Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks

Abdelaziz, Almoataz Y.; Hegazy, Yasser G.; El-Khattam, Walid; Othman, Mahmoud M.

doi:10.1016/j.epsr.2014.09.005

Cited by 78 publications

(48 citation statements)

References 15 publications

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“…The annual energy losses for the base case where no DG is connected is obtained by running the unbalanced load flow for the 96 hours and calculating the power loss for each hour, then the annual energy loss is calculated using (8). The load profile for the second test case is presented in Fig.…”

Section: ) Test Cases # 1 and #mentioning

confidence: 99%

A Novel Probabilistic Technique for Optimal Allocation of Photovoltaic Based Distributed Generators to Decrease System Losses

Mohamed¹,

Hegazy²,

Othman³

2016

Period. Polytech. Elec. Eng. Comp. Sci.

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This paper presents a novel algorithm for modeling photovoltaic based distributed generators for the purpose of optimal planning of unbalanced distribution networks. The proposed algorithm utilizes sequential Monte Carlo method in order to accurately consider the stochastic nature of photovoltaic based distributed generators. An efficient algorithm based on Firefly optimization method is proposed for optimal placement of photovoltaic based distributed generators in unbalanced distribution network. The proposed optimization algorithm aims to minimize the annual energy loss by determining the optimal locations of photovoltaic distributed generators. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are conducted to prove the effectiveness of the proposed algorithms. The results obtained are presented and discussed. IntroductionPhotovoltaic (PV) based power stations are good choice for replacement of the traditional electrical energy generation as it is infinite and less pollutant source of energy. However, due to its stochastic nature, PV increases the network uncertainties.The PV power is difficult to be accurately simulated because it is strongly correlated to the climate, ambient temperature, season, time and geography [1]. Thus, a probabilistic model of the PV power is needed in order to simulate the actual behavior of these stations.Models that considers the stochastic nature of the PV power can be classified into two categories; analytical methods [2-6] and Monte Carlo based techniques [7-10]. Authors in [2] Presented a modeling method that based on dividing the solar irradiance into states; finding the average solar irradiance and consequently the most likely power of each hour of the day after consecutive mathematical equations based on the photovoltaic module. In [3] the stochastic nature of the photovoltaic was handled by offering unsymmetrical two point estimation method and it was compared by symmetrical two point estimation method, Gram-Charlier and Latin Hypercube method. The authors in [4] presents a methodology to model PV based power stations for reliability studies by combining Markov Chain and Monte Carlo method for the generation of a multistate PV model based on the transition probability matrix. Reference [6] presents a chronological probability model of photovoltaic (PV) generation on the basis of conditional probability and nonparametric kernel density estimation. Reference [7] described an approach based on Monte Carlo Method to evaluate the uncertainty of the passive parameters of double diode photovoltaic cell using manufacturer's data for the panels, measured environmental parameters and semi empirical equations. The authors in [8] presents a Monte Carlo based strategy for modeling PV power generators considering their dependency with other renewable sources. In [9] a method based on the pseudo-sequential Monte Carlo simulation technique had been proposed to evaluate the reserve deployment and customers' nodal reliab...

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Section: ) Test Cases # 1 and #mentioning

confidence: 99%

A Novel Probabilistic Technique for Optimal Allocation of Photovoltaic Based Distributed Generators to Decrease System Losses

Mohamed¹,

Hegazy²,

Othman³

2016

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Thus, based on the selected study case network and groups of stochastic wind generation data in three scenarios, the computation results of Equations (4) and (6), which are obtained by the proposed multi-scenario considered DPF method, and credible capacity value, which is calculated through Equation (3), are shown in Tables 3 and 4. Interval distribution and main statistical feature of credible capacity value are analyzed based on the groups of discrete data, as shown in Table 4.…”

Section: Case Studymentioning

confidence: 99%

“…Thus, the average value of network active power loss P loss can be calculated through Equation (6), as shown below. T represents the length of studied time period of selected operation scenario.…”

Section: Definition Of Credible Capacity and Its Physical Meaningmentioning

confidence: 99%

“…A great number of studies considering this issue are emerging, which manage to analyze the impact quantitatively [2,3], statistically figure out the irregularity of intermittency and fluctuations, and predict and estimate future output in various ways [4,5]. Current literature can also be found investigating other domains such as grid planning [6][7][8][9], operation analysis, and optimization control considering the uncertainties of DG [10][11][12][13][14][15][16]. A power supply and storage capacity for operation optimization is proposed and studied [17].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Credible Capacity for Intermittent Distributed Energy Resources in Active Distribution Network

et al. 2017

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Abstract:The irregularity and randomness of distributed energy sources' (DERs) output power characteristic usually brings difficulties for grid analysis. In order to reliably and deterministically evaluate intermittent distributed generation's active power output, a credible capacity index for active distribution network (ADN) is proposed. According to the definition, it is a certain interval that the stochastic active power output of DERs may fall in with larger probability in all kinds of possible dynamic and time varying operation scenarios. Based on the description and analysis on the time varying scenarios, multiple scenarios considered dynamic power flow method for and are proposed. The method to calculate and evaluate credible capacity based on dynamic power flow (DPF) result is illustrated. A study case of an active distribution network with DERs integrated and containing 32 nodes is selected; multiple operation scenarios with various fractal dimension are established and used. Results of calculated credible capacity based on several groups of scenarios have been analyzed, giving the variance analysis of groups of credible capacity values. A deterministic value with the maximum occurrence probability representing credible capacity is given. Based on the same network case, an application of credible capacity to grid extension planning is given, which contributes to expenditure and cost reduction. The effectiveness and significance of the proposed credible capacity and solution method have been demonstrated and verified.

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“…Stochastic characteristics of wind power, photovoltaic power, and system demand incorporated for optimal placement of dispatchable distributed generation using Big Bang-Big crunch method [26]. The impact of both SVR and high penetration of PQ and PV distributed generation (DG) model on voltage profile and system power losses also studied [27].Naiem, A.F.…”

Section: Related Workmentioning

confidence: 99%

Review and Scientometric Analysis of IEEE 37 Radial Feeder Publications

Sharma¹,

Kamath²

2016

International Journal of Innovative Research in Electrical, Ele

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Abstract:In this paper, a review and scientometric analysis of IEEE 37 Radial Feeder publications is carried out to find out literature growth and researcher interest. An IEEE 37 Radial feeder is a rare, highly unbalance underground feeder in delta configuration and located in California. The publication of study is obtained from IEEE Xplore and Science direct digital library. It is observed that the IEEE Xplore digital library has total 34 publications during 1995 to 2015, while Science direct digital library has only 05 during 2008 to 2015. All these papers are discussed in the literature review. In IEEE Xplore direct digital library, maximum publication on this topic appeared in during 2014 and whereas Science direct digital library have only 01 publications for same. Authors from the USA have contributed the maximum as compared with other countries. In the IEEE Xplore digital library, the relative growth rates (RGR) have continuously decreased from 2003 (0.693) to 2011 (0.095) and maximum doubling time (DT) occurred in 2015 (11.431), while in science direct digital library RGR has continuously decreased from 2011 (0.693) and DT has increased from 1.709 to 3.106 during last three years.

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Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks

Cited by 78 publications

References 15 publications

A Novel Probabilistic Technique for Optimal Allocation of Photovoltaic Based Distributed Generators to Decrease System Losses

A Novel Probabilistic Technique for Optimal Allocation of Photovoltaic Based Distributed Generators to Decrease System Losses

Assessment of Credible Capacity for Intermittent Distributed Energy Resources in Active Distribution Network

Review and Scientometric Analysis of IEEE 37 Radial Feeder Publications

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