Hierarchical Energy Management in Islanded Networked Microgrids

Hong, Yi; Alano, Francisco I.

doi:10.1109/access.2022.3143307

Cited by 15 publications

(3 citation statements)

References 43 publications

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“…This article seeks to present and critically evaluate an in-depth review of hybrid renewable energy sources (HRES) [8]. Additionally, the article examines various approaches utilized in every category [9]. The battery charges to the average battery state of charge (SoC) state once the power generation surpasses the load requirement [10].…”

Section: Introductionmentioning

confidence: 99%

Distributed voltage unbalance mitigation in islanded microgrid using moth flame optimization and firebug swarm optimization

Gandikoti,

Jha,

Jha

et al. 2024

IJPEDS

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In recent trends, hybrid renewable energy sources (HRES) provide a better solution to meet energy demand, maximizing the productivity of electricity network. Due to the above-mentioned features, several researchers have given more attention to PV-wind-based HRES systems. A stable energy supply must be added, including batteries, diesel generator (DG), to meet demand in the event of a grid failure. To meet the voltage unbalance in islanded mode, the sizes of DG have need to be selected sensibly and HRES requires additional energy storage. To examine the voltage unbalance problems of an islanded microgrid, a hybrid optimization approach known as moth flame optimization (MFO) and firebug swarm optimization (FSO) is introduced. Due to various loads, harmonic distortion causes the voltage to unbalance, which can result in voltage collapse. To deliver a quick response in an island mode, a comprehensive algorithm called MFO-FSO control is proposed. MATLAB software is used to validate the results which demonstrate that the proposed MFO-FSO outperforms the conventional decoupling double synchronous reference frame (DDSRF) methods by reducing total harmonic distortion (THD) up to 1.21% and voltage unbalance factor (VUF) up to 1.427%.

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Section: Introductionmentioning

confidence: 99%

Distributed voltage unbalance mitigation in islanded microgrid using moth flame optimization and firebug swarm optimization

Gandikoti,

Jha,

Jha

et al. 2024

IJPEDS

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show abstract

“…Ref. [9] proposes a hierarchical energy management system to address the issues of energy trading, network interconnection, and the intermittency of renewable energy sources. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids

Wu,

Shan,

Fan

2024

Sustainability

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Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, and carries out multi-objective optimization in the tertiary control, i.e., optimizing the economic cost, environmental cost, and the degree of energy utilization of microgrids. As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to obtain a modified particle swarm algorithm, which is more advantageous in multi-objective optimization. Compared to the traditional particle swarm algorithm, the modified particle swarm algorithm increased the photovoltaic absorbed rate from 0.7724 to 0.8683 and the wind energy absorbed rate from 0.6064 to 0.7158 in one day, which resulted in an increase in energy utilization by 14.89%, and a reduction in financial environmental costs from RMB 135,870 to RMB 132,230. The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids. Then, the optimization results, i.e., the data of the power scheduling process of the four power sources, were made into a table and imported into the microgrid model in Simulink. The simulation results indicated that the microgrid was able to output stable voltage, current, and frequency. Finally, the changes in microgrids affected by the external environment were further investigated from the aspects of the market environment and natural environment. Moreover, we verified the presence of a contradiction between the optimization of the microgrid economy and environmental protection. Thus, microgrids need to adjust their optimization focus according to the natural conditions in which they are located.

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“…Energy management systems (EMS) are required to realize the ability of a wide range of applications in a microgrid. The EMS can coordinate the distributed energy resources (DER) with their loads, while each DER has its own local controller [3]. The microgrids use the power supply from the grid, RESs, and their local energy storage systems to meet power demands.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

et al. 2022

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This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid.

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Hierarchical Energy Management in Islanded Networked Microgrids

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 15 publications

References 43 publications

Distributed voltage unbalance mitigation in islanded microgrid using moth flame optimization and firebug swarm optimization

Distributed voltage unbalance mitigation in islanded microgrid using moth flame optimization and firebug swarm optimization

A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

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