Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
8-1-2019 12:00 AM
End Date
11-1-2019 12:00 AM
Description
Renewable generation, such as wind power, is commonly considered a must-take resource in power systems. In this work we show that, given the technical capabilities of current wind turbines, this approach could lead to major economic inefficiency as wind integration levels in power systems increase. We initially provide intuition for cases in which the optimal operating point involves shedding renewable generation, even though no cost is associated with it in the optimization objective, illustrated in small power systems. We then explore the expected benefit from dispatching wind resources at a lower level than their available output in a Stochastic Unit Commitment (SUC) framework. The modeling and evaluation approach adopted are described. A decomposition technique based on recent literature that utilizes global cuts and Lagrangian penalties to achieve convergence is used to solve the resulting large scale mixed integer optimization problem, in a high performance computing environment. A reduced California system is examined as a test case.
The Hidden Cost of Priority Dispatch for Wind Power
Grand Wailea, Hawaii
Renewable generation, such as wind power, is commonly considered a must-take resource in power systems. In this work we show that, given the technical capabilities of current wind turbines, this approach could lead to major economic inefficiency as wind integration levels in power systems increase. We initially provide intuition for cases in which the optimal operating point involves shedding renewable generation, even though no cost is associated with it in the optimization objective, illustrated in small power systems. We then explore the expected benefit from dispatching wind resources at a lower level than their available output in a Stochastic Unit Commitment (SUC) framework. The modeling and evaluation approach adopted are described. A decomposition technique based on recent literature that utilizes global cuts and Lagrangian penalties to achieve convergence is used to solve the resulting large scale mixed integer optimization problem, in a high performance computing environment. A reduced California system is examined as a test case.
https://aisel.aisnet.org/hicss-52/es/markets/8