Optimal Allocation and Sizing of Solar Panels Generation via Particle Swarm Optimization Algorithm

Zeb, Muhammad Zulqarnain; Imran, Kashif; Janjua, Abdul Kashif; Nadeem, Muhammad Faisal; Amin, Awais

doi:10.1109/icomet.2019.8673530

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…Many configurations of PSO have been presented in the literature for solving DGs' placement and sizing problems. 11,[135][136][137][138][139][140][141][142][143][144][145][146] In Krueasuk and Ongsakul, † PSO algorithm was differently utilised for determining optimal allocation of multiple DG units either to minimise total power losses, total operational cost, improve voltage profile, and stability index or to provide maximum power quality. PSO can be used for complex DG allocation problems and has greater efficiency and probability to find global optima solutions.…”

Section: Particle Swarm Optimisation (Pso) Methodsmentioning

confidence: 99%

“…It uses few parameters to adjust and converges faster. Many configurations of PSO have been presented in the literature for solving DGs' placement and sizing problems . In Krueasuk and Ongsakul, PSO algorithm was differently utilised for determining optimal allocation of multiple DG units either to minimise total power losses, total operational cost, improve voltage profile, and stability index or to provide maximum power quality.…”

Section: Optimisation Algorithms For Dg Allocation Planningmentioning

confidence: 99%

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Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

2020

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Summary Integrating renewable energy hybrid distributed generation (REHDG) into distribution network systems (DNSs) has become increasingly important because of various technical, economic, and environmental advantages accruing from it. However, the output power of REHDGs from photovoltaic (PV) and wind is highly variable because of its dependency on intermittent parameters such as solar irradiance, temperature, and wind speed. Such variability of generated power from large‐scale REHDGs or load introduces small signal instabilities (oscillations). Meanwhile, different locations of integration and sizes of REHDGs in the DNS affect the system oscillation modes by either improving or depriving the small‐signal stability (SSS) of the network. Consequently, a significant number of research has been conducted on the planning of optimal allocation of REHDGs in DNS. In this regard, this paper reviews the existing planning models, optimisation techniques, and resources' uncertainty modelling employed in REHDGs allocations in terms of their capability in obtaining optimal solutions and enhancing SSS of the system. Planning models with optimisation algorithms are evaluated for modelling renewable resource uncertainties and curtailing SSS variables. Research works on planning of optimal allocation of these generations attain minimum cost, but were unable to satisfy the SSS requirements of the system. The existing models for the planning and design of optimal timing, sizing, and placement of REHDGs will need to be improved to optimally allocate REHDGs and satisfy the SSS of the DNS after the integration.

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Section: Particle Swarm Optimisation (Pso) Methodsmentioning

confidence: 99%

Section: Optimisation Algorithms For Dg Allocation Planningmentioning

confidence: 99%

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

2020

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“…3. Power flow limits: On any line k, the power flow must be within the specified limits for that line as in (23) and (24).…”

Section: The Constraintsmentioning

confidence: 99%

“…Intelligent search (IS) based methods are differently employed to solve the optimal sizing and placement of DGs problems. IS methods utilize artificial intelligence (AI) algorithms like the genetic algorithm (GA) [21,22], particle swarm optimization (PSO) [23,24], simulated annealing (SA) [25][26][27], harmony search (HS) [28,29], big bang crunch (BBC) [30,31], the fireworks algorithm (FA) [32,33], and the water drop algorithm (WDA) [34,35].…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement

2019

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This paper solves the allocation planning problem of integrating large scale renewable energy hybrid distributed generations and capacitor banks into the distribution systems. Extraordinarily, the integration of renewable energy hybrid distributed generations such as solar photovoltaic, wind, and biomass takes into consideration the impact assessment of variable generations from PV and wind on the distribution networks’ long term dynamic voltage and small-signal stabilities. Unlike other renewable distributed generations, the variability of power from solar PV and wind generations causes small-signal instabilities if they are sub-optimally allocated in the distribution network. Hence, the variables related to small-signal stability are included and constrained in the model, unlike what is obtainable in the current works on the planning of optimal allocation of renewable distributed generations. Thus, the model is motivated to maximize the penetration of renewable powers by minimizing the net present value of total cost, which includes investment, maintenance, energy, and emission costs. Consequently, the optimization problem is formulated as a stochastic mixed integer linear program, which ensures limited convergence to optimality. Numerical results of the proposed model demonstrate a significant reduction in electricity and emission costs, enhancement of system dynamic voltage and small-signal stabilities, as well as improvement in welfare costs and environmental goodness.

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“…The optimal placement of DGs and sizing has been solved by a simulated annealing algorithm (SA) [20] and genetic algorithm (GA) [21], but with a higher convergence time and less accuracy when high-quality results are required. The particle swarm optimization algorithm (PSO) [22] suffers from partial optimization, due to which its velocity and direction is not maintained and it is inefficient for large and complex systems. In the firefly algorithm (FA) [23], cuckoo search algorithm (CSA) [24], and bat algorithm (BA) [25], the convergence rate is very much affected by the adjustment of parameters, which is always needed in such problems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Siting and Sizing of Distributed Generators by Strawberry Plant Propagation Algorithm

et al. 2021

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Increasing the unplanned penetration of Distributed Generators (DGs) has spurred active and reactive power losses in the distribution system. This article suggests using a novel Strawberry Plant Propagation Algorithm (SPPA) for planning the placement of the DGs with the aim of reducing the network (active) power losses and improving the overall voltage profile. The proposed method (SPPA) has been tested on 33 and 69 node radial systems in MATLAB. A cost analysis was also performed and compared with other contemporary methods. The results for the considered variables show the significance of the proposed method in comparison to various other counterparts, including the Mine Blast Algorithm and Particle Swarm Optimization.

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Optimal Allocation and Sizing of Solar Panels Generation via Particle Swarm Optimization Algorithm

Cited by 8 publications

References 19 publications

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement

Optimal Siting and Sizing of Distributed Generators by Strawberry Plant Propagation Algorithm

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