Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation

Khaled, Usama; Eltamaly, Ali M.; Béroual, Abderrahmane

doi:10.3390/en10071013

Cited by 70 publications

(36 citation statements)

References 37 publications

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“…Moreover, the charging and discharging of EVs from the DR programs could be identified and evaluated in optimal conditions for the customer type in terms of DR programs and DR potential benefits [23]. Therefore, optimization techniques are applied to find the optimal solution to problems that affect the PEVs increase and high consumption of energy from the electrical power system networks [9,[24][25][26]. Meanwhile, many current PEVs have been provided for users to replace the old internal combustion car, which are produced by different brands in the market.…”

Section: Introductionmentioning

confidence: 99%

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Kongjeen

Bhumkittipich

2018

Energies

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This paper proposes the impact of plug-in electric vehicles (PEVs) integrated into a power distribution system based on voltage-dependent control. The gasolinegate situation has many people turning to electric vehicles as a more environmentally friendly option, especially in smart community areas. The advantage of PEVs is modern vehicles that can use several types of fuel cells and batteries as energy sources. The proposed PEVs model was developed as a static load model in power distribution systems under balanced load conditions. The power flow analysis was determined by using certain parameters of the proposed electrical network. The main research objective was to determine the voltage magnitude profiles, the load voltage deviation, and total power losses of the electrical power system by using the new proposed methodology. Furthermore, it investigated the effects of the constant power load, the constant current load, the constant impedance load, and the plug-in electric vehicles load model. The IEEE 33 bus system was selected as the test system. The proposed methodology assigned the balanced load types in a steady state condition and used the new methodology to solve the power flow problem. The simulation results showed that increasing the plug-in electric vehicles load had an impact on the grids when compared with the other four load types. The lowest increased value for the plug-in electric vehicles load had an effect on the load voltage deviation (0.062), the total active power loss (120 kW) and the total reactive power loss (80 kVar), respectively. Therefore, this study verified that the load of PEVs can affect the electrical power system according to the time charging and charger position. Therefore, future work could examine the difference caused when PEVs are attached to the electrical power system by means of the conventional or complex load type.

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Section: Introductionmentioning

confidence: 99%

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Kongjeen

Bhumkittipich

2018

Energies

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“…Due to non-convexity and existence of local minima [45], PSO can be used to determine the solution. PSO is an evolutionary method and these methods are becoming popular in solving problems involving nonlinear power flow equations and in determination of the optimal solution in cost minimization problems involving multiple energy sources [23,46].…”

Section: Solution Of Energy Exchange Cost Minimization Problemmentioning

confidence: 99%

Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity

et al. 2019

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The increasing use of intermittent, renewable energy sources (RESs) for electricity generation in microgrids (MGs) requires efficient strategies for reliable and economic operation. Complementarity between RESs provides good prospects for integrating several local energy sources and reducing the costs of MG setup and operations. This paper presents a framework for maximizing the economic benefits of a grid-tied MG by exploiting the spatial and temporal complementarity between solar and wind energies (solar-wind complementarity). The proposed framework considers the cost of energy production from different RESs and the cost of bi-directional energy exchange with the main grid. For a given RES mix, a minimum system power loss (SPL) threshold can also be determined. However, at this SPL threshold, MG energy exchange cost is not always minimized. The framework determines the optimized SPL value (above the threshold) at which MG energy exchange cost gets minimized. Through this framework, MG operator can decide appropriate RES mix and can achieve various tradeoffs according to the energy production cost, solar-wind complementarity of the site and its required economic objectives.

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“…The ‐bus test system is used as a first case study. In order to demonstrate the capability of the PADE algorithm, a comparative study has been conducted with three widely used multi‐objective algorithms: nondominated sorting approach (NSGA‐III), multi‐objective differential evolution (MODE), and multi‐objective particle swarm optimization (MOPSO) . The same population size and the maximum iteration are used for the PADE, NSGA‐III, MODE, and MOPSO are 200 individuals and 500 generations, respectively.…”

Section: Case Studymentioning

confidence: 99%

“…A large number of multi‐objective evolutionary algorithms based on this dominance mechanism have been developed, such as evolutionary many‐objective optimization algorithm using reference‐point‐based NSGA‐III, multi‐objective particle swarm optimization (MOPSO), multi‐objective differential evolution with ranking‐based mutation operator (MODE‐RMO), Strenght Pareto Evolutionary Algorithm (SPEA), Territory Defining Evolutionary Algorithm (TDEA), Pareto Envelope‐based Selection Algorithm (PESA), and SPEA2 . This paper introduces a multi‐objective optimization method based on the Pareto archive DE proposed in …”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

Tarif

Amrane

Chabane

2019

Int Trans Electr Energ Syst

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Summary The current paper presents a multi‐objective approach for the optimal placement of phasor measurement units (PMUs) for small‐signal stability assessment of a power system. The proposed methodology is based on a multi‐signal Prony analysis of the voltage amplitude, voltage angle, and frequency signals measured by the PMUs. A Pareto Archive Differential Evolution (PADE) algorithm is used to determine the best location of PMUs minimizing the PMUs number and maximizing the signal‐to‐noise ratio (SNR) subject to full system constraints. The capability of the proposed approach has been demonstrated through several cases studies, where it gives an accurate estimation of electromechanical modes when confronted with modal analysis under various scenarios, disturbances, and measurement noise. Additionally, the PADE algorithm is compared with nondominated sorting approach (NSGA)‐III, multi‐objective differential evolution (MODE), and multi‐objective particle swarm optimization (MOPSO) algorithms, and the superiority of PADE algorithm for solving the optimal PMUs placement (OPP) problem is demonstrated.

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Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation

Cited by 70 publications

References 37 publications

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity

Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

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