Smart Energy Management System of Environmentally Friendly Microgrid Based on Grasshopper Optimization Technique

Gad, Yehia; Diab, Hatem; Abdelsalam, Mahmoud; Galal, Yasser

doi:10.3390/en13195000

Cited by 14 publications

(12 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CO 2 emissions from power generation cannot be reduced without a reliance on renewable energy. Energy management technologies are strongly encouraged as a means of improving microgrid dependability and lowering microgrid costs [15].…”

Section: Motivationsmentioning

confidence: 99%

Optimal Multi-Objective Power Scheduling of a Residential Microgrid Considering Renewable Sources and Demand Response Technique

et al. 2022

View full text Add to dashboard Cite

Microgrid optimization is one of the most promising solutions to power system issues and new city electrification. This paper presents a strategy for optimal power scheduling of a residential microgrid depending on renewable generating sources and hydrogen power. Five scenarios of the microgrid are introduced to show the effect of using biomass energy and a seawater electrolyzer on microgrid cost and CO2 emissions. Time of use demand response is applied to reshape the electric load demand and decrease the dependence on grid power. The obtained results from the multi-objective optimization verify that biomass has a significant role in minimizing the cost and CO2 emissions; the cost is decreased by 37.9% when comparing scenarios with and without biomass. Besides, the FC integration with seawater electrolyzer and tanks reduces the microgrid emissions by around 40%.

show abstract

Section: Motivationsmentioning

confidence: 99%

Optimal Multi-Objective Power Scheduling of a Residential Microgrid Considering Renewable Sources and Demand Response Technique

et al. 2022

View full text Add to dashboard Cite

show abstract

“…These young insects start rolling and eat everything that comes in their way. Then, they become adults and start swarming in the air (Gad et al, 2020). Each insect in the swarm is characterized by two important motions; cooperation between grasshoppers in larvae and insect phases and foraging for the food movement.…”

Section: Grasshopper Optimizationmentioning

confidence: 99%

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

Abdelkader

Elshaboury

Al-Sakkaf

2022

International Journal of Applied Metaheuristic Computing

View full text Add to dashboard Cite

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, ten meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. Besides, an integrative ranking of the models is achieved using average ranking algorithm.

show abstract

“…The wormhole is the path of the objects from a high verse to other lower inflation rate universes. The optimum case is whe receive objects from all other universes, which means that it has the lo To mathematically model the MVO algorithm, in Equation ( 22), tion for the roulette wheel mechanisms that can randomly arrange all u To mathematically model the MVO algorithm, in Equation (22), there is an illustration for the roulette wheel mechanisms that can randomly arrange all universes, assuming that d is the number of variables and n is the number of candidate solutions [40][41][42]:…”

Section: The Multi-verse Optimization Algorithmmentioning

confidence: 99%

A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques

2021

Self Cite

View full text Add to dashboard Cite

Optimal power flow (OPF) is considered one of the most critical challenges that can substantially impact the sustainable performance of power systems. Solving the OPF problem reduces three essential items: operation costs, transmission losses, and voltage drops. An intelligent controller is needed to adjust the power system’s control parameters to solve this problem optimally. However, many constraints must be considered that make the design process of the OPF algorithm exceedingly tricky due to the increased number of limitations and control variables. This paper proposes a multi-objective intelligent control technique based on three different meta-heuristic optimization algorithms: multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawks optimization (HHO) to solve the OPF problem. The proposed control techniques were validated by applying them to the IEEE-30 bus system under different operating conditions through MATLAB simulations. The proposed techniques were then compared with the particle swarm optimization (PSO) algorithm, which is very popular in the literature studying how to solving the OPF problem. The obtained results show that the proposed methods are more effective in solving the OPF problem when compared to the commonly used PSO algorithm. The proposed HHO, in particular, shows that it can form a reliable candidate in solving power systems’ optimization problems.

show abstract

Smart Energy Management System of Environmentally Friendly Microgrid Based on Grasshopper Optimization Technique

Cited by 14 publications

References 34 publications

Optimal Multi-Objective Power Scheduling of a Residential Microgrid Considering Renewable Sources and Demand Response Technique

Optimal Multi-Objective Power Scheduling of a Residential Microgrid Considering Renewable Sources and Demand Response Technique

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques

Contact Info

Product

Resources

About