IES integrates multiple energy sources within the region through coordinated planning and optimized operation to meet the diverse energy demands within the system. The IES operating framework considering efficient utilization of hydrogen energy is shown in Figure 1. As shown in Figure 1, the leading manufacturing equipment for hydrogen is an electrolytic tank, which generates hydrogen and oxygen through the electrolysis of water, belonging to the first stage of the P2G process. The energy equipment directly utilizing hydrogen includes hydrogen fuel cells, gas turbines, and methane reactors. The methane reactor uses hydrogen and carbon dioxide to generate natural gas, which belongs to the second stage of the P2G process. Hydrogen and natural gas are mixed and transported into the gas turbine proportionally, and all the generated electrical energy is supplied to the electrical load. At the same time, a portion of the generated heat energy flows to the thermal load through the waste heat boiler. A small amount is generated through the waste heat recovery device for waste heat generation and then transmitted to the electrical load. Equipping gas turbines with waste heat power generation equipment, to a certain extent, decouples the constraint of “determining electricity based on heat,” making their energy supply more flexible. The system is also equipped with a hydrogen fuel cell, which can directly utilize hydrogen to generate electrical and thermal energy, achieving the coupling of hydrogen energy with electrical and thermal energy. The system has added energy storage equipment to each energy flow link, enabling the transfer of electricity, heat, gas, and hydrogen energy sources in a specific time sequence, solving problems such as large fluctuations in new energy output and difficulty in prediction, and ensuring the safe and stable operation of the system.

A virtual energy storage system is a theory that utilizes other devices or scheduling strategies to balance the power system’s energy. By transferring or transforming the energy emitted by various types of power sources, the system energy is adjusted to improve the reliability and quality of system operation and economic benefits (Chen et al., 2021). Virtual energy systems offset the lack of energy storage by managing and guiding energy demand interventions and realizing the effect of reducing energy storage capacity and costs. Utilizing virtual energy storage technology to optimize energy at different periods without adding new energy storage facilities, peak-to-low system capacity requirements for damaging backup can improve grid security while improving terminal energy utilization and reducing charging costs.

Demand-side controllable loads (such as air conditioners, water heaters, refrigerators, and electric vehicles) have the characteristics of flexible and controllable operation modes (Wang et al., 2019). The dispatch center can directly control them according to the system needs, or economic measures (such as real-time electricity prices) can guide users to change their energy consumption mode selectively, achieving the purpose of demand-side management. Δ P 1 / P 1 Δ P 2 / P 2 ⋮ Δ P 24 / P 24 = R Δ C 1 / C 1 Δ C 2 / C 2 ⋮ Δ C 24 / C 24 , (5) R = R 1,1 R 1,2 ⋯ R 1,24 R 2,1 R 2,2 ⋯ R 2,24 ⋮ ⋮ ⋮ R 24,1 R 24,2 … R 24,24 , (6) where ΔP _i indicates the amount of electricity consumption change in the ith period, P _i indicates the power consumption of the ith period, ΔC _i indicates the amount of electricity price change in the ith period, C _i indicates the price of electricity in the ith period, and R is a correlation matrix composed of self-elastic coefficients and mutual elastic coefficients. 0 ≤ Δ P idr t ≤ P idr max Δ P idr t − Δ P idr t − 1 ≤ L idr , (7) where P idr max is the maximum response of the IDR load in the period t and L _idr is the response rate of the IDR load.

The decision variable of the IES upper-level model is the installed capacity of equipment within IES, and the objective function is a multi-objective function that includes the total cost of IES and the total carbon emissions of IES. The total price of IES consists of the equivalent equipment investment construction cost and system operation cost of IES. min ∑ t = 1 T F a + F c , ∑ t = 1 T F c o 2 , (8) where F _a indicates the cost of purchasing electricity and gas from the power grid and gas network and F _c indicates the maintenance and operation cost of each device.

The lower model is mainly divided into two parts: day-ahead and intraday. The day before dispatching takes 1 hour as the timescale, and the execution period is 24 h. The day-ahead optimal operation status of each energy unit in IES, the real-time price of IDR, and the call plan of the first-type IDR resources are determined and substituted into the day-ahead dispatching. The optimization of intraday scheduling takes 15 min as the timescale and 4 hours as the execution cycle to revise the pre-day scheduling plan. At this stage, it is necessary to determine the call plan for the second type of IDR and the output plan for each energy unit. min ∑ t = 1 T F a + F c + F d a − i d r , (9) F a t = λ e t P e y t + λ g t P g y t F c t = λ g t t P g t t + Q g t t + λ g b t P g b t + λ w h t P w h t + λ a c t P a c t + λ e l t P e l t + λ m r t P m r t + λ hfc t P hfc t + Q hfc t + λ g t t Q h s − c h a t + Q h s − d i s t F d a − i d r = ∑ t = 1 T K d a − i d r P d a − i d r (10) min ∑ t = 1 T F a + F c + F i a − i d r , (11) F i a − i d r = ∑ t = 1 T K i a − i d r P i a − i d r , (12) where λ e t and λ g t indicates the electricity price and gas price of the grid and gas network, respectively; λ i t indicates the maintenance price of GT, GB, WH, AC, EL, MR, and HFC, respectively; F _da−idr indicates the invocation cost of the day-ahead incentive demand response; K _da−idr indicates the call coefficient of the day-ahead incentive demand response load; F _ia−idr indicates the invocation cost of the intraday incentive demand response; and P _da−idr indicates the call volume of the day-ahead incentive demand response load period t. P g t min ≤ P g t t ≤ P g t max Q g t min ≤ Q g t t ≤ Q g t max Q g b min ≤ Q g b t ≤ Q g b max Q e b min ≤ Q e b t ≤ Q e b max Q w h min ≤ Q w h t ≤ Q w h max Q a r min ≤ Q a r t ≤ Q a r max Q a c min ≤ Q a c t ≤ Q a c max P e l min ≤ P e l t ≤ P e l max P m r min ≤ P m r t ≤ P m r max P hfc min ≤ P hfc t ≤ P hfc max Q h s − c h a min ≤ Q h s − c h a t ≤ Q h s − c h a max Q h s − d i s min ≤ Q h s − d i s t ≤ Q h s − d i s max Δ P g t min ≤ P g t t + 1 − P g t t ≤ Δ P g t max Δ P ele min ≤ P ele t + 1 − P ele t ≤ Δ P ele max Δ P mrh min ≤ P mrh t + 1 − P mrh t ≤ Δ P mrh max Δ P hfch min ≤ P hfch t + 1 − P hfch t ≤ Δ P hfch max S h s min ≤ S h s ≤ S h s max (13) Equation 13 represents the lower-level model constraints, mainly including the upper and lower limit constraints on the output of the equipment and the rate of climb constraints for specific equipment.

The system shown in Figure 1 is selected as the research object for the calculation example. The efficiency and other parameters of each energy conversion equipment are shown in Table 1. The time-of-use electricity price curve corresponding to typical days and the renewable energy and ambient temperature curves of the selected system on specific days in winter and summer are shown in Figure 3.

Considering the economy and environmental protection of the system at the same time, the NSGA-¢ò algorithm is used to solve the system model. The obtained Pareto optimal solution set is shown in Figure 4. The final capacity configuration results of the system are shown in Table 2.

Table 3 shows the cost comparison of current low-carbon dispatch under two options. Due to the significant increase in renewable energy output in Scenario 2, the system’s electricity purchase cost decreased by 29.88% compared to Scenario 1, and the natural gas power output of the methane reactor increased. The system’s gas purchase cost decreased by 25.86% compared to Scenario 1. Due to the decrease in demand for cooling, heating, and power loads on the user side, the IDR cost of the system has also decreased by 10.55% compared to Scenario 1, and the total cost of EH-IES has reduced by 25.16%.

Figure 5 shows the day-ahead dispatch optimization results of the integrated energy system, where the left image represents Scenario 1 and the right image represents Scenario 2. In the day-ahead optimization stage, the power, heating, and cooling systems jointly participate in energy dispatch. In terms of power supply, since the system incorporates electric boilers and refrigeration equipment, the electric energy generated by the system and the energy conversion equipment in the system must be provided to users. When the output of wind power and photovoltaic units exceeds the demand, the electrolyzer consumes the remaining electric energy to produce hydrogen. It is supplied to the methane reactor and fuel cell. The fuel cell is the leading power generation equipment of the electric–hydrogen-integrated energy system, and the gas turbine mainly provides electric energy to the system when the power supply is insufficient. In terms of heat supply, the heat generated by fuel cells and the thermal power caused by electric boilers serve as the leading heating equipment to provide thermal energy to users. At the same time, the absorption refrigerator absorbs part of the thermal power generated by the system for cooling. When the user’s heat demand is insufficient, the gas boiler and gas turbine work to provide heat power to the user.

Regarding cold power supply, absorption refrigerators and electric refrigerators provide hard power to users. Electric refrigeration provides most of the mean power demand when the system has excess electric power. The absorption refrigerator offers maximum cooling capacity when the system has excess thermal power to meet partial cooling power requirements.

The comparison of Scenarios 1 and 2 reveals that the increase in renewable energy output in Scenario 2 also significantly increases the production of the electrolytic cell. Therefore, adding the electrolytic cell to the system helps eliminate the redundancy in renewable energy. Due to the increase in cooling load demand and decrease in heating load demand in Scenario 2, the electricity consumption of electric refrigeration equipment increases, and the thermal power absorbed by absorption chillers decreases. Electric refrigeration is the primary cooling equipment. Due to the reduced demand for electricity and heat loads, fuel cells, as the main power supply and heating equipment, have relatively reduced operating power. The analysis of device operation in Scenarios 1 and 2 shows that the demand-side response can reduce energy consumption, increase demand when energy is redundant, and reduce need when energy supply is insufficient, which helps reduce IES operating costs. The reduction in operating expenses is mainly due to decreased power exchange costs, benefiting from peak shaving.

Table 4 shows the cost comparison of intraday scheduling under two schemes. Due to the significant increase in renewable energy production in Scenario 2, the system’s electricity purchase cost decreased by 25.48% compared to Scenario 1. Compared to Scenario 1, the natural gas power generation of the methane reactor increases, while the natural gas purchase cost in Scenario 2 decreases to 0. Due to the reduced demand for cooling, heating, and power loads from the user end, compared to Scenario 1, the IDR cost of the system has also decreased by 28.40%, and the total cost of EH-IES has reduced by 28.42%.The daily scheduling results of IES are shown in Figure 6. The output trend of each equipment in the daily plan is the same as the previous plan. Due to the use of data with higher prediction accuracy, it is closer to the actual situation. The output of coupling equipment and the interaction power between electricity, gas, and external networks have been corrected within the day. The interaction power curve with the power grid fluctuates with changes in electricity prices, and the purchase of electricity during peak hours significantly decreases to achieve economic optimization. Compared to natural gas, which has a longer power scheduling time, when the electricity price is high, it drives GT to generate electricity to maintain the system’s balance of supply and demand. However, when the price of natural gas is high, the output of GT is reduced. The optimization of cold/thermal energy within a day with a scheduling time of only hours is more accurate, and the production of the equipment may also change. The scheduling results before and during the day should be compared. Intraday scheduling can effectively track real-time power fluctuations on the demand side and new energy generation, improving the flexibility of system operation.