Reactive power planning and its cost allocation for distribution systems with distributed generation

Integrating Distributed Generation (DG) units into distribution systems can have an impact on the voltage profile, power flow, power losses, and voltage stability. In this paper, a new methodology for DG location and sizing are developed to minimize system losses and maximize voltage stability index (VSI). A proper allocation of DG has to be determined using the fuzzy ranking method to verify best compromised solutions and achieve maximum benefits. Synchronous machines are utilized and its power factor is optimally determined via genetic optimization to inject reactive power to decrease system losses and improve voltage profile and VSI. The Augmented Lagrangian Genetic Algorithm with nonlinear mixed-integer variables and Non-dominated Sorting Genetic Algorithm have been implemented to solve both single/multi-objective function optimization problems. For proposed methodology effectiveness verification, it is tested on 33-bus and 69-bus radial distribution systems then compared with previous works.

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Distributed Generation Placement in Radial Distribution Networks using a Bat-inspired Algorithm

DYNA ◽

10.15446/dyna.v82n192.48573 ◽

2015 ◽

Vol 82 (192) ◽

pp. 60-67 ◽

Cited By ~ 6

Author(s):

John Edwin Candelo-Becerra ◽

Helman Hernández-Riaño

Keyword(s):

Distributed Generation ◽

Radial Distribution ◽

Distribution Systems ◽

Reactive Power ◽

Distribution Networks ◽

Power Losses ◽

Number Of Generators ◽

Metaheuristic Techniques ◽

Distributed Generation Placement ◽

Made In

<p>Distributed generation (DG) is an important issue for distribution networks due to the improvement in power losses, but the location and size of generators could be a difficult task for exact techniques. The metaheuristic techniques have become a better option to determine good solutions and in this paper the application of a bat-inspired algorithm (BA) to a problem of location and size of distributed generation in radial distribution systems is presented. A comparison between particle swarm optimization (PSO) and BA was made in the 33-node and 69-node test feeders, using as scenarios the change in active and reactive power, and the number of generators. PSO and BA found good results for small number and capacities of generators, but BA obtained better results for difficult problems and converged faster for all scenarios. The maximum active power injections to reduce power losses in the distribution networks were found for the five scenarios.</p>

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Adopting fuzzy iterative learning control method for reactive power planning in distribution systems

2010 4th International Power Engineering and Optimization Conference (PEOCO) ◽

10.1109/peoco.2010.5559193 ◽

2010 ◽

Cited By ~ 1

Author(s):

Mazda Moattari ◽

Mehdi Ahrari Nouri ◽

Ahmadreza Argha

Keyword(s):

Iterative Learning Control ◽

Distribution Systems ◽

Reactive Power ◽

Control Method ◽

Learning Control ◽

Iterative Learning ◽

Reactive Power Planning

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Reactive Power Compensation of the Distribution Power System with Distributed Generation Using Improved Tabu Search Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2503 ◽

2013 ◽

Vol 765-767 ◽

pp. 2503-2508

Author(s):

Xiang Lei ◽

Yan Li ◽

Shao Rong Wang ◽

Hong Zhao ◽

Fen Zhou ◽

...

Keyword(s):

Tabu Search ◽

Distributed Generation ◽

Power Loss ◽

Cost Allocation ◽

Reactive Power ◽

Search Algorithm ◽

Test System ◽

Local Optimum ◽

Tabu Search Algorithm ◽

Optimal Position

Taking account of the mutual impacts of distributed generation and reactive power, to determine the optimal position and capacity of the compensation device to be installed, the paper proposed an improved Tabu search algorithm for reactive power optimization. The voltage quality is considered of the model using minimum network active power loss as objective Function. It is achieved by maintaining the whole system power loss as minimum thereby reducing cost allocation. On the basis of general Tabu search algorithm, the algorithm used memory guidance search strategy to focus on searching for a local optimum value, avoid a global search blindness. To deal with the neighborhood solution set properly and save algorithm storage space , some corresponding improvements are made, thus, it is easily to stop the iteration of partial optimization and it is more probable to achieve the global optimization by use of the improved algorithm. Simulations are carried out on standard IEEE 33 test system and results are presented.

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Reactive power planning in distribution networks with distributed generation

7th Mediterranean Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2010) ◽

10.1049/cp.2010.0886 ◽

2010 ◽

Cited By ~ 4

Author(s):

M. Alonso ◽

H. Amarís ◽

C. Álvarez ◽

R. Albarracín

Keyword(s):

Distributed Generation ◽

Reactive Power ◽

Distribution Networks ◽

Reactive Power Planning

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Evaluation of 20 kV Distribution Network Losses In Radial Distribution Systems Due to Distributed Generation Penetration

Journal of Physics Conference Series ◽

10.1088/1742-6596/2129/1/012085 ◽

2021 ◽

Vol 2129 (1) ◽

pp. 012085

Author(s):

H Arnawan ◽

I Muzamir ◽

I Y Mohd ◽

R A R Siti ◽

S Hadi

Keyword(s):

Distributed Generation ◽

Power Flow ◽

Distribution Systems ◽

Reactive Power ◽

Negative Impact ◽

Distribution Network ◽

Before And After ◽

Generation Capacity ◽

Network Losses ◽

Increasing Demand

Abstract The installation of distributed generation with renewable energy becomes a solution when the demand for electricity is increasing and electricity generation with fossil energy is increasingly limited. There has been a change in power flow before and after the installation of distributed generation. However there can be a negative impact on the distribution network losses applicable to reactive power flows. There are cases where the distributed generation capacity is greater than the supplied load, resulting in distributed generation operating as a system voltage regulator and requiring reactive power, so that DG will absorb the reactive power from the system. The increasing demand for reactive power in DG also causes an increase in the current flowing in the network, and causes an increase in losses in the network, especially for the losses in reactive power.

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