Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR

Fouladfar, Mohammad Hossein; Saeed, Nagham; Marzband, Mousa; Franchini, Giuseppe

doi:10.3390/en14185971

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…Although their approach showcases effectiveness via numerical examples, research on electricity price fluctuations remains limited. Fouladfar et al (2021) championed the integration of electric vehicles in home microgrids to bolster user demand response. Their study highlighted enhanced microgrid demand response performance through differential evolution (DE) methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective particle swarm optimization for optimal scheduling of household microgrids

Huang,

He,

et al. 2024

Front. Energy Res.

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Addressing the challenge of household loads and the concentrated power consumption of electric vehicles during periods of low electricity prices is critical to mitigate impacts on the utility grid. In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users’ travel patterns with electric vehicles. The model focuses on optimizing daily household electricity costs and minimizing grid-side energy supply variances. Specifically, the mathematical model incorporates the actual input and output power of each distributed energy source within the microgrid as optimization variables. Furthermore, it integrates an analysis of capacity variations for energy storage batteries and electric vehicle batteries. Through arithmetic simulation within the Pareto optimal solution set, the model identifies the optimal solution that effectively mitigates fluctuations in energy input and output on the utility side. Simulation results confirm the effectiveness of this strategy in reducing daily household electricity costs. The proposed optimization approach not only improves the overall quality of electricity consumption but also demonstrates its economic and practical feasibility, highlighting its potential for broader application and impact.

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Section: Introductionmentioning

confidence: 99%

Multi-objective particle swarm optimization for optimal scheduling of household microgrids

Huang,

He,

et al. 2024

Front. Energy Res.

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“…This problem is considered stochastic, and batteries can be used to overcome this problem. When PV generation is high compared to load demand, excess PV power is locally stored using BES and can be used to supply the load during peak periods [7][8][9][10]. For most commercial buildings that are built in city/urban areas where outer space is limited, the primary concern is the determination of the optimal capacity of PV-BES systems, considering their available rooftop space and the need to obtain maximum PV power output.…”

Section: Introduction 1motivationmentioning

confidence: 99%

A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings

et al. 2023

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In this article, the optimal sizing of hybrid solar photovoltaic and battery energy storage systems is evaluated with respect to rooftop space and feed-in tariff rates. The battery scheduling is performed using a proposed rule-based energy management strategy. The rules are formulated based on the demand limit, PV export power limit, and state of charge of the battery. Furthermore, optimization modeling with initial choices of parameters and constraints in terms of solar photovoltaic and battery energy storage capabilities is developed to minimize the total net present cost. The hourly values of solar irradiance, air temperature, electrical loads, and electricity rates are considered the inputs of the optimization process. The optimization results are achieved using particle swarm optimization and validated through an uncertainty analysis. It is observed that an optimal photovoltaic and battery energy storage system can reduce the cost of electricity by 12.33%, including the sale of 5944.029 kWh of electricity to the grid. Furthermore, energy consumption, peak demand, and greenhouse gas emissions are reduced by 13.71%, 5.85%, and 62.59%, respectively. A comprehensive analysis between the variable and fixed data for the load, energy from PV, batteries, and the grid, and costs demonstrates that the optimal sizing of photovoltaic and battery energy storage systems with the best mix of energy from PV, batteries, and the grid provides the optimal solution for the proposed configuration.

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An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties

Manoharan,

Chandrasekaran,

Chandran

et al. 2024

Environ Sci Pollut Res

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Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR

Cited by 6 publications

References 35 publications

Multi-objective particle swarm optimization for optimal scheduling of household microgrids

Multi-objective particle swarm optimization for optimal scheduling of household microgrids

A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings

An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties

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