TY - GEN
T1 - Demand Response Management using Non-Dominated Sorting Genetic Algorithm II
AU - Ray, Pravat Kumar
AU - Nandkeolyar, Shobhit
AU - Lim, Chee Shen
AU - Satiawan, I. Nyoman Wahyu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - In a smart grid environment, economic operation means not only economic scheduling of generation but also scheduling the load. Incentive-based Demand Response (DR) programs assume a noteworthy job in improving grid operation and reliability as well as cost management. Such a program enables utilities to decrease electrical energy use amid peak hours, which place burden on the electrical grid and result in high electricity prices or system outage as an after effect of issues present in the distribution system. This paper focus on the basic advantages of Demand Response Management (DRM) in smart households which includes optimization of customer loads leading to minimization of the bill borne by the customer. It also includes different methods (algorithms) to accomplish a lower inconvenience posed to the consumers. This is accomplished by an elitist Non-dominated Sorting Genetic Algorithm (NSGA II) and the result is compared with another optimization technique called Strength Pareto Evolutionary Algorithm (SPEA II). These will result to a profit-based environment; both to the consumers, and utility in an electricity market.
AB - In a smart grid environment, economic operation means not only economic scheduling of generation but also scheduling the load. Incentive-based Demand Response (DR) programs assume a noteworthy job in improving grid operation and reliability as well as cost management. Such a program enables utilities to decrease electrical energy use amid peak hours, which place burden on the electrical grid and result in high electricity prices or system outage as an after effect of issues present in the distribution system. This paper focus on the basic advantages of Demand Response Management (DRM) in smart households which includes optimization of customer loads leading to minimization of the bill borne by the customer. It also includes different methods (algorithms) to accomplish a lower inconvenience posed to the consumers. This is accomplished by an elitist Non-dominated Sorting Genetic Algorithm (NSGA II) and the result is compared with another optimization technique called Strength Pareto Evolutionary Algorithm (SPEA II). These will result to a profit-based environment; both to the consumers, and utility in an electricity market.
KW - Demand Response
KW - Non-Dominated Sorting Genetic Algorithm II
KW - Pareto-optimal solution
KW - Strength Pareto Evolutionary Algorithm II
UR - http://www.scopus.com/inward/record.url?scp=85084344643&partnerID=8YFLogxK
U2 - 10.1109/PESGRE45664.2020.9070522
DO - 10.1109/PESGRE45664.2020.9070522
M3 - Conference Proceeding
AN - SCOPUS:85084344643
T3 - 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, PESGRE 2020
BT - 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, PESGRE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, PESGRE 2020
Y2 - 2 January 2020 through 4 January 2020
ER -