Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control

Dimitrios Trigkas,Chrysovalantou Ziogou,Spyros Voutetakis,Simira Papadopoulou

doi:10.3390/en14041082

Dimitrios Trigkas, Chrysovalantou Ziogou + Show 2 more

Open Access

https://doi.org/10.3390/en14041082

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Abstract

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation.

Highlights

Climate change, global warming and ozone depletion are important issues for reducing carbon dioxide emissions
In order to demonstrate the benefits of the non-linear model predictive controller (NMPC), three scenarios were examined
To compare and highlight the capability of the controller to exploit the maximum amount of energy and balance the microgrid, in each scenario, two simulations of the microgrid operation are performed: one with the NMPC

Summary

Introduction

Global warming and ozone depletion are important issues for reducing carbon dioxide emissions. In combination with the increased energy demand and the reduced fossil fuel stocks, the global interest has shifted to alternative energy sources. Distributed renewable production, over the years evolved into an organized structure by integrating local energy storage systems and loads [1]. These structures known as microgrids have undertaken the fulfillment of energy demand, mostly based on renewable energy [2]. Microgrids contain electricity sources and loads and they consist of various sources of distributed generation, and most importantly renewable energy sources (RES) [3]. The integration of numerous power devices in microgrids along with the stochastic nature of renewable generation and the uncertainty concerning the load demand resulted in the need of appropriate management and control [4,5,6]

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