TY - GEN
T1 - A stochastic geometry-based demand response management framework for cellular networks powered by smart grid
AU - Farooq, Muhammad Junaid
AU - Ghazzai, Hakim
AU - Kadri, Abdullah
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23
PY - 2016/9/12
Y1 - 2016/9/12
N2 - In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized alpha-fair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.
AB - In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized alpha-fair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.
UR - http://ieeexplore.ieee.org/document/7564929/
UR - http://www.scopus.com/inward/record.url?scp=84989855991&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2016.7564929
DO - 10.1109/WCNC.2016.7564929
M3 - Conference contribution
SN - 9781467398145
BT - IEEE Wireless Communications and Networking Conference, WCNC
PB - Institute of Electrical and Electronics Engineers Inc.
ER -
