ADMM-Based distributed online algorithm for energy management in hybrid energy powered cellular networks (2019)
investigate the energy management problem by jointly optimizing the data admission rate, transmit power, energy sharing among base stations, battery charging and discharging rate, and the energy purchased from the grid
long term average total cost minimization problem under the constraints on limited battery size and users data rate req - formulated as stochastic optimization problem
employ Lyapunov optimization technique and alternating direction method of multipliers (ADMM)
propose an online distributed algorithm - distributed online energy management algorithm (DOEMA)
need current system states, without requiring system statistic or future information
can be implemented in parallel and completely distributed fashion
to tackle the unreliable and random of RES, THREE approaches commonly used:
each base station (BS) can purchase back-up power from the traditional grid to ensure a reliable service for the users
deploy batteries to store the surplus energy and to discharge when the electricity price is high
exploiting the geographical diversity of RE
further utilize the limited renewable energy effectively
and reduce the grid expenditure significantly
in order to investigate EM problem in cellular network with hybrid energy supply, an abundant work have been done which classified into two:
EM policies without energy cooperation
energy cooperation enabled strategies
previous work referred:
based on non-causal RE & traffic info, proposed an energy allocation algorithm with infinite battery capacity to reduce the total on grid energy consumption [1]
REFERENCES
leveraging the priori distributions of the RE arrival and data arrival, an optimal deterministic offline resource scheduling policy exhibited to minimize the energy consumption [2]
[1] D. Liu, Y. Chen, K. K. Chai, T. Zhang, and M. Elkashlan, ‘‘Twodimensional optimization on user association and green energy allocation for hetNets with hybrid energy sources,’’ IEEE Trans. Commun., vol. 63, no. 11, pp. 4111–4124, Nov. 2015
[2] I. Fawaz, M. Sarkiss, and P. Ciblar, ‘‘Joint resource scheduling and computation offloading for energy harvesting communications,’’ in Proc. 25th Int. Conf. Telecommun. (ICT), Jun. 2018, pp. 26–28
devised an offline resource allocation algorithm for the system power costs minimization [3]
[3] S. Lohani, E. Hossain, and V. K. Bhargava, ‘‘Joint resource allocation and dynamic activation of energy harvesting small cells in OFDMA hetNets,’’IEEE Trans. Wireless Commun., vol. 17, no. 3, pp. 1768–1783, Mar. 2018.
hard to obtain the exact non-causal information during time-varying energy harvesting process in real world applications and the capacity of battery also limited
without require the prior knowledge of channel and harvested energy info, online resource allocation algorithm was developed to fully exploit the harvested energy [4]
[4] D. Zhai, M. Sheng, X. Wang, and Y. Li, ‘‘Leakage-aware dynamic resource allocation in hybrid energy powered cellular networks,’’ IEEE Trans.Commun., vol. 63, no. 11, pp. 4591–4603, Nov. 2015
3 online EM strategies
1
2
Main contribution:
formulated a stochastic programming problem
to minimize the time average total cost of the system with the consideration of many practical factors
users' data rate requirement
limited battery capacity
dynamic of electricity price
the stochastic data arrivals
time varying wireless channels
intermittent energy harvesting
employ the Lyapunov optimization technique and ADMM
an online algorithm is proposed to solve the formulated problem
fully distributed algorithm where each BS can make decisions on their own at each time slot
the asymptotic optimally of the proposed algorithm is analyzed in detail by selecting an appropriate value of the control parameter V
SYSTEM MODEL
topology example of the hybrid energy powered cellular network
Network model
Energy supply model
Battery model
Problem formulation
LYAPUNOV OPTIMIZATION and DISTRIBUTED ONLINE ALGORITHM
two virtual queues
users' data rate requirement
battery level
lyapunov optimization
distributed online algorithm
ADMM-based distributed energy management algorithm
solution procedure of the local optimization problem