Simultaneous allocation of DGs and remote controllable switches in distribution networks considering multilevel load model

Raoofat, Mahdi

doi:10.1016/j.ijepes.2011.06.023

Cited by 71 publications

(69 citation statements)

References 11 publications

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“…ENS is a common index for reliability evaluation in power systems [1,10,11,48,49,[52][53][54][55]. In the present work, its value is obtained directly from the OPF output in the form of the aggregation of all-nodal load sheddings per scenario ϑ ℓ : …”

Section: Energy Not Suppliedmentioning

confidence: 99%

“…Indeed, DG allocation on areas close to the customers allows the power flowing through shorter paths, and therefore, decreasing the amount of unsatisfied power demand and enhancing the power and voltage profiles. Thus, the eventual intermittence of the centralized power supply can be smoothed [11]. In addition, the modular structure of the DG technologies implies lower financial risks [12,13] and thus the investments on the power system can be deferred [1,3].…”

Section: Introductionmentioning

confidence: 99%

“…Given the complexity of the optimization problem, heuristic optimization techniques belonging to the class of Evolutionary Algorithms (EAs) have been proposed as a most effective way of solution [10], including particle swarm optimization (PSO) [23,24,27,31,32], differential evolution (DEA) [18] and genetic algorithms (GA) [3,7,11,13,14,16,26,33,34]. …”

Section: Introductionmentioning

confidence: 99%

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A risk-based simulation and multi-objective optimization framework for the integration of distributed renewable generation and storage

Mena

Hennebel

et al. 2014

Renewable and Sustainable Energy Reviews

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Abstract:We present a simulation and multi-objective optimization framework for the integration of renewable generators and storage devices into an electrical distribution network. The framework searches for the optimal size and location of the distributed renewable generation units (DG). Uncertainties in renewable resources availability, components failure and repair events, loads and grid power supply are incorporated. A Monte Carlo simulation -optimal power flow (MCS-OPF) computational model is used to generate scenarios of the uncertain variables and evaluate the network electric performance. As a response to the need of monitoring and controlling the risk associated to the performance of the optimal DG-integrated network, we introduce the conditional valueat-risk (CVaR) measure into the framework. Multi-objective optimization (MOO) is done with respect to the minimization of the expectations of the global cost (C g ) and energy not supplied (ENS) combined with their respective CVaR values. The multi-objective optimization is performed by the fast non-dominated sorting genetic algorithm NSGA-II. For exemplification, the framework is applied to a distribution network derived from the IEEE 13 nodes test feeder. The results show that the MOO MCS-OPF framework is effective in finding an optimal DG-integrated network considering multiple sources of uncertainties. In addition, from the perspective of decision making, introducing the CVaR as a measure of risk enables the evaluation of trade-offs between optimal expected performances and risks.

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Section: Energy Not Suppliedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A risk-based simulation and multi-objective optimization framework for the integration of distributed renewable generation and storage

Mena

Hennebel

et al. 2014

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

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“…Immune algorithm [14], linear programming [15], ant colony [16] and simulated annealing [17] are some other methods that are proposed to find the best locations for switches. In [18] a GA based algorithm is proposed for simultaneous allocation of DGs and RCSs to decrease energy losses and energy not supplied (ENS) of the system. The main contribution of [18] is that it considers a multilevel load model rather than a simple peak load consideration to obtain a better solution.…”

Section: Introductionmentioning

confidence: 99%

“…In [18] a GA based algorithm is proposed for simultaneous allocation of DGs and RCSs to decrease energy losses and energy not supplied (ENS) of the system. The main contribution of [18] is that it considers a multilevel load model rather than a simple peak load consideration to obtain a better solution.…”

Section: Introductionmentioning

confidence: 99%

Restricting minimum size of DGs to confirm correct operation of fast directional protection switches in their simultaneous allocation with DGs

Dorkhosh

Samet

2015

Energy Conversion and Management

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Solving a novel multiobjective placement problem of recloser and distributed generation sources in simultaneous mode by improved harmony search algorithm

et al. 2014

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This article deals with optimal placement of Distributed Generation (DG) sources and recloser in simultaneous mode and develops an improved harmony search (iHS) algorithm to solve it. For this, two important control parameters have been adjusted to reach better solution from simple HS algorithm to obtain better solution from simple HS algorithm. The proposed multiobjective function consists of two parts; first is improving reliability indices and second is minimizing power loss. The reliability indices have been selected based on satisfactory requirements of costumer and electric company as well as response to transient and permanent faults. Then, four reliability indices has been used in objective function; that is, system average interruption duration index (SAIDI), cost of energy not supplied, momentary average interruption frequency index, and system average interruption frequency index (SAIFI). Simulation has been performed on a practical distribution network in North West of Iran. Three scenarios have been introduced; that is, scenario (i) First, placement of DGs, and then recloser, scenario (ii) First, placement of recloser, and then DG, and scenario (iii) simultaneous placement of DG and recloser. Also, three cases are defined based on the number of used DG and recloser. Results of the proposed algorithm have been compared with related values of particle swarm optimization and simple HS algorithms. The core contribution of the presented study is introducing several novel indices to analyze and discuss the obtained results from simulation. © 2014 Wiley Periodicals, Inc. Complexity 21: 328–339, 2015

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Simultaneous allocation of DGs and remote controllable switches in distribution networks considering multilevel load model

Cited by 71 publications

References 11 publications

A risk-based simulation and multi-objective optimization framework for the integration of distributed renewable generation and storage

A risk-based simulation and multi-objective optimization framework for the integration of distributed renewable generation and storage

Restricting minimum size of DGs to confirm correct operation of fast directional protection switches in their simultaneous allocation with DGs

Solving a novel multiobjective placement problem of recloser and distributed generation sources in simultaneous mode by improved harmony search algorithm

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