Short-term Forecasting in Power Systems: A Guided Tour

Muñoz, Antonio; Sánchez‐Úbeda, Eugenio F.; Cruz, Albert C.; Anzurez-Marin, J.

doi:10.1007/978-3-642-12686-4_5

Cited by 41 publications

(19 citation statements)

References 86 publications

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“…Depending on the time horizon and specific application, different price models can be considered. These can be obtained from time series modeling (e.g., Diongue 2005;Muñoz et al 2010;Pedregal et al 2012), mathematical finance (e.g., Oudjane et al 2006;Higgs and Worthington 2008;Benth et al 2012;NguyenHuu 2012;Pepper et al 2012) or can be based on electricity fundamentals (e.g., van Ackooij and Wirth 2007;Ea 2012). For the case where the producer is a price taker, that is, small enough so that its production can be deemed to have little or no effect on the realized prices, UC can typically be independently solved for each individual unit (thus being styled as the self-scheduling problem), and it is therefore much easier (Arroyo and Conejo 2000), although uncertainty in prices then becomes a critical factor (Conejo et al 2002b;Nogales et al 2002;Baringo and Conejo 2011).…”

Section: The Objective Functionmentioning

confidence: 99%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty

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Section: The Objective Functionmentioning

confidence: 99%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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“…Several models have been developed for hourahead and day-ahead load forecasting that are often referred to short-term load forecasting (STLF) (Muñoz et al 2010).…”

Section: Short-term Load Forecast Problemmentioning

confidence: 99%

Evolving Takagi–Sugeno model based on online Gustafson-Kessel algorithm and kernel recursive least square method

Shafieezadeh-Abadeh

Kalhor

2015

Evolving Systems

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“…Se supone que estos contratos con distintas fechas y distintos períodos de vencimiento, reflejan las expectativas reales del comportamiento de los precios futuros. En los mercados eléctricos liberalizados, el precio se caracteriza por su alta volatilidad (Muñoz et al, 2010), es decir, es fluctuante.…”

Section: Modelo De Gestión De La Demanda Para Autogeneradoresunclassified

Beneficios Económicos de la Gestión de la Demanda y la Energía Autogenerada en el Contexto de la Regulación Colombiana

2018

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ResumenEn este trabajo se determinan los beneficios económicos del funcionamiento de un sistema de autogeneración eléctrica con tecnologías de energías renovables. El sistema está conectado a la red tradicional y, tiene las opciones de participar en programas de respuesta a la demanda y exportar excedentes de generación. Las variables del modelo de gestión, fueron identificadas a partir de las leyes y lineamientos establecidos por las entidades del sector eléctrico colombiano. Se demuestra que el modelo puede ser aplicado a empresas de diferentes sectores económicos y con diferentes curvas de carga. La incertidumbre de la generación y del cálculo de los beneficios se aborda mediante Simulación de Monte Carlo. Se presenta un caso de estudio en el que, utilizando un sistema de generación fotovoltaico se aplica el modelo propuesto a los sectores industriales y de servicios. Esto permite analizar los beneficios que pueden tener los inversionistas variando su capacidad de energía autogenerada. Palabras clave: autogeneración, respuesta a la demanda, incertidumbre, energías renovables, beneficios económicos Economic Benefits of Demand Management and SelfGenerated energy in the Context of Colombian Regulations AbstractIn this paper the economic benefits of the operation of an electric self-generation system with renewable energy sources are determined. The system is connected to the traditional grid and, it has the options to participating in the programs of demand response and to export its surpluses of generation. The variables of the management model were identified based on the Colombians laws and guidelines, established by the government entities of the electricity sector. The results show that the model can be applied to companies from different economic sectors using different curves of load. The uncertainty of the generation and the calculation of the profits were approached with Monte Carlo Simulation. A case of study is presented where a photovoltaic generation system was included, and the proposed model was applied to the industrial and service sectors. This allows analyzing the different benefits that investors can receive varying the capacity of self-generation.

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Short-term Forecasting in Power Systems: A Guided Tour

Cited by 41 publications

References 86 publications

Large-scale Unit Commitment under uncertainty

Large-scale Unit Commitment under uncertainty

Evolving Takagi–Sugeno model based on online Gustafson-Kessel algorithm and kernel recursive least square method

Beneficios Económicos de la Gestión de la Demanda y la Energía Autogenerada en el Contexto de la Regulación Colombiana

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