A profit-centric strategy for distributed generation planning considering time varying voltage dependent load demand

Ebrahimi, Reza; Ehsan, Mehdi; Nouri, Hassan

doi:10.1016/j.ijepes.2012.07.039

Cited by 35 publications

(16 citation statements)

References 38 publications

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“…Expansion planning of DG based on dynamic fuzzy interactive approach with network reinforcement was well explained in [12], which optimized total cost, emission cost and voltage profile of the network by determining the best possible design of http://dx.doi.org/10.12785/ijcds/080609 http://journals.uob.edu.bh timing, rating and placement of DG. Investigation of DNO profit and selection of optimal DG size options for various loading conditions were proposed in [13].…”

Section: Introductionmentioning

confidence: 99%

Loading Capability Enhancement of Existing Unbalanced Distribution System with Distributed Generation

al.¹

2019

IJCDS

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The distribution system supplies power to the consumers of the different nature that may be residential, commercial, and industrial. It is very difficult to have perfect balance in all the phases due the stochastic nature of the load and the switching behavior of the consumers. It is thus important to study the impact of unbalances on the loadability of the network and the impact of the presence of distributed generation. This paper proposed a method for finding the economic size of Distributed generation (DG) and its optimal location in unbalanced radial distribution system (URDS). The main contributions of the paper are: (i) Determination of economic rating of DG based on variational algorithm subjected to minimize the total cost, (ii) Examine the comparative impact of DG power factor in reduction of losses, improvement in voltage profile and cost saving (iii) Estimation of total released feeder capacity after DG placement, (iv) Analysis of P-V, Q-V curve for finding the improvement of loading capability. The results have been determined for IEEE 25 bus unbalanced radial distribution system.

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Section: Introductionmentioning

confidence: 99%

Loading Capability Enhancement of Existing Unbalanced Distribution System with Distributed Generation

al.¹

2019

IJCDS

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“…Most of the previous studies have considered the power utility as the DG owner. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]19,[25][26][27][28][29][30][31][32][33][34][35] Nowadays, since the interest of the private investors to participate in DG investment is increasing, more DGEP research should be conducted at the viewpoint of these investors, with more details and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A new approach for DG planning at the viewpoint of the independent DG investor, a case study of Iran

Barati

Jadid

Zangeneh

2017

Int. Trans. Electr. Energ. Syst.

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Summary Over the last decade, distributed generation units have been proposed to provide a part of the required energy of end‐use customers. In this paper, a comprehensive model is presented to perform the distributed generation expansion planning from the independent investor's viewpoint. The objective function is suggested based on the payback period and with considering the budget limit. The investment and operation costs are included in the problem. The investment cost consists of the costs of equipment, distribution line installation, upgrading of upstream substation, gas and water pipe installations, and land. The genetic algorithm is applied to determine the optimal technology, location, and size of the distributed generation units. The performance of the proposed model is evaluated via a realistic case study, and the effect of a bank loan is discussed in the numerical results. Finally, the impact of the investor's investment decisions on the power system is analyzed.

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“…The methodologies proposed in the literature can be stratified by objective function, algorithm applied and additional control variables utilized. In relation to algorithms, the most common applied are: Exhaustive Search [3], Genetic [4][5][6][7][8][9][10], Particle Swarm Optimization [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], Mixed-Integer Linear Programming [27,28], Artificial Neural Networks [29], Kalman Filter [30], Evolutionary [31], Non-dominated Sorting Genetic Algorithm II [32], Tabu Search [33], Multi-Objective Nonlinear Programming [34], Chaotic Artificial Bee Colony [35] and Fuzzy Approach [36]. There are also new algorithms proposed by the authors: Chaotic Local Search and Modified Honey Bee Mating Optimization [37], Modified Discrete Particle Swarm Optimization [38], Modified Teaching-Learning Based Optimization [39], Plant Growth Simulation [40], Imperialist Competitive Algorithm [41] and Improved Multi-Objective Harmony Search [42].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Distributed Generation Units in a Distribution System with Uncertain Topologies using Monte Carlo Simulation

Donadel¹,

Fardin

Encarnação

2015

International Journal of Emerging Electric Power Systems

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In the literature, several papers propose new methodologies to determine the optimal placement/sizing of medium size Distributed Generation Units (DGs), using heuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). However, in all methodologies, the optimal placement solution is strongly dependent of network topologies. Therefore, a specific solution is valid only for a particular network topology. Furthermore, such methodologies does not consider the presence of small DGs, whose connection point cannot be defined by Distribution Network Operators (DNOs). In this paper it is proposed a new methodology to determine the optimal location of medium size DGs in a distribution system with uncertain topologies, considering the particular behavior of small DGs, using Monte Carlo Simulation.

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A profit-centric strategy for distributed generation planning considering time varying voltage dependent load demand

Cited by 35 publications

References 38 publications

Loading Capability Enhancement of Existing Unbalanced Distribution System with Distributed Generation

Loading Capability Enhancement of Existing Unbalanced Distribution System with Distributed Generation

A new approach for DG planning at the viewpoint of the independent DG investor, a case study of Iran

Optimal Placement of Distributed Generation Units in a Distribution System with Uncertain Topologies using Monte Carlo Simulation

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