DG Source Allocation by Fuzzy and Clonal Selection Algorithm for Minimum Loss in Distribution System

Lalitha, M. Padma; Reddy, Vijay; Reddy, N. Sivarami; Reddy, V. Usha

doi:10.1080/21563306.2011.10462202

Cited by 32 publications

(30 citation statements)

References 8 publications

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“…In the literature [20][21][22][23][24][25], the authors investigated the problem of deployment of distributed electricity sources to reduce active power losses and improve voltage characteristics. Lalitha et al [20] used the fuzzy artificial immune system to determine the location of distributed sources, to improve the characteristics of voltage and reduce active power losses.…”

Section: Non-traditional Methods For Determining Optimal Location Andmentioning

confidence: 99%

“…In [24] authors used fuzzy logic to determine the optimal location of distributed sources to reduce losses of the active power and maintain the voltage within the allowable limits. Artificial bee colonies algorithm was used to determine the optimal power of distributed generation; the method was successfully tested on IEEE 33 bus test system.…”

Section: Non-traditional Methods For Determining Optimal Location Andmentioning

confidence: 99%

“…Shorter computation time [24] FL Energy management of the EES, balancing EES in microgrid, reducing fluctuations in RES production simplicity compression of complex algorithms [49,50], [51,53] Matrix real-coded genetic algorithm (MRCGA)…”

Section: Psomentioning

confidence: 99%

“…Considering that certain methods for optimization are more appropriate for determining optimal location, [21,24,25,31,36] and other methods for determining the optimal size of DG, the researchers used a combination of methods for optimization to achieve better results, [20,21,22, 24÷30] and [36]. For example, a mix of the fuzzy logic to determine the optimal location and the artificial bee colonies algorithm to determine the optimum power were used in [24].…”

Section: Future Trendsmentioning

confidence: 99%

“…For example, a mix of the fuzzy logic to determine the optimal location and the artificial bee colonies algorithm to determine the optimum power were used in [24]. A hybrid method of genetic algorithm and sequential quadratic programming with the aim of reducing the total cost of the distribution system has proved successful in [36].…”

Section: Future Trendsmentioning

confidence: 99%

See 4 more Smart Citations

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

Šipoš¹,

Klaić²,

Fekete³

et al. 2018

Teh. vjesn.

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Abstract:The integration of distributed energy sources transforms passive distributed grid, in which the energy flows only in one direction (from the source to the consumer), in an active one, in which energy flows in both directions. To maximize positive impacts, which distributed generation (DG) can provide to the distribution network, it is necessary to determine the optimal allocation of distributed generation. The optimal allocation can be determined by using the optimization method. There are two main categories: exact methods (traditional) and heuristic (non-traditional) methods. Exact methods search for global optimum while heuristic methods achieve satisfactory solutions with greater computation speed. This paper gives a brief review of non-traditional methods used for determining optimal location and optimal power of DG with the aim to reduce real power losses and to improve voltage characteristics. Also, there is a review of the application of those methods in determining the optimal power, optimal location and optimal cycle of charging/discharging of electrical energy storage systems.

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Section: Non-traditional Methods For Determining Optimal Location Andmentioning

confidence: 99%

Section: Non-traditional Methods For Determining Optimal Location Andmentioning

confidence: 99%

Section: Psomentioning

confidence: 99%

Section: Future Trendsmentioning

confidence: 99%

Section: Future Trendsmentioning

confidence: 99%

See 3 more Smart Citations

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

Šipoš¹,

Klaić²,

Fekete³

et al. 2018

Teh. vjesn.

View full text Add to dashboard Cite

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Olympic ranking–based allocation of distributed generation units in distribution networks

Gholami

Zakariazadeh

2019

Int Trans Electr Energ Syst

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Summary This paper presents a new analytical approach for optimum placement of distributed generations (DGs) in distribution systems. The proposed algorithm is inspired by the Olympic Games ranking process, which is able to simultaneously consider a wide range of goals such as voltage profile, losses, security, loading, and short‐circuit level without worrying about increasing in complexity or computational burden of the problem. This approach takes advantages of nonsensitivity to initial optimization control parameters, a proper normalization of objective values, less computation time, and providing a unique response in different runs. In viewpoint of distribution system operators and planners, the proposed method can provide a simple and comprehensive tool for analytical and validation purposes due to its transparent decision‐making process. The proposed method is implemented on the IEEE 37‐Bus test system, and the results are compared with the ones obtained by two commonly used methods. The simulation results show the ability, effectiveness, and flexibility of the proposed DG placement method.

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Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

2020

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Summary Integrating renewable energy hybrid distributed generation (REHDG) into distribution network systems (DNSs) has become increasingly important because of various technical, economic, and environmental advantages accruing from it. However, the output power of REHDGs from photovoltaic (PV) and wind is highly variable because of its dependency on intermittent parameters such as solar irradiance, temperature, and wind speed. Such variability of generated power from large‐scale REHDGs or load introduces small signal instabilities (oscillations). Meanwhile, different locations of integration and sizes of REHDGs in the DNS affect the system oscillation modes by either improving or depriving the small‐signal stability (SSS) of the network. Consequently, a significant number of research has been conducted on the planning of optimal allocation of REHDGs in DNS. In this regard, this paper reviews the existing planning models, optimisation techniques, and resources' uncertainty modelling employed in REHDGs allocations in terms of their capability in obtaining optimal solutions and enhancing SSS of the system. Planning models with optimisation algorithms are evaluated for modelling renewable resource uncertainties and curtailing SSS variables. Research works on planning of optimal allocation of these generations attain minimum cost, but were unable to satisfy the SSS requirements of the system. The existing models for the planning and design of optimal timing, sizing, and placement of REHDGs will need to be improved to optimally allocate REHDGs and satisfy the SSS of the DNS after the integration.

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DG Source Allocation by Fuzzy and Clonal Selection Algorithm for Minimum Loss in Distribution System

Cited by 32 publications

References 8 publications

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

Olympic ranking–based allocation of distributed generation units in distribution networks

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

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