Optimal probabilistic location of DGs using Monte Carlo simulation based different bio-inspired algorithms

Hemeida, Mahmoud G.; Alkhalaf, Salem; Senjyu, Tomonobu; Ibrahim, Abdalla Ahmed; Ahmed, Mahrous; Bahaa-Eldin, Ayman M.

doi:10.1016/j.asej.2021.02.007

Cited by 34 publications

(24 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 3 shows the scenarios generated in the Monte Carlo simulation with σ = 10, μ = 70, and sample size 1000. The backward reduction procedure can be used to reduce the number of the generated scenarios, as explained in [3]. Table 1 shows the generated scenarios and the corresponding loading, wind speed, and solar irradiance.…”

Section: Modeling Load Demand Uncertaintymentioning

confidence: 99%

“…In contrast, the cost of renewable energy systems, such as solar photovoltaic (PV) and wind energy, has dropped significantly [2]. In order to reduce the number of scenarios formed by Monte Carlo simulations, a backward reduction algorithm was used [3]. Determining the optimal allocation of DGs provides a number of benefits, including lower energy costs, reduced emissions, and improved voltage profiles [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the authors employed various bio-inspired methods, such as grey wolf optimization, manta ray foraging optimization, satin bowerbird optimization, and whale optimization (WOA), as well as Monte Carlo simulation, to determine the optimal position of DGs. In [14], an effective algorithm was presented, the equilibrium optimizer, with the purpose of resolving the energy management issue of a microgrid and optimizing DG location and size.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

et al. 2022

View full text Add to dashboard Cite

Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.

show abstract

Section: Modeling Load Demand Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…These techniques can be divided into three acting categories: heuristic, numerical, and analytical based [9]. The authors of [10] presented the DG allocation problem to optimize system losses, voltage stability and voltage deviations using the Monte Carlo simulation (MCS) integrated with some bio-inspired algorithms, which are, Manta-ray Foraging Optimization (MRFO), Grey wolf optimizer (GWO), WOA and Satin Bird Optimization (SBO) under load uncertainties. The study was implemented and applied to IEEE 33 and 69-bus radial systems and resulted in determining the optimal locations which provide an improvement in the system's monitored parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

Hassan

Othman

Bendary

et al. 2022

Technol Econ Smart Grids Sustain Energy

View full text Add to dashboard Cite

The massive increase in the utilization of Distributed Generation (DG) units in the traditional Electric Distribution Networks (EDNs) enforces the distribution companies' operators to enhance the technical performance of EDNs while considering economic perspectives. This challenge paves the way for developing a multi-objective optimization platform to tackle the techno-economic problems while respecting system uncertainties as well as the operational policy of the distribution companies. As a motivating solution for this multi-objective problem, this paper introduces the application of three natureinspired algorithms as multi-objective optimization techniques for enhancing the techno-economic performance of EDNs through the integration of multiple Renewable Energy Resources (RERs). Grasshopper Optimization Algorithm (GOA), Salp Swarm Algorithm (SSA) and Moth Flame Optimization Algorithm (MFO), have been employed in this comparative study to minimize the active power losses, enhance the Fast Voltage Stability Index (FVSI) and reduce the total costs, considering the penetration level specified margin as well as and the framework of the DG units' operating power factor constraints. The proposed algorithms have been implemented in the MATLAB environment and applied on various benchmark IEEE test systems (33-bus, 57-bus and 300-bus) as a mimic, small and large EDNs. A realistic part of the Egyptian distribution network (171-bus) is also introduced as a practical, applicable case study. The attained results show that the suggested optimization platform especially using MFO, is more effective and successful in determining and finding the optimal locations and capacities of different DG types for getting the optimal value of the objective function in minimum time within a minimum number of iterations.

show abstract

“…A PSO and hybrid enhanced gray wolf optimizer (EGWO-PSO) approach was presented in [10] for the optimal DG allocation in order to reduce system costs, emissions, active power losses, and voltage deviation index (VDI), as well as increase voltage stability index (VSI). For electrical power planning with load uncertainties, the authors of [11] suggested a Monte Carlo simulation-based bioinspired algorithm. To reduce the overall energy loss when sizing and deploying DG units, a method known as coefficient particle swarm optimization (CPSO) was used in [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

et al. 2021

View full text Add to dashboard Cite

For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

show abstract

Optimal probabilistic location of DGs using Monte Carlo simulation based different bio-inspired algorithms

Cited by 34 publications

References 50 publications

The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

Contact Info

Product

Resources

About