Load and Renewable Energy Forecasting for a Microgrid using Persistence Technique

Dutta, Soumi; Li, Yanling; Venkataraman, Aditya; Costa, Luís; Jiang, Tianyi; Plana, R.; Tordjman, Philippe; Choo, Fook Hoong; Foo, C.F.; Püttgen, Hans-Björn

doi:10.1016/j.egypro.2017.12.736

Cited by 65 publications

(32 citation statements)

References 3 publications

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“…Thus, one forecasting approach based on another forecasting gives inaccuracy. Thus, a persistence technique for both renewable energy and load forecasting is presented in [44] which is based on historical power data instead of weather data. Other techniques presented in the literature for load and generation forecasting include fuzzy logic [45], [46], statistic approach [47], [48], intelligent algorithm [49], adaptive neuro-fuzzy inference system (ANFIS) [50].…”

Section: A Generation and Load Forecastingmentioning

confidence: 99%

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Al–Ismail

2021

IEEE Access

338

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In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cuttingedge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic. INDEX TERMS DC microgrids, renewable energy sources, batteries, supercapacitors, DC bus voltage, power management, state of charge, microgrid operation, and planning.

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Section: A Generation and Load Forecastingmentioning

confidence: 99%

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Al–Ismail

2021

IEEE Access

338

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“…All the abovementioned approaches are usually compared with a benchmark, which is represented by the persistence. A persistence forecasting method consists of imposing the next value of the forecast parameter to be equal to the last measured one [23]. In the very short term, this method achieves very good results, especially in stationary conditions, and presents the advantage of a negligible computational cost.…”

Section: Introductionmentioning

confidence: 99%

Sun Position Identification in Sky Images for Nowcasting Application

Niccolai

Nespoli

2020

Forecasting

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Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic production, whole sky images can be used effectively. The first step in the implementation of new and effective nowcasting algorithms is the identification of Sun positions. In this paper, three different techniques (solar angle-based, image processing-based, and neural network-based techniques) are proposed, described, and compared. These techniques are tested on real images obtained with a camera installed at SolarTechLab at Politecnico di Milano, Milan, Italy. Finally, the three techniques are compared by introducing some performance parameters aiming to evaluate of their reliability, accuracy, and computational effort. The neural network-based technique obtains the best performance: in fact, this method is able to identify accurately the Sun position and to estimate it when the Sun is covered by clouds.

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“…The second one is indirect forecasting, which forecasts the solar irradiance and then uses simulation software to calculate the power output of the PV. Most PV power forecasting research has focused on direct forecasting with various techniques including the persistence model [12], statistical approaches [13], machine learning approaches [14], and hybrid techniques [15]. Mitsuru et al [16] already studied both direct and indirect methods to compare their efficiencies.…”

Section: Introductionmentioning

confidence: 99%

Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

Khumma

Tamee

2020

ECTI-CIT

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This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm ﬁltering technique to estimate power output and the creation of a stochastic forecasting model. As a result, aﬀected power can be forecasted from stochastic factors with machine learning and an artiﬁcial neural network. This model focuses on very short-term forecasting over a ﬁve minute period. As it uses only endogenous data, no exogenous data is needed. To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. This proposed PV forecasting model gained higher accuracy than the persistence model using stochastic factors.

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Load and Renewable Energy Forecasting for a Microgrid using Persistence Technique

Cited by 65 publications

References 3 publications

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Sun Position Identification in Sky Images for Nowcasting Application

Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

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