  In this paper, to minimize the on-grid energy cost in a large-scale green
cellular network, we jointly design the optimal base station (BS) on/off
operation policy and the on-grid energy purchase policy from a network-level
perspective. Due to the fluctuations of the on-grid energy prices, the
harvested renewable energy, and the network traffic loads over time, as well as
the BS coordination to hand over the traffic offloaded from the inactive BSs to
the active BSs, it is generally NP-hard to find a network-level optimal
adaptation policy that can minimize the on-grid energy cost over a long-term
and yet assures the downlink transmission quality at the same time. Aiming at
the network-level dynamic system design, we jointly apply stochastic geometry
(Geo) for large-scale green cellular network analysis and dynamic programming
(DP) for adaptive BS on/off operation design and on-grid energy purchase
design, and thus propose a new Geo-DP design approach. By this approach, we
obtain the optimal BS on/off policy, which shows that the optimal BSs' active
operation probability in each horizon is just sufficient to assure the required
downlink transmission quality with time-varying load in the large-scale
cellular network. We also propose a suboptimal on-grid energy purchase policy
with low-complexity, where the low-price on-grid energy is over-purchased in
the current horizon only when the current storage level and the future
renewable energy level are both low. We compare the proposed policy with the
existing schemes and show that our proposed policy can more efficiently save
the on-grid energy cost over time.
