Low-Carbon Economic Dispatch Strategy for Integrated Energy Systems with Blue and Green Hydrogen Coordination under GHCT and CET Mechanisms

1  Introduction

1.1 Motivation

Against the backdrop of depleting fossil fuel reserves and continuously growing energy demand, the low-carbon transition of the energy structure has become increasingly imperative. Meanwhile, with the rapid expansion of renewable energy capacity, the power system is facing challenges in renewable energy accommodation. As a high-quality secondary energy carrier complementary to electricity, hydrogen exhibits broad application prospects in sectors such as transportation, industry, and power systems [1]. IES, characterized by multi-energy flow coordination and optimization, provide an effective platform for coupling hydrogen with electricity and thermal energy, thereby offering a promising pathway to address the aforementioned challenges.

1.2 Literature Review

A large number of studies have been focused on the coupled utilisation of hydrogen energy in IES. Ref. [2] developed a collaborative hydrogen production model based on multiple renewable energy sources, significantly enhancing energy conversion efficiency; Ref. [3] proposed a planning methodology for electricity-hydrogen coupled systems under high-renewable penetration scenarios. These studies lay a solid theoretical foundation for advancing hydrogen integration into IES. Hydrogen as energy carrier can be produced however, freshwater supply for hydrogen production at largescale would be major challenge as electrolyser requires high quality freshwater as suggested by several researchers [4,5].

Hydrogen can be produced via multiple pathways, including fossil fuel-based hydrogen production, water electrolysis, and industrial by-product hydrogen. The resulting hydrogen is generally categorized as blue and green hydrogen. Among them, green hydrogen is produced by electrolysis of water, a process with near-zero carbon emissions, which is a key direction for the future development of hydrogen energy [6,7]. Ref. [8] highlighted the substantial potential of producing green hydrogen from surplus hydropower, which can help alleviate the energy crisis and reduce carbon emissions. Ref. [9] emphasized the feasibility of integrated renewable-driven green hydrogen systems as a transformative solution for achieving energy independence. Ref. [10] assessed the impact of renewable hydrogen production on future power systems, providing valuable insights for energy transition planning. Ref. [11] developed a two-level planning and operation model for water-hydrogen integrated energy systems, demonstrating that hydrogen systems can enhance overall system stability and economic performance. Ref. [12] established a multi-timescale scheduling model for integrated energy systems based on zero-carbon hydrogen, effectively reducing system costs while achieving zero carbon emissions. The above studies on hydrogen-integrated energy systems primarily focus on green hydrogen. To reduce hydrogen production costs and explore the feasibility of a gradual transition to low-carbon green hydrogen systems, integrating both green and blue hydrogen within an IES framework holds significant research value.

Blue hydrogen, on the other hand, is produced from fossil fuels and currently has a cost advantage. Ref. [13] suggests that in the short term, leveraging the cost advantage of fossil fuels to develop hydrogen-related infrastructure and integrate the hydrogen supply chain is a pragmatic approach, thereby enabling a gradual transition from fossil-based hydrogen to renewable-based hydrogen production. Ref. [14] explored the complementary and competitive dynamics between blue and green hydrogen, concluding that prioritizing the development of blue hydrogen at this stage is more conducive to market expansion and can facilitate the future growth of green hydrogen. Ref. [15] highlights the critical role of blue hydrogen as a major low-cost source during the energy transition phase. Ref. [16] incorporated gas-based hydrogen production into a multi-energy integrated system and proposed a flexible energy supply mode that couples electrolytic and methane-based hydrogen production, enhancing the system’s economic performance, carbon reduction, and operational flexibility. Ref. [17] further analyzed the impact of different hydrogen production pathways on carbon emissions in integrated energy networks. Against the backdrop of the still high cost of green hydrogen and an undeveloped industrial chain, blue hydrogen has significant economic viability in the early stages of hydrogen energy promotion.

In addition, there is a waste of resources in the existing hydrogen production pathways. PEME is considered an ideal approach for green hydrogen production from renewable energy sources, owing to its high efficiency, strong dynamic response, and wide power regulation range. SMR, on the other hand, is currently the most widely adopted and technologically mature hydrogen production method based on fossil fuels. However, both SMR and PEME processes involve resource wastage: SMR discharges waste heat, while the oxygen by-product from PEME is typically vented and unused [18]. The secondary utilization of these resources may provide valuable support for the transition from blue hydrogen to green hydrogen. To this end, the waste heat generated by SMR can be recovered, and OCC technology can be introduced. Specifically, the oxygen produced by PEME can be used for oxygen-enriched combustion in the GT unit. Ref. [19] proposed a coordinated operation mode for IES that considers OCC and hydrogen blending in GT units, and verified the energy-saving and carbon-reduction benefits of integrating OCC with hydrogen energy resources. Ref. [20] addressed the issue of unused by-product oxygen from hydrogen production and proposed a configuration and optimal scheduling method for an integrated energy system that incorporates hydrogen-based ammonia production in thermal power units, along with ammonia co-firing and oxygen-enriched combustion, thereby enabling full utilization of system by-products. OCC features high carbon reduction efficiency, and its integration with hydrogen energy systems holds significant low-carbon potential that warrants further research.

In terms of market regulatory mechanisms, the CET mechanism and the GHCT mechanism provide strong support for promoting low-carbon operation of IES and the development of green hydrogen. Ref. [21] developed a hydrogen-based IES operation framework under a carbon trading environment, achieving reductions in cost, carbon emissions, and wind power curtailment. Ref. [22] developed an IES model incorporating offshore wind-powered hydrogen production under the CET framework. Ref. [23] investigated the environmental value of green hydrogen by modeling hydrogen transmission networks in detail and accounting for the benefits of green certificates associated with power-to-hydrogen processes. Ref. [24] proposed a coordinated optimization model for integrated energy systems under multi-market coupling conditions, in which hydrogen energy, carbon certificates, and green certificates are deeply integrated through hydrogen and carbon price mechanisms to enhance the economic performance and low-carbon operation of the system. Ref. [25] investigated a multi-timescale optimization strategy for hydrogen-based integrated energy systems under the coupling of green certificate and carbon trading mechanisms, achieving improvements in both economic efficiency and carbon reduction. Most existing studies consider either the CET or GHCT mechanism in isolation and have not sufficiently explored their combined impact on the optimal operation of IES integrating both green and blue hydrogen pathways.

1.3 Contributions

To address the gaps in existing research, this paper proposes a low-carbon economic dispatch strategy for IES that incorporates both green and blue hydrogen under the GHCT and CET mechanisms.

First, a hydrogen production-storage-utilization model is established, which includes a PEME model considering dynamic efficiency characteristics and an SMR model incorporating waste heat recovery.

Second, the GT unit is retrofitted by introducing OCC technology, where the oxygen produced by PEME is utilized to create an oxygen-enriched combustion environment, thereby improving energy utilization and enabling low-carbon power supply.

Furthermore, the GHCT mechanism is incorporated into the system, and its complementary effect with the ladder-type CET mechanism is analyzed.

Finally, an integrated optimization model is developed to simultaneously achieve low carbon emissions and economic performance. The effectiveness of the proposed strategy is validated through case studies, with results showing a 7.77% reduction in wind power curtailment, a 65.98% decrease in carbon emissions, and a 12.57% reduction in total system cost.

A comparison between the proposed method and existing studies is presented in Table 1.

2  IES Operation Architecture

2.1 Basic Structure of IES

The IES, as shown in Fig. 1, is constructed to encompass multiple stages of energy supply, conversion, storage, and utilization. The system derives its energy from the power grid, wind power, water sources, and natural gas. On the demand side, the energy loads include electricity, hydrogen, and thermal loads. The energy conversion side consists of PEME, SMR, GT unit, hydrogen fuel cell (HFC), and electric boiler (EB). The storage devices include an electrical storage tank (EST), thermal storage tank (TST), hydrogen storage tank (HST), and oxygen storage tank (OST). The purple section in the figure represents the overall operational framework of the IES under market mechanisms. The green section illustrates the specific devices included in the IES, while the brown section highlights the GT unit retrofitted with the OOC technology.

Figure 1: Integrated energy system operation architecture

2.2 Hydrogen Production-Utilization-Storage Model

2.2.1 Refined Model of PEME

PEME utilizes electrical energy to produce green hydrogen, with no carbon emissions during the entire process. However, it consumes both electricity and water. The model is as follows:

{Vpem,H2(t)=Ppem,E(t)ηpem/qH2Vpem,O2(t)=βpem,O2Vpem,H2(t)Mpem,H2O(t)=βpem,H2OVpem,H2(t)Ppem,Emin≤Ppem,E≤Ppem,Emax(1)

where Vpem,H2(t) represents the hydrogen production at time t by PEME; Ppem,E(t) is the electrical power input to PEME at time t; ηpem is the efficiency of the conversion between electrical and hydrogen energy; qH2 is the calorific value of hydrogen, with a value of 3.539 kWh/Nm3; Vpem,O2(t) is the oxygen production at time t; βpem,O2 is the oxygen production coefficient; Mpem,O2(t) represents the water consumption at time t; and βpem,H2O is the water consumption coefficient of PEME, with a value of 2 kg/(Nm3).

To ensure the hydrogen production equipment model more accurately reflects real-world conditions, this paper treats the hydrogen production efficiency of the PEME as a variable dependent on the input power. The electric power consumption of the PEME at time t can be expressed as:

{Ppem,E(t)=UpemIpemIpem=SipemUpem=Urev+Uact+Uohm+UdiffUrev=1.229−0.009(Tpem−298)+RTpemzFln⁡(PH2PO2αH2O)(2)

where Ppem,E(t) represents the electric power consumption of the PEME; Upem is the operating voltage of the PEME; Ipem denotes the operating current of the PEME; ipem is the current density of the PEME; and S represents the effective reaction area. Urev, Uohm, Uact, Udiff represent the open-circuit voltage, ohmic overvoltage, activation overvoltage, and diffusion overvoltage; Udiff can be neglected. PH2, PO2 represent the partial pressures of hydrogen and oxygen; R is the gas constant; αH2O is the activity of water; F is the Faraday constant; Tpem is the electrolytic temperature, and z is the number of moles of electrons involved in the electrolysis reaction.

The calculation of Uact and Uohm is typically accompanied by complex electrochemical mechanism analysis. To simplify the computation, this paper adopts the approach outlined in Ref. [26], using the Uirrev to represent their relationship. For the calculation of Uirrev, the empirical model from Ref. [27] is used:

{Upem=Urev+UirrevUirrev=Uact+UohmUirrev=(a1+a2Tpem)ipem+(b1+b2+b3Tpem2)log⁡[(c1+c2Tpem+c3Tpem2)ipem+1](3)

where a1, a2, b1, b2, b3, c1, c2 and c3 are empirical coefficients. This allows for an accurate representation of the nonlinear characteristics of Upem, which is jointly influenced by Tpem and ipem.

The hydrogen production efficiency of the PEME is determined by both the Faradaic efficiency and the voltage efficiency. Based on Faraday’s law, it is expressed as follows:

{ηpem=ηfηvηv=ΔHzFUpemηf=ipem2(d1+d2Tpem)+ipem2(d3+d4Tpem)(4)

where d1 to d4 are empirical coefficients, ΔH is the reaction enthalpy change, which is 285.84 kJ/mol, ηf is the Faradaic efficiency, and ηv is the voltage efficiency. The equation shows that the dynamic efficiency of the PEME is a high-dimensional function of current density and operating temperature.

Based on the above equations, a semi-empirical model of PEME power and efficiency can be derived by introducing current density as an intermediate variable. Under a fixed temperature of 333 K, the power–efficiency and power–hydrogen production relationships of PEME are illustrated in Fig. 2. The blue lines represent the hydrogen production efficiency, while the red lines indicate the hydrogen production output. As shown in the figure, during the initial stage of operation, the hydrogen production efficiency of PEME increases rapidly with rising input power. After reaching its peak, further increases in power lead to a gradual decline in efficiency. Meanwhile, as the power level continues to rise, the hydrogen output increases accordingly. However, due to fluctuations in efficiency, the growth slope of hydrogen production first increases and then decreases.

Figure 2: Characteristic power-efficiency-yield curve of PEME hydrogen production at 333 K

2.2.2 Refined Model of PEME

SMR produces blue hydrogen from natural gas and is currently one of the most mature industrial hydrogen production technologies. It is also considered the simplest and most cost-effective method [28]. During the hydrogen production process, SMR consumes water and releases waste heat and CO2. The model is as follows:

{Vsmr,H2(t)=ηsmrqCH4Vsmr,CH4(t)/qH2Msmr,H2O(t)=βsmr,H2OηsmrqCH4Vsmr,CH4(t)/qH2Msmr,CO2(t)=esmr,CO2ηsmrqCH4Vsmr,CH4(t)/qH2Hsmr(t)=αsmrηsmrqCH4Vsmr,CH4(t)/qH2Vsmr,H2min≤Vsmr,H2(t)≤Vsmr,H2max(5)

where Vsmr,H2(t) represents the blue hydrogen production at time t; ηsmr is the hydrogen production efficiency; qCH4 is the calorific value of natural gas, set at 10.122 kWh/(Nm3); Vsmr,CH4(t) is the gas consumption at time t; Hsmr(t) is the waste heat recovery power of SMR at time t; αsmr is the recovery coefficient of the SMR process; Msmr,H2O(t) is the water consumption of SMR at time t; βsmr,H2O is the water consumption coefficient of SMR; Msmr,CO2(t) is the carbon emissions of SMR at time t; and esmr,CO2 is the carbon emission coefficient of SMR.

2.2.3 Hydrogen Fuel Cell

HFC is clean and emission-free, serving as a key coupling component for hydrogen-to-electricity conversion. The model is as follows:

{Phfc(t)=ηhfceqH2Vhfc,H2(t)Hhfc(t)=ηhfchqH2Vhfc,H2(t)Phfcmin≤Phfc(t)≤Phfcmax(6)

where Phfc(t) and Hhfc(t) represent the electrical and thermal power outputs of the HFC, respectively; ηhfce denotes the hydrogen-to-electricity conversion efficiency of the HFC; ηhfch is the hydrogen-to-heat conversion efficiency; and Vhfc,H2(t) indicates the hydrogen consumption of the HFC at time t.

2.2.4 Hydrogen Storage Tank

As a mature hydrogen storage technology, HST have been widely adopted. The corresponding model is given as follows:

{Hhst(t+1)=(1−σhst)Hhst(t)+ηhstcVhstc(t)−1ηhstdVhstd(t)εhstc+εhstd≤10≤Vhstc(t)≤εhstcVhstcmax0≤Vhstd(t)≤εhstdVhstdmax0≤Hhst≤Hhstmax(7)

where Hhst(t) denotes the hydrogen storage level at time t; σhst is the hydrogen leakage rate; ηhstc and ηhstd represent the charging and discharging efficiencies, respectively; Vhstc(t) and Vhstd(t) are the hydrogen charging and discharging volume flow rates at time t; εhstc and εhstd are binary variables indicating the charging and discharging states; Vhstcmax and Vhstdmax denote the maximum hydrogen charging and discharging flow rates; and Hhstmax is the maximum storage capacity of HST.

2.3 Oxygen-Enriched Combustion Carbon Capture

Unlike conventional combustion technologies, OCC employs high-purity oxygen as the oxidant to increase the CO2 concentration in the flue gas, thereby facilitating carbon capture by downstream equipment. The energy flow of the oxygen-enriched combustion unit is illustrated by the brown section in Fig. 1. In this study, a back-pressure GT unit is selected for OCC-based retrofit, with a constant heat-to-power ratio. The corresponding model is as follows:

{Pgt(t)=ηgtqCH4Vgt,CH4(t)Hgt(t)=κgtPgt(t)Vgt,O2(t)=βgt,O2Pgt(t)Mgt,CO2(t)=egt,CO2Pgt(t)Pgtmin≤Pgt≤Pgtmax−ΔPgt≤Pgt(t+1)−Pgt(t)≤ΔPgt(8)

where Pgt(t) and Hgt(t) denote the electrical and thermal power outputs of the GT unit at time t, respectively; Vgt,CH4(t) is the natural gas consumption at time t; κgt represents the heat-to-power ratio; Vgt,O2(t) is the oxygen demand at time t; βgt,O2 denotes the oxygen consumption per unit of power output at time t; Mgt,CO2(t) is the mass of CO2 emissions at time t; egt,CO2 represents the carbon emission intensity coefficient of the GT unit; and ΔPgt is the ramping capability of the GT unit.

The internal energy consumption of the oxygen-enriched combustion unit includes the air separation unit (ASU) and the carbon capture equipment (CCE). Their power consumption can be dynamically adjusted based on the capture intensity of the CCE and the power demand of the ASU, thereby regulating the power delivered to the electrical and thermal loads. During the hydrogen production process via PEME, a large amount of high-purity oxygen is simultaneously generated and can be supplied to the GT unit for combustion. Since the GT unit requires an oxygen source in every time period, the ASU is configured to produce oxygen for constructing the oxygen-enriched combustion environment, while the oxygen storage tank is used to store the oxygen generated by the ASU for cross-period utilization. The corresponding model is as follows:

{Pgt(t)=Pnet(t)+Pasu(t)+Pcce(t)Pasu(t)=βasu,O2Vasu,O2(t)Pcce(t)=λcceMcce,CO2(t)0≤Mcce,CO2(t)≤γcceMgt,CO2(t)Mair,CO2(t)=Mgt,CO2(t)−Mcce,CO2(t)(9)

where Pnet(t) denotes the net power output of the oxygen-enriched combustion unit at time t; Pasu(t) is the energy consumption of the ASU at time t; βasu,O2 represents the operational coefficient of the ASU for oxygen production; Vasu,O2(t) is the oxygen output of the ASU at time t; Pcce(t) denotes the energy consumption of the CCE at time t; λcce is the energy consumption coefficient of the CCE; Mcce,CO2(t) is the mass of CO2 captured by the CCE at time t; γcce indicates the carbon capture level at time t; and Mair,CO2(t) is the actual CO2 emission from the GT unit at time t.

The OST model is similar to that of the HST and can also be represented by Eq. (7).

2.4 Models of Other Devices

2.4.1 Electric Boiler

EB converts electrical energy into thermal energy, its model is given as follows:

{Heb(t)=Peb(t)ηeb0≤Peb≤Pebmax(10)

where Heb(t) denotes the thermal power output of EB at time t; Peb(t) is the input electrical power at time t; and ηeb represents the efficiency of EB.

2.4.2 Energy Storage Models

The energy storage models include thermal storage and electrical storage, both of which can be uniformly represented by the following formulation:

Eest(t+1)=(1−σest)Eest(t)+ηestcPestc(t)−1ηestdPestd(t)(11)

where Eest(t) denotes the electricity stored in EST at time t; σest is the leakage rate; ηestc and ηestd represent the charging and discharging efficiencies, respectively; and Pestc(t) and Pestd(t) are the charging and discharging power at time t.

3  Market Mechanisms

3.1 Ladder-Type Carbon Emission Trading Mechanism

The CET mechanism serves as an important instrument for promoting system-wide carbon reduction. Through the carbon allowance trading market, power producers are incentivized to formulate rational production and emission plans based on their allocated carbon quotas, thereby achieving emission reduction targets. In the proposed system, only the GT unit is granted free carbon allowances, which are determined based on the unit’s electricity and heat outputs and can be expressed as follows:

Mfree,CO2(t)=mePgt(t)+mhHgt(t)(12)

where Mfree,CO2(t) denotes the carbon emission allowance for the GT unit, and me and mh are the baseline coefficients for free carbon quota allocation.

Considering the carbon emissions from grid electricity purchases and hydrogen production via SMR, the actual carbon emissions of the system are given by:

{Mbuy,CO2(t)=mbuyPbuy(t)0≤Pbuy(t)≤Pbuymax(t)Mall,CO2(t)=Mair,CO2(t)+Mbuy,CO2(t)+Msmr,CO2(t)RCET=∑t=1T(Mall,CO2(t)−Mfree,CO2(t))(13)

where Mbuy,CO2 represents the carbon emissions from grid electricity purchases; mbuy is the grid carbon emission factor; Pbuy(t) denotes the electricity purchased from the grid at time t; Mall,CO2 is the actual carbon emissions of the system; and RCET represents the system’s carbon emission allowance.

The ladder-type CET mechanism defines multiple carbon allowance purchase tiers, where a higher quantity of carbon purchases corresponds to a higher unit price. This mechanism provides stronger incentives for emission reduction. The corresponding carbon trading cost is expressed as follows:

fCET={−xCET(2+3ν)ΔL+xCET(1+3ν)(RCET+2ΔL),RCET≤−2ΔL−xCET(1+ν)ΔL+xCET(1+2ν)(RCET+ΔL),−2ΔL<RCET≤−ΔLxCET(1+ν)RCET,−ΔL<RCET≤0xCETRCET,0<RCET≤ΔLxCETΔL+xCET(1+ν)(RCET−ΔL),ΔL<RCET≤2ΔLxCET(2+ν)ΔL+xCET(1+2ν)(RCET−2ΔL),2ΔL<RCET(14)

where xCET is the base carbon trading price; ν represents the price increment; and ΔL denotes the length of each carbon emission tier.

3.2 Green Hydrogen Certificate Trading Mechanism

At present, the production cost of green hydrogen remains relatively high; however, its environmental benefits are significant. To address this, the green certificate trading mechanism in the electricity market is introduced into the hydrogen market, and the trading price of green hydrogen certificates is determined based on certificate demand. Holding a green hydrogen certificate indicates that the hydrogen is produced via PEME. If the amount of green hydrogen production fails to meet the government-mandated quota, the system is required to purchase green certificates; otherwise, a penalty will be imposed. Conversely, if the green hydrogen production exceeds the quota, the system receives an equivalent number of green certificates, which can be sold for economic gain. A schematic diagram of the GHCT mechanism is shown in Fig. 3.

Figure 3: Schematic diagram of green hydrogen certificate trading

Under market dynamics, the trading price of green certificates varies with their quantity. According to the Cournot model formulation [29], the market trading price of green hydrogen certificates can be expressed as:

{CGHCT=CGHCTmax−μ∑t=1TVpem,H2(t)0≤CGHCT≤CGHCTmaxfGHCT=CGHCT(Rd−Rs)Rd=αGH2∑t=1TPload,H2(t)Rs=ωGH2∑t=1TVpem,H2(t)(15)

where CGHCT denotes the trading price of green hydrogen certificates; CGHCTmax is the maximum acceptable price; μ represents the demand ratio, i.e., the rate at which price decreases with respect to unit increase in demand; fGHCT is the system’s green certificate trading cost; Rd is the certificate quota assigned to the system; αGH2 denotes the quota coefficient; Pload,H2(t) is the hydrogen demand; Rs is the number of green certificates acquired by the system; and ωGH2 is the conversion coefficient from the quantity of green hydrogen produced to the number of green certificates obtained.

4  Dispatch Model and Solution

4.1 Objective Function

From the perspective of economic cost, this paper proposes an objective function aimed at minimizing the total system cost, under the premise of meeting the electricity, hydrogen, and thermal load demands of the IES. A 24-h scheduling horizon is adopted with a time step of 1 h. The objective function is formulated as follows:

F=min(fbuy+fwind+fpem+fgt+fsmr+fGHCT+fCET)(16)

fbuy=∑t=1TcbuyPbuy(t)(17)

fwind=∑t=1TcwPwind(t)+∑t=1TccwPcwind(t)(18)

fpem=∑t=1Tcc|Ppem,E(t)−Ppem,E(t−1)|+∑t=1TcpemVpem,H2(t)+∑t=1TcH2OMpem,H2O(t)(19)

fgt=∑t=1TcgtPgt(t)+∑t=1TcCH4Vgt,CH4(t)(20)

fsmr=∑t=1TcCH4Vsmr,CH4(t)+∑t=1TcsmrVsmr,H2(t)+∑t=1TcH2OMsmr,H2O(t)(21)

where fbuy is the cost of electricity purchased from the grid, and cbuy denotes the time-of-use electricity price. fwind is the wind power cost, consisting of operating cost and wind curtailment penalty; cw is the wind power operating cost coefficient, Pwind(t) is the wind power, Pcwind(t) is the curtailed wind power, and ccw is the curtailment penalty coefficient. fpem is the cost of PEME, including degradation cost due to power fluctuation, operating cost, and water cost; cc is the degradation coefficient, cpem is the PEME operating cost coefficient, and cH2O is the water price. fgt is the cost of GT unit, comprising operating and fuel costs; cgt is the GT operating cost coefficient, and cCH4 is the natural gas price. fsmr is the cost of SMR, including fuel cost, operating cost, and water cost; csmr is the SMR operating cost coefficient.

4.2 Constraints

Constraints, including electricity, thermal, hydrogen, and oxygen balances:

Pbuy(t)+Pnet(t)+Pwind(t)+Phfc(t)+Pestd(t)=Pload(t)+Ppem,E(t)+Pestc(t)+Peb(t)+Pasu(t)+Pccs(t)(22)

Hhfc(t)+Hgt(t)+Heb(t)+Htstd(t)+Hsmr(t)=Hload(t)+Htstc(t)(23)

Vpem,H2(t)+Vhstd(t)+Vsmr,H2(t)=Vload,H2(t)+Htstc(t)+Vhfc,H2(t)(24)

Vasu,O2(t)+Vostd(t)+Vpem,O2(t)=Vgt,O2(t)+Vostc(t)(25)

where Pload, Pestd, and Pestc represent the predicted electrical load, discharging power, and charging power of EST at time t, respectively. Hload(t), Htstd(t), and Htstc(t) denote the predicted thermal load, discharging power, and charging power of TST at time t. Vload,H2(t) represents the predicted hydrogen load at time t.

4.3 Model Linearization and Solution

Considering the power–efficiency characteristics of PEME introduces nonlinearity into the model, thereby increasing the complexity of the solution process. To address this, a piecewise linearization strategy is adopted, in which the power–output curve is divided into N segments and approximated linearly within each segment. This approach simplifies the model structure and improves computational efficiency. The principle of piecewise linearization is illustrated as follows:

{Ppem,E(t)=∑j=1NZj(t)Vpem,H2(t)=∑j=1NαjZj(t)+δjεj(t)bj(t)Zjmin≤Zj(t)≤bj(t)Zjmax∑j=1Nbj(t)=1(26)

where Zj(t) is the independent variable of the piecewise function, i.e., the input power; αj and δj represent the slope and intercept of the j linear segment, respectively; and εj(t) is a binary variable indicating the active segment.

Since the absolute value term in Eq. (19) introduces nonlinearity, an auxiliary variable representing power fluctuation, Pc(t), is introduced to achieve linearization:

{fpem=∑t=1TccPc(t)+∑t=1TcpemVpem,H2(t)+∑t=1TcH2OMpem,H2O(t)Pc(t)≥Ppem,E(t−1)−Ppem,E(t)Pc(t)≥Ppem,E(t)−Ppem,E(t−1)(27)

The mixed-integer nonlinear programming (MINLP) problem is thereby transformed into a mixed-integer linear programming (MILP) problem, which can be solved using the Gurobi solver within the YALMIP environment in MATLAB. The solution procedure is illustrated in Fig. 4.

Figure 4: Schematic overview of the solving process

5  Case Studies

To validate the effectiveness of the proposed model, a case study is conducted based on the IES architecture shown in Fig. 1. The time-of-use electricity price, natural gas price, and water price are listed in Table A1. The hydrogen production system consists of PEME and SMR, with relevant parameters provided in Table A2. Operating parameters for the remaining devices are detailed in Table A3. Mechanism parameters and cost coefficients are presented in Table A4. The hourly forecasts of load demand and wind turbine output over a day are shown in Fig. 5. The blue bars represent the predicted output of the wind turbines, the red line indicates the thermal load, the black line denotes the electrical load, and the green line represents the hydrogen load.

Figure 5: Load demand and wind output forecasts

To evaluate the overall effectiveness of the proposed model in optimizing IES operation, five cases are designed for comparative analysis: Case 1: Coordinated utilization of green and blue hydrogen, with hydrogen produced jointly by PEM and SMR. Case 2: Utilization of oxygen produced by PEME for oxygen-enriched combustion in the GT unit, and recovery of waste heat from SMR. Case 3: Only the GHCT mechanism is considered. Case 4: Only the ladder-type CET mechanism is considered. Case 5: Both the GHCT mechanism and the ladder-type CET mechanism are considered. The dispatch results for the five scenarios are presented in Table 2.

As shown in Table 2, compared with Case 1, the system operating cost in Case 2, Case 3, and Case 4 is reduced by 2.59%, 6.95%, and 7.86%, respectively; the wind power curtailment rate is reduced by 4.13%, 7.74%, and 4.72%; and carbon emissions are reduced by 25.26%, 25.60%, and 65.74%, respectively. These results highlight the positive effects of resource reuse, as well as the CET and GHCT mechanisms. Compared with Case 2, Case 5 considers both the GHCT and CET mechanisms simultaneously. Under this configuration, the system operating cost, wind curtailment rate, and carbon emissions are further reduced by 12.57%, 7.77%, and 65.98%, respectively. In addition, the revenue from green hydrogen certificate trading increases by 12.46%. These results demonstrate the effectiveness of jointly considering GHCT and CET in improving renewable energy utilization, enhancing system decarbonization, and reducing operational costs.

5.1 Effect Analysis of Oxygen-Enriched Combustion Carbon Capture Unit

To evaluate the effectiveness of the OCC unit, a comparison is made between Case 1 and Case 2 in terms of GT unit operation, carbon emissions, and wind curtailment. As shown in Table 2 and Fig. 6, Case 1 adopts conventional air combustion, whereas Case 2 employs oxygen-enriched combustion. Compared with Case 1, the carbon emissions in Case 2 are reduced by 25.25%. Figs. 7 and 8 show that CCE primarily operates during periods 1–7 and 22–24, which coincide with high wind generation intervals. By consuming the forced electrical output of the GT unit, additional space is created for wind power integration, resulting in a 4.13% reduction in the wind curtailment rate.

Figure 6: Carbon emissions by time period

Figure 7: Wind power consumption

Figure 8: Composition of GT unit output

In Case 2, the OOC effectively reduces carbon emissions during the early morning and evening hours. However, since oxygen-enriched combustion requires a sufficient oxygen supply, the GT unit must operate under an oxygen-enriched environment. As shown in Fig. 9, HST enables the storage of oxygen produced by ASU and PEME during nighttime, which is then released during daytime operation. This facilitates cross-period oxygen utilization and supports the low-carbon retrofit of the GT unit.

Figure 9: System scheduling results

Considering the “heat-defined power” characteristic of the GT unit and their utilization of SMR waste heat, the system faces a power shortage during daytime periods with low thermal demand. When wind power output is weak, the electrical output of the GT unit is constrained by its thermal output, leading to insufficient system power supply. Moreover, the requirement of oxygen-enriched combustion for oxygen production via the ASU further exacerbates the power deficit. To ensure load supply, the system tends to prioritize allocating the GT unit’s power output to meet load demand rather than to drive the CCE. Since the grid electricity price is lower than the GT unit’s generation cost, the system also purchases a large amount of electricity from the grid. Although this strategy increases carbon emissions on the grid side, it results in an overall reduction in system-wide carbon emissions.

5.2 Effect Analysis of Green Hydrogen Certificate Prices

To compare the variations in total system cost and PEME-based hydrogen production under different green hydrogen certificate prices, six scenarios are designed based on whether the GHCT mechanism is considered and the specific certificate pricing levels. The green hydrogen certificate prices and corresponding dispatch results for each scenario are summarized in Table 3. The data indicate that incorporating the GHCT mechanism helps improve system economic performance and reduce wind curtailment. Specifically, compared to Case 6 (the baseline scenario), the total system cost in Case 9 and Case 11 decreases by 4.47% and 13.25%, respectively, while hydrogen production increases by 49.77% and 81.97%, respectively.

As shown in Fig. 10, the output of PEME is primarily concentrated during periods 1–9 and 22–24, mainly due to high wind power generation and significant wind curtailment in these intervals. The GHCT mechanism incentivizes increased hydrogen production by PEME. Considering the revenue from green hydrogen certificates, the effective operating cost of PEME becomes lower than the combined cost of wind curtailment and wind power operation. As a result, PEME increases its output and facilitates greater wind power utilization.

Figure 10: PEME output by time period and comparison of price results by certificat

During periods 10–21, when the green hydrogen certificate price is below 200 ¥/book, the cost of hydrogen production via SMR remains lower than that of PEME, and the system tends to prioritize SMR. However, when the certificate price exceeds 200 ¥/book, the economic incentive from certificate revenue makes PEME-based hydrogen production more favorable, prompting the system to gradually increase the share of green hydrogen. The hydrogen production of PEME in each period is shown in Fig. 10. A comparison of Cases 7–11 reveals that higher green hydrogen certificate prices lead to more operating periods for PEME and greater hydrogen production in each period.

In addition, Fig. 10 shows that the overall wind curtailment rate decreases with the increase in green hydrogen certificate trading prices. When the certificate price is 110 ¥/book, the PEME output remains low, and the system’s wind curtailment rate is 9.63%, representing only a 0.48% reduction compared to the baseline scenario. This result indicates that a low certificate price fails to effectively promote wind power integration, thereby limiting the production and utilization of green hydrogen. In Cases 9–11, when the certificate price exceeds 170 ¥/book, the wind curtailment rate tends to stabilize, while carbon emissions begin to rise. This is because during periods 1–9 and 22–24, the PEME has already reached its maximum operating capacity and cannot further absorb excess wind power. Meanwhile, during periods 10–21, the increased certificate incentive drives the system to produce more green hydrogen by purchasing electricity from the grid, leading to a rise in total system carbon emissions. Considering both wind curtailment and carbon emissions, a green hydrogen certificate price of 170 ¥/book is recommended as the optimal setting.

Finally, the results in Fig. 11 indicate that the hydrogen produced by PEME not only meets the hydrogen demand but also provides surplus hydrogen to the HFC to support electricity and heat supply. This reduces the thermal output of the GT unit, lowers the forced power generation, increases the system’s capacity to accommodate wind power, and decreases the fuel cost of the GT unit.

Figure 11: Hydrogen equilibrium and heat equilibrium

5.3 Effect Analysis of the GHCT and CET Mechanisms

In this section, four scenarios (Case 2 to Case 5) are designed to compare system performance in terms of economic operation, wind curtailment rate, and carbon emissions. A comprehensive analysis is conducted to evaluate the impact of the GHCT and CET mechanisms on IES operation.

According to the data in Table 2, compared with Case 2, the wind curtailment rate in Case 3 decreases by 3.61%. With the introduction of the GHCT mechanism, wind power integration is enhanced, and the total system cost is reduced by 4.47%. In Case 4, carbon emissions are reduced by 54.15% and the total cost decreases by 5.40% relative to Case 2, indicating that the CET mechanism effectively reduces system carbon emissions. However, when applied independently, the GHCT mechanism has a limited impact on emission reduction, while the CET mechanism alone does not significantly improve wind power utilization. Case 5 considers the combined effect of both GHCT and CET mechanisms and achieves the best performance in terms of wind curtailment, carbon emissions, and total cost. Compared with Case 2, Case 5 reduces the wind curtailment rate by 3.64%, lowers carbon emissions by 54.47%, and decreases total cost by 10.24%.

Fig. 12 illustrates the green and blue hydrogen production under different scenarios, as well as the variations in GT unit output, electricity purchase, and PEME power consumption. It can be observed that Case 5 achieves the highest proportion of green hydrogen production. Under the influence of the GHCT mechanism, green hydrogen output in Cases 3 and 5 increases compared to Cases 2 and 4, enabling greater wind power utilization. In contrast, under the CET mechanism, blue hydrogen production in Cases 4 and 5 decreases relative to Cases 2 and 3 as a result of carbon reduction incentives. The output of the GT unit is jointly determined by the energy consumption of ASU, CCE, and load. Driven by the GHCT mechanism, Case 3, compared with Cases 2 and 4, reduces GT output on the supply side to create additional grid space for wind power and increases PEME power demand on the load side, further expanding wind power integration. This dual-sided adjustment improves wind power utilization and enhances GHCT-related revenue.

Figure 12: Hydrogen production and system output by scenario

Fig. 13 presents the wind curtailment across different time periods and the carbon emissions and carbon capture volumes of each device under various scenarios. The results show that scenarios incorporating the GHCT mechanism significantly reduce wind curtailment. Additionally, due to the OCC-based retrofit of the GT unit, its carbon emissions are effectively lowered. By increasing the power supply to CCE, the actual carbon emissions fall below the allocated carbon allowance. The rise in CCE power consumption enhances the CET revenue. Under the incentive of the CET mechanism, the CCE power in Cases 4 and 5 increases compared to Cases 2 and 3. Furthermore, the carbon emission intensity of the GT unit, after OCC retrofit, is lower than that of the grid. As a result, the system tends to reduce electricity purchases from the grid. The results indicate that electricity purchases in Cases 4 and 5 are lower than those in Cases 2 and 3.

Figure 13: Wind abandonment by time period and carbon emissions by scenario

5.4 Reliability of Results

To verify the reliability of the conclusion that “the coordinated utilization of blue and green hydrogen can balance cost advantages and low-carbon benefits”, this section introduces two additional comparative scenarios: (1) the system operates with blue hydrogen only, and (2) the system operates with green hydrogen only. These scenarios are compared with the baseline scenario involving the coordinated use of both blue and green hydrogen. The scheduling results are presented in Table 4.

In the scenario using only blue hydrogen, the wind power curtailment rate increased by 9.45% compared to the baseline scenario. The total system cost increased only slightly due to the low production cost of blue hydrogen, all hydrogen demand was met by SMR throughout the day, resulting in a 6.22% increase in carbon emissions.

In the scenario using only green hydrogen, the curtailment rate decreased by 1.58%. However, due to the high production cost of PEME-based hydrogen, the total system cost rose by 5.68%. Meanwhile, carbon emissions also increased by 6.20%. The main reason is that the high cost limited the amount of green hydrogen available for HFC-based power generation. Consequently, the system relied more heavily on GT units and electricity purchases from the grid to meet demand, with their respective costs increasing by 17.68% and 46.18%. Since both power sources have higher carbon emission factors than SMR, this offset the inherent zero-carbon advantage of green hydrogen.

In contrast, the scenario with coordinated utilization of blue and green hydrogen can better leverage the respective advantages of each. During daytime periods of low wind power output, low-cost blue hydrogen produced by SMR is used to meet the hydrogen demand, while excess hydrogen is supplied to HFC units, thereby reducing the output of GT units and electricity purchases. During morning and evening wind power peaks, green hydrogen is produced by PEME electrolyzers, making full use of renewable electricity and reducing carbon emissions. Therefore, the coordinated use of blue and green hydrogen not only ensures economic performance but also maximizes the zero-emission advantage of green hydrogen, thereby validating the reliability of the proposed conclusion.

6  Conclusions

To reduce the carbon emissions of IES, improve its economic performance, and enhance wind power integration, this paper proposes a low-carbon economic dispatch strategy under the GHCT and CET mechanisms, incorporating both green and blue hydrogen pathways. The strategy involves retrofitting the GT unit with OCC technology, utilizing oxygen generated by PEME for oxygen-enriched combustion in the GT unit, and recovering waste heat from the SMR process. The main conclusions are as follows:

(1)   The coordinated utilization of blue and green hydrogen not only leverages the cost competitiveness of blue hydrogen but also maximizes the zero-emission advantage of green hydrogen. This synergy provides a cost-effective transitional solution for the hydrogen industry. As green hydrogen production technologies mature and costs decline, the market share of green hydrogen is expected to expand gradually, thereby enabling a green transition of the hydrogen economy.

(2)   Utilizing oxygen for oxygen-enriched combustion in GT unit and recovering waste heat from the SMR process contribute to lower-carbon energy production. These measures provide additional thermal support to the system, reduce dependence on external energy sources, and minimize resource waste.

(3)   Accounting for the benefits of green hydrogen certificates helps to capture the environmental value of green hydrogen and provides additional economic incentives for its production and utilization, thereby enhancing its market competitiveness. The GHCT mechanism also effectively improves wind power integration, particularly during periods of high wind generation, enabling the system to better utilize surplus electricity and reduce wind curtailment.

(4)   The CET mechanism encourages the system to prioritize low-carbon and zero-carbon technologies, such as PEME and carbon capture, effectively reducing carbon emissions. A comprehensive consideration of both GHCT and CET mechanisms enables full utilization of their complementary advantages. This not only reduces system operating costs but also lowers carbon emissions and enhances wind power integration, thereby achieving optimal performance in both economic and environmental dimensions.

Acknowledgement: This paper was completed with the hard help of every author.

Funding Statement: This work is supported by National Natural Science Foundation of China (52477101); Natural Science Foundation of Jiangsu Province (BK20210932).

Author Contributions: Conceptualization, Zirui Wang and Aidong Zeng; methodology, Zirui Wang; software, Zirui Wang; validation, Zirui Wang, Aidong Zeng and Jiawei Wang; formal analysis, Zirui Wang, Aidong Zeng and Jiawei Wang; resources, Aidong Zeng, Mingshen Wang and Sipeng Hao; data curation, Zirui Wang and Jiawei Wang; writing—original draft preparation, Zirui Wang; writing—review and editing, Zirui Wang, Aidong Zeng, Sipeng Hao and Mingshen Wang; supervision, Aidong Zeng; funding acquisition, Aidong Zeng and Mingshen Wang. All authors reviewed the results and approved the final version of the manuscript.

Availability of Data and Materials: Data will be made available on request.

Ethics Approval: Not applicable.

Conflicts of Interest: The authors declare no conflicts of interest to report regarding the present study.

Appendix A

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