados na industria

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects

Pointwise forecast, confidence and prediction intervals in electricity demand and price

Programa Oficial de Doutoramento en Estatística e Investigación Operativa. 555V01[Abstract] Analysis of the electricity demand and price is presented, within the Spanish Electricity Market, applying statistical tools from the field of functional data. It begins with a descriptive analysis of the electrical data, studying its particular features. This kind of data conform a functional time series. Functional outlier detection methods are proposed to deal specifically with functional time series, taking dependence in this data structure into account. Then, a comparative study among different prediction techniques for next-day electricity demand and price is performed. It includes naïve procedures, time series ARIMA models and robust functional principal components analysis. The use of functional regression methods is proposed in this field. Specifically, the functional nonparametric regression model is used together with the semi-functional partial linear regression model, which allows incorporating external covariates as temperature and wind power production. Bootstrap procedures are proposed to build confidence intervals for the considered functional regression models. Validity of these bootstrap procedures is proved theoretically and they are applied to both a simulation study and the electricity demand and price data. Finally, bootstrap procedures are proposed to build prediction intervals and prediction density, which are also applied to the electrical data.[Resumen] Se presenta un análisis de la demanda y el precio de la electricidad, dentro del Mercado Eléctrico Español, aplicando técnicas estadísticas del ámbito de los datos funcionales. En primer lugar, se realiza un análisis descriptivo de los datos eléctricos, en el que se estudian sus principales características. Este tipo de datos conforman una serie de tiempo funcional. Se proponen métodos de detección de atípicos diseñados específicamente para series de tiempo funcionales, teniendo en cuenta la dependencia presente en esta estructura de datos. A continuación, se realiza un estudio comparativo de diferentes técnicas para la predicción de la demanda y precio de la electricidad al día siguiente. Este estudio incluye métodos naïve, modelos ARIMA de series de tiempo y métodos basados en componentes principales funcionales robustas. Se propone el uso de métodos de regresión funcional en este ámbito. En concreto, se utiliza el modelo de regresión funcional no paramétrico y el modelo semi-funcional parcialmente lineal, en el que se incorporan covariables externas como la temperatura y la producción de energía eólica. Considerando los métodos de regresión funcional indicados, se proponen procedimientos bootstrap para el cálculo de intervalos de confianza, cuya validez se prueba teóricamente y se aplican en un estudio de simulación y en los datos eléctricos de demanda y precio. Finalmente, se proponen procedimientos bootstrap para construir intervalos y densidades de predicción, los cuales se aplican al mismo conjunto de datos eléctricos.[Resumo] Preséntase unha análise da demanda e prezo da electricidade, dentro do Mercado Eléctrico Español, aplicando técnicas do ámbito dos datos funcionais. En primeiro lugar, realízase unha análise descritiva dos datos eléctricos, estudando as súas principais características. Este tipo de datos conforman unha serie de tempo funcional. Propóñense métodos de detección de atípicos dese ñados especificamente para series de tempo funcionais, tendo en conta a dependencia presente nesa estrutura de datos. A continuación, lévase a cabo un estudo comparativo de diferentes técnicas para predición da demanda e prezo da electricidade no día seguinte. Este estudo inclúe métodos naïve, modelos ARIMA de series de tempo e métodos baseados en compoñentes principais funcionais robustas. Proponse o uso de métodos de regresión funcional neste ámbito. En concreto, utilízase o modelo de regresión funcional non paramétrico e o modelo semi-funcional parcialmente lineal, no que se incorporan covariables externas como a temperatura e a produción de enerxía eólica. Considerando os métodos de regresión funcional indicados, propóñense procedementos bootstrap para o cálculo de intervalos de confi- anza, nos que a súa validez se proba na teoría e que son aplicados tanto nun estudo de simulación como nos datos eléctricos de demanda e prezo. Finalmente, propóñense procedementos bootstrap para construír intervalos e densidades de predición, que se aplican ao mesmo conxunto de datos eléctricos

Advanced Optimization and Data-Driven Control in Smart Grid

The power grids are continuously evolving over the past decades, where new challenges and opportunities are embraced at the same time. On one hand, the penetration of renewable generations and other distributed energy resources (DER) is growing rapidly, whose different generation and control patterns could significantly impact the daily operation. On the other hand, the new communication, monitoring and regulating devices are gradually installed, which enable more control abilities of the generations, demands, and grids, and the feasibility to deploy more sophisticated control schemes.To leverage the new technique and overcome the new challenges in the smart girds, different optimization and control problems need to be solved for different roles including the system operator, demand, and financial traders. For the system operators, it is critical to maximizing the total social welfare while satisfying the operational constraints. To better coordinate the DER and improve the efficiency of distribution systems, the three-phase optimal power flow (OPF) problem algorithms are developed including the DCOPF algorithm for robustness and the ACOPF algorithm for optimality. Moreover, the deep reinforcement learning-based Volt-VAR control schemes are proposed to better maintain the voltage stability and electricity service quality.For demands resources, minimizing their energy bills will satisfy the energy needs is always their goal. Providing ancillary services by proactively adjusting their total demand is one of the potential choices. Through the provision of the services, the demands can not only receiving incentives from the system operators but also help to improve the reliability and stability of power grids. We develop control schemes specifically for the data centers to provide the phase balancing service in the distribution system and the frequency regulation service in the transmission system. The financial traders, it is desired to maximize their total profits. A better trading strategy with a more accurate forecast model can help increase the traders' gain and further improve the price convergence of the electricity market. Our machine learning based trading framework outperforms the existing approach and lays the foundation for market efficiency evaluation across the markets

Column-generation and interior point methods applied to the long-term electric power-planning problem

Aquesta tesi s'adreça al problema de planificació de la generació elèctrica a llarg termini per a una companyia específica (SGC) que participa en un mercat liberalitzat organitzat en un pool. Els objectius de la tesi són: modelitzar aquest problema, i desenvolupar i implementar tècniques apropiades i eficients que el resolguin. Un planificació òptima a llarg termini és important, per exemple, per a la confecció de pressupostos, o per a la gestió de compres/consum de combustibles. Una altra aplicació és la de guiar la planificació a curt termini perquè aquesta tingui en compte decisions preses sota una òptica de llarg termini. La nostra proposta per a fer la planificació de la generació és optimitzar la generació esperada de cada unitat (o la unió de diverses unitats de característiques semblants) del pool per a cada interval en que dividim el llarg termini. El model bàsic per la planificació de la generació a llarg termini (LTGP) maximitza el benefici de totes les unitats del pool. La constricció més important és la satisfacció de la demanda, ja que el sistema està sempre balancejat. Utilitzem la formulació de Bloom i Gallant, la qual modela la càrrega a través d'una monòtona de càrrega per cada interval i requereix un número exponencial de constriccions lineals de desigualtat, anomenades LMCs. Altres constriccions (lineals) incloses en el model són: garantia de potència, límits en la disponibilitat de combustibles, emissions màximes de CO2 o una quota de mercat mínima per a la SGC. Una extensió d'aquest model és la planificació conjunta de l'assignació de manteniments de les unitats tèrmiques d'una SGC amb la planificació de la generació. El model conjunt és un problema quadràtic amb variables binàries i contínues. Per resoldre aquest model es proposa un parell d'heurístiques i s'ha implementat un prototipus de branch and bound en AMPL.Aquesta tesi també proposa una manera per coordinar el model LTGP proposat amb una planificació a curt termini. Es desenvolupa un model de curt que inclou els resultats de llarg termini. Donat que el model de planificació a llarg termini s'ha de resoldre sovint (principalment per passar informació acurada al model de curt), les tècniques emprades per a resoldre'l han de donar resultats fiables en un espai de temps curt. Les tècniques aplicades han estat:· Donat que les constriccions de recobriment i les fites de no negativitat defineixen un políedre convex els vèrtexs del qual són fàcils de trobar el model es transforma i les variables esdevenen els coeficients convexos que defineixen un punt. Aquest nou problema es resolt amb l'algoritme de Murtagh i Saunders, que és un procediment òptim. Aquest algoritme s'aplica sota un esquema de generació de columnes donat que el número de vèrtexs del políedre és comparable al número de constriccions. L'avantatge d'aquest mètode és que els vèrtexs es van generant a mesura que es necessiten.· L'aplicació de mètodes directes és computacionalment costós donat el número exponencial de LMCs. De totes maneres, a l'òptim només un conjunt reduït de constriccions de recobriment seran actives. Hem desenvolupat una heurística, anomenada heurística GP, la qual genera un subconjunt de constriccions, entre les quals hi ha les LMCs que són actives a l'òptim. L'heurística resol una seqüència de problemes quadràtics, els quals incrementen el número de LMCs considerades a cada iteració. Els problemes es resolen amb mètodes de punt interior que s'inicialitzen amb tècniques de warm start per tal d'accelerar la convergència cap a la nova solució. Aquest procediment resulta ser molt més eficient que el de generació de columnes. La modelització i els casos de prova estan basats en dades d'un sistema de pool pur i de mercat com ha estat a Espanya fins el juliol de 2006.This thesis presents an approach to the long-term planning of power generation for a company (SGC) participating in a liberalized market organized as a pool. The goal of this thesis is two-fold: to model the problem and to develop and implement appropriate and efficient techniques for solving it.The optimization of the long-term generation planning is important for budgeting and planning fuel acquisitions, and to give a frame where to fit short-term generation planning.Our proposal for planning long-term generation is to optimize the expected generation of each unit (or the merger of several units of the same type) in the power pool over each interval into which the long-term horizon is split.The basic model for long-term generation planning (LTGP) maximizes the profit for all the units participating in the pool. The most important constraint is matching demand, since the market always clears. The Bloom and Gallant formulation is used, which models the load with a load-duration curve for each interval and requires an exponential number of linear inequality constraints, called herein LMCs. Other (linear) constraints included in the model are: minimum generation time, limits on the availability of fuel, maximum CO2 emission limits or the market share of the SGC. This thesis also proposes the way in which coordination between the LTGP model developed and a short-term plan should be considered and provides a model for short-term electrical power planning adapted to the LTGP proposed and which includes the long-term results.Another decision that needs to be taken from a long-term point of view is the joint scheduling of thermal unit maintenances with the generation planning of a particular SGC. The results of a prototype of a Branch and Bound implemented in AMPL are included in this thesis.Long-term planning needs to be considered before short-term planning and whenever the real situation deviates from the forecasted parameters, so the techniques implemented must be efficient so as to provide reliable solutions in a short time. Two methods for handling the LMCs are proposed and compared:● A decomposition technique exploits the fact that the LMCs plus the non-negativity bounds define a convex polyhedron for each interval whose vertices are easy to find. Thus, the problem is transformed and the variables become the coefficients of a convex combination of the vertices. The transformed problem is quadratic with linear constraints, making it suitable to be solved with the Murtagh & Saunders algorithm, which gives an optimal solution. A column-generation approach is used because the number of vertices of the polyhedron is comparable to the number of LMCs. The advantage of this method is that it does not require previous computation of all of the vertices, but rather computes them as the algorithm iterates.● The application of direct methods is computationally difficult because of the exponential number of inequality LMCs. However, only a reduced subset of LMCs will be active at the optimizer. A heuristic, named GP heuristic, has been devised which is able to find a reduced set of LMCs including those that are active at the optimizer. It solves a sequence of quadratic problems in which the set of LMCs considered is enlarged at each iteration. The quadratic problems are solved with an interior point method, and warm starts are employed to accelerate the solution of the successively enlarged quadratic problems. This procedure is more efficient than the column generation one.The modeling and tests of this thesis are based on the pure pool system and market data from the Spanish system up to July 2006