Driven by government incentive policies and heightened environmental awareness by individuals, many regions around the world have seen a rapid rise in distributed energy resource (DER) penetration in electricity distribution networks. While high penetration of DER significantly helps facilitate the decarbonization in power and utitlies, it also brings unexpected operational challenges, among which voltage compliance has been a significant concern. To address this issue, the efficient load profile forecast, the operational framework and related strategies are critical challenges that need to be addressed urgently. Hence, this paper presents a cloud-edge computing-based framework to effectively operate the coupled medium-voltage (MV) and low-voltage (LV) distribution network. The high computational efficiency in cloud computing and low data latency in edge computing are presented and explored to coordinate the day-ahead and intraday operations ranging from different framework layers. Under the framework, a customer-level forecasting algorithm is employed to predict both day-ahead and real-time load profiles. Based on the prediction results, an optimization model based on unbalanced-three phase optimal power flow is proposed and solved by an efficient and accurate linearization-based approach that considers the controllability of on-load tap changers, distributed static var generator and the PV inverters. Simulations based on an extensive mocked MV-LV distribution network show the proposed forecasting method is adopted in real-time operations in terms of high accuracy and demonstrate the efficiency of the proposed optimization method in enhancing the voltage compliance in the network.

MOTIVATION: There is a pressing need for improved proteomic screening methods allowing for earlier diagnosis of disease, systematic monitoring of physiological responses and the uncovering of fundamental mechanisms of drug action. The combined platform of LC-MS (Liquid-Chromatography-Mass-Spectrometry) has shown promise in moving toward a solution in these areas. In this paper we present a technique for discovering differences in protein signal between two classes of samples of LC-MS serum proteomic data without use of tandem mass spectrometry, gels or labeling. This method works on data from a lower-precision MS instrument, the type routinely used by and available to the community at large today. We test our technique on a controlled (spike-in) but realistic (serum biomarker discovery) experiment which is therefore verifiable. We also develop a new method for helping to assess the difficulty of a given spike-in problem. Lastly, we show that the problem of class prediction, sometimes mistaken as a solution to biomarker discovery, is actually a much simpler problem. RESULTS: Using precision-recall curves with experimentally extracted ground truth, we show that (1) our technique has good performance using seven replicates from each class, (2) performance degrades with decreasing number of replicates, (3) the signal that we are teasing out is not trivially available (i.e. the differences are not so large that the task is easy). Lastly, we easily obtain perfect classification results for data in which the problem of extracting differences does not produce absolutely perfect results. This emphasizes the different nature of the two problems and also their relative difficulties. AVAILABILITY: Our data are publicly available as a benchmark for further studies of this nature at http://www.cs.toronto.edu/~jenn/LCMS