OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration

Angelina D Bintoudi,Lampros Zyglakis,Athanasios Tryferidis,Dimitrios Tzovaras,Apostolos C Tsolakis,Paschalis A Gkaidatzis,Dimosthenis Ioannidis

doi:10.3390/en14102752

Angelina D Bintoudi, Lampros Zyglakis + Show 5 more

Open Access

https://doi.org/10.3390/en14102752

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Abstract

As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results.

Highlights

Microgrids (MG), foreseen as the avant-garde building blocks of the future Distribution Network (DN), have attracted much attention from various research fields, ranging from power electronics [1] and control systems [2] to machine learning [3]
To comprehend how OptiMEMS works, the MG scheduling and actual operation are demonstrated for two sample summer days: Figures 4 and 5 depict the MG operation under
To highlight the importance of MG operation monitoring and the adaptive nature of the optimal MG scheduling provided in Scenario B, the following interesting remark is made: the drop on the PV production curve shown with the black line in Figure 4b corresponds to the actual production, which was not accurately predicted by the PV forecasting engine in the day ahead horizon

Summary

Introduction

Microgrids (MG), foreseen as the avant-garde building blocks of the future Distribution Network (DN), have attracted much attention from various research fields, ranging from power electronics [1] and control systems [2] to machine learning [3]. It is essential to move past simulations [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] and hardware-in-the-loop (HIL) configurations [24,25,26,27,28,29,30] towards the evaluation of technologies in real-life applications [31,32,33,34]. The MG day-ahead scheduling problem essentially consists of the implementation of an optimised energy management strategy. The optimisation objectives may vary: cost [37]

Methods

Results

Conclusion