Multi-objective Optimization Approach for Optimal Distributed Generation Sizing and Placement

Darfoun, Mohamed; El‐Hawary, Mohamed E.

doi:10.1080/15325008.2014.1002589

Cited by 42 publications

(17 citation statements)

References 17 publications

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“…Front (surface) which is given by :Statement 2 Pareto Optimality: A solution

\overset{x}{true}

ɛX is named Pareto optimal if:

updiagonalstrike \exists \overset{y}{true} \in X ∣ F ((), \overset{false\to}{y}) ≻ F ((), \overset{false\to}{x})

…”

Section: Description Of Multi‐objective Optimization Algorithmsmentioning

confidence: 99%

Optimal Fractional Order BELBIC to Ameliorate Small Signal Stability of Interconnected Hybrid Power System

Falehi

2019

Env Prog and Sustain Energy

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The small signal stability of power systems integrated with fallback large‐scale distributed energy resources is of utmost importance during the perturbation conditions. Among the various renewable resources in power systems: photovoltaic, fuel cell, wind turbine, diesel engine, aqua electrolyser, battery, ultra‐capacitor, and flywheel is becoming more favored in last decades. AGC, a main control system of frequency modulation, must correctly alleviate the low frequency oscillations caused by the perturbation and variable output power of renewable resources. In this regard, FOBELBIC is here introduced to enhance the damping capability of AGC. Since both the frequency and tie‐line power oscillations must be concomitantly alleviated, the small signal stability problem has been multi‐objectively formulated. Due to the exploration capability of MOALO, it has been chosen to extract the optimal parameters of FOBELBIC. Furthermore, MOPSO, MOBA, and NSGA‐II have been studied to demonstrate the capability of MOALO. To verify and validate the damping performance of suggested controller, it has been evaluated in three‐area reconstructed power system under the load perturbations as well as wind speed and sun radiation variations, and at the same time compared with BELBIC and PID controllers. Finally, the simulation results have evidently confirmed the damping the performance of MOALO‐based FOBELBIC to enhance the small signal stability of hybrid power system. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019

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“…Front (surface) which is given by :Statement 2 Pareto Optimality: A solution

\overset{x}{true}

ɛX is named Pareto optimal if:

updiagonalstrike \exists \overset{y}{true} \in X ∣ F ((), \overset{false\to}{y}) ≻ F ((), \overset{false\to}{x})

…”

Section: Description Of Multi‐objective Optimization Algorithmsmentioning

confidence: 99%

Optimal Fractional Order BELBIC to Ameliorate Small Signal Stability of Interconnected Hybrid Power System

Falehi

2019

Env Prog and Sustain Energy

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“…DSTATCOM is a power electronic-based synchronous voltage generator capable of providing rapid and uninterrupted capacitive and inductive reactive power supply [2]. Various renewable and nonrenewable DG technologies are available on the market today, such as microturbines, fuel cells, combustion gas turbines, photovoltaic, wind turbines, and combined heat and power [3]. The integration of renewable DG technologies such as photovoltaic and wind turbines is becoming more popular in distribution systems on account of smart grid initiatives and strict environmental laws.…”

Section: Introductionmentioning

confidence: 99%

Improved Cat Swarm Optimization for Simultaneous Allocation of DSTATCOM and DGs in Distribution Systems

Kanwar

Gupta

Niazi

et al. 2015

Journal of Renewable Energy

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This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO) is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO) technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.

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“…Pareto front has been obtained in both and compromised solution has been selected from set of solutions. The best solution has been selected by fuzzy decision-making in [27] and while it has been done by max-min technique in [28].…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy decision-making has been employed to select single solution from Pareto front. Sequential quadratic programming has been employed in [27] to obtain set of optimal solutions to minimize total real power loss and cost of DG. In addition to loss and voltage deviation minimization, cost minimization is also considered as objective [28].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Approach for Placement of Multiple DGs for Voltage Sensitive Loads

Kaur

Jain

2017

Energies

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This paper presents the optimal placement of multiple Dispersed Generators using multi-objective optimization. The optimization is carried out with objectives namely active power loss, reactive power loss, voltage deviation and overall economy. The multi-objective optimization and accounting conflicting objectives are realized through Particle Swarm Optimization with fuzzy decision approach to find the optimal sizes and sites of Dispersed Generators for voltage dependent residential, commercial and industrial loads. The clusters of buses are formulated from base case load flow to limit the search space for finding the placement of the Dispersed Generators. The effectiveness of the proposed approach is tested on a 69-bus radial distribution. It is found that the optimal placement of the Dispersed Generators improves the overall performance of the system and the optimal allocation is affected by the type of load.

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Multi-objective Optimization Approach for Optimal Distributed Generation Sizing and Placement

Cited by 42 publications

References 17 publications

Optimal Fractional Order BELBIC to Ameliorate Small Signal Stability of Interconnected Hybrid Power System

Optimal Fractional Order BELBIC to Ameliorate Small Signal Stability of Interconnected Hybrid Power System

Improved Cat Swarm Optimization for Simultaneous Allocation of DSTATCOM and DGs in Distribution Systems

Multi-Objective Optimization Approach for Placement of Multiple DGs for Voltage Sensitive Loads

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