Photovoltaic and solar power forecasting for smart grid energy management

Wan, Can; Zhao, Jian; Song, Yonghua; Xu, Zhao; Lin, Jin; Hu, Zechun

doi:10.17775/cseejpes.2015.00046

Cited by 460 publications

(198 citation statements)

References 66 publications

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“…and g A,ub0 should be chosen in the interval of [0,1]. In this study, g A,lb0 and g A,ub0 are chosen as 0.8, which is equivalent to 80% of 1 pu.…”

Section: Design Stage 3: Initially Adjust the Scaling Parameters In Tmentioning

confidence: 99%

“…Efficient control methods for managing renewable energy resources have also been designed and implemented. The high uncertainty of weather conditions makes forecasts of power profiles from renewable energy resources important for energy management, especially in large solar PV and wind power plants [1,2]. The time horizon of estimating and forecasting methods can be divided into very short-term, short-term, medium-term and long-term predictions [1].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems

Thao

Uchida

2018

Energies

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This paper proposes an improved interval fuzzy modeling (imIFML) technique based on modified linear programming and actual boundary points of data. The imIFML technique comprises four design stages. The first stage is based on conventional interval fuzzy modeling (coIFML) with first-order model and linear programming. The second stage defines reference lower and upper bounds of data using MATLAB. The third stage initially adjusts scaling parameters in the modified linear programming. The last stage automatically fine-tunes parameters in the modified linear programming to realize the best possible model. Lower and upper bounds approximated by the imIFML technique are closely fitted to the reference lower and upper bounds, respectively. The proposed imIFML is thus significantly less conservative in cases of large variation in data, while robustness is inherited from the coIFML. Design flowcharts, equations, and sample MATLAB code are presented for reference in future experiments. Performance and efficacy of the introduced imIFML are evaluated to estimate solar photovoltaic, wind and battery power in a demonstrative renewable energy system under large data changes. The effectiveness of the proposed imIFML technique is also compared with the coIFML technique.

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“…and g A,ub0 should be chosen in the interval of [0,1]. In this study, g A,lb0 and g A,ub0 are chosen as 0.8, which is equivalent to 80% of 1 pu.…”

Section: Design Stage 3: Initially Adjust the Scaling Parameters In Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems

Thao

Uchida

2018

Energies

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“…En todo caso, es imposible para un módulo estar conectado a más de una cadena, por tanto, las probabilidades de cada columna, i.e. asociadas a un mismo módulo, deben sumar 1 (100%) como se describe en (4). Igualmente, las probabilidades iniciales se definen de acuerdo con (5) para garantizar que todas las configuraciones iniciales tienen la misma probabilidad en el inicio de la optimización.…”

Section: Reconfiguración Basada En El Algoritmo Aibpunclassified

“…Esta característica les permite suplir electricidad en aplicaciones aisladas sin contaminar el medio ambiente, de forma similar a los generadores renovables difíciles de predecir, por ejemplo, fotovoltaicos (FV) [2], [3] y eólicos. Debido a que las CC permiten predecir la cantidad de energía que se produce, lo que presenta gran dificultad con los sistemas FV o eólicos [4], las CC son ideales para suplir sistemas críticos tales como protecciones, sistemas de comunicación, entre otros. Adicionalmente, las CC tienen la desventaja de requerir un suministro continuo de combustible, lo que hace necesario almacenar y transportar ese combustible.…”

Section: Introductionunclassified

Reconfiguración de paneles fotovoltaicos para reducción del consumo de hidrógeno en las celdas de combustible de sistemas híbridos

Montoya

Ramos‐Paja

Bolaños-Martínez

et al. 2017

TecnoL.

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“…F-H weather would still exist in vast areas of China, and strategies of the operation, maintenance, prediction and dispatch control of power systems under F-H weather need improving. As for the predictions, the early studies about photovoltaic output and load forecasts only consider traditional meteorological factors, such as temperature, humidness and all kinds of weather types, rarely focusing on F-H [12,13]. The literature [14] has proven that illumination intensity is directly blocked by F-H, and the instantaneous output and effective operating time of photovoltaic panels (PV panels) are greatly restricted.…”

Section: F-h Impacts On Photovoltaic Output and Load Forecastsmentioning

confidence: 99%

The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

Gao

Zhu

et al. 2016

Sustainability

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Abstract:With the growing influence of fog and haze (F-H) weather and the rapid development of distributed energy resources (DERs) and smart grids, the concept of the virtual power plant (VPP) employed in this study would help to solve the dispatch problem caused by multiple DERs connected to the power grid. The effects of F-H weather on photovoltaic output forecast, load forecast and power system dispatch are discussed according to real case data. The wavelet neural network (WNN) model was employed to predict photovoltaic output and load, considering F-H weather, based on the idea of "similar days of F-H". The multi-objective optimal dispatch model of a power system adopted in this paper contains several VPPs and conventional power plants, under F-H weather, and the mixed integer linear programming (MILP) and the Yalmip toolbox of MATLAB were adopted to solve the dispatch model. The analysis of the results from a case study proves the validity and feasibility of the model and the algorithms.

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Photovoltaic and solar power forecasting for smart grid energy management

Cited by 460 publications

References 66 publications

An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems

An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems

Reconfiguración de paneles fotovoltaicos para reducción del consumo de hidrógeno en las celdas de combustible de sistemas híbridos

The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

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