Probability distributions of outputs of stochastic economic dispatch

Shahirinia, Amir; Soofi, Ehsan S.; Yu, David C.

doi:10.1016/j.ijepes.2016.02.043

Cited by 18 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Simulation results showed that this method is superior to the existing robust optimization model in cost and reliability [9]. In references [10][11][12], the uncertainty of wind power output is dealt with by using stochastic simulation technology. Xiong Hu et al took the output and load of a large-scale intermittent power supply as fuzzy parameters, and studied the clear equivalence class of fuzzy opportunity constraints with multiple fuzzy parameters, which was used to deal with the opportunity constraints [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint

et al. 2018

View full text Add to dashboard Cite

With the increasing expansion of wind power, its impact on economic dispatch of power systems cannot be ignored. Adding a heat storage device to a traditional cogeneration unit can break the thermoelectric coupling constraint of the cogeneration unit and meet the economic and stable operation of a power system. In this paper, an economy-environment coordinated scheduling model with the lowest economic cost and the lowest pollutant emissions is constructed. Economic costs include the cost of conventional thermal power generating units, the operating cost of cogeneration units, and the operating cost of wind power. At the same time, green certificate costs are introduced into the economic costs to improve the absorption of wind power. Pollutant emissions include SO2 and NOx emissions from conventional thermal power units and cogeneration units. The randomness and uncertainty of wind power output are fully considered, and the prediction error of wind power is fuzzy treated according to the fuzzy random theory, and the electric power balance and spinning reserve fuzzy opportunity conditions are established, which are converted into the explicit equivalent. The improved multi-objective particle swarm optimization (MOPSO) was used to solve the model. With this method, the validity of the model is verified by taking a system with 10 machines as an example.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Anteriormente, con el uso de fuentes convencionales (termoeléctricas e hidroeléctricas) el error asociado al ED programado dependía exclusivamente de la demanda, pues determinaba la cantidad de potencia a inyectar en la red y consecuentemente el error solo dependía del pronóstico de la misma. Sin embargo, con la entrada masiva de las RER en los sistemas eléctricos, se establecen otras fuentes de error que afectan la programación del ED y que están representadas por la predicción de la velocidad del viento y la radiación solar (Shahirinia, Soofi , & Yu, 2016) (Dean, Ortmeyer, & Wu, 2016). Estas variables dependen de las condiciones climáticas específicas de cada lugar y su comportamiento puede ser modelado por Funciones de Densidad de Probabilidad (Probability Density Function, PDF), de modo que se pueden predecir los valores futuros necesarios para realizar un despacho económico óptimo con un error asociado, el cual se puede expresar a partir de funciones de costo por sobreestimar o subestimar (Rivera & Romero, 2018).…”

Section: Introductionunclassified

“…Rivera y Arévalo (2018) adoptan una distribución de probabilidad Weibull para la velocidad del viento, una distribución de probabilidad Lognormal para la radiación solar y una distribución Normal para los EV, que después integran en un despacho económico estático para calcular las funciones por sobreestimación y subestimación. En Shahirinia (2016), se realiza un despacho económico estocástico que lidia con las incertidumbres de los generadores eólicos. Dicho acercamiento hace uso de datos de velocidad de viento simulados que se integran en el despacho económico para estimar las distribuciones de probabilidad de la generación óptima de plantas fósiles, pérdidas por transmisión y el costo total de la generación del sistema.…”

Section: Introductionunclassified

Modelado de cargas controlables en el despacho de sistemas con fuentes renovables y vehículos eléctricos

Guzman

Osorio

Rivera

2017

Ingeniería y Región

View full text Add to dashboard Cite

ResumenEl presente documento muestra el despacho económico realizado en un sistema de potencia con penetración de fuentes renovables y carros eléctricos, con énfasis en el modelado de cargas controlables. Para ello, se estudió el comportamiento de la irradiancia solar, la velocidad del viento y los patrones de conducción de carros eléctricos por medio de distribuciones de probabilidad Log-Normal, Weibull y Normal, respectivamente. Se definió el concepto de carga controlable, así como los requisitos del contrato con el operador de red para que un centro de consumo pueda declararse como controlable, mediante el uso de un modelo de minimización de costos de compensación por bloque de potencia no despachada, que permitiera modelar el comportamiento energético-económico de dichos nodos desde el punto de vista del operador de red. La optimización del despacho (flujo óptimo de potencia), se hizo por medio del algoritmo de optimización DEEPSO (Differential Evolutionary Particle Swarm Optimization) mediante la inclusión de 7 nodos controlables, escogidos en base a la posible flexibilidad de carga en el nodo respectivo. Se encontró que las cargas controlables pueden presentar dos grandes beneficios para el sistema dependiendo de los parámetros establecidos en el contrato permitiendo la suavización del perfil de demanda (desplazamiento de picos de máxima potencia y disminución de pérdidas) y disminución del costo total de generación. Palabras clave: Optimización heurística; despacho económico; fuentes renovables de energía; cargas controlables AbstractThis document shows the economic dispatch carried out in a power system with penetration of renewable sources, electric cars and with special emphasis on the modeling of controllable loads. For this, the behavior of solar irradiance, wind speed and electric carriage driving patterns were studied by means of Log-Normal, Weibull and Normal probability distributions, respectively. The concept of controllable load was defined as well as the requirements of the contract with the network operator so that a consumption center could be declared as controllable and a model of minimization of costs of compensation by unbundled power block was used to model the behavior energetic-economic of said nodes from the point of view of the network operator. The optimization of the dispatch (optimum flow of power) was made by means of the DEEPSO optimization algorithm by the inclusion of 7 controllable nodes, chosen based on an established selection criterion. It was found that the controllable loads can present two great benefits for the system depending on the parameters established in the contract: smoothing the demand profile (displacement of maximum power peaks and decrease of losses) and decrease of the total cost of generation.

show abstract

“…Statistical methods have been applied to three different time scales: short-term, medium-term and long-term. Short-term wind speed forecasts play a central role in estimating various engineering parameters, such as power outputs, extreme wind loads, and fatigue loads [1]. The wind speed forecasts for this time scale are only a few hours ahead of target time [2]- [6].…”

Section: Introductionmentioning

confidence: 99%

“…An overview of the wind speed models used in recent literature is as follows. [1], [12] and [13] used a Weibull distribution to forecast the wind speed and assessed wind energy potential. [14] compared fit of a Rayleigh distribution and another Weibull distribution to wind speed data and showed that the Weibull model provided a better fit.…”

Section: Introductionmentioning

confidence: 99%

Bayesian predictive models for Rayleigh wind speed

Shahirinia

Hajizadeh

2017

2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB)

View full text Add to dashboard Cite

Abstract-One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view the distributions of the wind speed over a wind farm as being homogeneous. However, the uncertainty about these wind speed models has not yet been considered. In this paper the Bayesian approach to taking into account the uncertainty inherent in the wind speed model has been presented. The proposed Bayesian predictive model of the wind speed aggregates the nonhomogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines' locations in a wind farm. More specifically, instead of using a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented.

show abstract

Probability distributions of outputs of stochastic economic dispatch

Cited by 18 publications

References 24 publications

Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint

Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint

Modelado de cargas controlables en el despacho de sistemas con fuentes renovables y vehículos eléctricos

Bayesian predictive models for Rayleigh wind speed

Contact Info

Product

Resources

About