Photovoltaic and Wind Turbine Integration Applying Cuckoo Search for Probabilistic Reliable Optimal Placement

Swief, R.A.; Abdel-Salam, T. S.; El-Amary, Noha H.

doi:10.3390/en11010139

Cited by 22 publications

(16 citation statements)

References 21 publications

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“…Many studies have been presented for different multi objective problems. Application of participating RERs has been investigated for improving reliability, reducing losses, reducing production cost, and reducing emission costs in [2][3][4]. High RER penetration comes with operational challenges due to their high level of intermittency.…”

Section: Pgmentioning

confidence: 99%

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

2020

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This paper introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB ® Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.

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Section: Pgmentioning

confidence: 99%

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

2020

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“…The main objective is to minimize the transmission losses. Swief et al [16] proposed a cuckoo search optimization (CSO) technique for optimally determining the locations and sizes of photovoltaic (PV) and wind turbine (WT) DGs. The main objective is to maximize the reliability in the system.…”

Section: Related Workmentioning

confidence: 99%

“…where DG Li represents the location of the DG in bus i and B L max represents the maximum location of the bus [4] CSA 33, 69, and 119 nodes Minimize active power losses and maximize voltage magnitude [5] Analytical and PSO 33 and 69 buses Minimize the power distribution loss [6] Analytical 33 and 69 buses Minimize power losses [7] LSF and IWO 33 and 69 buses Minimize losses and operational cost and improve the voltage stability [8] PSO 33 and 69 buses Minimize power losses [9] AGPSO 69 buses Minimize power losses [10] GWO 33 and 69 buses Minimize power losses [11] GA-PSO 33 and 69 buses Minimize losses and maintain acceptable voltage profiles [12] GA-ABC 33 and 69 buses Reduce the cost of the system and decrease RPLs [13] GA and Fuzzy 34 buses Minimize cost, emission, power losses, and voltage deviation [14] PBIL and PSO 33 and 69 buses Reduce active power losses and improve the nodal voltage profiles [15] PSO 30 buses Minimize the transmission losses [16] CSO 69 buses Maximize the reliability in the system [17] BSA 69 and 136 buses Reduce power losses and improve network voltage profile [18] BSA and Fuzzy expert rules 33 and 94 nodes Minimize the network power losses, consolidate the static voltage stability indices, and ameliorate the bus's voltage profile.…”

Section: Constraintsmentioning

confidence: 99%

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Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

Alzaidi

Bayat

Uçan

2019

International Journal of Photoenergy

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Distributed generators (DGs) are currently extensively used to reduce power losses and voltage deviations in distribution networks. The optimal location and size of DGs achieve the best results. This study presents a novel hybridization of new metaheuristic optimizations in the last two years, namely, salp swarm algorithm (SSA) and whale optimization algorithm (WOA), for optimal placement and size of multi-DG units in radial distribution systems to minimize total real power losses (kW) and solve voltage deviation. This hybrid algorithm is implemented on IEEE 13- and 123-node radial distribution test systems. The OpenDSS engine is used to solve the power flow to find the power system parameters, such power losses, and the voltage profile through the MATLAB coding interface. Results describe the effectiveness of the proposed hybrid WOA-SSA algorithm compared with those of the IEEE standard case (without DG), repeated load flow method, and WOA and SSA algorithms applied independently. The analysis results via the proposed algorithm are more effective for reducing total active power losses and enhancing the voltage profile for various distribution networks and multi-DG units.

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“…The PV output power depends on solar irradiance and is calculated using the historical data within one year in [10,11]. The probability of solar irradiance is obtained by counting the number of hours per year for each state of solar irradiance and dividing it by total number of hours per year [12,15]. The PV output power of solar irradiance can be calculated by using the multi-state model referred to in [9,14] .…”

Section: Photovoltaic Output Power Modelmentioning

confidence: 99%

Composite Reliability Index of Practical Distribution Network with PVDG Integration

Moe¹

2019

CSSP

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The benefits of the photovoltaic distributed generation (PVDG) installation in the distribution network include the total system loss reduction, voltage profile improvement and the reliability enhancement of the distribution network by minimizing the interruption and duration time to customers due to the loss of utility or due to the faults in transmission lines/equipments. in this research, the optimal size of PVDG is connected at the optimal location of practical distribution network (town pyone feeder). The multi-state model is used to calculate the probabilities of the solar irradiance data and the output power of PVDG. These probabilities of solar irradiance are applied to assess the reliability of the practical distribution network with PVDG integration. The reliability within the island operation mode of the distribution system with PVDG is presented. Analytical approach is described to assess the impact of PVDG on the system reliability due to the stochastic behavior of PV output power. a composite index organizing the impact of PVDG on the overall reliability of distribution network including interruption frequency, interruption duration and expected energy not supplied is presented. The results show that the integration of PVDG placement in the practical distribution network can improve the reliability and the applying proposed methodology can enable determining PVDG configuration concerning the overall system reliability improvement.

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Photovoltaic and Wind Turbine Integration Applying Cuckoo Search for Probabilistic Reliable Optimal Placement

Cited by 22 publications

References 21 publications

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

Composite Reliability Index of Practical Distribution Network with PVDG Integration

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