Research on Grid Connected Optimization Scheduling of Micro-grid Utilizing on Improved Bee Colony Method

Zhang, Qiangshan

doi:10.13052/dgaej2156-3306.3712

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…RL-GAN combines the power of reinforcement learning (RL) and generative adversarial networks (GANs) to optimize energy consumption in smart grids using demand response techniques. Figure 1 presents the architecture of the proposed model [8,33].…”

Section: Methodsmentioning

confidence: 99%

Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

M L,

Joseph P

et al. 2023

DGAEJ

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Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time (<0.4 seconds), are used to evaluate the model’s performance. The output of the method excelled all of the other current methods that were taken into account. The system’s rapid response time and its positive environmental impact further highlight its potential in managing smart grid resources effectively.

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Section: Methodsmentioning

confidence: 99%

Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

M L,

Joseph P

et al. 2023

DGAEJ

View full text Add to dashboard Cite

show abstract

Research on Improved BBO Algorithm and Its Application in Optimal Scheduling of Micro-Grid

2022

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Aiming at the cooperative optimization problem of economy and environmental protection of the traditional microgrid, including micro gas turbine and diesel engine, carbon capture and storage, and a power to gas system which can consume wind and light and deal with carbon dioxide, is introduced, and three optimization scheduling models of the microgrid based on improved BBO algorithm are proposed. Firstly, a micro-grid with a power to gas system is constructed, and an optimal scheduling model is built which takes into account the system operation cost, environmental governance cost and comprehensive economic benefit. Secondly, the ecological expansion operation is introduced, an improved BBO algorithm is explored by improving the mobility model, and its convergence is derived in detail. Finally, the microgrid system energy optimization scheduling is realized based on the improved BBO algorithm. Compared with the scheduling model that only considers the operation cost or pollution gas control cost, the total cost of the comprehensive economic benefit scheduling model is reduced by 15.5% and 5.5%, respectively, which reflects the reasonableness of the scheduling model and the effectiveness of the improved algorithm.

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Research on Grid Connected Optimization Scheduling of Micro-grid Utilizing on Improved Bee Colony Method

Cited by 2 publications

References 13 publications

Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

Research on Improved BBO Algorithm and Its Application in Optimal Scheduling of Micro-Grid

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