<p>The advent of small-scale, distributed generators of energy has resulted in the problem of integrating them in the conventional electric power system, which is characterized by large-scale, centralized energy generators. MicroGrids have emerged as a promising solution to the integration problem and have duly received increasing research attention. Microgrids are semi-autonomous collections of controllable microsources and loads, which present themselves to the utility grid as single, controlled entities. In order to achieve the semi-autonomous and controlled nature of microgrids, especially,overcoming the challenge of balancing demand and power generation, an intelligent energy management scheme is required.</p> <p>Developing an energy management scheme is an interesting and challenging task, which provides the potential to exploit ideas from a plethora of fields like Artificial Intelligence and Machine Learning, Constrained Optimization, etc. However, testing energy management strategies on a microgrid would pose a multitude of problems,the most important of them being the unreliability and inconvenience of testing an energy management strategy, which is not optimal, on a functional microgrid. Errors in a test strategy might cause power outages and damage installed devices. Hence it is necessary to test energy management strategies on simulated microgrids.</p> <p>This thesis presents a Software Testbed of MicroGrids, specifically designed to suit the purposes of development of energy management strategies. The testbed consists of two components: Simulation Framework and Analysis Tool. The modular simulation framework enables simulation of a microgrid with microsources and loads,whose configurations can be specified by the user. The analysis tool enables visual analysis of data generated using simulations, which would enable the improvement of not only the management strategy and prediction techniques, but also the computer models used in the simulation framework. A demonstration of the software testbed's simulation and analysis capabilities is presented and possible directions for future research are suggested.</p> / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/11295 |
| Date | 10 1900 |
| Creators | Ravichandran, Adhithya |
| Contributors | Haykin, Simon, T. Kirubarajan, Rafael Kleiman, T. Kirubarajan, Rafael Kleiman, Computational Engineering and Science |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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