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

Alanazi, Abdulaziz; Alanazi, Mohana; Abdelaziz, Almoataz Y.; Kotb, Hossam; Milyani, Ahmad H.; Azhari, Abdullah Ahmed

doi:10.3390/pr10112179

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…In [20], the allocation of PVs in the networks was performed using an improved human learning optimization algorithm (IHLOA) to minimize power losses and improve its voltage profile and stability considering the generation and load uncertainty. In [21], the optimization of a hybrid photovoltaic/wind/fuel cell was implemented using meta-heuristic techniques to constantly supply three typical demands in Kousseri, Cameroon. The determination of the site and size of the PVs and WTs in the distribution network for minimization of the losses and to improve voltage stability using the moth-flame optimizer (MFO) was presented in [21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [21], the optimization of a hybrid photovoltaic/wind/fuel cell was implemented using meta-heuristic techniques to constantly supply three typical demands in Kousseri, Cameroon. The determination of the site and size of the PVs and WTs in the distribution network for minimization of the losses and to improve voltage stability using the moth-flame optimizer (MFO) was presented in [21]. In [22], the scheduling of an HPV/WT/Batt system was presented for minimizing its active losses and enhancing its voltage profile using an improved whale optimizer algorithm (IWOA).…”

Section: Literature Reviewmentioning

confidence: 99%

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Stochastic Multi-Objective Scheduling of a Hybrid System in a Distribution Network Using a Mathematical Optimization Algorithm Considering Generation and Demand Uncertainties

Hadi Abdulwahid,

Al-Razgan,

Fakhruldeen

et al. 2023

Mathematics

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In this paper, stochastic scheduling of a hybrid system (HS) composed of a photovoltaic (PV) array and wind turbines incorporated with a battery storage (HPV/WT/Batt) system in the distribution network was proposed to minimize energy losses, the voltage profile, and the HS cost, and to improve reliability in shape of the energy-not-supplied (ENS) index, considering energy-source generation and network demand uncertainties through the unscented transformation (UT). An improved escaping-bird search algorithm (IEBSA), based on the escape operator from the local optimal, was employed to identify the optimal location of the HS in the network in addition to the optimal quantity of PV panels, wind turbines, and batteries. The deterministic results for three configurations of HPV/WT/Batt, PV/Batt, and WT/Batt were presented, and the results indicate that the HPV/WT/Batt system is the optimal configuration with lower energy losses, voltage deviation, energy not supplied, and a lower HS energy cost than the other configurations. Deterministic scheduling according to the optimal configuration reduced energy losses, ENS, and voltage fluctuation by 33.09%, 53.56%, and 63.02%, respectively, compared to the base network. In addition, the results demonstrated that the integration of battery storage into the HPV/WT enhanced the various objectives. In addition, the superiority of IEBSA over several well-known algorithms was proved in terms of obtaining a faster convergence, better objective value, and lower HS costs. In addition, the stochastic scheduling results based on the UT revealed that the uncertainties increase the power losses, voltage deviations, ENS, and HPV/WT/Batt cost by 2.23%, 5.03%, 2.20%, and 1.91%, respectively, when compared to the deterministic scheduling.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Stochastic Multi-Objective Scheduling of a Hybrid System in a Distribution Network Using a Mathematical Optimization Algorithm Considering Generation and Demand Uncertainties

Hadi Abdulwahid,

Al-Razgan,

Fakhruldeen

et al. 2023

Mathematics

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“…• high penetration of PV, which can cause overloading of the transformers or overvoltages [13,22]; • asymmetry in the voltage and current flow of neutral conductors in low-voltage distribution systems, which is caused by single-phase production/consumption devices; • improper integration of EVs into distribution systems, which can result in the technology not being reliable [15,23]; • thermal effects caused by the high current in phase or neutral conductors, which increase losses.…”

Section: Current State Of Pv Systemsmentioning

confidence: 99%

Stochastic Approach for Increasing the PV Hosting Capacity of a Low-Voltage Distribution Network

et al. 2022

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In recent years, the emerging fear of an energy crisis in central Europe has caused an increased demand for distributed energy resources (DER), especially small photovoltaic rooftop installations up to 10 kWp. From a technical point of view, distributed PV in low-voltage networks is associated with the risk of power quality violation, overvoltage, voltage unbalance, harmonics, and violation of the thermal limit of phase conductors, neutral conductors, and transformers. Distribution system operators (DSO) are currently in a position to determine the amount of installed PV power for which reliable and safe network operation is ensured, also known as the photovoltaic hosting capacity (PVHC). The presented study describes a stochastic methodology for PVHC estimation and uses it to analyze a typical LV rural network in the Slovak Republic. Detailed and precise calculations are performed on the 4-wire LV model with accurate results. In this study, we, thus, profoundly analyze the problems with voltage violation, unbalanced voltage energy losses, and the thermal loading effect of increasing PV penetration. The results show that overvoltage events are the main factor limiting the PVHC in LV systems. This conclusion is in accordance with the experience of the DSO in the Slovak and Czech Republic. Subsequently, the study focuses on the possibilities of increasing PVHC using those tools typically available for DSO, such as changes in PV inverter power factors and no-load tap changer transformers. The results are compared with those derived from similar analyses, but we ultimately find that the proposed solution is problematic due to the high variability of approaches and boundary conditions. In conclusion, the paper discusses the issue of the acceptable risk of overvoltage violation in the context of PVHC and lowering losses in LV networks.

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“…Improving stability and reliability was also the primary focus of the study. In [30][31][32], the crow search algorithm (CrOA), the improved human learning optimization algorithm (IHLOA), and the improved equilibrium optimization algorithm (IEOA) were also employed to optimize the allocation of the renewable energy systems to different RDPG. However, the most striking feature of these studies is considering the uncertainties of renewable-based DGs, which makes the considered problem even more complicated.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

Phan,

Duong,

Doan

et al. 2024

Applied Sciences

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This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.

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

Cited by 5 publications

References 43 publications

Stochastic Multi-Objective Scheduling of a Hybrid System in a Distribution Network Using a Mathematical Optimization Algorithm Considering Generation and Demand Uncertainties

Stochastic Multi-Objective Scheduling of a Hybrid System in a Distribution Network Using a Mathematical Optimization Algorithm Considering Generation and Demand Uncertainties

Stochastic Approach for Increasing the PV Hosting Capacity of a Low-Voltage Distribution Network

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

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