###### Abstract
introduced the potential renewable penetration index (PRPI) to select the appropriate capacity for P2G facilities, aiming to enhance the penetration of renewable energy sources [11]. Nevertheless, with the increase in power consumption, the electrolysis efficiency of water gradually decreases from around 85% to 65%, leading to a gradual decline in the overall efficiency of P2G systems [12; 13]. Thus, [PERSON] et al. enhanced the process of P2G by dividing it into two stages: electrolytic hydrogen production and hydrogen methanation. This approach established a low-carbon economic scheduling model for regional integrated energy systems, taking into account heating networks and P2G [14]. To efficiently utilize the hydrogen produced through water electrolysis in P2G devices, [PERSON] injected the generated hydrogen into a gas-fired device, thereby enhancing the efficiency of the gas-fired apparatus [15]. The hydrogenation process can utilize a carbon capture system (CCS) to obtain the necessary carbon source [16]. [PERSON] et al. employed a stochastic dynamic programming approach to design a P2G integrated energy hub system, aiming to investigate the impact of P2G-CCS systems on carbon reduction and system operational costs [17]. Reference [18] considered the coupling of CCS and P2G, establishing a bi-level optimization scheduling model, and verified that the coupling of P2G-CCS can enhance the economic viability and the ability to accommodate wind and solar power in the comprehensive energy system. However, the aforementioned studies primarily focus on the impact of P2G on improving the economic efficiency of the system, neglecting the influence of utilizing hydrogen produced through P2G as a hydrogen source to reduce carbon emissions in industrial production. Demand response technology is an essential method used in the IES to flatten load curves and optimize the economic functioning of the system [19]. Demand response seeks to achieve peak shaving and valley filling by directing user behavior and facilitatingcoordinated interaction between supply and demand. A customer satisfaction model is introduced in [20], which enhances the existing load models for demand response in electricity, gas, cooling, and heating. This model also takes into account price-based demand response. To investigate the economic and environmental potential of price-responsive demand response, [PERSON] et al. proposed two preference-based methods to approximate the hourly marginal emission factor (MEF) and demonstrated that when carbon prices are sufficient, price-responsive demand response can be an effective tool for improving both economic and environmental outcomes [21]. [PERSON] et al. employed the microeconomic behavioral framework of overlapping generations (OLG) to establish a price-responsive demand model, accurately representing customer load behavior [22]. The objective of [23] is to decrease system running expenses and manage the surplus electricity generated by wind power by integrating price-based demand response with a hydrogen energy storage system. [PERSON] et al. introduced incentive-based demand response and employed the Stackelberg game method to encourage residential users to actively participate in demand response programs [24]. [PERSON] et al. proposed a decentralized multi-agent reinforcement learning method for incentive-based demand response, aiming to reduce energy consumption and prevent grid congestion [25]. To encourage more users to participate in demand response, [PERSON] et al. employed real-time feedback to adaptively modify participants' incentive measures and enhance the flexibility of the distribution system's demand through the integration of electric vehicles [26]. However, the existing literature primarily concentrates on examining the influence of demand response on the demand side while overlooking the evaluation of energy-saving potential among various types of demand response.