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

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.

A novel energy storage and demand side management for entire green smart grid system for NEOM city in Saudi Arabia

“…For feasible use of the PHES in any site, it is recommended to use L/H less than 10. 33 Based on the data of the PHES system that will be used in NEOM, L/H = 1200/650 = 1.85 which is much lower than 10 which means that the PHES system proposed in this study will be a superior option.…”

“…[31] FLC has been introduced to regulate the optimal electrolyzer/fuel cell operation according to the discrepancy between RESgenerated production and load demand. Besides the FLC, there are other strategies used in determining the optimal tariff for the HRES such as a multi-agent system, 32 linear programming approach 10,11 model predictive control, 33 and rule-based approach. 34 A significant reduction in the system size, the system's reliability, energy cost, and GHG emissions will be the result of all the advantages gained through the DSM application during the sizing and operation of the HRESs.…”

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

“…A real-time energy optimization solution for EV coordinated microgrids is developed using the Lyapunov stochastic optimization technique in [51]. In [52], a non-linear model predictive controller (NMPC) technique for coordinating the operation of linked multi-node microgrids with energy storage capabilities is introduced. A hybrid model is developed for series-parallel plugin hybrid electric vehicles (PHEVs) energy optimization in islanded mode, using a rule-based genetic algorithm (GA).…”

Nonlinear Technologies in Advanced Power Systems: Analysis and Control

“…Predictive control is one the more interesting strategies used in automatic control application today and Trigkas and his coauthors in the paper [11] present an original contribution where nonlinear predictive control strategies are introduced for storage energy management in interconnected microgrid networks. The mathematical model of the management system is presented and the platform including the SCADA system is proposed for real-time applications.…”