Single- and multi-objective optimization for photovoltaic distributed generators implementation in probabilistic power flow algorithm

Kamel, Salah; Selim, Ali; Ahmed, Walaa

doi:10.1007/s00202-019-00878-7

Cited by 15 publications

(10 citation statements)

References 27 publications

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“…Numerous algorithms have been formulated based on this methodology such as; Pareto archived evolution strategy (PAES), nondominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA), improved version SPEAII, and multiobjective particle swarm optimization (MOPSO) [20]. For the DG allocation problem, MOPSO has been applied with fuzzy decision making to minimize the power loss and improve the VD in [21]. Multi-objective whale optimization (MOWOA) has been proposed to enhance the VSI and reduce the VD and active power loss [22].…”

Section: B Literature Reviewmentioning

confidence: 99%

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Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

et al. 2020

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In this paper, improved single-and multi-objective Harris Hawks Optimization algorithms, called IHHO and MOIHHO, respectively are proposed and applied for determining the optimal placement of distribution generation (DG) in the radial distribution systems. Harris Hawks optimizer (HHO) is a new inspired meta-heuristic optimization technique that is mainly based on the intelligence behavior of the Harris hawks in chasing prey. The IHHO and MOIHHO are applied for determining the optimal size and location of DG at different operating power factors (p.f) with the aim of minimizing the total active power loss, reducing the voltage deviation (VD), and increasing the voltage stability index (VSI) considering the operational constraints of distribution system. In IHHO, the performance of the conventional HHO algorithm is improved using the rabbit location instead of the random location. In MOIHHO, grey relation analysis is applied for identifying the best compromise solution among the non-dominance Pareto solutions. To verify the effectiveness of the proposed algorithms, IEEE 33-bus and IEEE 69-bus radial distribution systems are used, and the obtained results are compared with those obtained by other optimization techniques. The results prove the efficiency of the proposed algorithms in terms of best solutions obtained so far for the single-and multi-objective scenarios. INDEX TERMS Harris hawks optimizer, single-and multi-objective optimization, DG placement, distribution systems, power loss reduction, voltage deviation, voltage stability index. I. INTRODUCTION A. MOTIVATION AND INCITEMENT ALI SELIM received the B.Sc. and M.Sc. degrees in electrical engineering from Aswan University, Aswan, Egypt, in 2010 and 2016, respectively. He is currently pursuing the Ph.D. degree with the Department of Electrical Engineering, University of Jaén, Spain. He is also an Assistant Lecturer with the Electrical Engineering Department, Aswan University. His research interests include mathematical optimization, planning, and control of power systems, renewable energies, energy storage, and smart grids.

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Section: B Literature Reviewmentioning

confidence: 99%

“…In this case, r < 0.5 and |E| < 0.5, the rabbit is exhausted and it has been surrounded hardly by the hawks. Similarly, Levy flight (LF) concept is employed to state this besiege as in Eq (18) to (21), but Y are estimated by follows:…”

Section: ) Hard Besiege With Progressive Rapid Divesmentioning

confidence: 99%

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

et al. 2020

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“…The capacity of ESS is determined between maximum and minimum of that in (16). The bound of maximum and minimum charge and discharge state of ESS is set between 10% and 90% of ESS capacity described in (17). Charge capacity of ESS is similar to the discharge capacity as stated in (18).…”

Section: Problem Formulation and Proposed Methodsmentioning

confidence: 99%

“…Crow search algorithm is used for problem optimization in the paper. In Reference 17, the optimal allocation of PVs using PSO and fuzzy logic decision is stated to minimize total power loss and voltage deviation.…”

Section: Introductionmentioning

confidence: 99%

Loss cost reduction and power quality improvement with applying robust optimization algorithm for optimum energy storage system placement and capacitor bank allocation

Rajamand

2020

Int J Energy Res

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Power losses cause the underutilization of distributed generation (DG) units in addition to the cost increasing in microgrid. Minimizing these losses has been focused in many papers. Using energy storage system (ESS) is a crucial solution for loss reduction. ESS can balance the power exchange in on-peak times where its location and size optimization can improve the microgrid efficiency and reduce the loss cost significantly. Moreover, to ensure the power quality by improving the voltage profile, capacitor bank can be installed optimally on some buses. Optimization of size and location of the capacitor bank can enhance the reactive power that is leading to power loss reduction. In other words, the capacitor bank is applied to compensate the total reactive power and consequently, the current is reduced that results in power loss reduction. In this article, the problem is defined as the optimum location and size of ESS and capacitor bank in the microgrid. Due to the complexity of the problem in many options for selecting the buses to implement these elements (ESS and capacitor bank), robust approach using the particle swarm optimization algorithm and general algebraic modeling system are applied for optimization process. In addition, the uncertainty of renewable DGs such as photovoltaic and wind turbine is modeled by probability density functions and Monte-Carlo is used for selecting more probable cases in optimization processes. The results show the loss cost reduction and improvement in voltage and power profile with less fluctuations and more stability.

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“…ese algorithms include PAES, NSGA-II, SPEA (strength Pareto evolutionary algorithm), SPEA-II, and MOPSO [20]. MOPSO with fuzzy has been used in [19] to minimize power losses and VDI improvement. MOWOA has been applied in [20] to enhance VSI and reduce VDI and power losses.…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement and Sizing Problem for Power Loss Minimization and Voltage Profile Improvement of Distribution Networks under Seasonal Loads Using Harris Hawks Optimizer

Habib

Waqar

Sohail

et al. 2022

International Transactions on Electrical Energy Systems

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Improving efficiency with sustainable radial distribution networks (RDNs) is challenging for larger systems and small grid-connected RDNs. In this paper, the optimal placement of DGs with the Harris hawks optimizer (HHO) under seasonal load demands is proposed to simultaneously reduce total active and reactive power losses and minimize bus voltage drops with the consideration of operational constraints of RDNs. HHO is a newly inspired metaheuristic optimization algorithm primarily based on the Harris hawks’ intelligent behaviors during the chasing of the prey. Furthermore, the authors have investigated four stages of DGs. The first stage involves the optimal allocation of one DG. The second stage includes an investigation with two DGs, the third stage considers three DGs, and the fourth stage investigates the integration of four DGs. The effectiveness of the applied HHO is validated on IEEE 33 and 69 bus RDNs, and results are analyzed by comparing with the standard optimization methods. The Big-O test is also executed for statistical analysis with standard algorithms. The simulation results reveal the better performance of the applied HHO under different circumstances than other algorithms. Furthermore, the total active and reactive power losses and bus voltage drops are improved by adding more DGs into IEEE 33 and 69 bus RDNs.

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Single- and multi-objective optimization for photovoltaic distributed generators implementation in probabilistic power flow algorithm

Cited by 15 publications

References 27 publications

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

Loss cost reduction and power quality improvement with applying robust optimization algorithm for optimum energy storage system placement and capacitor bank allocation

Optimal Placement and Sizing Problem for Power Loss Minimization and Voltage Profile Improvement of Distribution Networks under Seasonal Loads Using Harris Hawks Optimizer

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