Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids – A review

Kumar, K. Prakash; Saravanan, B.

doi:10.1016/j.rser.2016.12.063

Cited by 115 publications

(67 citation statements)

References 30 publications

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“…References [103,104] describes the implementation details of various forecasting techniques for PV power generation. More details about uncertainty modelling for the RES systems can be found in References [105][106][107][108]. The literature of PV based uncertainty modelling is scarce as the cumulative effect of PV power on the system is small compared to other uncertain variables (load, EV demand).…”

Section: Pv Generationmentioning

confidence: 99%

“…Table 5 shows an overview of various common methods used for modelling uncertainty in electrical loads. Readers can refer to [105,108,[111][112][113] for implementation details of these and other methods used to model uncertainties present in electrical load. The convolution and cumulant based techniques are traditional methods popular in the late nineties' era.…”

Section: Electrical Load Demandmentioning

confidence: 99%

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Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling

2020

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Electric vehicles (EVs) are one of a prominent solution for the sustainability issues needing dire attention like global warming, depleting fossil fuel reserves, and greenhouse gas (GHG) emissions. Conversely, EVs are shown to emit higher emissions (measured from source to tailpipe) for the fossil fuel-based countries, which necessitates renewable energy sources (RES) for maximizing EV benefits. EVs can also act as a storage system, to mitigate the challenges associated with RES and to provide the grid with ancillary services, such as voltage regulation, frequency regulation, spinning reserve, etc. For extracting maximum benefits from EVs and minimizing the associated impact on the distribution network, modelling optimal integration of EVs in the network is required. This paper focuses on reviewing the state-of-the-art literature on the modelling of grid-connected EV-PV (photovoltaics) system. Further, the paper evaluates the uncertainty modelling methods associated with various parameters related to the grid-connected EV-PV system. Finally, the review is concluded with a summary of potential research directions in this area. The paper presents an evaluation of different modelling components of grid-connected EV-PV system to facilitate readers in modelling such system for researching EV-PV integration in the distribution network.

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Section: Pv Generationmentioning

confidence: 99%

Section: Electrical Load Demandmentioning

confidence: 99%

Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling

2020

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“…An ADN is typically equipped with PV plants to supply usable solar power. The beta distribution is used to model the uncertainty of solar irradiance [15]:…”

Section: Model Of Pvsmentioning

confidence: 99%

Probabilistic active distribution network equivalence with correlated uncertain injections for grid analysis

Huang

Zheng

2020

IET Renewable Power Generation

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Equivalent modelling for active distribution networks (ADNs) is essential for improving the efficiency of analysing transmission networks. Current equivalent modelling methods for ADNs neglect the probabilistic characteristics of renewable energy sources (RESs) and loads. To address this issue, this study proposes a probabilistic equivalent modelling method (PEMM) for ADNs considering the uncertainty of RESs and loads. The uncertainty of the RESs and loads is transferred to the equivalent boundary bus injection using the properties of cumulant and power transfer matrices. The PEMM is extended to incorporate the correlations of RESs through an orthogonal transformation. A sampling method using the Gaussian copula function is employed to generate the correlated samples and the joint cumulants, providing the input data for the PEMM. The comparative results of the case studies on two different test systems demonstrate the effectiveness of the PEMM. The equivalent model developed in this study is a practical solution for analysing the transmission network efficiently and taking the uncertainty of RESs and loads in the ADNs into account simultaneously.

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“…Let there be an exclusive uncertainty set for each of renewable generators over the time period ∈ and another exclusive uncertainty set for the total renewable power ∑ ( ) , over a time sub-horizon ∈ and say all these uncertainty sets form the subsets of the main uncertainty set . Then (19) relates all the above uncertainty sets, Numeric values of these terms can be estimated from historical data analysis using any of the data inference techniques reported in literature [3].…”

Section: Modelling Uncertainty Of Res Generationmentioning

confidence: 99%

“…The idea of tapping solar and wind energy which are distributed sources by availability has encouraged the concept of connecting smaller and distributed generation sources to the utility grid at tail ends (distribution systems) at low and medium voltages levels. This lead to a whole new concept of autonomous microgrids [3]. The microgrids operate in either grid connected mode or in autonomous mode.…”

Section: Introductionmentioning

confidence: 99%

Day-ahead Management of Energy Sources and Storage in Hybrid Microgrid to reduce Uncertainty

Kumar

Saravanan

2019

Gazi University Journal of Science

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Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids – A review

Cited by 115 publications

References 30 publications

Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling

Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling

Probabilistic active distribution network equivalence with correlated uncertain injections for grid analysis

Day-ahead Management of Energy Sources and Storage in Hybrid Microgrid to reduce Uncertainty

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