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The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as a key element of the system to achieve a successful operation. Additionally, the relatively low cost of wireless communication technologies and the advantages offered by remote monitoring have promoted the inclusion of the Internet of Things (IoT) and Wireless Sensor and Actuator Network (WSAN) technologies in the energy sector. In this article is presented the development of an IoT EMS based on a WSAN (IoT-EMS-WSAN) for the management of a DC-µG. The proposed EMS is composed of a WiFi-based WSAN that is interconnected to a DC-µG, a cloud server, and a User Web App. The proposed system was compared to a conventional EMS with a high latency wired communication layer. In comparison to the conventional EMS, the IoT-EMS-WSAN increased the updating time from 100 ms to 1200 ms; also, the bus of the DC-µG maintained its stability even though its variations increased; finally, the DC bus responded to an energy-outage scenario with a recovery time of 1 s instead of 150 ms, as seen with the conventional EMS. Despite the reduced latency, the developed IoT-EMS-WSAN was demonstrated to be a reliable tool for the management, monitoring, and remote controlling of a DC-µG. [ABSTRACT FROM AUTHOR]

Microgrids have increased in popularity thanks to both the integration of renewable energy resources and their energy distribution capability for remote locations. Moreover, the microgrids, mainly using multiple generators connected in parallel, acquire additional advantages by using both Hot-Swap and Current-Sharing techniques. This paper presents the development of a Hardware in the Loop platform to test Current-Sharing algorithms. It is reinforced that the use of a real-time simulation based on Hardware in the Loop is a viable and cost-effective alternative in the validation of controllers. The platform was developed in a graphical programming environment (LabVIEW 2015) and implemented with NI MyRIO 1900 (National Instruments Corp., Austin, TX, USA) development boards for easier reproducibility. The entire code project is openly available and provided in this paper. A system of photovoltaic energy generators was used to evaluate the performance of the HIL platform. As a result, the platform was able to reproduce a similar behavior to the photovoltaic generator, presenting average mean errors of 0.4 V and 0.2 A in its voltage and current, respectively. Additionally, the platform showed its capability to test Current-Sharing algorithms in the occurrence of Hot-Swap events. This work contributes with a validation tool for energy management systems applied to microgrids. [ABSTRACT FROM AUTHOR]

The use of renewable energies contributes to the goal of mitigating climate change by 2030. One of the fastest-growing renewable energy sources in recent years is wind power. Large wind generation systems have drawbacks that can be minimized using small wind systems and DC microgrids (DC-µGs). A wind system requires a control system to function correctly in different regions of its operating range. However, real-time analysis of a physical wind system may not be feasible. An alternative to counteract this disadvantage is using real-time hardware in the loop (HIL) simulation. This article describes the implementation of an HIL platform in an NI myRIO 1900 to evaluate the performance of control algorithms in a small wind system (SWS) that serves as a distributed generator for a DC-µG. In the case of an SWS, its implementation implies nonlinear behaviors and, therefore, nonlinear equations, and this paper shows a way to do it by distributing the computational work, using a high-level description language, and achieving good accuracy and latency with a student-oriented development kit. The platform reproduces, with an integration time of 10 µs, the response of the SWS composed of a 3.5 kW turbine with a fixed blade pitch angle and no gear transmission, a permanent magnet synchronous generator (PMSG), and a three-phase full-bridge AC/DC electronic power converter. The platform accuracy was validated by comparing its results against a software simulation. The compared variables were the PMSG currents in dq directions, the turbine's angular speed, and the DC bus's voltage. These comparisons showed mean absolute errors of 0.04 A, 1.9 A, 0.7 rad/s, and 9.5 V, respectively. The platform proved useful for validating the control algorithm, exhibiting the expected results in comparison with a lab-scale prototype using the same well-known control strategy. Using a well-known control strategy provides a solid reference to validate the platform. [ABSTRACT FROM AUTHOR]

The hardware in the loop (HIL) technique allows you to reproduce the behavior of a dynamic system or part of it in real time. This quality makes HIL a useful tool in the controller validation process and is widely used in multiple areas including photovoltaic systems (PVSs). This study presents the development of an HIL system to emulate the behavior of a PVS that includes a photovoltaic panel (PVP) and a DC-DC boost converter connected in series. The emulator was embedded into an NI-myRIO development board that operates with an integration time of 10 µs and reproduces the behavior of the real system with a mean percent error of 2.0478%, compared to simulation results. The implemented emulator is proposed as a platform for the validation of control systems. With it, the experimental stage is carried out on two controllers connected to the PVS without having the real system and allowing to emulate different operating conditions. The first controller is based on the Hill Climbing algorithm for the maximum power point tracking (MPPT), the second is a proportional integral (PI) controller for voltage control. Both controllers generate settling times of less than 3 s; the MPPT controller generates variations in the output in steady state inherent to the algorithm used. For both cases, the comparison of the experimental results with those obtained through software simulation show that the platform fulfills its usefulness when evaluating control systems. [ABSTRACT FROM AUTHOR]