Data-driven uncertainty analysis of distribution networks including photovoltaic generation

In order to make significant progress in the operation of power systems and minimizing cost and air pollution, multi-carrier energy systems have been proposed by implementing various solutions and using different sources. In this regard, renewable energy sources, fuel cells, and electric vehicles (EVs) as a mobile storage system are applied, but these units due to uncertain behavior create a big problem in balancing between the demand and generated energy. In this study, an energy hub (EH) system includes a wind turbine (WT), photovoltaic (PV), and EVs that are exchanged energy with energy and reserve market considering energy, thermal, and gas demand response program is proposed. Also, the uncertainty of WT, PV, load, and electricity market price are considered. Besides that, all parameters of the EVs with uncertainty behavior are modeled using a new method. This method is employed a stochastic optimization approach to simplify the uncertainty modeling for increasing the system reliability. Hence, two objective functions, namely economic cost, and environmental cost are considered. In addition, a three-step strategy is introduced to solve the multi-objective problem. Finally, EH management performance is investigated by implementing the proposed method and elements.

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“…Beta PDF is used to model the uncertainty of solar irradiance. Beta PDF is a function of two parameters as 𝜔 and ψ [36]:…”

Section: Solar Irradiance Uncertaintymentioning

confidence: 99%

An Optimal Energy Hub Management Integrated EVs and RES Based on Three-Stage Model Considering Various Uncertainties

et al. 2022

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“…where α here denotes the PV penetration level, i.e., the ratio between the installed PV power ¾¼¾ and the nominal active one, while x n are normalized random variables defined as (10) ex-¾¼¿ tracted from a representative wide measurement data set [26]. On the whole, the number ¾¼ of random parameters x d is D = 15 × 3 = 45.…”

Section: ¾¼½mentioning

confidence: 99%

“…First, they have to rely on realistic statistical models of PV uncertainty and be able to exploit the information supplied by measurements and historic data set [6]. Several approaches to PV modeling have been proposed in the literature [7], some of which are focused on reproducing metereological conditions variability [8,9] while some others directly exploit PV delivered power data [10].…”

Section: Introductionmentioning

confidence: 99%

“…Evidences gained from such data-driven approaches show that: (a) PV delivered power follows non-standard statistical distributions [10,11], (b) geographically close PV plans tend to exhibit a certain degree of correlation. Techniques for including sources correlations are mostly based on Copula Method (CM) [12,13], or on Gaussian Mixture Models (GMM) [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration

2022

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This paper aims at presenting a novel effective approach to probabilistic analysis of distribution power grid with high penetration of PV sources. The novel method adopts a Gaussian Mixture Model for reproducing the uncertainty of correlated PV sources along with a piece-wise-linear approximation of the voltage-power relationship established by load flow problem. The method allows the handling of scenarios with a large number of uncertain PV sources in an efficient yet accurate way. A distinctive feature of the proposed probabilistic analysis is that of directly providing, in closed-form, the joint probability distribution of the set of observable variables of interest. From such a comprehensive statistical representation, remarkable information about grid uncertainty can be deduced. This includes the probability of violating the safe operation conditions as a function of PV penetration.

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“…. , ξ 6 were used to scale the maximum active power absorbed by four loads in the nets following the method previously presented in [23]. Such load-related stochastic parameters are independent and beta distributed (with parameters a = 1.1 and b = 22).…”

Section: Test Networkmentioning

confidence: 99%

Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks

2021

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Deterministic load flow analyses of power grids do not include the uncertain factors that affect the network elements; hence, their predictions can be very unreliable for distribution system operators and for the decision makers who deal with the expansion planning of the power network. Adding uncertain probability parameters in the deterministic load flow is vital to capture the wide variability of the currents and voltages. This is achieved by probabilistic load flow studies. Photovoltaic systems represent a remarkable source of uncertainty in the distribution network. In this study, we used a Gaussian copula to model the uncertainty in correlated photovoltaic generators. Correlations among photovoltaic generators were also included by exploiting the Gaussian copula technique. The large sets of samples generated with a statistical method (Gaussian copula) were used as the inputs for Monte Carlo simulations. The proposed methodologies were tested on two different networks, i.e., the 13 node IEEE test feeder and the non-synthetic European low voltage test network. Node voltage uncertainty and network health, measured by the percentage voltage unbalance factor, were investigated. The importance of including correlations among photovoltaic generators is discussed.

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Data-driven uncertainty analysis of distribution networks including photovoltaic generation

Cited by 20 publications

References 27 publications

An Optimal Energy Hub Management Integrated EVs and RES Based on Three-Stage Model Considering Various Uncertainties

An Optimal Energy Hub Management Integrated EVs and RES Based on Three-Stage Model Considering Various Uncertainties

Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration

Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks

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