With increasing penetration of renewable energy, uncertainty challenges ISOs to keep power balance in real-time. As ramping issues draw public... Show moreWith increasing penetration of renewable energy, uncertainty challenges ISOs to keep power balance in real-time. As ramping issues draw public attention, many ISOs have instituted flexible ramping products to ensure ramping reserve at generation side. However, not all the ramping reserves are deliverable when a transmission line is already congested. In the real-time market, if an uncertain load estimation is known at peak time t+10mins previously, SCUC/SCED is able to spare transmission reserve by changing the dispatch at time t with additional uncertain load constraints at t. To spare transmission reserve under uncertainty, this research proposes an uncertain load estimation to generate an estimated uncertain load and uncertainty constraints at t+10 in SCUC/SCED: with the help of a stochastic optimization model, uncertainties are quantified as a random actual load y and utilized in a modified stochastic model for undeliverable ramping reserve issues; once the optimal total system generation x is obtained, treated as an estimated uncertain load, uncertainty constraints are added at t+10mins in SCUC/SCED to obtain a secure dispatch at t. Therefore, transmission ramping reserve is ensured by a change in dispatch at t. Numerical results show that this design enhances the economy and scalability of power systems. In addition, scalability analysis proves it works for any scale of power systems with multiple local peak loads. M.S. in Electrical Engineering, December 2016 Show less
A recent thread of research in ordinal data analysis involves a class of mixture models that designs the responses as the combination of the... Show moreA recent thread of research in ordinal data analysis involves a class of mixture models that designs the responses as the combination of the two main aspects driving the decision pro- cess: a feeling and an uncertainty components. This novel paradigm has been proven flexible to account also for overdispersion. In this context, Groebner bases are exploited to estimate model parameters by implementing the method of moments. In order to strengthen the validity of the moment procedure so derived, alternatives parameter estimates are tested by means of a simulation experiment. Results show that the moment estimators are satisfactory per se, and that they significantly reduce the bias and perform more efficiently than others when they are set as starting values for the Expectation-Maximization algorithm. Show less
