# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 3 Selected Case Studies and Numerical Results
### Decentralized State Estimation
#### 3.4.2 Incorporation of Decentralized Bad Data Processing into Decentralized SE  
The increasing support of information and communication technology (ICT) enables the conversion of legacy power grids into smart grids, wherein the tasks of grid operation, control, and estimation are achieved using the continuous flows of data measured from remote power facilities. However, ironically, the development of ICT increases the surfaces that malicious actors can attack. In particular, a power grid spans a wide geographic area, and hence the public and private communications between remote facilities and control centers are likely to increase the numbers of sites that hackers and attackers can readily access to pollute measured data with missing data and bad data (BD). This will cause disruptions to the operational stability of power grid and further large-scale power outages, leading to serious and harmful consequences in society and economy. It was reported in [73; 74] that various types of cyberattacks successfully occurred in essential parts of the power grid. This implies that cyber security should be recognized as a critical issue, as comprehensively discussed in [75; 76], when moving toward power grid decarbonization. BD acquired from critical measurements reduce the accuracy and computational efficiency of BD processing (BDP) methods, for example, using largest normalized residual tests (LNRTs), least absolute values, and sparse \(1_{1}\)-relaxation. Multiple BD conforming are likely to further decrease the accuracy and efficiency [77]. This is because the advantages afforded by PMU measurements have not been fully exploited, compromising the real-world hybrid DSE performance. In [77; 78], a phasor-aided SE (PHASE) method was used to improve BDP performance via more intensive cross-validation of the SCADA and PMU data. However, the PHASE method was implemented only in a centralized manner. Figure 16 compares a common, centralized hybrid SE strategy to a new hybrid DSE strategy [79]. In the latter, the PHASE method is integrated with ADMM-based DSE to improve BDP performance and thus also the DSE. Here, we term the new hybrid DSE, \"decentralized PHASE (DPHASE)\"; this was developed using a parallel multi-stage approach. Specifically, in each sub-area, the SCADA- and PMU-based estimators obtain the local SE results in parallel using SCADA and PMU measurements, respectively, and the corresponding measurement functions. The local estimators need exchange only small numbers of state estimates with their neighboring estimators, exploiting the ADMM. Given the network topology and measurement locations, the estimators then extend the sets of SCADA- and PMU-based state estimates in a manner that includes the same entries. The final estimates are determined based on fusion of the extended sets. Figure 17 and Tables 11 and 12 show the absolute error and average maximum absolute error (AMAE) results of comparative case studies when conducted on IEEE 14-, 118-, and 1062-bus networks. All the test networks include sub-areas that are locally unobservable to the PMU-based estimators. Specifically, the 14-bus network is divided into four sub-areas [79]. The SCADA systems acquired the voltage magnitudes, power injections, and line power flows at measurement points 4, 5, and 16, respectively. The PMUs measured both the magnitudes and phase angles of the voltages and incident currents at buses 2, 6, 7, and 9. The locations were optimally determined to achieve global observability using the minimum number of PMUs. Similarly, the IEEE 118-bus network was divided into five sub-areas, as shown in Table 13. The PMUs and SCADA sensors were located optimally and arbitrarily, respectively, such that the network was observable globally. The IEEE 1062-bus network includes nine sub-areas, each  
of which corresponds to the IEEE \(118\)-bus network. The locations of the PMUs and SCADA sensors in each sub-area are the same as those in the \(118\)-bus network.