A framework for remote Energy Management System development using Model Predicitive Control
Challenge and object
Energy Management Systems (EMS) are software tools that support the grid operators to monitor, control and optimize power system assets.
Deployment of an Energy managment system on a real system is time-consuming and may be hard to implement.
This work draws inspiration from the Software as a Service (SaaS) business model to propose an Energy managment system-as-a-service framework.
Work performed
Created an Energy managment system-as-a-service framework for researchers and developers consisting of i) Cloud-based Energy management system that scales for microgrid HWIL, ii) Probabilistic load forecast model, iii) Stochastic Model predictive control.
The software framework is based on the “Docker” technology (www.docker.com).
Significant results
The tool has been tested on a computer model of the Skagerak Energilab microgrid.
The stochastic Model predictive control reduced the operational cost by 26 % for the test case.
Impact for distribution system innovation
Energy management system development based on Software as a Service can ease the implementation, testing and verification of advanced control strategies for microgrids.
