Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
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...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.