Optimal Integration of Multi Distributed Generation Sources in Radial Distribution Networks Using a Hybrid Algorithm

Bhullar, Suman; Ghosh, Swaroop

doi:10.3390/en11030628

Cited by 24 publications

(17 citation statements)

References 30 publications

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“…Voltage stability is also considered in [22] but optimization is carried out via an improved genetic algorithm. The hybrid artificial bee colony and cuckoo search of [23] is shown to outperform PSO and genetic algorithm variants for DG placement and sizing. Stochastic PV generation scenarios are introduced in the multi-objective PSO algorithm of [24] assuming that solar irradiance follows the beta distribution, but no reactive support is provided by the PV units.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic Planning of Distributed PV Generation

Bazrafshan

Yalamanchili²,

Gatsis

et al. 2019

Energies

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Recent studies by electric utility companies indicate that maximum benefits of distributed solar photovoltaic (PV) units can be reaped when siting and sizing of PV systems is optimized. This paper develops a two-stage stochastic program that serves as a tool for optimally determining the placing and sizing of PV units in distribution systems. The PV model incorporates the mapping from solar irradiance to AC power injection. By modeling the uncertainty of solar irradiance and loads by a finite set of scenarios, the goal is to achieve minimum installation and network operation costs while satisfying necessary operational constraints. First-stage decisions are scenario-independent and include binary variables that represent the existence of PV units, the area of the PV panel, and the apparent power capability of the inverter. Second-stage decisions are scenario-dependent and entail reactive power support from PV inverters, real and reactive power flows, and nodal voltages. Optimization constraints account for inverter’s capacity, PV module area limits, the power flow equations, as well as voltage regulation. A comparison between two designs, one where the DC:AC ratio is pre-specified, and the other where the maximum DC:AC ratio is specified based on historical data, is carried out. It turns out that the latter design reduces costs and allows further reduction of the panel area. The applicability and efficiency of the proposed formulation are numerically demonstrated on the IEEE 34-node feeder, while the output power of PV systems is modeled using the publicly available PVWatts software developed by the National Renewable Energy Laboratory. The overall framework developed in this paper can guide electric utility companies in identifying optimal locations for PV placement and sizing, assist with targeting customers with appropriate incentives, and encourage solar adoption.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic Planning of Distributed PV Generation

Bazrafshan

Yalamanchili²,

Gatsis

et al. 2019

Energies

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“…By subtracting both equality sides of (15) from both sides of equality (14) and taking into consideration (17), (18) and (20) it can be derived:…”

Section: Of 26mentioning

confidence: 99%

“…Other groups of power systems problems to solve using the GA are the energy consumption optimization tasks [14,15] and optimizations for forecasting purposes [16,17]. The nature of the problem under consideration is very similar to the optimal power flow [18], optimal automation devices [19] or distributed generation sources [20] allocation, optimal operating and scheduling of micro grids [21] and other problems [22,23] but with different objective functions. These are the entire tasks based on network operation mode calculations with the pre-defined non-linear constraints to find global extremum, i.e., the minimal losses, the minimal or the maximal flows, the minimal investments, or the worst-case error for the case under consideration.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids

et al. 2018

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Smart energy meters supporting bidirectional data communication enable novel remote error monitoring applications. This research targets characterization of the systematic worst-case error of the previously published remote watthour meter’s gain estimation method based on the comparison of synchronous measurements by the reference and meter under test. To achieve the research aim a methodology based on global maximization of the systematic error objective function assuming the typical low voltage electrical distribution network operation parameters ranges as defined by the standard recommendations for network design. To cross verify the reliability of the assessed solutions the suggested error analysis methodology was implemented utilizing two stochastic global extremum search techniques (genetic algorithms, pattern search) and the third one utilizing nonlinear programming solver. It was determined that the wattmeter adjustment gain worst-case error does not exceed 0.5% if the remote wattmeter monitored load power factor is larger than 0.1 and a network is designed according to the recommendation of the acceptable voltage drop less than 5%. For a load exhibiting power factor larger than cos φ = 0.9 the worst-case error was found to be less than 0.1%. It is concluded therefore that considering the systematic worst-case error the previously suggested remote wattmeter adjustment gain estimation method is suitable for remote error monitoring of Class 2 and Class 1 wattmeters.

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“…In addition, photovoltaic energy systems in homes increases the efficiency of networks both in capital and utilization rates [7]. DG units could also be used to maximize the voltage profile and minimize power loss [8]. These units vary between non-traditional units such as wind turbines, photovoltaic farms, and fuel cells, as well as traditional generators such as micro turbines, therefore, modern grids have become more complex [9].…”

Section: Introductionmentioning

confidence: 99%

Voltage Quality and Power Factor Improvement in Smart Grids Using Controlled DG Units

et al. 2019

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The increased penetration of renewable energy sources in the electrical grid, due to the rapid increase of power demand and the need of diverse energy sources, has made distributed generation (DG) units an essential part of the modern electrical grid. The integration of many DG units in smart grids requires control and coordination between them, and the grid to maximize the benefits of the DG units. Smart grids and modern electronic devices require high standards of power quality, especially voltage quality. In this paper, a new methodology is presented to improve the voltage quality and power factor in smart grids. This method depends on using voltage variation and admittance values as inputs of a controller that controls the reactive power generation in all DG units. The results show that the controller is efficient in improving the voltage quality and power factor. Real data from an electrical network have been used in the simulation model in MATLAB Simulink to test the new approach.

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Optimal Integration of Multi Distributed Generation Sources in Radial Distribution Networks Using a Hybrid Algorithm

Cited by 24 publications

References 30 publications

Stochastic Planning of Distributed PV Generation

Stochastic Planning of Distributed PV Generation

An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids

Voltage Quality and Power Factor Improvement in Smart Grids Using Controlled DG Units

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