Longoria, Genaro (2020) Open-market Energy Procurement Strategies to Integrate Wind Energy. Doctoral thesis, Waterford Institute of Technology.
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Abstract
Technological breakthroughs in recent years have no precedent. From the hard to the fog, basically all infrastructures have been influenced or have had an impact from the strepitous progress. To make this possible at the core lies a key factor : Energy. Blessed by the geographic location, Ireland has one of the best wind resources in the world. Wind farms incur in nearly zero marginal costs. Reduce dependence on imported fossil fuels. Also, can be deployed at different scales with ease. Also decentralizes power generation and helps alleviate distribution congestion. The hard infrastructure (e.g. blade and nacelle) has reached a mature state which is reflected in the falling LCOE. Nevertheless, generating electricity from wind power is still dependent on support schemes. Incorporating wind energy into portfolios represents a significant challenge for Load Serving Entities. Therefore the sought after characteristics of wind energy hinder hefty balancing costs percolating to the end-customer, and thus meager or negative profit. Governments around the globe have incentivized renewable generation with tax-based safety nets to provide significant risk mitigation, foster investment and low cost finance. This work focuses on reducing the need for exogenous support to integrate wind energy. Firstly, the research identifies the operational context of a typical price-taker wind power producer (WPP) and the gaps in the research literature. This gives place to a splitting of the portfolio concocting in two strategical horizons. Following, the research applies non-cooperative behavioral techniques and evolutionary programming to determine long-term bilateral contracts among power wholesalers and electricity retailers. The next step addresses the short term interaction among the WPP and the market. This work also sets the challenges of a micro grid peer-to-peer energy trading setting. Finally an analytics driven trading scheme is developed whereby the exposure to the more volatile balancing markets and penalty fees is automated and optimized.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Wind energy |
| Departments or Groups: | *NONE OF THESE* |
| Divisions: | School of Science > Department of Computing, Maths and Physics |
| Depositing User: | Derek Langford |
| Date Deposited: | 18 Sep 2020 10:42 |
| Last Modified: | 18 Sep 2020 10:42 |
| URI: | https://repository.wit.ie/id/eprint/3449 |
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