Synergizing Renewable Distributed Generation Allocation with Demand Response for Enhanced Energy Efficiency

The integration of distributed generation (DG) technology into distribution networks (DN) has garnered significant attention in recent years due to its potential to enhance system efficiency, reduce carbon emissions, and improve overall power system reliability. However, the complex interplay of factors such as load demand, renewable energy sources, and energy storage systems (ESS) complicates the determination of optimal DG system placement within DN. Demand response (DR) programs offer a promising avenue for improving DG system efficiency by enabling customers to adjust their energy consumption during peak hours, thereby alleviating strain on the grid. Recognizing the potential synergy between DR and solar photovoltaic (PV) systems, this study investigates their combined impact on DN efficiency. Utilizing a particle swarm optimization (PSO) method, this research addresses the challenge of maximizing DG distribution while coordinating with DR initiatives. The iterative nature of these optimizations ensures a comprehensive approach to system enhancement. Application of this methodology to the IEEE 33 bus system demonstrates significant improvements in power quality metrics. The proposed methodology not only enhances system efficiency and reliability but also effectively coordinates DG placement with DR strategies. The findings underscore the potential of DG and DR integration in addressing contemporary challenges faced by distribution networks, offering a pathway towards a more resilient and environmentally sustainable electricity grid.

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Published
2024-08-10
DOI
https://doi.org/10.1109/iccccm61016.2024.11039964
Primary Topic
Smart Grid Energy Management
Type
article
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article

Synergizing Renewable Distributed Generation Allocation with Demand Response for Enhanced Energy Efficiency

Anil Kumar Yadav, Vivek Saxena, Shyama Kant Jha, Praveen Kumar
Smart Grid Energy Management
article

Synergizing Renewable Distributed Generation Allocation with Demand Response for Enhanced Energy Efficiency

Anil Kumar Yadav, Vivek Saxena, Shyama Kant Jha, Praveen Kumar
article en

Abstract

The integration of distributed generation (DG) technology into distribution networks (DN) has garnered significant attention in recent years due to its potential to enhance system efficiency, reduce carbon emissions, and improve overall power system reliability. However, the complex interplay of factors such as load demand, renewable energy sources, and energy storage systems (ESS) complicates the determination of optimal DG system placement within DN. Demand response (DR) programs offer a promising avenue for improving DG system efficiency by enabling customers to adjust their energy consumption during peak hours, thereby alleviating strain on the grid. Recognizing the potential synergy between DR and solar photovoltaic (PV) systems, this study investigates their combined impact on DN efficiency. Utilizing a particle swarm optimization (PSO) method, this research addresses the challenge of maximizing DG distribution while coordinating with DR initiatives. The iterative nature of these optimizations ensures a comprehensive approach to system enhancement. Application of this methodology to the IEEE 33 bus system demonstrates significant improvements in power quality metrics. The proposed methodology not only enhances system efficiency and reliability but also effectively coordinates DG placement with DR strategies. The findings underscore the potential of DG and DR integration in addressing contemporary challenges faced by distribution networks, offering a pathway towards a more resilient and environmentally sustainable electricity grid.

Netaji Subhas University of Technology (IN), Dr. B. R. Ambedkar National Institute of Technology Jalandhar (IN)
Affordable and clean energy
Openalex Percentile: Top 72%
Smart Grid Energy Management
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Synergizing Renewable Distributed Generation Allocation with Demand Response for Enhanced Energy Efficiency — Anil Kumar Yadav, Vivek Saxena, et al. · (2024) | TGRS Research Map | TGRS