• Energy Research
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This paper presents a novel distributed control strategy for frequency control, congestion management, and optimal dispatch (OD) in isolated microgrids. The proposed strategy drives the distributed generators (DGs) within the microgrid to a dispatch that complies with the Karush–Kuhn–Tucker (KKT) conditions of a linear optimal power flow (OPF) formulation. The controller relies on local power and frequency measurements, information from neighboring DGs, and line-flow measurements transmitted through a communications network. Extensive simulations show a good performance of the controller against sudden changes in the load, congested lines and availability of DGs in the microgrid, and the ability to successfully drive the system to an optimal economic operation.

Dynamic thermal rating (DTR) has been seen as an important tool for planning and operation of power systems, and recently, for smart-grid applications. To implement an effective DTR system, it is necessary to install monitoring stations along the studied lines, with a tradeoff between accurate estimations and equipment investments. In this paper, a novel heuristic is developed for identifying the number and locations of critical monitoring spans for the implementation of DTR. The heuristic is based on the use of historical-simulated weather data, obtained from a Mesoscale Weather Model, and the statistical analysis of the thermal capacities computed in each span along the line. The heuristic is applied to a line that is 325 km long in North Chile. Optimal monitoring sets, including the number and location of required monitoring stations, are determined for different confidence levels in all line segments. The results are compared to an equidistant monitoring strategy. The proposed heuristic shows robustness since it outperforms the equidistant monitoring strategy in all of the analyzed cases, especially for the longer line segments, which are subject to more complex weather patterns.

Islanded microgrids (IMGs) provide a promising solution for reliable and environmentally friendly energy supply to remote areas and off-grid systems. However, the operation management of IMGs is a complex task including the coordination of a variety of distributed energy resources and loads with an intermittent nature in an efficient, stable, reliable, robust, resilient, and self-sufficient manner. In this regard, the energy management system (EMS) of IMGs has been attracting considerable attention during the last years, especially from the economic and emissions point of view. This paper provides an in-depth overview of the EMS optimization problem of IMGs by systematically analyzing the most representative studies. According to the state-of-the-art, the optimization of energy management of IMGs has six main aspects, including framework, time-frame, uncertainty handling approach, optimizer, objective function, and constraints. Each of these aspects is discussed in detail and an up-to-date overview of the existing EMSs for IMGs and future trends is provided. The future trends include the need for improved models, advanced data analytic and forecasting techniques, performance assessment of real-time EMSs in the whole MG’s control hierarchy, fully effective decentralized EMSs, improved communication and cyber security systems, and validations under real conditions. Besides, a comprehensive overview of the widely-used heuristic optimization methods and their application in EMSs of IMGs as well as their advantages and disadvantages are given. It is hoped that this study presents a solid starting point for future researches to improve the EMS of IMGs.