Energy management of a virtual power plant with a battery-ultracapacitor based Hybrid Energy Storage System

Avila, Eduardo; Pozo, Marcelo; Pozo, Nataly; Camacho, Oscar; Leica, Paulo; Ortega, Leonardo; Gallardo, Carlos; Dominguez, Xavier; Feuersanger, Simon; Pacas, Mario

doi:10.1109/spec.2017.8333635

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The possible utilization of this prediction model and the generated curve of predicted solar irradiance is in a virtual plant that consists of different storages and is based on integration with solar photovoltaic cells. Storages can be based on a battery [22], hydrogen cell [23], and supercapacitor [24], each with different characteristics related to capacity, efficiency, and response speed. With the generated curve of predicted solar irradiance in the next hour, the amount of energy generated can be predicted.…”

Section: Introductionmentioning

confidence: 99%

Solar Irradiance Forecast Based on Cloud Movement Prediction

Radovan¹,

Šunde

Kučak

et al. 2021

Energies

View full text Add to dashboard Cite

Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.

show abstract

Section: Introductionmentioning

confidence: 99%

Solar Irradiance Forecast Based on Cloud Movement Prediction

Radovan¹,

Šunde

Kučak

et al. 2021

Energies

View full text Add to dashboard Cite

show abstract

“…It uses a hybrid combination of battery and UC as the energy storage device and compensates the wind/load power fluctuations . The control strategies based on sliding mode control using UC and battery as energy storage device demonstrates fast and precise dynamic response against changing irradiance level, wind speed, and load . Various hybrid energy storage systems such as battery and electrolyzer with control strategies manage the power flow among the renewable energy sources and load by distributing the current at the DC bus .…”

Section: Introductionmentioning

confidence: 99%

Energy storage management of hybrid solar/wind standalone system using adaptive neuro‐fuzzy inference system

Varghese

Reji

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper proposes a charge controller for ultracapacitor that acts as an energy storage device so that it can minimize the effect of irregular solar radiation and widely varying wind velocity. A suitable supervision for each generating unit and ultracapacitor are mandatory for a standalone system. The primary energy sources solar and wind systems are operating at maximum power point. Adaptive neuro‐fuzzy inference system (ANFIS) is utilized to foresee the voltage of panel at which extreme power is obtained for a solar system and also to ascertain the most extreme power delivered by the nonuniform and unpredictable characteristics of solar and wind energy sources. A common energy management technique is developed to manage the control flow between the distinctive power sources, loads, and ultracapacitor in the system. The results obtained by ANFIS are compared with artificial neural network. A Simulink model of the hybrid standalone system was developed and tested for various insolation, temperature, and wind velocity.

show abstract