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With the ever-intensive utilization of distributed generators (DGs) and smart devices, distribution networks are evolving from a hierarchal structure to a distributed structure, which imposes significant challenges to network operators in system dispatch. A distributed energy-management method for a networked microgrid (NM) is proposed to coordinate a large number of DGs for maintaining secure and economic operations in the electricity-market environment. A second-order conic programming model is used to formulate the energy-management problem of an NM. Network decomposition was first carried out, and then a distributed solution for the established optimization model through invoking alternating-direction method of multipliers (ADMM). A modified IEEE 33-bus power system was finally utilized to demonstrate the performance of distributed energy management in an NM.Energies 2018, 11, 2555 2 of 18 for implementing the control center as well as communication infrastructures [3,4]. Furthermore, storing all data in one control center carries the risk of exposing the privacy of customers, as well as unavoidable single-point failures [3]. More importantly, the distributed method is more computationally efficient than the centralized one. Therefore, it is desirable to effectively coordinate NMs in a distributed manner for improving reliability and economics. Literature ReviewConventionally, a distribution-level microgrid is centrally controlled by a central coordination center [5]. More specifically, the central coordination center collects relevant information from dispersed controllable devices and forecasting data to perform an optimal dispatch of distributed resources for the next period [6]. Centralized energy-management architecture has been widely studied in existing publications. Reference [7] proposed a two-layer energy-management model wherein the schedule level attains the economic-operation scheme based on forecasts, while the dispatch level dispatches controllable DGs based on real-time data. In Reference [8], a centralized scheduling algorithm was proposed for an electric vehicle-dominated microgrid to optimize the charging scheme considering the charging cost and convenience of microgrid users. In Reference [9], the centralized energy-management problem for a household-level microgrid was formulated as a utility-maximization problem, subject to capacity constraints. In Reference [10], a centralized energy-management optimization model was formulated for a residential-quarter microgrid including a concentrating solar-power unit with an objective of minimizing the involved operation costs. A two-stage stochastic demand-side management model for a commercial-building microgrid is formulated in Reference [11], considering the uncertainties in solar-generation outputs, loads, microgrid availabilities, and microgrid energy demands.The fully distributed optimization method includes two broad categories, i.e., the Lagrangian relaxation-based and the optimality-condition decomposition-based category. The Lagrangian r...

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With the extensive installation of intelligent electronic devices with bi-directional fault monitoring capabilities, richer fault direction information can be collected and utilized to achieve an accurate fault diagnosis. In this paper, we consider the fault diagnosis problem in active distribution systems with distributed generators connected, such as rotating electrical machine power sources and centralized inverter interfaced renewable energy resources. The fault diagnosis problem is modeled as a linear integer programming problem with an objective to minimize the numbers of fault zones and false alarms. A novel functional form is derived to capture the expected alarms sent by bi-directional fault monitoring devices to be compared with the actual alarms received by the dispatch center. Uncertainties in both the monitoring and communication stages are considered in the model by formulating the numbers of false alarms in the objective function. Three types of suspected false alarms can be detected: "missing alarms", "distorted alarms", and "reverse alarms". By solving the developed optimization model for fault diagnosis, false alarms and suspected fault zones can be found. Case studies in a modified IEEE 33-bus system and a 55-bus system in Guangzhou, China are carried out in several different scenarios with multiple faults to demonstrate the performance of the proposed model. Numerical tests show that the proposed approach is superior in computational time such that it can be used for real-time fault diagnosis in active distribution systems. INDEX TERMS Active distribution system, bi-directional fault monitoring devices, fault diagnosis, linear integer programming.