## 4 Case Studies
### Comparative Analysis of the Proposed Incentive Mechanism and the Existing Mechanisms
#### 4.3.2 Scenario 2: Annual Wind Power Utilization of Approximately 3000 h  
In China, the annual utilization hours of onshore wind power in areas with good wind resources can reach approximately 3000 h. To investigate the effect of the proposed model in different wind resource scenarios, three groups of Case 4, Case 5, and Case 6, with different incentive mechanisms, were set in a scenario with approximately 3000 h of wind power utilization per year.  
Figure 8: Planning results of different cases in Scenario 1.  
Figure 9: (**a**) Expectation of bus carbon intensity in Case 2; (**b**) expectation of bus carbon intensity in Case 3.  
* Case 4: The proposed bus carbon intensity incentive mechanism;
* Case 5: The energy storage capacity subsidy incentive mechanism;
* Case 6: The energy storage capacity subsidy incentive mechanism and renewable energy certificate trading.  
The planning results of the three incentive mechanisms for the new wind resource scenario are shown in Table 8. As the wind resource improves, the total carbon emissions of the original system prior to planning was 37,205.9 tCO\({}_{2}\). Compared with the original system, Case 4, Case 5, and Case 6 reduce carbon emissions by 15.38%, 3.93%, and 6.88%, respectively. Due to the enhancement of wind resources, 58 MW of wind power capacity is planned in Case 5 with an embedded energy storage capacity subsidy. It can be noted that Case 4 has less wind power planned than Case 1. This is because Case 4 is in a scenario with better wind resources, and the wind power output already existing in the original system is increased, thus promoting the reduction of the bus carbon intensity level in the system. In the proposed mechanism, the bus carbon intensities of the pre-planning system affect the planning results. When the carbon intensity in the original system decreases, the incentive to stimulate wind power planning decreases. It can be seen that the proposed mechanism is more conducive to the government directing a low-carbon energy injection into high-carbon intensity areas. This has important practical significance for decarbonizing the power system. In addition, it can be found that the siting of the wind turbines and energy storage in Case 4 is consistent with Case 1. In contrast, there is no obvious pattern in the planned siting between Case 5 and Case 2 or between Case 6 and Case 3. As can be seen in Figure 10, in the scenario with better wind resources, Case 4, which has the proposed incentive mechanism, still has significant advantages in promoting the reasonable capacity planning of wind turbines and energy storage and promoting the system to reduce carbon emissions with similar total fixed investment costs as in Case 5 and Case 6.  
Figure 11 shows the bus carbon intensity plots for the original system, Case 4, Case 5, and Case 6 in Scenario 2. This once again verifies that the proposed incentive mechanism is more conducive to motivating the system to reduce the bus carbon intensities by planning wind turbines and energy storage, thus reducing the system's carbon emissions. This is because the proposed mechanism, i.e., the incentive mechanism based on the nodal, historical carbon emission intensity, is an incentive mechanism that directly uses carbon as an indicator and can more directly and finely guide planning in the direction favorable to carbon reduction. In contrast, the actual energy storage capacity subsidy and renewable energy certificate trading are incentive mechanisms from the energy perspective.  
Figure 11: (**a**) Expectation of bus carbon intensity of the pre-planning system; (**b**) expectation of bus carbon intensity in Case 4; (**c**) expectation of bus carbon intensity in Case 5; (**d**) expectation of bus carbon intensity in Case 6.