Mixed-Integer Distributed Ant Colony Optimization of Dump Load Allocation with Improved Islanded Microgrid Load Flow

Kreishan, Maen Z.; Zobaa, Ahmed F.

doi:10.3390/en16010213

Cited by 5 publications

(4 citation statements)

References 58 publications

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“…Future work will involve further refinement of the NSCSO algorithm and its application to more intricate practical scenarios, such as microgrid allocation problems 62 , WSN node coverage problems 63 . Additionally, the proposed algorithm’s multi-objective version holds promise as a valuable contribution for future research endeavors.…”

Section: Discussionmentioning

confidence: 99%

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

Huang,

Zheng,

Wei

et al. 2024

Sci Rep

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Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.

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Section: Discussionmentioning

confidence: 99%

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

Huang,

Zheng,

Wei

et al. 2024

Sci Rep

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“…It is assumed that the ants remember

places visited, but also places to go. The ant determines the graph of the state space and therefore finds the most advantageous route to travel [ 58 , 67 ]. Ants follow the designated path according to the decision matrix

, which is created according to the node

created from the local values of the pheromone trail and the local heuristic values, as shown by Equation (6) [ 57 , 58 , 68 ]:

where:

—amount of pheromone at the edge

in moment

—heuristic shift value from point

to point

—the set of nearby (adjacent) points to point

—coefficient determining the degree of influence of heuristic values.…”

Section: Procedures To Identify Critical Causes Of Materials Incompat...mentioning

confidence: 99%

“…After the ants have travelled all the paths, it is possible to evaporate the pheromone that is on all the edges. Afterwards, each ant

leaves the pheromone

on the edges it has visited (8) [ 66 , 67 , 68 ]:

where:

—the path found by the ant

in iteration

—length of the found road, q—dose factor of the pheromone left by the ant.…”

Section: Procedures To Identify Critical Causes Of Materials Incompat...mentioning

confidence: 99%

A Novelty Procedure to Identify Critical Causes of Materials Incompatibility

Siwiec

Pacana

2023

Materials

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Ensuring the expected quality of materials is still a challenge, mainly in order to precisely plan improvement actions that allow for stabilization of the production process. Therefore, the purpose of this research was to develop a novel procedure to identify critical causes of material incompatibility—the causes that have the largest negative impact on material deterioration, and the natural environment. The main originality of this procedure is developing a way to coherent analyse the mutual influence of the many causes of incompatibility of any material, after which the critical causes are identified and a ranking of improvement actions to eliminate these causes is created. A novelty is also developed in the algorithm supporting this procedure, which can be realized in three different ways to solve this problem, i.e.; by considering the impact of material incompatibility on: (i) the deterioration of the material quality; (ii) the deterioration of the natural environment; and (iii) simultaneously the deterioration of the quality of the material and the natural environment. The effectiveness of this procedure was confirmed after tests on 410 alloy, from which a mechanical seal was made. However, this procedure can be useful for any material or industrial product.

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“…MILP has been also used in [9] to optimally determine the MG's components' sizes considering demand response program. In [10], Ant Colony Optimization (ACO) was utilized to resolve an MG's energy management issue. Sources of power generations was scheduled with the use of an Artificial Fish Swarm Algorithm (AFSA) in [11].…”

Section: Introductionmentioning

confidence: 99%

A comparative study for two Novel Optimization Algorithms used to solve Microgrid Energy Management problem considering Energy Storage System

Hassanin,

Abdel-Rahim,

Mansour

et al. 2024

Preprint

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In this paper, an optimization solution is introduced to address an Energy Management problem for a Microgrid comprising Photovoltaic arrays, Wind Turbines, Combined Heat and Power units, and a Battery Energy Storage System. The goal is to establish an optimal Energy Management System that minimizes operational costs by efficiently dispatching the required load demand among the available electricity resources. Traditionally, Diesel generators have served as the main source of load supply in microgrids. However, the integration of renewable energy sources and storage systems into microgrids has led to a decrease in electricity generation costs and an enhanced ability to reduce harmful emissions from conventional resources. Given the intermittent nature of renewable resources, the existence of diverse distributed generators with varying operational costs, and the fluctuating electricity consumption patterns, there is a continuous demand for a robust Energy Management System. Such a system enables grid operators to maintain a cost-effective, efficient, and reliable balance between supply and demand. Consequently, optimization algorithms have become indispensable for developing the most efficient Energy Management System. This study utilizes the Political Optimizer and Pelican Optimization Algorithm to tackle the Energy Management problem in the microgrid. The primary objectives of these innovative optimization algorithms are to minimize operational costs, ensure supply-load balance, and increase the penetration of renewable resources for electricity generation in the microgrid. Through extensive numerical results and statistical analyses, the effectiveness and superiority of the proposed optimization algorithms are demonstrated. The outcomes illustrate cost reductions of up to 25% compared to conventional algorithms. Thus, it is evident from the results and statistical analyses that the Political Optimizer and Pelican Optimization Algorithms exhibit the capability to efficiently address various optimization challenges and deliver optimal results in the initial iterations.

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Mixed-Integer Distributed Ant Colony Optimization of Dump Load Allocation with Improved Islanded Microgrid Load Flow

Cited by 5 publications

References 58 publications

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

A Novelty Procedure to Identify Critical Causes of Materials Incompatibility

A comparative study for two Novel Optimization Algorithms used to solve Microgrid Energy Management problem considering Energy Storage System

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