## Case Study
### Results and Analysis of LA Bidding Strategies
#### 6.3.4 Summary of the Impact of Resident Response Uncertainty  
In the demand side bidding, without considering the willingness of residents to respond, the LA bids for respond quantity based on the ideal maximum response potential of residents, which may make the actual response quantity of the LA the next day less than the previous bidding quantity. This is because the willingness of residents to participate in DR is different, and it is costly for the LA to involve all residents in DR. Therefore, the LA need to consider the residents' willingness and choose the residents with high response willingness to participate in DR, which can greatly reduce the demand response cost of the LA. The case simulation in this section shows that without considering the uncertainty of residents' participation in DR, the LA's eventual net income would be substantially reduced, perhaps even negative. However, considering the uncertainty of the residents' response, the LA prefers to choose the residents with high willingness to participate in the response, which can reduce the response cost of the LA and increase net income. The response cost of LA during the load increase period is decreased by up to 71% and by an average of 40%; and during the load decrease period is decreased by up to 23% and by an average of 12%.  
In addition, to quantify the uncertainty of the residential consumers, the uncertainty evaluation model considers the three influences of residents' consciousness, response capacity and external incentive. Compared with the model based on consumer psychology, which only considers external incentives, the model presented in this paper is more representative of the actual situation. It is found that there is little difference in the response quantity of different residential groups. Therefore, the awareness factor is the main reason for the difference in response willingness. From the LA's perspective, at the beginning of the DR, the LA can increase residents' willingness to participate in DR projects by increasing engagement of familiar residents, increasing publicity and promotion of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DR Period** & 1:00–6:00 & 16:00–20:00 \\
**Net profit (yuan)** & \(-\)59.25 & 872.21 \\
**Incentive unit price (yuan/kWh)** & 0.89 & 1.33 \\
**Bidding unit price (yuan/kWh)** & 1.34 & 2.00 \\
**Response quantity (MWh)** & 12.26 & 8.43 \\
**Average willingness** & 0.81 & 0.82 \\
**Compensation value (yuan)** & 2056.49 & 1294.11 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Conventional bidding strategies without considering the willingness.  
Figure 18: Different DR plans and corresponding residents’ average willingness \(\overline{p}\) to participate in DR.  
favorable news about DR, supporting relevant policies, and increasing financial subsidies to residents. Then, the LA can obtain data on residents' electricity usage behavior and DR participation in a large number of historical DR projects. Combined with the artificial neural network, the LA can adjust the parameters of the fuzzy inference system, which can more accurately assess the residents' response uncertainty.