Name
Abstract | ||
|---|---|---|
This paper presents a demand response (DR) framework that utilizes the flexibility inherent to the thermodynamic behavior of four groups of independently-controlled medical freezers in a privately-owned business park microgrid that contains rooftop photovoltaics (PV). The optimization objectives may be chosen from the following 3 options: minimizing electricity exchanges with the public grid; minimizing costs by considering prices and RES availability; and minimizing peak load. The proposed DR framework combines thermodynamic models with automated, genetic-algorithm-based optimization, resulting in demonstrable benefits in terms of cost, energy efficiency, and peak power reduction for the consumer, local energy producer, and grid operator. The resulting optimal DR schedules of the freezers are compared against unoptimized, business-asusual scenarios with- and without PV. Results show that flexibility can be harnessed from the thermal mass of the freezers and their contents, improving the cost- and energy performance of the system with respect to the business-as-usual scenarios. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.5220/0006801001200129 | PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON SMART CITIES AND GREEN ICT SYSTEMS (SMARTGREENS) |
Keywords | Field | DocType |
Demand Response, Genetic Algorithm, Local RES Integration, Physical System Modeling, Smart Grid | Smart grid,Electricity,Efficient energy use,Thermal mass,Computer science,Photovoltaics,Demand response,Microgrid,Reliability engineering,Grid | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rosa Morales Gonzalez | 1 | 0 | 0.68 |
| Madeleine Gibescu | 2 | 30 | 7.86 |
| Sjef Cobben | 3 | 0 | 1.69 |
| Martijn Bongaerts | 4 | 0 | 0.68 |
| Marcel de Nes-Koedam | 5 | 0 | 0.34 |
| Wouter Vermeiden | 6 | 1 | 1.03 |