MARTINE—A Platform for Real-Time Energy Management in Smart Grids
Abstract
:1. Introduction
- three realistic case studies to validate the use of the platform;
- scheduling of the energy resources available in each scenario;
- laboratory emulation of the developed models;
- evaluating the activity of an aggregator during DR implementation, namely the ramp period, regarding scheduling and remuneration;
- improvement of aggregator resources management in short and real-time DR;
- development of real-time simulation models using a set of laboratory equipment;
- use of multi-agents to support the simulations, namely the players modeling and resource models integration.
2. Materials and Methods
- multi-agent based platform integrating several multi-agent systems;
- agents representing energy resources can be of 3 types (real buildings, other installations and equipment, physically emulated components, or software agents) allowing mixing physical resources with simulated ones in real-time simulation;
- includes a real-time data acquisition and historic data repository, storing measured real-time data, data obtained in measurement campaigns, and other data from multiple sources;
- provides reality augmented by realistic simulation in real-time, enabled by GECAD algorithms in conjunction with hardware in the loop simulation;
- enables integration of external applications and physical installation (buildings, houses, labs);
- receives real-time data from remote partners, used in real-time management and simulation;
- integrates most of the GECAD models for smart grids (for example, energy resource scheduling optimization, demand response, market simulation);
- provides effectiveness/efficiency to intelligent management (according to the time constraints to provide the solution) by the use of reinforced learning.
3. Results
- -
- an agriculture irrigation management model is proposed to show the application of the MARTINE platform in the context of agriculture, in Section 3.1;
- -
- a practical model of the DR program is presented with a specific focus on the ramping of DR programs, in Section 3.2;
- -
- finally, in the last case, a practical strategy is shown for load modeling by using various network parameters and configurations, in Section 3.3.
3.1. Energy Scheduling in an Agriculture Irrigation System
3.1.1. Implemented Solution
3.1.2. Case Study
3.1.3. Experimental Results
3.2. Practical Implementation of Ramping in Distinct Demand Response Programs
3.2.1. Implemented Solution
3.2.2. Experimental Results
3.3. Real-Time Simulation System for Load Modeling
3.3.1. Implemented Solution
3.3.2. Experimental Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Vale, Z.; Faria, P.; Abrishambaf, O.; Gomes, L.; Pinto, T. MARTINE—A Platform for Real-Time Energy Management in Smart Grids. Energies 2021, 14, 1820. https://doi.org/10.3390/en14071820
Vale Z, Faria P, Abrishambaf O, Gomes L, Pinto T. MARTINE—A Platform for Real-Time Energy Management in Smart Grids. Energies. 2021; 14(7):1820. https://doi.org/10.3390/en14071820
Chicago/Turabian StyleVale, Zita, Pedro Faria, Omid Abrishambaf, Luis Gomes, and Tiago Pinto. 2021. "MARTINE—A Platform for Real-Time Energy Management in Smart Grids" Energies 14, no. 7: 1820. https://doi.org/10.3390/en14071820