# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
## 4 Result and Discussion
### Carbon Allowance Reallocation Results  
Given the complexity of the power structure in China, the electric power system should allocate CO\({}_{2}\) emission allowances on the basis of the actual conditions of regional power production and electricity consumption to minimize the phenomenon of \"carbon transfer\". The core issue, under the shared responsibility principle, is how to determine carbon emissions' shared proportions among provinces. In the absence of detailed data on energy consumption by provincial power grids and inter-provincial or regional electricity transfers, the actual production-side ratio is used as a proxy for the shared responsibility coefficient, considering the interaction between economic output and electricity load. \[\theta_{i}=0.35(1-k_{i})/P_{eci}+0.65s_{i} \tag{30}\]  
In the formula, \(\theta_{i}\) represents the actual production-side ratio (i.e., the emission reduction responsibility coefficient), \(k_{i}\) denotes the power generation efficiency in the region, and \(s_{i}\) represents the electricity consumption efficiency in the region. \(P_{eci}\) refers to the per capita electricity consumption (kWh/person) in region iii. The calculations for \(k_{i}\) and \(s_{i}\) are provided in the following formulas. \[k_{i}=\mathbf{ECV}/G_{ci} \tag{31}\]  
\[s_{i}=GDP_{i}/E_{ci} \tag{32}\]  
_ECV_ represents the power equivalent value of 0.1229 kgce/kWh, \(G_{ci}\) denotes the green and post-generation (10,000 kWh), and \(E_{ci}\) represents the electricity consumption  
Figure 10: Results of carbon allowance projections. in region \(i\) (100 million kWh). The shared responsibility coefficient \(\theta_{i}\) for each regional power grid is determined by calculating the values of \(k_{i}\) and \(s_{i}\), with the results presented in Table 5.  
Therefore, on the premise that the overall carbon peak volume limits in the national power system remain unchanged, considering and avoiding the risk of \"carbon transfer\", the actual carbon dioxide emission of each regional power grid \(EP_{i}\) is as follows:  
\[EP_{i}=\theta_{i}.EM_{g2030i} \tag{33}\]  
Among them, \(EM_{g2030i}\) represents the value of green energy and CO\({  
Based on the above model building and the final carbon quota allocation measurement, the initial carbon quota for each regional grid is determined by the total carbon quota in 2030 and the corresponding shared emission reduction responsibility coefficient for each region. Based on the prediction of the total amount of carbon quota for the power industry in the previous section, this paper uses the predicted carbon emission value after the neutralization of green energy power generation as the total amount of carbon quota for the allocation of carbon quota for each regional power grid. According to the above table of shared emission reduction responsibility coefficients of each region, the initial carbon quota allocation results of each region in 2025-2030 are calculated. In order to study the current status of carbon quota allocation of each regional power grid under the carbon peak target, this paper takes 2030 as the study year, and the initial carbon emission quotas of its six power grids are mainly distributed in three interval bands, which are 0-1 billion tons, 5-1 billion tons, and 1-1.5 billion tons. Among them, the Northeast Power Grid, located in the east of China, has the smallest initial carbon emission quota, receiving only 194.146 million tons of carbon quota, which is mainly because the Northeast region accounts for a large proportion of thermal power generation (the proportion of thermal power generation is more than 90%), and its population and economic development level are in a negative growth trend, so the level of emission reduction potential is low, and therefore it receives a smaller share of carbon emission quota. The initial carbon emission quotas for the North China Power Grid and the Northwest Power Grid are between 500 million and 1 billion tons.