Insulet Acquires Assets of Automated Glucose Control LLC (AGC) streetinsider.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from streetinsider.com Daily Mail and Mail on Sunday newspapers.
The focal point of this research is to increase the usage of solar energy for charging electric vehicles by using a coordinated control of the power distribution system.
The technical challenges of large-scale integration of rooftop solar PV systems is a major concern for distribution network service providers (DNSPs) due to bidirectional power flow and voltage regulation issues. Energy storage devices have the potential to mitigate these adverse effects by storing excess energy during the day and providing peak shaving support at peak load. This paper presents a two-level dual-objective model predictive control (MPC) based algorithm to control DNSP owned community energy storage devices in LV residential distribution feeders. The proposed control method considers the feed-in tariff, spot price of energy and storage system operational costs. Both the economics ofthe system and voltage regulation are addressed. The individual modes of operation are activated by a high-level controller and a low-level controller provides the optimized charging/discharging rates according to the predefined objective cost functions. A case study 300m 4-wire LV feeder from
This paper considers energy delivery by a Hybrid Access Point (HAP) to one or more Radio Frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal Channel State Information (CSI), and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a Mixed Integer Linear Program (MILP) coupled with a Sample Average Approximation (SAA) method. The second is a Model Predictive Control (MPC) solution that utilizes Gaussian Mixture Model (GMM) and a so called backoff that is used to tighten probabilistic constraints. The results show that the performance of the MPC based solution is within 8% of the optimal solution with a probability of 90.8%.