Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties

Zou, Kai; Agalgaonkar, Ashish P.; Muttaqi, Kashem M.; Perera, Sarath

doi:10.1109/tste.2011.2166281

Cited by 336 publications

(205 citation statements)

References 25 publications

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“…Therefore, it is necessary to consider its uncertainty when the optimizing the configuration of DG. [4] 2.1 Wind power generation probability model.…”

Section: Dg Output Probability Modelmentioning

confidence: 99%

Maximum Admission Capacity Model of Distribution Generation in Active Distribution Network

Cao¹

2017

Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

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Abstract. The optimal planning of distributed generation which intends to maximize the capacity genetration of distributed generations (DG) is mainly studied in this paper. Firstly, the probabilistic models of wind power generation and photovoltaic power generation are established under the premise of considering the uncertainties of DGs. Secondly, on the basis of this, the DG maximum admission capacity model with the maximum capacity of the DGs as the objective function is established. The model introduces different types of constraints, including voltage level constraint, line power constraint for the traditional distribution network. In addition, in order to adapt to the active management mode, we introduce DG output constraint, OLTC tap constraint, and reactive power compensation constraint.

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“…Therefore, it is necessary to consider its uncertainty when the optimizing the configuration of DG. [4] 2.1 Wind power generation probability model.…”

Section: Dg Output Probability Modelmentioning

confidence: 99%

Maximum Admission Capacity Model of Distribution Generation in Active Distribution Network

Cao¹

2017

Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

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“…However, installing any extra devices for providing reactive power provision leads to extra payments and consequently higher investment costs. It should be mentioned here that the power capability of DG units to provide active/reactive power highly depends on the technologies as shown in [21,22]. Briefly, utilizing the reactive power capability of DG units can significantly affect active energy losses in ADNs.…”

Section: Figurementioning

confidence: 99%

“…Generally, the capability of a power conditioning system (PCS) used for connecting DG units to a power system depends on DG technologies, e.g., see [21]. In this paper, a full-scale power electronic converter [22] is modeled as an ideal PCS as seen in Figure 3.…”

Section: Wind Stationmentioning

confidence: 99%

“…The introduction of a curtailment factor (0 ď β c.w ď 1) at each WS is necessary to curtail the wind power generation to prevent violations of system constraints. More importantly, we consider the situation in which the available reactive power need to be restricted by different limits on PFs [21,22] (Figure 3a). This is represented with the dark-area in Figure 3b.…”

Section: Wind Stationmentioning

confidence: 99%

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On Variable Reverse Power Flow-Part I: Active-Reactive Optimal Power Flow with Reactive Power of Wind Stations

Gabash

2016

Energies

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It has recently been shown that using battery storage systems (BSSs) to provide reactive power provision in a medium-voltage (MV) active distribution network (ADN) with embedded wind stations (WSs) can lead to a huge amount of reverse power to an upstream transmission network (TN). However, unity power factors (PFs) of WSs were assumed in those studies to analyze the potential of BSSs. Therefore, in this paper (Part-I), we aim to further explore the pure reactive power potential of WSs (i.e.

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“…A stochastic multi-objective model for placement of DG in electrical distribution networks is proposed in [13] with a binary particle swarm optimization (PSO) algorithm. The DS expansion planning strategy with DG systems with intermittent power generation is presented in [14] to obtain optimal solutions for system planners. A DG placement planning study framework is brought in [15] that includes a coordinated reconfiguration feeder and voltage control to calculate the maximum allowable DG capacity at a given node in the electrical distribution system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Distributed Generation Using Bacterial foraging Optimization in an Electrical Distribution System

Baskar¹,

Hariprakash²,

Vijayakumar³

2017

IJERT

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Abstract-Distributed generation (DG) has been placed in electrical distribution networks widely due to line loss reduction, environmental benefits, better voltage profile, postponement of system upgrading, and increasing reliability. Optimization techniques are tools which can be used for placement and sizing of the DG units in the distribution system. The impacts of DG placement issues, such as power loss minimization and voltage profile, should be analyzed effectively by proposing a method of locating and sizing DG units. This paper proposes Bacterial Foraging Optimization (BFO) algorithm technique for optimal placement of DG units in Distribution System (DS) to minimize the total real power loss, improve power factor and to regulate the voltage profile. BFO is a recently developed nature-inspired optimization technique, which is based on the foraging behavior of E. coli bacteria. To determine the validity of the BFO algorithm, a standard IEEE 69 bus radial distribution feeder system is examined with two test cases. The results show that the proposed BFO algorithm is more efficient, and capable of handling mixed integer nonlinear optimization problems effectively.

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Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties

Cited by 336 publications

References 25 publications

Maximum Admission Capacity Model of Distribution Generation in Active Distribution Network

Maximum Admission Capacity Model of Distribution Generation in Active Distribution Network

On Variable Reverse Power Flow-Part I: Active-Reactive Optimal Power Flow with Reactive Power of Wind Stations

Optimal Placement of Distributed Generation Using Bacterial foraging Optimization in an Electrical Distribution System

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