Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures

This study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques.JEL Codes - G13; G14; Q4

Analyzing the impact of renewable generation on the locational marginal price (LMP) forecast for California ISO

abstract: Accurate forecasting of electricity prices has been a key factor for bidding strategies in the electricity markets. The increase in renewable generation due to large scale PV and wind deployment in California has led to an increase in day-ahead and real-time price volatility. This has also led to prices going negative due to the supply-demand imbalance caused by excess renewable generation during instances of low demand. This research focuses on applying machine learning models to analyze the impact of renewable generation on the hourly locational marginal prices (LMPs) for California Independent System Operator (CAISO). Historical data involving the load, renewable generation from solar and wind, fuel prices, aggregated generation outages is extracted and collected together in a dataset and used as features to train different machine learning models. Tree- based machine learning models such as Extra Trees, Gradient Boost, Extreme Gradient Boost (XGBoost) as well as models based on neural networks such as Long short term memory networks (LSTMs) are implemented for price forecasting. The focus is to capture the best relation between the features and the target LMP variable and determine the weight of every feature in determining the price. The impact of renewable generation on LMP forecasting is determined for several different days in 2018. It is seen that the prices are impacted significantly by solar and wind generation and it ranks second in terms of impact after the electric load. The results of this research propose a method to evaluate the impact of several parameters on the day-ahead price forecast and would be useful for the grid operators to evaluate the parameters that could significantly impact the day-ahead price prediction and which parameters with low impact could be ignored to avoid an error in the forecast.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Service Revenue Evaluation Methodologies to Maximize the Benefits of Energy Storage

The objective of this research is to develop novel methodologies and tools for service revenue evaluation of electrical energy storage systems. Energy storage systems can provide a wide range of services and benefits to the entire value chain of the electricity industry and, therefore, are becoming a favorable technology among stakeholders. The U.S. Government and various states have set initiatives and mandated energy storage deployment as part of their grid modernization roadmap. The key to an increased deployment of energy storage projects is their economic viability. Because of the significant potential value of energy storage as well as the complexity of the decision-making problem, sophisticated service evaluation methodologies and service optimization tools are highly needed. The maximum potential value of energy storage cannot be captured with the evaluation methodologies that have been developed for conventional generators or other distributed energy resources. Previous research studies mostly operational strategies for energy storage coupled with renewable energy sources and the benefits and business models of privately-owned energy storage systems are not well understood. Most of the existing literature focuses on evaluating energy storage systems providing a single service while multiservice operation and evaluation is often not considered. The few available methods for multiservice evaluation study a limited number of services and cannot be readily implemented into a computational tool due to complexity and scalability issues. Accordingly, this research proposes novel service evaluation methodologies with two main objectives: a. Discover the maximum value of energy storage systems for single and multiservice applications, b. Provide flexibility, scalability and tractability of implementation. In order to meet these objectives, various methodologies based on statistical analysis, dynamic control, mixed integer linear programming, convex optimization and decomposition have been proposed. The challenges, complexities, and the benefits of modeling energy services using a scalable approach are analyzed, solutions are proposed and simulated with realistic data in three main chapters of this research: a) energy storage in wholesale energy markets, b) generic multiservice revenue analysis of energy storage, and c) temporal complexities of energy storage optimization models: value and decomposition. Simulation results show the feasibility of the proposed approaches, and significant added values to the economic viability of energy storage projects using the proposed methodologies. Energy storage decision makers including public utility commissioners, transmission/distribution system operators, aggregators, private energy storage owners/investors, and end-use customers (residential and commercial loads) can benefit from the proposed methodologies and simulation results. A software tool has been developed for multiservice benefit cost analysis of energy storage projects. It is hoped that with the significant unlocked value of energy storage systems using the proposed tools and methodologies, more of these technologies be deployed in the future grids to help communities with their sustainability and environmental goals.Ph.D

Modelo agregador para la gestión de la Demanda en Grandes Consumidores con la inclusión de Generación Distribuida en Sistemas de Distribución de Energía Eléctrica

La acelerada evolución tecnológica y el cambio en los patrones de consumo energético han llevado a la industria eléctrica a investigar a fondo la gestión activa de la demanda y la generación de recursos renovables distribuidos. Esto implica evaluar cómo los consumidores responden a variaciones en los precios de la energía eléctrica, optimizando así la eficiencia técnica y económica del sistema. Las redes inteligentes no solo gestionan técnicamente la energía, sino que generan datos que permiten a terceros formar sistemas de gestión de datos técnicos y económicos. Sin embargo, los programas de respuesta a la demanda suelen ser diseñados por empresas y reguladores que carecen de información precisa sobre las estrategias de los clientes. Esta investigación analiza programas de respuesta de la demanda en países en desarrollo, destacando la falta de participación de grandes consumidores en el diseño de estos programas. Se propone una metodología innovadora que utiliza técnicas de analítica de datos, optimización y modelos matemáticos para diseñar y evaluar respuestas a la demanda. Se estratifica la demanda, considerando cargas gestionables y no gestionables, junto con la generación distribuida renovable que los grandes consumidores pueden aportar. Se proyecta en función de los precios del mercado eléctrico, y se resuelve mediante un enfoque de optimización bi-nivel que maximiza beneficios del agregador y minimiza costos de equilibrar la oferta y la demanda en tiempo real. La investigación evalúa restricciones técnicas y operativas desde la perspectiva del operador de distribución, buscando decisiones técnicamente viables y rentables para consumidores y distribuidores en un entorno de redes inteligentes.The accelerated technological evolution and the change in energy consumption patterns have led the electricity industry to thoroughly investigate the active management of demand and the generation of distributed renewable resources. This involves evaluating how consumers respond to variations in electricity prices, thus optimizing the technical and economic efficiency of the system. Smart grids technically manage energy and generate data that allows third parties to form technical and financial data management systems. However, demand response programs that lack accurate information about customer strategies are often designed by companies and regulators. This research analyzes demand response programs in developing countries, highlighting the lack of participation of large consumers in creating these programs. An innovative methodology that uses data analytics, optimization and mathematical model techniques to design and evaluate responses to demand is proposed. Order is stratified, considering manageable and non-manageable loads and the renewable distributed generation that large consumers can contribute. It is projected based on electricity market prices. It is resolved through a bi-level optimization approach that maximizes the aggregator's benefits and minimizes the real-time costs of balancing supply and demand. The research evaluates technical and operational constraints from the perspective of the distribution operator, seeking technically viable and profitable decisions for consumers and distributors in a smart grid environment.0000-0001-5777-525