Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

Hossain, Md. Alamgir; Pota, Hemanshu R.; Squartini, Stefano; Abdou, Ahmed

doi:10.20944/preprints201808.0037.v1

Cited by 31 publications

(43 citation statements)

References 16 publications

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“…Authors MD Alamgir Hossain, Hemanshu Roy emphasizes on the difficulty to determine optimal operating point of a storage system for saving cost & minimising energy waste [39]. Particle Swarm Optimization (PSO) algorithm is used for the application of real time energy management.…”

Section: Energy Management Techniques Based On Approximate Modelsmentioning

confidence: 99%

Optimization Techniques for Energy handling System in a Microgrid

Joseph*,

Jeyaprabha

2020

IJRTE

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The energy from the renewable energy sources primarily depends on the geographical factors. Intermittency is a major issue for renewable energy sources which will result in comparatively high electricity generation cost and a proper need of an energy management system. In an intelligent era, the increasing penetration level of solar photovoltaic (PV) energy is becoming a tedious task. Hence, an energy management system is required to control the energy flow when there is more than one source connected. Real time energy management becomes challenging because of the time varying prices of electricity, mismatch of generation and demand etc. So, they are widely related to the optimization techniques. This paper explains a broader review of various challenges on integration of Renewable energy sources to the grid. Also, an extensive review of numerous approaches of Energy management system is given in this paper. Different optimization techniques are discussed along with its advantages and limitations for better and more optimized Energy management in the micro grid. A proper energy management system helps to decrease the electricity prices as well as increases the power system reliability. Also, improves the grid stability.

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Section: Energy Management Techniques Based On Approximate Modelsmentioning

confidence: 99%

Optimization Techniques for Energy handling System in a Microgrid

Joseph*,

Jeyaprabha

2020

IJRTE

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“…Particle Swarm Optimization (PSO), an intelligent algorithm which was originally inspired by the foraging behavior of group birds, was firstly proposed by J. Kennedy and RC Eberhart. PSO uses the sharing of information by individuals in the group to solve the problem of group motion and obtain the optimal solution, making evolution process from disorder to order in vector space [1]. In this paper, particle swarm optimization is used to find the optimal parameters in LSSVM.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Application Research of PSO-LSSVM in Carbon Emission Prediction in Hebei Province

Sun

Liu

2019

E3S Web Conf.

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This paper firstly introduces the background and significance of carbon emission prediction in Hebei Province, then collects the data and uses the PSO-LSSVM prediction model to train and predict the data collected through MATLAB software, finally obtains the prediction results. The results show that the prediction of PSO-LSSVM are accurate, and it can be used to predict carbon emissions in Hebei Province, Providing a new solution to the carbon emission forecasting of Hebei Province.

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“…They introduced a calculation strategy for energy utilization system. For the applications in real time energy management, system a regularised particle swarm optimization algorithm is introduced by Hossain et al [11].…”

Section: Introductionmentioning

confidence: 99%

Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

Joy

Krishnakumar

2020

IJECE

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For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.

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Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

Cited by 31 publications

References 16 publications

Optimization Techniques for Energy handling System in a Microgrid

Optimization Techniques for Energy handling System in a Microgrid

Application Research of PSO-LSSVM in Carbon Emission Prediction in Hebei Province

Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

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