Distributed deep reinforcement learning for functional split control in energy harvesting virtualized small cells

To meet the growing quest for enhanced network capacity, mobile network operators (MNOs) are deploying dense infrastructures of small cells. This, in turn, increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering mobile networks with harvested ambient energy to achieve both environmental and cost benefits. In this paper, we consider a network of virtualized small cells (vSCs) powered by energy harvesters and equipped with rechargeable batteries, which can opportunistically offload baseband (BB) functions to a grid-connected edge server depending on their energy availability. We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution. Coordination among vSCs is enabled via the exchange of battery state information. The evaluation of the network performance in terms of grid energy consumption and traffic drop rate confirms that enabling coordination among the vSCs via knowledge exchange achieves a performance close to the optimal. Numerical results also confirm that the proposed DDRL solution provides higher network performance, better adaptation to the changing environment, and higher cost savings with respect to a tabular multi-agent reinforcement learning (MRL) solution used as a benchmark

Decentralized Sliding Mode Control of Islanded AC Microgrids with Arbitrary Topology

The present paper deals with modelling of complex microgrids and the design of advanced control strategies of sliding mode type to control them in a decentralized way. More specifically, the model of a microgrid including several distributed generation units (DGus), connected according to an arbitrary complex and meshed topology, and working in islanded operation mode (IOM), is proposed. Moreover, it takes into account all the connection line parameters and it is affected by unknown load dynamics, nonlinearities and unavoidable modelling uncertainties, which make sliding mode control algorithms suitable to solve the considered control problem. Then, a decentralized second order sliding mode (SOSM) control scheme, based on the Suboptimal algorithm is designed for each DGu. The overall control scheme is theoretically analyzed, proving the asymptotic stability of the whole microgrid system. Simulation results confirm the effectiveness of the proposed control approach

A Robust Consensus Algorithm for Current Sharing and Voltage Regulation in DC Microgrids

In this paper a novel distributed control algorithm for current sharing and voltage regulation in Direct Current (DC) microgrids is proposed. The DC microgrid is composed of several Distributed Generation units (DGUs), including Buck converters and current loads. The considered model permits an arbitrary network topology and is affected by unknown load demand and modelling uncertainties. The proposed control strategy exploits a communication network to achieve proportional current sharing using a consensus-like algorithm. Voltage regulation is achieved by constraining the system to a suitable manifold. Two robust control strategies of Sliding Mode (SM) type are developed to reach the desired manifold in a finite time. The proposed control scheme is formally analyzed, proving the achievement of proportional current sharing, while guaranteeing that the weighted average voltage of the microgrid is identical to the weighted average of the voltage references.Comment: 12 page