Stochastic Allocation of Photovoltaic Energy Resources in Distribution Systems Considering Uncertainties Using New Improved Meta-Heuristic Algorithm

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Stochastic Allocation of Photovoltaic Energy Resources in Distribution Systems Considering Uncertainties Using New Improved Meta-Heuristic Algorithm
by Abdulaziz Alanazi 1ORCID,Mohana Alanazi 2ORCID,Almoataz Y. Abdelaziz 3,*ORCID,Hossam Kotb 4ORCID,Ahmad H. Milyani 5,6,*ORCID andAbdullah Ahmed Azhari 7
1
Department of Electrical Engineering, College of Engineering, Northern Border University, Ar’Ar 73222, Saudi Arabia
2
Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
3
Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt
4
Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
5
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
6
Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
7
The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Processes 2022, 10(11), 2179; https://doi.org/10.3390/pr10112179
Submission received: 16 September 2022 / Revised: 15 October 2022 / Accepted: 18 October 2022 / Published: 24 October 2022
(This article belongs to the Special Issue Modeling and Operation of Renewable Energy System)
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Abstract
In this paper, a stochastic-metaheuristic model is performed for multi-objective allocation of photovoltaic (PV) resources in 33-bus and 69-bus distribution systems to minimize power losses of the distribution system lines, improving the voltage profile and voltage stability of the distribution system buses, considering the uncertainty of PV units’ power and network demand. The decision-making variables, including installation location and the size of PVs, are determined optimally via an improved human learning optimization algorithm (IHLOA). The conventional human learning optimization algorithm (IHLOA) is improved based on Gaussian mutation to enhance the exploration capability and avoid getting trapped in local optimal. The methodology is implemented in two cases as deterministic and stochastic without and with uncertainties, respectively. Monte Carol Simulation (MCS) based on probability distribution function (PDF) is used for uncertainties modeling. The deterministic results proved the superiority of the IHLOA compared with conventional HLOA, particle swarm optimization (PSO), to obtain better values of the different objectives and faster convergence speed and accuracy. The results are clear that enhancing the conventional HLOA has increased the algorithm’s ability to explore and achieve the optimal global solution with higher convergence accuracy. Moreover, the stochastic results were clear that considering the uncertainties leads to correct and robust decision-making against existing uncertainties and accurate knowledge of the network operator against the exact values of various objectives compared to the deterministic case.