Forecasting Mid-Term Electricity Market Clearing Price Using Support Vector Machines

In a deregulated electricity market, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance. The forecasting of electricity market clearing price (MCP) is a prediction of future electricity price based on given forecast of electricity demand, temperature, sunshine, fuel cost, precipitation and other related factors. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. The mid-term electricity MCP forecasting focuses electricity MCP on a time frame from one month to six months. Developing mid-term electricity MCP forecasting is essential for mid-term planning and decision making, such as generation plant expansion and maintenance schedule, reallocation of resources, bilateral contracts and hedging strategies. Six mid-term electricity MCP forecasting models are proposed and compared in this thesis: 1) a single support vector machine (SVM) forecasting model, 2) a single least squares support vector machine (LSSVM) forecasting model, 3) a hybrid SVM and auto-regression moving average with external input (ARMAX) forecasting model, 4) a hybrid LSSVM and ARMAX forecasting model, 5) a multiple SVM forecasting model and 6) a multiple LSSVM forecasting model. PJM interconnection data are used to test the proposed models. Cross-validation technique was used to optimize the control parameters and the selection of training data of the six proposed mid-term electricity MCP forecasting models. Three evaluation techniques, mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square root error (MSRE), are used to analysis the system forecasting accuracy. According to the experimental results, the multiple SVM forecasting model worked the best among all six proposed forecasting models. The proposed multiple SVM based mid-term electricity MCP forecasting model contains a data classification module and a price forecasting module. The data classification module will first pre-process the input data into corresponding price zones and then the forecasting module will forecast the electricity price in four parallel designed SVMs. This proposed model can best improve the forecasting accuracy on both peak prices and overall system compared with other 5 forecasting models proposed in this thesis

A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting

Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibit low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimization technique of Bacterial Foraging Optimization Algorithm (BFOA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. Monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index are selected as the input features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than the existing models

The use of computational intelligence techniques for mid-term electricity price forecasting

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementWe currently live in a world ruled by large amounts of data. Organizations’ success is highly determined by the way they foresee and assess changes occurring in the future. Predictive data analytics is the art of building and using models that create forecasts based on patterns extracted from historical data. So, it is a process of making projections about a specific event which the outcome is still unknown in the present. One of the main applications is price prediction (Kelleher, Namee, & D’Arcy, 2015). Price prediction can be applied in innumerous types of business, including the energy sector. Additionally, Big Data has created opportunities for development of new energy services and bears a promise of better energy management and conservation (Grolinger, L’Heureux, Capretz, & Seewald, 2016). Whenever prediction deals with time-series data, it can be designated as forecasting. The electricity spot prices (ESP) represent the result of the market bidding prices outcome, in the electric wholesale market. Predicting these prices is an important and impactful task for market participants, like producers, consumers and retailers, since the principal objective for such players is to achieve the lowest cost in comparison with competitors. ESP play a huge role in energy market’s decision making. It is important both for developing proper bidding strategies as well as for making conscient and sustainable investment decisions (Keynia & Heydari, 2019). Additionally, it impacts the decision of the technologies to use, for example, choosing between renewable energy generators or classic gas turbines. Furthermore, the topic of electricity prices forecasting is extremely relevant for both developed and developing countries. Developed countries search for their economic prospect’s improvement. Electric energy efficiency is a crucial metric for that improvement. Electric energy efficiency can decrease the electricity prices thanks to the reduction of consumption, thus decreasing the need of having new expensive power generation and diminishing the pressure on energy resources. Therefore, ESP behavior is an important factor in their economy. Regarding developing economies, which have faced problems to take the populations out of poverty, the electricity sector restructuring has been fundamental for helping increase the levels of economic development (Ebrahimian, Barmayoon, Mohammadi, & Ghadimi, 2018)

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

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

Predictive Trading Strategy for Physical Electricity Futures

This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading

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