Distribution systems techno-economic performance optimization through renewable energy resources integration

Hassan, Ahmed S.; Othman, ElSaeed A.; Bendary, Fahmy; Ebrahim, Mohamed A.

doi:10.1016/j.array.2020.100050

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…Digital simulation and electrical network computation were used to simulate the Delta Egypt electrical network (66, 11, 6.6, and 0.4 kV) to improve the voltage profile and achieve voltage quality [7]. The Whale Optimization Algorithm, Sine Cosine Algorithm, and Multi-Verse Optimization Algorithm have been developed as multi-objective optimization methods for identifying the optimal mix sizing and positioning RES integrated into IEEE 33-bus and 118-bus test systems, as well as a realistic portion of the Egyptian distribution network [8]. The digital model was developed using the DIgSILENT power system calculation package to simulate the Egyptian power system at 500 kV and 220 kV levels to conduct power system studies such as load flow, short circuit, fault, and stability to evaluate performance and ensure compliance with grid code technical requirements [9].…”

Section: Related Workmentioning

confidence: 99%

Modified Analytical Technique for Multi-Objective Optimal Placement of High-Level Renewable Energy Penetration Connected to Egyptian Power System

et al. 2023

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The 2022 United Nations Climate Change Conference (COP27) recommended that Egypt be converted to green energy, in addition to increasing the demand for annual energy consumption, which will lead to an increase in the use of renewable energy sources (RES) in Egypt. The Egyptian Ministry of Energy and Electricity plans to build RES (photovoltaic systems and wind farms) connected to the Egyptian power system (EPS). It is a defect to choose the position and size of the RES based on only power calculations because the RES is an intermittent source. This paper presents a modified analytical energy technique for locating RES in IEEE 33-bus and 69-bus distribution networks and a realistic 25-bus 500 kV EPS. An analytical multi-objective function has been developed to determine the optimal locations of DGs or RESs based on power losses and annual energy loss calculations of the system depending on weather conditions. The efficiency and feasibility of the proposed algorithm based on the IEEE 33-bus and 69-bus distribution networks and the realistic 25-bus 500 kV EPS have been tested and compared with PSO and GA. The impact of RESs on the performance of the 25-bus 500 kV EPS has been investigated based on annual energy losses and operation stability depending on weather conditions. The results showed that the proposed technique used these effective values to obtain optimal weather-adjusted locations. The optimal locations of PV systems or wind systems based on energy calculation improved the voltage profile better than power calculation by about 2%, and the annual energy losses decreased by about 7%. The performance of the 25-bus 500 kV EPS, due to the addition of RES, resulted in a decrease in the annual energy losses of 47% and an improvement in the voltage profile and system stability.

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Section: Related Workmentioning

confidence: 99%

Modified Analytical Technique for Multi-Objective Optimal Placement of High-Level Renewable Energy Penetration Connected to Egyptian Power System

et al. 2023

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“…The output power of the PV plant, operating at maximum power point at solar irradiance s may be estimated using Equation (17) [64].…”

Section: Practical Sizing Of Pv Dgmentioning

confidence: 99%

“…The expected output power from the PV module considering the effect of solar irradiance s and ambient temperature T A may be calculated using Equation (18), and the expected total output power for a specific time period may be calculated using Equation (19) [64].…”

Section: Practical Sizing Of Pv Dgmentioning

confidence: 99%

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

et al. 2021

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The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.

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“…In recently published articles, several optimization techniques have been employed to solve ODGA problems in RDN. The objectives include minimizing active power loss (RPL) [2,[8][9][10][11], minimizing reactive power loss (QPL) [2,[12][13][14], maximizing the voltage stability index (VSI) [9,11,[15][16][17], and reducing the total voltage deviation (TVD) [2,9,[14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation

et al. 2023

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With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.

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Distribution systems techno-economic performance optimization through renewable energy resources integration

Cited by 9 publications

References 23 publications

Modified Analytical Technique for Multi-Objective Optimal Placement of High-Level Renewable Energy Penetration Connected to Egyptian Power System

Modified Analytical Technique for Multi-Objective Optimal Placement of High-Level Renewable Energy Penetration Connected to Egyptian Power System

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation

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