## 5 Case Study Analysis
### Analysis of Energy-Saving Potential for Different Demand Response Types  
Figures 2 and 3 clearly demonstrate that the implementation of incentive-driven demand response results in a proactive user response to incentives between the hours of 8:00 \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Equipment Name**} & **Maximum Power** & **Energy Conversion** & **Operational Costs** \\  & **(kW)** & **Efficiency** & **(CNY/kW)** \\ \hline GT & 2000 & 0.44/0.39 & 0.0946 \\ HRSG & 500 & 0.9 & 0.032 \\ GB & 800 & 0.8 & 0.032 \\ EC & 700 & 5 & 0.0846 \\ AC & 1500 & 0.8 & 0.032 \\ P2G & 7000 &
\begin{tabular}{c} EL: 0.85 \\ MR: 0.7 \\ \end{tabular} & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main equipment parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Equipment Name**} & **Initial** & **Capacity** & **Charge/Discharge** & **Operational Costs** \\  & **Capacity** & **Limit** & **Efficiency** & **(CNY/kW)** \\ \hline ES & 1000 kW & 2000 kW & 0.95 & 0.045 \\ HS & 800 kW & 1600 kW & 0.95 & 0.045 \\ CS & 800 kW & 1600 kW & 0.95 & 0.045 \\ QS & 20 kg & 180 kg & 0.95 & 0.045 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Energy storage equipment parameters. and 21:00. Users modify their energy consumption behavior, resulting in a substantial decrease in the electricity load. By incorporating price-responsive demand, users strategically adjust their electricity usage, increasing it during the low-price period from 23:00 to early morning hours and decreasing it during peak price periods, with the goal of minimizing procurement costs. The immediate impact of price-responsive demand on reducing peak electricity consumption is more significant than that of incentive-driven demand response. The integration of both incentive-driven and price-responsive demand responses leads to a more substantial decrease in electricity load compared to utilizing each strategy separately, resulting in a more efficient load optimization. Table 3 demonstrates that in Scenario 1, when demand response is not taken into account, the energy supply remains constant, leading to notably elevated total costs and carbon emissions, as well as a noticeable occurrence of wind curtailment. Figure 3: The electricity price variation following demand response. Figure 2: Comparison of electrical loads after demand response. In comparison to Scenario 1, in Scenario 2, with the addition of price-responsive demand response, the system adjusts its energy supply methods flexibly to adapt to market price fluctuations, increases the proportion of wind power, leading to a reduction of 7.71% in wind curtailment costs and a decrease of 9.53% in carbon emissions. Additionally, it reallocates part of the load that was originally during peak electricity pricing periods to off-peak periods, resulting in a 5.16% reduction in purchasing energy costs and an overall cost reduction of 4.03%. Compared to Scenario 1, in Scenario 3, with the addition of incentive-based demand response, the application of incentives effectively changes users' energy consumption habits, aligning them more closely with the grid's operational needs. This results in the shifting of some peak-period loads to nighttime when wind power generation is high, promoting the utilization of wind power and leading to a reduction of 3.86% in wind curtailment costs, a decrease of 6.25% in carbon emissions, subsequently lowering purchasing energy costs by 3.36%, and achieving an overall cost reduction of 2.24%. Compared to Scenario 3, in Scenario 2, the electricity price signal is more effective in eliciting user responses than incentive measures. This results in a larger reduction in electricity demand, a relatively higher increase in the proportion of wind power, and significant reductions in carbon emissions and wind curtailment costs. However, the introduction of incentive measures, compared to price changes, offsets some of the reductions in total costs and purchasing energy costs.