Abstract
Without a proper observation of the energy demand
of the receiving terminals, the retailer may be obliged to purchase
additional energy from the real-time market and may take the
risk of losing profit. This paper proposes two combinatorial multiarmed
bandit (CMAB) strategies in green cloud radio access
network (C-RAN) with simultaneous wireless information and
power transfer under the assumption that no initial knowledge
of forthcoming energy demand and renewable energy supply
are known to the central processor. The aim of the proposed
strategies is to find the set of optimal sizes of the energy packages
to be purchased from the day-ahead market by observing the
instantaneous energy demand and learning from the behaviour of
cooperative energy trading, so that the total cost of the retailer can
be minimized. Two novel iterative algorithms, namely, ForCMAB
energy trading and RevCMAB energy trading are introduced to
search for the optimal set of energy packages in ascending and
descending order of package sizes, respectively. Simulation results
indicate that CMAB approach in our proposed strategies offers
the significant advantage in terms of reducing overall energy cost
of the retailer, as compared to other schemes without learningbased
optimization.
of the receiving terminals, the retailer may be obliged to purchase
additional energy from the real-time market and may take the
risk of losing profit. This paper proposes two combinatorial multiarmed
bandit (CMAB) strategies in green cloud radio access
network (C-RAN) with simultaneous wireless information and
power transfer under the assumption that no initial knowledge
of forthcoming energy demand and renewable energy supply
are known to the central processor. The aim of the proposed
strategies is to find the set of optimal sizes of the energy packages
to be purchased from the day-ahead market by observing the
instantaneous energy demand and learning from the behaviour of
cooperative energy trading, so that the total cost of the retailer can
be minimized. Two novel iterative algorithms, namely, ForCMAB
energy trading and RevCMAB energy trading are introduced to
search for the optimal set of energy packages in ascending and
descending order of package sizes, respectively. Simulation results
indicate that CMAB approach in our proposed strategies offers
the significant advantage in terms of reducing overall energy cost
of the retailer, as compared to other schemes without learningbased
optimization.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Communications (IEEE ICC 2016) |
| Publisher | IEEE |
| DOIs | |
| Publication status | Published - 14 Jul 2016 |
