## 5 Case Studies and Discussions
### Impact Due to Congestion and Demand Response
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Iteration** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{2}{*}{**Gap-S**} & **PF point** & 1385.308 & 2.707 & 0.652 & 0.366 & 0.048 & N/A \\ \cline{2-7}  & **Zero point** & 707557.820 & 471.389 & 365.477 & 205.691 & 0.300 & 0.038 \\ \hline \multirow{2}{*}{**Gap-Z**} & **PF point** & 143.076 & 139.152 & 0.730 & 0.552 & 0.090 & N/A \\ \cline{2-7}  & **Zero point** & 305592.919 & 70906.237 & 48.071 & 15.128 & 164.559 & 0.067 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gap of iteration process with various expansion point. Figure 10: Electricity load profile without and with demand response. Case 3: The demand response program is considered, and the electricity load is shown in Figure 10. There is increased load in the pipeline congestion period when DR becomes effective. This will cause a more serious gas shortage of NGFUs, and insufficient generation of electricity network as compared to Case 2. Table 5 provides gas shortage and load shedding information with different operation modes. The unbalanced amount of coupled dispatch increased due to the addition of demand response action. The reason is that, due to the consideration of time-of-use electricity prices, electricity price-sensitive loads will be adjusted according to changes in electricity prices, and electricity demand will increase during the period when the gas network pipeline is blocked. This adjustment supply gap is enlarged. As shown in Figure 11, due to the addition of price-based demand response action, the power demand shifts from the peak period (14-19 h) to the valley period (3-6 h), and the load in the valley period increases. The peak load is reduced, so the peak-valley difference is reduced, and the effect of demand response is reflected. However, at 20-22 h, because the demand response action increases the power demand, the load curve is larger than the original curve, so the unbalanced amount of coupled dispatch increases. At 19 h, due to the peak load period, regardless of whether demand response is considered or not, G1 and G3 remain at full output. The impact of the gas network on scheduling does not change due to demand response, so the unbalanced amount of coupled scheduling remains unchanged. Table 6 shows the total cost of the electricity network with different operation modes in different cases. In DO pipeline congestion cases, the addition of a demand response program not only promotes the reduction in costs but also increases profit. CO mode can solve this problem with a great extent, not only minimizing the negative impact of DR, but also reducing load shedding. Figure 11: Hourly generation and total cost of ENO. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case 2**} & \multicolumn{2}{c}{**Case 3**} \\ \cline{2-5}  & **DO** & **CO** & **DO** & **CO** \\ \hline Gas shortage of NGFUs (kcf) & 428.3 & 0 & 728.8 & 0 \\ \hline Load shedding (MW) & 29.7 & 0 & 46 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Gas shortage and load shedding in DO. The work demonstrates that demand response can achieve benefits, for example, to reduce congestion so as to improve energy supply security and enhance low-carbon economy. The use of gas storage is no double can improve co-ordination with electricity network operation. This gives a direction for future work to study the gas storage investment in detail. A similar approach derived from electrical energy storage (EES) may be considered. At present, EES, such as lithium-ion (Li-ion) batteries, can reduce curtailment of renewables, maximizing renewable utilization by storing surplus electricity. Several techno-economic analyses have been performed on EES, but few have investigated the financial performance. However, [58] presents a state-of-the-art financial model obtaining novel and significative financial and economics results when applied to Li-ion EES.