A modified particle swarm optimization algorithm for scheduling renewable generation in a micro-grid under load uncertainty

Gholami, Khalil; Dehnavi, Ehsan

doi:10.1016/j.asoc.2019.02.042

Cited by 102 publications

(30 citation statements)

References 44 publications

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“…Desde sua autoria o PSO vem sofrendo modificações [10,30] e influenciando a criação de novos métodos como, por exemplo, o algoritmo de Colônia de Vagalumes (ACV).…”

Section: O Algoritmo De Colônia De Vagalumesunclassified

Estudo de Sensibilidade do Algoritmo de Colônia de Vagalumes para um Problema de Engenharia Envolvendo Dimensionamento de Treliças

Pereira

Santos

Moraes

et al. 2020

Tend. Mat. Apl. Comput.

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Recebido em 8 de setembro de 2020 / Aceito em 14 de setembro de 2020 RESUMO. A treliçaé uma estrutura triangular rígida, com resistência aos esforços normais, podendo ser utilizada em telhados, mezaninos, torres de energia, de telecomunicações e pontes. Logoé possível afirmar que esse sistema estrutural apresenta uma grande relevância no cenário da engenharia de estruturas. Nesta pesquisaé utilizado um método probabilístico de otimização global baseado em inteligência coletiva ou inteligência de enxame, com aplicações promissoras em diversos campos das ciências aplicadas, o Algoritmo de Colônia de Vagalumes (ACV), para determinação do peso mínimo de uma treliça de benchmark. Foi conduzida uma análise de sensibilidade com os parâmetros do algoritmo como: população (Npop), número de iterações (Ngen), parâmetro de aleatoriedade α, fator de atratividade β e parâmetro de absorção de luz (γ). A treliça utilizada nos testes foi uma estrutura de benchmark com 10 barras e essa foi otimizada obtendo um valor de peso mínimo em torno de 2284 kg, tal valor quando comparado a outros trabalhos da literatura mostram a efetividade do método adotado nesse trabalho. O software utilizado para as implementações e simulação das treliças foi o MATLAB ® .

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“…Desde sua autoria o PSO vem sofrendo modificações [10,30] e influenciando a criação de novos métodos como, por exemplo, o algoritmo de Colônia de Vagalumes (ACV).…”

Section: O Algoritmo De Colônia De Vagalumesunclassified

Estudo de Sensibilidade do Algoritmo de Colônia de Vagalumes para um Problema de Engenharia Envolvendo Dimensionamento de Treliças

Pereira

Santos

Moraes

et al. 2020

Tend. Mat. Apl. Comput.

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“…Introduction to Particle Swarm Optimization Algorithm. e particle swarm optimization algorithm [28] is a mathematical simulation model of the process of birds looking for food. In particle swarm optimization (PSO), two simple equations of motion are designed to guide particles to find the global optimal solution in order to simulate the predator-prey flight behavior of birds, thus realizing the mathematical modelling of swarm behavior.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm

Yao

Ding

2020

Complexity

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Aiming at the shortcomings of standard particle swarm optimization (PSO) algorithms that easily fall into local optimum, this paper proposes an optimization algorithm (LTQPSO) that improves quantum behavioral particle swarms. Aiming at the problem of premature convergence of the particle swarm algorithm, the evolution speed of individual particles and the population dispersion are used to dynamically adjust the inertia weights to make them adaptive and controllable, thereby avoiding premature convergence. At the same time, the natural selection method is introduced into the traditional position update formula to maintain the diversity of the population, strengthen the global search ability of the LTQPSO algorithm, and accelerate the convergence speed of the algorithm. The improved LTQPSO algorithm is applied to landscape trail path planning, and the research results prove the effectiveness and feasibility of the algorithm.

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“…In [24], a PSO of the cost function is proposed which aims to feed highly fluctuating industrial load using PV generation, wind farms, and conventional energy generation. In order to optimally maintain the state-of-charge in batteries, a multi-objective particle swarm optimization (MOPSO) algorithm is introduced by [25] in an AC/DC microgrid based on renewable energy production.…”

Section: Artificial Intelligence (Ai) Techniques In Microgrid Energy mentioning

confidence: 99%

“…Each individual (or candidate solution) is assigned a fitness value (based on its objective function value) and the fitter individuals are given a higher chance to mate and yield more "fitter" individuals. This is in line with the Darwinian theory of "Survival of the Fittest".In [24], a PSO of the cost function is proposed which aims to feed highly fluctuating industrial load using PV generation, wind farms, and conventional energy generation. In order to optimally maintain the state-of-charge in batteries, a multi-objective particle swarm optimization (MOPSO) algorithm is introduced by [25] in an AC/DC microgrid based on renewable energy production.…”

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confidence: 99%

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

et al. 2019

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In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.The major share of the energy production in microgrids usually comes from renewable energy sources such as photovoltaic panels (PV) or wind turbines (WT). The intermittent nature of such units can cause fluctuation and power imbalance in the system. Hence, energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3-6]. State-of-the-ArtResearch on all aspects of microgrids has become very popular in the recent past. In this review we will concentrate on optimization objectives in microgrids only since this is the topic of this paper.Reference [3] presents a review of existing optimization objectives, tools, and solution approaches used in microgrid energy management. In [6], a summary of various uncertainty quantification methods along with a comparative analysis on utilized communication technologies are presented. An energy management solution based on a Bayesian optimization algorithm (BOA) is proposed by [7] in which the optimization problem is formulated without a closed-form objective function expression, and solved using a BOA-based data-driven framework. In [8], a two-layer predictive energy management system (EMS) for microgrids was proposed. The upper layer reduces the total operational cost and the lower layer try to remove the forecast fluctuations. In [9], an optimal scheduling of a standalone microgrid under system uncertainties is proposed. This model uses a dynamic programming method to solve a single-objective optimization problem to reduce the operation and emission costs.Flexibility and cooperative behavior of multi-agent systems (MASs) make them appropriate candidates for ener...

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A modified particle swarm optimization algorithm for scheduling renewable generation in a micro-grid under load uncertainty

Cited by 102 publications

References 44 publications

Estudo de Sensibilidade do Algoritmo de Colônia de Vagalumes para um Problema de Engenharia Envolvendo Dimensionamento de Treliças

Estudo de Sensibilidade do Algoritmo de Colônia de Vagalumes para um Problema de Engenharia Envolvendo Dimensionamento de Treliças

Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

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