Maximizing hosting capacity of renewable energy sources in distribution networks: A multi-objective and scenario-based approach

Rabiee, Abbas; Mohseni‐Bonab, Seyed Masoud

doi:10.1016/j.energy.2016.11.095

Cited by 81 publications

(65 citation statements)

References 27 publications

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“…A typical day for each month is defined, and 90 wind speed points (30 days of each month of 3 years) are used to generate the typical 24 hours frequency distribution (24 hours for each month). A total of 288 time segments are used for a year (24 hours × 12 months), and Rayleigh probability distribution function (PDF) is applied for simulation of the wind speed:

f_{R} ((), v_{w}) = ((), \frac{2 v_{w}}{c_{R}^{2}}) . exp ((), - {(\frac{v_{w}}{c_{R}})}^{2}), c_{R} = \frac{2}{\sqrt{π}} v_{mean},

where f R is Rayleigh PDF, v w and v mean are the actual and mean wind speed, respectively, c R is the scale parameter.…”

Section: System Modelmentioning

confidence: 99%

“…With regard to the voltage stability index expressed in the literature (Appendix A), the voltage stability index of each bus is obtained as below (from ):

{VS}_{m} = {|V_{n}|}^{4} - 4 ((), P_{normalm - 1} r_{m} Q_{normalm - 1} x_{m}) {|V_{n}|}^{2} - 4 {({normalQ}_{m - 1} {normalr}_{normalm} - {normalP}_{m - 1} {normalx}_{normalm})}^{2}, normalm = 1, 2, \dots, normalN .

…”

Section: Objective Function and Its Constraintsmentioning

confidence: 99%

“…With regard to the voltage stability index expressed in the literature [5][6][7] (Appendix A), the voltage stability index of each bus is obtained as below (from (A3)):…”

Section: Voltage Stabilitymentioning

confidence: 99%

“…On the other hand, DG owners, especially owners having renewable energy sources (RESs), want to sell more energy, and distribution network operator is attended to attract more local power generation capacity to control his/her network, reduce power loss, emission, and costs, and receive more connection fees . However, the incorrect and overadjustment of DG penetration level can increase the power loss, which is unfavorable for distribution network operators …”

Section: Introductionmentioning

confidence: 99%

“…his/her network, reduce power loss, emission, and costs, and receive more connection fees. 7 However, the incorrect and overadjustment of DG penetration level can increase the power loss, which is unfavorable for distribution network operators. 8 In this paper, a multiobjective optimization function is presented to determine the DG penetration level subject to linear and nonlinear operational constraints of the distribution network.…”

mentioning

confidence: 99%

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A combined probabilistic modeling of renewable generation and system load types to determine allowable DG penetration level in distribution networks

Naghdi

Shafiyi

Haghifam

2018

Int Trans Electr Energ Syst

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Summary Determining the penetration level of the distributed generation (DG) is an effective tool to site and size DGs in distribution networks. A multiobjective function of optimizing the DG penetration level, subject to constraints including short circuit capacity, voltage limits, transformer capacity, reverse power flow, and congestion of lines, is introduced to minimize the real and reactive losses and to ensure the voltage stability considering the variations of loads, different locations and power factors of DGs, and statistical nature of DG powers especially renewable energy resources. This optimization is performed based on a combined probabilistic modeling of the chronological behavior of wind speed, solar irradiance, and load with different types. The dynamical behavior of the compensators and on‐load tap changers is modeled by optimization subfunctions. An improved bee algorithm is used through conducting searches in the neighboring areas based on a new nonlinear function for a higher fitness and coherency speed.

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f_{R} ((), v_{w}) = ((), \frac{2 v_{w}}{c_{R}^{2}}) . exp ((), - {(\frac{v_{w}}{c_{R}})}^{2}), c_{R} = \frac{2}{\sqrt{π}} v_{mean},

where f R is Rayleigh PDF, v w and v mean are the actual and mean wind speed, respectively, c R is the scale parameter.…”

Section: System Modelmentioning

confidence: 99%

“…With regard to the voltage stability index expressed in the literature (Appendix A), the voltage stability index of each bus is obtained as below (from ):

{VS}_{m} = {|V_{n}|}^{4} - 4 ((), P_{normalm - 1} r_{m} Q_{normalm - 1} x_{m}) {|V_{n}|}^{2} - 4 {({normalQ}_{m - 1} {normalr}_{normalm} - {normalP}_{m - 1} {normalx}_{normalm})}^{2}, normalm = 1, 2, \dots, normalN .

…”

Section: Objective Function and Its Constraintsmentioning

confidence: 99%

“…With regard to the voltage stability index expressed in the literature [5][6][7] (Appendix A), the voltage stability index of each bus is obtained as below (from (A3)):…”

Section: Voltage Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A combined probabilistic modeling of renewable generation and system load types to determine allowable DG penetration level in distribution networks

Naghdi

Shafiyi

Haghifam

2018

Int Trans Electr Energ Syst

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Impact of optimal integration of renewable energy sources and electric vehicles in practical distribution feeder with uncertain load demand

Dixit

2020

Int Trans Electr Energ Syst

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Distributed energy resources (DERs) are a better option for fulfillment of load demand near the load centers. Optimal location and rating of DERs plays a vital role in the enhancement of distribution system performance in terms of power loss reduction, voltage profile enhancement, environmental friendliness, increasing reliability, and postponement of system upgrading. This paper addresses the Gbest-guided artificial bee colony (GABC) optimization technique for the optimal integration of renewable energy sources (RESs) and electric vehicles (EVs) in distribution feeder to improve the power loss reduction, voltage level, cost reduction, and emissions reduction under load demand uncertainty. In this work, mathematical models of RESs, that is, wind turbine generation system (WTGS) and photovoltaic generation system (PVGS), are employed to determine the generated output power. The foremost aim of this research work is simultaneous abatement of total active power loss, entire cost, and emissions generation; therefore a multiobjective-based fitness function is formed. Moreover, the impact of charging/discharging behavior of EVs on the distribution grid has also been considered in this study. The proposed methodology is applied on two test systems: (a) 28-bus practical rural distribution network located at Kakdwip zone in the province of West Bengal, India, and (b) standard 69-bus distribution feeders. Finally, from the achieved numerical outcomes, it is analyzed that, GABC algorithm generates better outcomes as compared to other optimization techniques. This indicates the viability and effectiveness of the proposed methodology.

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Coordinated Operation and Planning of the Modern Heat and Electricity Incorporated Networks

Golshanfard

Noorollahi

2022

Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks

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Maximizing hosting capacity of renewable energy sources in distribution networks: A multi-objective and scenario-based approach

Cited by 81 publications

References 27 publications

A combined probabilistic modeling of renewable generation and system load types to determine allowable DG penetration level in distribution networks

A combined probabilistic modeling of renewable generation and system load types to determine allowable DG penetration level in distribution networks

Impact of optimal integration of renewable energy sources and electric vehicles in practical distribution feeder with uncertain load demand

Coordinated Operation and Planning of the Modern Heat and Electricity Incorporated Networks

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