Capturing Transmission and Distribution Connected Wind Energy Variability
Although renewable energy provides a viable solution to address ongoing challenges of the economy and the environment in modern power systems, the variable generation of this technology results in major technical challenges for system operators. This issue is becoming more severe as the penetration of renewable generation is increasing. This dissertation addresses the variability challenge of renewable energy resources in transmission and distribution levels of modern power systems.
For transmission level, this dissertation focuses on wind generation fluctuation. Three methods of reducing wind generation fluctuation are investigated from an economic perspective, including (a) dumping the wind generation, (b) using battery energy storage system (BESS) to capture excess wind generation, and (c) a hybrid method combining these two approaches. The economic viability of the hybrid method is investigated via a developed linear programming model with the objective of profit maximization, which in extreme cases will converge to one of the other methods. This dissertation further proposes a BESS planning model to minimize wind generation curtailment and accordingly maximize the deployment of this viable technology.
For distribution level, this dissertation investigates the issue of microgrids net load variability stemmed from renewable generation. This is accomplished by investigating and comparing two options to control the microgrid net load variability resulted from high penetration of renewable generation. The proposed options include (a) Local management, which limits the microgrid net load variability in the distribution level by enforcing a cap constraint, and (b) Central management, which recommends on building a new fast response generation unit to limit aggregated microgrid net load variability in the distribution level. Moreover, the aggregated microgrid net load variability is studied in this dissertation by considering the distribution system operator (DSO). DSO would calculate the microgrids net load in day-ahead basis by receiving the aggregated demand bid curves. Accordingly, two models are proposed considering the DSO role in managing the grid operation and market clearing. The first one is securityconstrained distribution system operation model which maximizes the system social welfare. The system security consists of distribution line outage as well as microgrid islanding. None of these two security events are in the control of the DSO, so associated uncertainties are considered in the problem modeling. The second one aims at reconfiguring the distribution grid, i.e., a grid topology control, using the smart switches in order to maximize the system social welfare and support grid reliability.
The conducted numerical simulations demonstrate the effectiveness and the merits of the proposed models in identifying viable and economic options in capturing renewable generation variability.