4.7 Article

Dynamic Energy Management for Smart-Grid-Powered Coordinated Multipoint Systems

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2016.2520220

关键词

CoMP systems; downlink beamforming; smart grids; high-penetration renewables; stochastic optimization; Lyapunov optimization

资金

  1. China Recruitment Program of Global Young Experts
  2. Program for New Century Excellent Talents in University
  3. Innovation Program of Shanghai Municipal Education Commission
  4. National Science and Technology Major Project of the Ministry of Science and Technology of China [2012ZX03001013]
  5. U.S. NSF [1509040, 1508993, 1442686, 1423316, 1343248]
  6. Direct For Computer & Info Scie & Enginr
  7. Division of Computing and Communication Foundations [1423316] Funding Source: National Science Foundation
  8. Directorate For Engineering
  9. Div Of Electrical, Commun & Cyber Sys [1509005, 1508993] Funding Source: National Science Foundation
  10. Division of Computing and Communication Foundations
  11. Direct For Computer & Info Scie & Enginr [1442686] Funding Source: National Science Foundation
  12. Div Of Electrical, Commun & Cyber Sys
  13. Directorate For Engineering [1343248, 1509040] Funding Source: National Science Foundation

向作者/读者索取更多资源

Due to increasing threats of global warming and climate change concerns, green wireless communications have recently drawn intense attention toward reducing carbon emissions. Aligned with this goal, the present paper deals with dynamic energy management for smart-grid powered coordinated multipoint (CoMP) transmissions. To address the intrinsic variability of renewable energy sources, a novel energy transaction mechanism is introduced for grid-connected base stations that are also equipped with an energy storage unit. Aiming to minimize the expected energy transaction cost while guaranteeing the worst-case users' quality of service, an infinite-horizon optimization problem is formulated to obtain the optimal downlink transmit beamformers that are robust to channel uncertainties. Capitalizing on the virtual-queue-based relaxation technique and the stochastic dual-subgradient method, an efficient online algorithm is developed yielding a feasible and asymptotically optimal solution. Numerical tests with synthetic and real data corroborate the analytical performance claims and highlight the merits of the novel approach.

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