Authors :
Monty Kumar; Dr. Gurpreet Kaur
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/sk8rem78
DOI :
https://doi.org/10.5281/zenodo.10039256
Abstract :
Due to the many benefits that come along
with using photovoltaic (PV) systems, they are now in
the driver's seat when it comes to using solar power as a
renewable energy source (RES). This trend is becoming
more prevalent, particularly in grid-connected
applications, as a direct result of the many advantages
brought about by the use of RES inside distributed
generation (DG) systems. This new scenario makes it
necessary to have an efficient tool for evaluating
photovoltaic (PV) systems that are connected to the grid.
This thesis focuses on addressing load fluctuation
challenges in residential environments by developing an
optimization framework for scheduling elastic
residential appliances and integrating solar PV systems.
The objective is to flatten the load profile and enable
effective demand response implementation in smart grid
systems. The proposed methodology utilizes the Particle
Swarm Optimization (PSO) algorithm to optimize the
scheduling of appliances and the utilization of solar PV
energy. The thesis provides an introduction to the
problem, outlines the methodology, presents the
obtained results, and evaluates the effectiveness of the
scheduling approach in load curve flattening.
Keywords :
PSO Particle Swarm Optimization, DG Distributed Generation, PV Photo Voltaic
Due to the many benefits that come along
with using photovoltaic (PV) systems, they are now in
the driver's seat when it comes to using solar power as a
renewable energy source (RES). This trend is becoming
more prevalent, particularly in grid-connected
applications, as a direct result of the many advantages
brought about by the use of RES inside distributed
generation (DG) systems. This new scenario makes it
necessary to have an efficient tool for evaluating
photovoltaic (PV) systems that are connected to the grid.
This thesis focuses on addressing load fluctuation
challenges in residential environments by developing an
optimization framework for scheduling elastic
residential appliances and integrating solar PV systems.
The objective is to flatten the load profile and enable
effective demand response implementation in smart grid
systems. The proposed methodology utilizes the Particle
Swarm Optimization (PSO) algorithm to optimize the
scheduling of appliances and the utilization of solar PV
energy. The thesis provides an introduction to the
problem, outlines the methodology, presents the
obtained results, and evaluates the effectiveness of the
scheduling approach in load curve flattening.
Keywords :
PSO Particle Swarm Optimization, DG Distributed Generation, PV Photo Voltaic
