This paper presents two methods for online rolling horizon optimal control of an energy storage unit in a gridconnected microgrid, subject to uncertainty in demand and electricity pricing. The proposed methods are based on the concept of rolling horizon control, where battery charge/discharge activities are determined by repeatedly solving a linear optimization problem over a moving control window. The predicted values of the microgrid net electricity demand and electricity prices over the control horizon are assumed to be uncertain. The first formulation of the control is based on the scenario-based stochastic conditional value at risk (CVaR) optimization, where the cost function includes electricity usage cost, battery operation costs, and grid signal smoothing objectives. Multivariate Gaussian distribution is used to model the variations of electricity prices and net demand power around their predicted nominal values. The second formulation of the control reduces the computations by taking a worst-case CVaR stochastic optimization approach. In this case, the uncertainty in demand is still stochastic but the problem constraints are made robust with respect to price variations in a range. Simulation results under different scenarios are presented to demonstrate the effectiveness of the proposed methods.

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As the number of Internet of Things (IoT) devices keeps increasing, data is required to be communicated and processed by these devices at unprecedented rates. Cooperation among wireless devices by exploiting Device-to-Device (D2D) connections is promising, where aggregated resources in a cooperative setup can be utilized by all devices, which would increase the total utility of the setup. In this paper, we focus on the resource allocation problem for cooperating IoT devices with multiple heterogeneous applications. In particular, we develop Application-Aware Cooperative Time allocation (AACT) framework, which optimizes the time that each application utilizes the aggregated system resources by taking into account heterogeneous device constraints and application requirements. AACT is grounded on the concept of Rolling Horizon Control (RHC) where decisions are made by iteratively solving a convex optimization problem over a moving control window of estimated system parameters. The simulation results demonstrate significant performance gains.