Fast yet Accurate Energy-Loss-Assessment Approach for Analyzing/Sizing PV in Distribution Systems Using Machine Learning

Mahmoud, Karar; Abdel-Nasser, Mohamed

doi:10.1109/tste.2018.2859036

Cited by 35 publications

(18 citation statements)

References 33 publications

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“…Suppose that there are two independent random variables X and Y, and their probability density functions and , respectively, then Z = X + Y is still a random variable. The probability density function of Z is: (8) Its distribution function is (9)…”

Section: B Convolution Calculationmentioning

confidence: 99%

“…Decreasing the power losses can be achieved with integrated renewable energy resources. Recently, different methods have been presented using neural networks and machine learning to decrease the power and energy losses in the distribution system in existence of photovoltaic (PV) [7,8]. In addtion, some methods have been presented to determine the power loss in smart grids [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

Ahmed¹,

Selim²,

Kamel³

et al. 2018

IJIMAI

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Section: B Convolution Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

Ahmed¹,

Selim²,

Kamel³

et al. 2018

IJIMAI

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show abstract

“…The PV power generation systems have proven themselves as substitute candidates for replacing conventional fossil fuel-based generation systems. Being available everywhere, new and renewable, environmentally friendly, and having continuously reduced production cost represent the main advantages behind these ambitious plans [1], [2]. However, the fluctuated nature of PV generation systems during the day-time in addition to unavailability at night-time have given rise to energy storage systems (ESSs) installations for ensuring reliable power supply.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Reactive Power Dispatch Method for Hybrid Photovoltaic and Superconducting Magnetic Energy Storage Inverters in Utility Grids

2020

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The intermittent property and increased grid restrictions have become the most critical elements for increasing penetration levels of clean renewable energy sources (RESs). Smart inverters with combined RESs integration and reactive power support for utility grids have recently found widespread applications due to their techno-economic benefits. In smart inverters, the distribution-static compensator (DSTATCOM) functionality is inherently integrated to the RESs inverters. However, optimized reactive power-sharing between these inverters has become a big issue for the control systems of utility grids. There are numerous existing attempts presented in the literature for addressing these challenges, although they disadvantage low efficiency, uneven sharing, complex implementations, and/or costly added devices. Therefore, this paper proposes an efficient reactive power dispatch method between hybrid renewable energy generation and energy storage systems. The proposed method enhances the energy efficiency of the utility grid by adopting the reactive power share between interfacing inverters according to the estimated power losses. Besides, the proposed method enhances the reliability of smart inverters by relieving their thermal stresses through adopting their reactive power share according to the estimated power losses. The hybrid photovoltaic (PV) generation with superconducting magnetic energy storage (SMES) systems is selected as a case study for validating the new proposed reactive power dispatch method. The results, comprehensive discussions, and performance comparisons have verified the superior performance of the new proposed reactive power dispatch method. INDEX TERMS distribution static compensator, photovoltaic (PV), reactive power dispatch, smart inverters, superconducting magnetic energy storage (SMES)

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“…There is another type of hybridization, which is combining metaheuristic algorithms together such as, genetic algorithm (GA) with imperialist competitive algorithm [19], ant colony optimization with artificial bee colony (HACO) [20], hybrid grey wolf optimizer (HGWO) [21], backtracking search optimization algorithm (BSOA) [22], and in [23], which used particle ant bee colony with harmony search (PABC). Other studies used the analytical approach such as in [24], which uses efficient analytical with optimal power flow (EA-OPF), an improved analytical (IA) method in [25] and machine learning method in [26]. In addition to Naresh, who used an analytical expression for optimum location for DG [27].…”

Section: Introductionmentioning

confidence: 99%

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

Mohamed¹,

Mohamed²,

Mitani³

2019

IJIMAI

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This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units.

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Fast yet Accurate Energy-Loss-Assessment Approach for Analyzing/Sizing PV in Distribution Systems Using Machine Learning

Cited by 35 publications

References 33 publications

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

An Efficient Reactive Power Dispatch Method for Hybrid Photovoltaic and Superconducting Magnetic Energy Storage Inverters in Utility Grids

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

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