Analysis of Smart Grid and Demand Response Technologies for Renewable Energy Integration: Operational and Environmental Challenges
Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the existing power system, which cannot cope effectively with highly variable and distributed energy resources. The emergence of smart grid technologies in rec...
| Main Author: | |
|---|---|
| Other Authors: | |
| Language: | English en |
| Published: |
2015
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1828/6000 |
| Summary: | Electricity generation from wind power and other renewable energy sources is increasing, and their variability
introduces new challenges to the existing power system, which cannot cope effectively with highly variable and distributed energy resources. The emergence of smart grid technologies in recent year has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This thesis investigates the impact of smart grid technologies on the integration of wind power into the power system. A smart grid power system model is developed and validated by comparison with a real-life smart grid experiment: the Olympic Peninsula Demonstration Experiment. The smart grid system model is then expanded to include 1000 houses and a generic generation mix of nuclear, hydro, coal, gas and oil based generators. The effect of super-imposing varying levels of wind penetration are then investigated in conjunction with a market model whereby suppliers and demanders bid into a Real-Time Pricing (RTP) electricity market. The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation. It is also found that the degree to which Greenhouse Gas (GHG) emissions can be mitigated is highly dependent on the generation mix. A displacement of natural gas based generation during peak demand can for instance lead to an increase in GHG emissions. Of practical significance to power system operators, the simulations also demonstrate that Demand Response (DR) can reduce generator cycling and improve generator efficiency, thus potentially lowering GHG emissions while also reducing wear and tear on generating equipment. === Graduate |
|---|