Optimal sizing and operation for microgrid with renewable energy considering two types demand response

The integration of renewable energy sources into the power grid poses several challenges due to their variability in output power as they primarily depend on weather conditions. As a countermeasure, battery energy storage systems are introduced in order to mitigate output power fluctuations of the r...

Full description

Saved in:
Bibliographic Details
Published in:Journal of renewable and sustainable energy 2020-11, Vol.12 (6)
Main Authors: Sugimura, Makoto, Gamil, Mahmoud M., Akter, Homeyra, Krishna, Narayanan, Abdel-Akher, Mamdouh, Mandal, Paras, Senjyu, Tomonobu
Format: Article
Language:English
Subjects:
Alternative energy sources
Clustering
Computing costs
Distributed generation
Electric power grids
Electrical loads
Energy management
Energy resources
Energy storage
Fossil fuels
Mixed integer
Optimization
Renewable energy sources
Renewable resources
Schedules
Simulation
Storage systems
Weather
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The integration of renewable energy sources into the power grid poses several challenges due to their variability in output power as they primarily depend on weather conditions. As a countermeasure, battery energy storage systems are introduced in order to mitigate output power fluctuations of the renewable energy sources. Moreover, demand response programs have been attracting huge attention as an effective mechanism for efficient energy management. It enables power suppliers to cope with the output uncertainty of renewable energy systems. First step, we propose an HRES design methodology that takes into account one year. In order to find the optimal power supply configuration, it is necessary to take into account the changes in load demand due to weather conditions and seasons for one year. However, the computational cost of simulating one year is huge and requires a rather long simulation time. Therefore, we propose a simplified simulation method that uses clustering to account for changes in load demand due to weather and seasonality in one year. Second, the first proposed method is used to propose a method for optimizing the HRES equipment capacity and operation schedule considering demand response. In this paper, mixed-integer linear programing is used as the optimization method. The proposed method considers two types of demand response and achieves cost reduction by controlling load demand in response to generated power fluctuations.
ISSN:1941-7012
1941-7012
DOI:10.1063/5.0008065