Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Abstract

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emissions and electricity expenses of base stations. However, renewable energy is inherently stochastic and intermittent, imposing formidable challenges on reliably satisfying users' time-varying wireless traffic demands. In addition, the probability distribution of the renewable energy or users’ wireless traffic demand is not always fully known in practice. In this paper, we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations, the battery charging and discharging rates, and the energy purchased from the grid under the constraint of a limited battery size at each base station. In solving the formulated non-convex chance-constrained stochastic optimization problem, a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance. Using this ambiguity set, the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach. Furthermore, a low-complexity distributionally robust chance-constrained energy management algorithm, which requires only interval sets of the mean and covariance of stochastic parameters, is proposed. The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity, energy cost, and reliability.

Keywords

• Cellular networks
• Energy harvesting
• Energy management
• Chance-constrained
• Distributionally robust optimization