# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 3 Selected Case Studies and Numerical Results
### Network Reconfiguration and Distributed Generation  
Climate change affects the technical and economic considerations of the power industry. Extreme weather events, such as floods and storms, increasingly threaten grid reliability [60]. Given the rapid increase in the frequency of such events, network reconfiguration (NR) has received significant attention because it can mitigate adverse effects on grid reliability by changing the network topology using on-off switches (SWs). For example, in Figure 6a, an islanded microgrid (MG) is implemented using the IEEE 37-node test feeder. The MG includes tie SWs (TSWs) and sectionalizing SWs (SSWs) that can adaptively change the MG topology in real time. Initially, the TSWs and SSWs are open and closed, respectively. Figure 6b shows an NR scenario, in which the SW operations trigger variation in the MG topology to restore the critical loads in LA\({}_{1}\) and LA\({}_{3}\). The non-critical loads in LA\({}_{2}\) were shed to ensure sufficient reserve capacity of the distributed generators (DGs) and then recovered after all critical loads became energized.  
Most NR studies focus on the operational scheduling of SSWs and TSWs in the steady state, for example, to maximize the restored load demand and minimize the restoration time. SSW and TSW scheduling principally considers the steady-state operations of distribution grids and MGs using constant or hourly sampled load demand forecasts before and after NR. During such scheduling, DGs featuring synchronous machines and inverters (referred to as SGs and IGs, respectively) have often been treated as point sources (i.e., PQ or PV nodes) without consideration of the dynamic responses to SW operations. In practice, consecutive SW operations and the resulting abrupt variations in load demand can trigger severe fluctuations in grid frequency and node voltages in the transient state, given the small capacities and low moments of inertia of DGs, which can thus unexpectedly trip DGs and cascade the collapse of network voltages.  
Two recent studies [61; 62] focused on how real-time control of DGs, working with the SWs, enhanced grid stability and reliability during load restoration. Both studies used DG feedback control loops to ensure that the DG reference power outputs and terminal  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Maximum Variations in Figure 5a** & \(|\Delta v_{0}|_{\text{max}}\left(\times 10^{-5}\right)\) & \(|\Delta v_{\text{C}}|_{\text{max}}\left(\times 10^{-5}\right)\) & \(1\Delta v_{W1}|_{max}\) & \(1\Delta v_{W2}|_{max}\) \\ \hline
**Case 1[pu]** & 6.4840 & 4.3595 & 0.0363 & 0.0354 \\ \hline
**Case 2 [pu]** & 18.1581 & 5.8330 & 0.0610 & 0.0614 \\ \hline
**Case 3 [pu]** & 348.8569 & 5.8436 & 0.0478 & 0.0474 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of continuous response test results.  
Figure 6: (**a**) A reconfigurable network with switches and synchronous machine-based and inverter-interfaced generators and (**b**) the variations in the topology that reduce power loss and restore and shed load.  
voltages were determined based on information from the SWs. The feedback loops were activated only after the load demand variation caused by NR significantly affected the MG operation. These studies motivated the development of new control strategies; DGs can pre-emptively compensate for the load variations given that, in general, NR initiation is controlled. The pre-emptive control of power grids has become increasingly possible as modeling and parameter estimation techniques have evolved over time.