Optimal DG Allocation in Radial Distribution Systems with High Penetration of Non-linear Loads

Abdelsalam, Abdelazeem A.; Zidan, Aboelsood; El-Saadany, Ehab F.

doi:10.1080/15325008.2015.1043601

Cited by 25 publications

(15 citation statements)

References 31 publications

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“…Furthermore, the studies in [25,26] were about multiobjective problem with trade-off between two objectives. In this regard, we have found four studies [27][28][29][30] that proposed single objectives for the task of determining the location and sizing of PVDGUs. As pointed in [27], the power losses occur naturally and depend on the type of conductor used, the capacity of the transformer, and other components used in the distribution grid.…”

Section: Proposals Novelties Andmentioning

confidence: 99%

“…On the contrary, study [29] has considered technical issue as a main goal and improving voltage profile has been proposed to be an objective function. Study [30] has pointed out serious damages of nonlinear loads on distribution grids since they have caused voltage distortion. Hence, the main goal should be the minimization of harmonic distortions.…”

Section: Proposals Novelties Andmentioning

confidence: 99%

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Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Nguyen

Pham²,

Kien

et al. 2020

Complexity

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This paper proposes an improved coyote optimization algorithm (ICOA) for optimizing the location and sizing of solar photovoltaic distribution generation units (PVDGUs) in radial distribution systems. In the considered problem, four single objectives consisting of total power losses, capacity of all PVDGUs, voltage profile index, and harmonic distortions are minimized independently while satisfying branch current limits, voltage limits, and harmonic distortion limits exactly and simultaneously. The performance of the proposed ICOA method has been improved significantly since two improvements were carried out on the two new solution generations of the conventional coyote optimization algorithm (COA). By finding four single objectives from two IEEE distribution power systems with 33 buses and 69 buses, the impact of each proposed improvement and two proposed improvements on the real performance of ICOA has been investigated. ICOA was superior to COA in terms of capability of finding higher quality solutions, more stable search ability, and faster convergence speed. Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. The result comparisons have also indicated the outstanding performance of ICOA because it could find much better results than these methods, especially SFO, SSA, and GA. Consequently, the proposed ICOA is a very effective method for finding the optimal location and capacity of PVDGUs in radial distribution power systems.

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Section: Proposals Novelties Andmentioning

confidence: 99%

Section: Proposals Novelties Andmentioning

confidence: 99%

Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Nguyen

Pham²,

Kien

et al. 2020

Complexity

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“…Investment cost contains unit construction, installation and essential equipment cost. Operation cost includes the cost of replacing components during their technical lifetime and maintenance cost contains costs of renewing, repairing, and restoring unit equipment in case of necessity [24][25][26].…”

Section: Dg Power Costmentioning

confidence: 99%

“…where P DG;g and C INV ;g are the rated real power and inverter cost of g th DG unit; C 0 OP and C 0 M are the operating cost and maintenance cost of each DG unit; R INF and R INT are the inflation rate and interest rate of each DG unit; T h and Ny are total number of hours in a year and total the number of years; and N DG is the number of DGs considered for the study. The above-mentioned parameters required to calculate DG power cost are taken from [26].…”

Section: Dg Power Costmentioning

confidence: 99%

Multi-objective simultaneous optimal planning of electrical vehicle fast charging stations and DGs in distribution system

Battapothula

Yammani

Sydulu

2019

J. Mod. Power Syst. Clean Energy

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The large-scale construction of fast charging stations (FCSs) for electrical vehicles (EVs) is helpful in promoting the EV. It creates a significant challenge for the distribution system operator to determine the optimal planning, especially the siting and sizing of FCSs in the electrical distribution system. Inappropriate planning of fast EV charging stations (EVCSs) cause a negative impact on the distribution system. This paper presented a multiobjective optimization problem to obtain the simultaneous placement and sizing of FCSs and distributed generations (DGs) with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system. The problem is formulated as a mixed integer non-linear problem (MINLP) to optimize the loss of EV user, network power loss (NPL), FCS development cost and improve the voltage profile of the electrical distribution system. Non-dominated sorting genetic algorithm II (NSGA-II) is used for solving the MINLP. The performance of the proposed technique is evaluated by the 118-bus electrical distribution system.

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“…These are some basic parameters of GA and GA optimization problems formulated are according to [16].…”

Section: Fitness Functionmentioning

confidence: 99%

Volt/VAr Optimization of Distribution System with Integrated Distributed Generation

Mughees

Awan

Mughees

2017

Mehran Univ. res. j. eng. technol.

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Optimal DG Allocation in Radial Distribution Systems with High Penetration of Non-linear Loads

Cited by 25 publications

References 31 publications

Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Multi-objective simultaneous optimal planning of electrical vehicle fast charging stations and DGs in distribution system

Volt/VAr Optimization of Distribution System with Integrated Distributed Generation

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