Method for Determining a PV Generation Limit on Low Voltage Feeders for Evenly Distributed PV and Load

Heslop, Simon; MacGill, Iain; Fletcher, John; Lewis, Simon

doi:10.1016/j.egypro.2014.10.025

Cited by 23 publications

(13 citation statements)

References 20 publications

(21 reference statements)

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“…According to the power flow equations, changing the active and reactive powers of loads and DG units affects effectively the amplitude and angle of system voltages . This effect on system characteristics is especially seen in the heavy load of distributed network:

Power flow constraits : ({, \begin{matrix} P_{G, k} - P_{L, k} = \sum_{i = 1}^{N} (||, V_{i}) (||, V_{k}) (||, Y_{italicik}) cos ((), δ_{ki} + θ_{k} - θ_{i}) \\ Q_{G, k} - Q_{L, k} = \sum_{i = 1}^{N} (||, V_{i}) (||, V_{k}) (||, Y_{italicik}) sin ((), δ_{ki} + θ_{k} - θ_{i}) \end{matrix}), \forall k \in N

where Y ik is element i ‐ k in Y BUS (network admittance matrix); δ ik is phase angle of Y ik ; and P G , k and Q G , k are the generated active and reactive powers of bus k , respectively.…”

Section: Subroutines Of Optimization Functionmentioning

confidence: 99%

“…Using voltage stability index, the voltage stability subroutine (dVS) is defined as a function of the voltage magnitude and (generated and consumed) power of system buses: According to the power flow equations, changing the active and reactive powers of loads and DG units affects effectively the amplitude and angle of system voltages. 17 This effect on system characteristics is especially seen in the heavy load of distributed network 8 :…”

Section: Voltage Stabilitymentioning

confidence: 99%

“…The methods presented in the above papers [14][15][16][17][18][19][20][21][22][23][24][25] have considerable weaknesses due to simplifications, and the applied models do not cover all constraints or their nonlinear behavior in the objective function. Location, number, and capacity of energy resources, especially renewable resources with variable output (like solar and wind energy), have serious implications that influence the optimization algorithms of DG penetration and placement and tend to spread the interconnection points of DG units.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Naghdi

Shafiyi

Haghifam

2018

Int Trans Electr Energ Syst

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Summary Increasing integration of local generators is a challenge in the planning, design, and operation of the distribution system. The distributed energy resources, especially renewable ones, have caused dispersion of generation units along the feeder. This paper presents a dual index combination of the penetration level and the dispersion factor of distributed generation. Maximum penetration level of distributed generation was determined by a multiobjective function of the proposed dual index. This function has subroutines based on voltage stability, active and reactive power flows, voltage profile limits, and power loss that are restricted by technical constraints associated system with shunt compensator. A limited memory quasi‐Newton trust‐region algorithm was developed to optimize the objective function by using the first‐ and second‐order sensitivities of the nonlinear objective function and a trust region based on the constraints. Two networks were used for testing, and obtained results revealed accuracy and validity of the proposed method.

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Power flow constraits : ({, \begin{matrix} P_{G, k} - P_{L, k} = \sum_{i = 1}^{N} (||, V_{i}) (||, V_{k}) (||, Y_{italicik}) cos ((), δ_{ki} + θ_{k} - θ_{i}) \\ Q_{G, k} - Q_{L, k} = \sum_{i = 1}^{N} (||, V_{i}) (||, V_{k}) (||, Y_{italicik}) sin ((), δ_{ki} + θ_{k} - θ_{i}) \end{matrix}), \forall k \in N

where Y ik is element i ‐ k in Y BUS (network admittance matrix); δ ik is phase angle of Y ik ; and P G , k and Q G , k are the generated active and reactive powers of bus k , respectively.…”

Section: Subroutines Of Optimization Functionmentioning

confidence: 99%

Section: Voltage Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Naghdi

Shafiyi

Haghifam

2018

Int Trans Electr Energ Syst

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show abstract

“…Banerjee et al presented a reactive loading index to predict voltage stability margin (VSM) for 33 node feeder with composite loads [9]. At specified load, maximum allowable PV penetration assessment has been done with the help of terminal count and total feeder impedance [10]. In restructured environment, distributed generation integration brought economic, environmental and technical benefits for unities and consumers.…”

Section: Introductionmentioning

confidence: 99%

Stochastic voltage stability margin in unbalance feeder with fuzzy based distributed generation placement

Sharma

Kamath²

2018

IJET

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In this paper, the impact of distributed generation (DG) integration on worst stochastic voltage stability margin is investigated for a modified IEEE 37 node test system. This unbalance test system has voltage sensitive load model for industrial, commercial and residential consumers and load flow computed in MATLAB environment with 15 minutes metering time interval for a whole day. DG integration is based on fuzzy expert system and integrated between 35 to 73 period of metering time interval. The stochastic voltage stability margin for all phase are evaluated under three different DG operational scenarios and compared with results obtained in the base case. The cause and consequence of unbalance phenomena is also broadly discussed in detail.

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“…Generally, by limiting the amount of power injected into the grid, voltage rise and energy quality degradation may cause a reverse power flow (Heslop et al, 2014) [1]. In order to overcome the negative impacts of high active DG used, and to avoid power curtailment, various voltage control schemes involving DG production have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Novel Optimal Coordinated Voltage Control for Distribution Networks Using Differential Evolution Technique

Ánh

Kien

2018

JCC

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Abstract. This paper investigates a Distributed Generators (DG) connected to distribution networks offering multiple benefits for grids and environments in the case of renewable sources used. Nevertheless, this task requires an appropriate planning and control strategy, if not several drawbacks can issue, including voltage rise problems and increased power losses. To overcome such disadvantages, this paper proposes a coordinated voltage control CVC method for distribution networks with multiple distributed generators. This new method is based on a differential evolution DE approach to obtain the optimal setting points for each control component. Furthermore this proposed method considers both of time-varying load demand and production, leading to not only an improvement in the voltage profile but also to optimally minimizing the active power loss.

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Method for Determining a PV Generation Limit on Low Voltage Feeders for Evenly Distributed PV and Load

Cited by 23 publications

References 20 publications

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Stochastic voltage stability margin in unbalance feeder with fuzzy based distributed generation placement

Novel Optimal Coordinated Voltage Control for Distribution Networks Using Differential Evolution Technique

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