Research on the Optimization of Urban Electric Heating Hydrogen Integrated Energy System Under Carbon Pricing Mechanism: A Case Study of Guangzhou City

Abstract

This study establishes an urban-scale integrated energy system model, innovatively incorporating carbon emission cost constraints. Utilizing a source-load-storage collaborative planning approach, the research quantitatively evaluates the interplay between hydrogen energy penetration and carbon pricing policies on system evolution. A dynamic optimization algorithm is employed to identify the cost-minimal development pathway. Results reveal that a 30% increase in photovoltaic capacity and a 50% expansion in biomass power generation reduce annual system carbon emission intensity by 1.3 basis points, highlighting the decarbonization potential of renewable energy scaling. Hydrogen-based transportation substitution for fuel vehicles contributes 2.7% of the system’s total CO₂ reduction through full lifecycle emission savings. At a carbon price of 140 yuan/ton CO₂, market-driven energy structure optimization enhances renewable utilization by 11.2 percentage points, achieving a 0.3% annual reduction in societal emissions (equating to 297,000 tons). However, this scenario induces a 5% rise in end-user energy costs (approximately 530,000 yuan annually), underscoring the critical balance between decarbonization and economic viability. The study demonstrates that urban energy system planning must integrate dual objectives of carbon constraints and cost efficiency. Policy incentives are coupled with technological cost reductions in environmental and economic performance. This study provides quantitative evidence to guide low-carbon transition strategies for municipal energy systems.

1. Introduction

With the deepening of global decarbonization targets and the rapid growth of renewable energy, traditional power systems are facing increasing challenges in terms of flexibility, reliability and economic performance. Integrated energy systems (IES), which coordinate electricity, heat and other energy carriers, have been widely recognized as an effective pathway to enhance the overall utilization efficiency of energy resources and to support large-scale renewable integration [1,2,3,4,5]. By exploiting the complementary characteristics of multiple energy forms, IES can improve peak shaving capacity, reduce curtailment of wind and photovoltaic power, and provide ancillary services such as load leveling, peak shaving and spinning reserve [6,7,8].

Existing research on IES has primarily focused on optimizing the configuration and operation of electricity–heat multi-energy systems at different spatial scales, such as buildings, communities and industrial parks. These studies have demonstrated that multi-energy coupling and coordinated dispatch can effectively reduce system operating costs and carbon emissions while improving the coordination capability of the power grid [9,10]. However, most of the current IES planning frameworks still emphasize the electricity–heat sector, and the role of hydrogen as a flexible, cross-sectoral energy carrier has not been fully integrated into city-level IES models.

Under prevailing IES planning approaches, hydrogen is often treated either as an isolated end-use fuel or as an auxiliary technology without explicitly linking it to urban integrated planning and carbon pricing policies. Although some studies have considered hydrogen production, storage and utilization for transportation or industrial applications, they usually analyze hydrogen projects at a project or regional level, and seldom embed hydrogen demand into a unified urban IES planning framework under explicit carbon-price signals [11,12,13,14,15,16,17,18,19,20]. As a result, it remains unclear how hydrogen penetration at the city level interacts with renewable energy development and carbon pricing in shaping the optimal configuration of urban electricity–heat–hydrogen systems.

To address these gaps, this paper develops a city-scale electricity–heat–hydrogen integrated energy system planning model for Guangzhou and embeds an explicit carbon pricing mechanism into the optimization framework [21,22,23,24,25,26]. Compared with previous HOMER Pro–based studies on hydrogen-integrated urban IES, this paper provides several methodological advances. First, an explicit carbon-pricing module is embedded into the planning model, enabling a scenario-based assessment of how different carbon prices (including 80 and 140 CNY/t CO₂) reshape the least-cost configuration of the electricity–heat–hydrogen system. Second, the model couples the city-scale electricity–heat IES with a dedicated hydrogen demand module for road transportation, and quantifies the life-cycle emission reduction attributable to hydrogen vehicle substitution rather than only focusing on power-sector emissions. Third, a set of coordinated development pathways (S1–S4) is designed to jointly vary renewable capacity expansion, hydrogen penetration levels and carbon-price signals, which allows us to decompose the contributions of renewable energy scaling, hydrogen substitution and carbon pricing to the overall CO₂ reduction. Finally, the case study for Guangzhou provides policy-oriented indicators such as changes in system LCOE (Levelized Cost of Energy, LCOE), fuel cost structure, and the share of hydrogen-driven emission reduction, offering practical evidence for urban low-carbon energy planning under carbon-pricing constraints.

2. Model Construction of the Electricity–Heat–Hydrogen Integrated Energy System

2.1. Current Status of Energy Utilization in Guangzhou

Energy development in Guangzhou urban area is characterized by a continuous enhancement of energy supply capacity and a transition towards a cleaner energy structure, adhering to principles of controlling coal, reducing oil, and increasing the shares of natural gas, non-fossil fuels, and imported clean electricity.

Pertaining to the energy structure, by 2025, coal consumption is targeted to be controlled within the provincial mandate. The consumption of clean energy natural gas is projected to exceed 9 billion cubic meters, surpassing the share of coal in the total energy consumption. The combined installed capacity of PV (Photovoltaic, PV) and wind power is planned to reach over 1.16 GW, with PV accounting for more than 1 GW.

Regarding hydrogen energy, Guangzhou’s Huangpu District has been designated a pilot zone for hydrogen development [27]. As of April 2023, Huangpu District had constructed 5 hydrogen refueling stations and deployed over 700 hydrogen fuel cell vehicles in sectors such as public transportation, cold chain logistics, sanitation, as well as specialized trucks and logistics vehicles across the city. Currently, Guangzhou has initiated planning in districts including Huangpu, Nansha, and Panyu, preliminarily forming a hydrogen refueling network covering the urban area. Xiaohu Island in Nansha District is positioned as a hydrogen hub, intended to integrate various hydrogen production resources [28].

2.2. City-Level Electricity–Heat–Hydrogen IES Model Architecture

The framework of the electricity–heat–hydrogen IES is depicted in Figure 1. The IES is divided into four segments: energy supply, energy transmission, energy storage, and energy consumption, corresponding to the “source-grid-load-storage” paradigm. The energy supply side comprises electricity, heat, and hydrogen supply. Electricity supply primarily originates from fossil fuel-based generation (coal power, natural gas power, etc.), renewable generation (solar, wind, hydro, biomass, etc.). Heat supply mainly derives from combined heat and power (CHP) generation based on fossil fuels and heat pumps. Hydrogen supply primarily comes from fossil fuel-based reforming (e.g., steam methane reforming) and water electrolysis. The external power grid is considered a supplementary electricity source, while heat and hydrogen are primarily supplied within the system. Energy transmission is facilitated by three interconnected networks: the power grid, hydrogen network, and thermal network, serving as channels for interaction among components. The energy storage segment currently includes lithium battery storage and pumped hydro storage; hydrogen storage and thermal storage are not considered at this stage. The energy consumption end encompasses electrical loads (industrial, commercial, residential), thermal loads, and hydrogen loads.

Within the model, electricity, heat, and hydrogen can be interconverted. For instance, CHP units simultaneously generate electricity and heat; the system’s electrical and thermal output can be controlled by adjusting the unit’s heat-to-power ratio. Electrolyzer units facilitate the conversion of electricity to hydrogen. Based on fluctuations in electrical load and generator operation, surplus electricity can be converted into hydrogen via electrolyzers, supplying hydrogen loads through the hydrogen network. Conversely, waste heat generated during hydrogen production in electrolyzers can be supplied to thermal loads via the thermal network.

In this study, hydrogen storage and thermal storage modules are not modeled in detail. Instead, we focus on the planning of generation and conversion capacities under different combinations of renewable energy development, hydrogen penetration and carbon price levels. This simplification is motivated by two considerations. First, in the current stage of Guangzhou’s energy system, large-scale dedicated hydrogen and thermal storage facilities are still in the early demonstration phase, and reliable cost and performance data for city-scale deployment are limited. Second, the main objective of this paper is to investigate how hydrogen penetration and carbon pricing affect the overall configuration and emission performance of the integrated system, rather than to optimize short-term temporal shifting strategies in storage. The flexibility needs arising from renewable variability are partly captured by the combination of multiple generation technologies and the external grid interaction. We acknowledge that explicitly modeling hydrogen and thermal storage would provide additional insights, and this will be a direction for future work.

2.3. Objective Function

This study employs the minimization of the Levelized Cost of Energy (LCOE) for useful electricity generated by the system as the optimization objective function. The LCOE is calculated by dividing the annualized power generation cost (total annualized cost minus the cost associated with serving the thermal load) by the total electrical load served [29], as shown in Equation (1):

$$LCOE=\left(C_{ann,tot}-C_{boiler}{H}_{served}\right)/E_{served}$$

(1)

where $C_{ann,tot}$ is the total annualized cost, $C_{boiler}$ is the marginal cost of the heat pump, $H_{served}$ is the total thermal energy served to the thermal load, $E_{served}$ is the total electrical energy served, encompassing DC load consumption, AC load consumption, and electricity sold to the grid.

$$E_{served}=E_{AC,L}+E_{DC,L}+E_{GRID,SALE}$$

(2)

where $E_{AC,L}$ is the AC load consumption, $E_{DC,L}$ is the DC load consumption, and $E_{GRID,SALE}$ is the electricity sold to the grid by the IES.

Given that the annual growth of the IES load consumption is exogenously set, minimizing the average cost per kilowatt-hour of useful energy is equivalent to minimizing the total annualized cost $C_{ann,tot}$:

$$min{C}_{ann,tot}=CRF(i,R_{proj})·C_{NPN,tot}$$

(3)

$$CRF(i,N)={\displaystyle \frac{i{(1+i)}^N}{{(1+i)}^N-1}}$$

(4)

where i is the real discount rate, N is the number of years, $C_{NPN,tot}$ is the net present cost of the system, and $R_{proj}$ is the project lifetime.

2.4. Constraints

The model’s primary constraints include electricity, heat, and hydrogen supply-demand balance constraints, unit output constraints, and electrolyzer hydrogen production constraints.

(1)

Power Balance Constraint

The power supply must instantaneously meet the demand of the electrical load:

$$P_{gen}+P_{hydro}+P_{pv}+P_{wind}+P_{grid}+P_{biomass}\ge P_{load}+P_{H_2}$$

(5)

where $P_{gen}$ is the power from fossil fuel generators, $P_{hydro}$ is hydropower, $P_{pv}$ is solar PV power, $P_{wind}$ is wind power, $P_{grid}$ is power from the external grid, $P_{biomass}$ is biomass power, $P_{load}$ is the electrical load power, and $P_{H_2}$ is the electrolyzer power draw.

(2)

Heat Supply Balance Constraint

The heat supply from CHP units and heat pumps must satisfy the thermal load demand:

$$H_{gen}+H_{boiler}=H_{load}$$

(6)

where $H_{gen}$ is the heat supply from CHP units, $H_{boiler}$ is the heat supply from heat pumps, and $H_{load}$ is the thermal load.

(3)

Hydrogen Supply Constraint

The hydrogen production must meet the hydrogen load demand:

$$G_{reform}+G_{electrolyzer}\ge H_{2load}$$

(7)

where $G_{reform}$ is the hydrogen production from reformer units (kg/day), $G_{electrolyzer}$ is the hydrogen production from electrolyzers (kg/day), and $H_{2load}$ is the hydrogen consumption by the load (kg/day).

(4)

Generator Output Constraint

The power output of fossil fuel generators must remain within their minimum and maximum limits:

$$P_{gen}^{min}\le P_{gen}\le P_{gen}^{max}$$

(8)

where $P_{gen}^{min}$ is the minimum stable generation level and $P_{gen}^{max}$ is the maximum capacity.

(5)

Electrolyzer Hydrogen Production

A state-transition model is adopted for the electrolyzer. In standby mode, it operates at low power for maintenance or hot standby. In operating mode, it produces hydrogen at rated power.

$$P_{ec,t}=({\sigma }_{s,t}^{ec}-{\sigma }_{o,t}^{ec})\times {\delta }_s^{ec}{P}_{ec}+{\sigma }_{o,t}^{ec}{P}_{op,t}^{ec}$$

(9)

$${\sigma }_{s,t}^{ec}-{\sigma }_{o,t}^{ec}\ge 0$$

(10)

where ${\delta }_s^{ec}$ is the ratio of standby power to rated power for the electrolyzer. ${\sigma }_{s,t}^{ec}$ and ${\sigma }_{o,t}^{ec}$ are binary variables representing the standby and operating states of the electrolyzer at time t, respectively. If ${\sigma }_{s,t}^{ec}$ = 1, the electrolyzer is in either standby or operating mode. If ${\sigma }_{s,t}^{ec}$ = 0, the electrolyzer consumes no power.

2.5. Carbon Emissions Calculation

The carbon emissions considered in this study primarily include those generated from energy production within the IES and the emission reductions achieved by substituting hydrogen for oil products.

The production-related carbon emissions of the IES mainly originate from three sources: (1) Emissions from fossil fuels consumed by generators for electricity production; (2) Emissions from fossil fuels consumed by heat pumps for heat production; (3) Indirect emissions associated with electricity losses from the grid. The model calculates these based on the energy consumption multiplied by the corresponding carbon emission factors recommended by the IPCC (Intergovernmental Panel on Climate Change, IPCC) [30], as shown in Equation (11):

$$E_s={\tau }_1{W}_{coal1}+{\tau }_2{W}_{gas}+{\tau }_3{W}_{grid}+E_{coal2}$$

(11)

where $E_s$ is the total system CO₂ emissions, ${\tau }_1$ is the CO₂ emission factor for coal (t/MWh), $W_{coal1}$ is the electricity generated by coal-fired units, ${\tau }_2$ is the CO₂ emission factor for natural gas (t/MWh), W_gas is the electricity generated by gas-fired units, ${\tau }_3$ is the CO₂ emission factor for the grid (t/MWh); for Guangzhou’s grid, this factor is 0.6379 t/MWh [31], $W_{grid}$ is the electricity drawn from the grid, $E_{coal2}$ is the CO₂ emissions from the heat pump (if fossil-fueled).

In the model, hydrogen production is primarily intended to supply the hydrogen consumption for transportation in Guangzhou. The emission reduction is calculated by determining the CO₂ emissions that would have been produced by gasoline vehicles traveling the equivalent distance replaced by hydrogen fuel cell vehicles.

The average daily driving distance for hydrogen vehicles is 32.24 km [32]. The corresponding CO₂ emission reduction per 100 km for hydrogen transportation infrastructure is approximately 141.7 kg (Note: Unit ‘g’ in original text is likely a typo, should be ‘kg’ based on context and standard values).

By incorporating the CO₂ emission reduction into the system, the overall CO₂ emissions for each scenario can be expressed as:

$$E_{whole}=E_{system}-E_{reduce}$$

(12)

where $E_{whole}$ is the overall CO₂ emissions for the scenario, $E_{system}$ is the CO₂ emissions from the IES itself within the scenario, and $E_{reduce}$ is the CO₂ emission reduction achieved through hydrogen use.

2.6. Solution and Calculation Process

The study utilizes HOMER Pro software(version 3.18) for system modeling and optimization. HOMER Pro is a optimization tool designed for microgrid and distributed generation analysis. Using power, heat, and hydrogen production and consumption data for Guangzhou urban area, with 2020 as the base year, different development pathways are established to construct scenarios for 2025 and 2030. Inputs include the installed capacities of different generation components within the IES, fuel types used, lower heating values (LHV), heat-to-power ratios, etc. Load inputs primarily comprise industrial, commercial, and residential electrical loads; residential load profiles are designed based on Guangzhou residents’ electricity consumption habits, incorporating peak and off-peak periods [33]. Input data also includes parameters for hydrogen reformers, electrolyzers, and hydrogen loads, forming the hydrogen network. A carbon price is applied system-wide, imposing additional costs on subsystem units that generate carbon emissions.

The model calculation workflow is illustrated in Figure 2. The generated model operation modes are evaluated against the constraints. If the system satisfies the power balance, hydrogen supply balance, and heat balance constraints, it then calculates the IES configuration, power output, energy consumption, and carbon emissions under the condition of minimizing the total energy cost.

3. Methodology and Data

3.1. Scenario Design

Based on Guangzhou’s urban IES in 2020, this study categorizes the city’s energy structure into a thermal network, a power grid, and a hydrogen network. The thermal network comprises combined heat and power (CHP) units on the generation side and thermal loads. The power grid design includes coal power, gas power, photovoltaic (PV), wind power, hydropower, and the external grid on the supply side, with industrial and residential loads on the demand side. The hydrogen network includes hydrogen loads, hydrogen reforming units, electrolyzers, and hydrogen storage tanks. The energy storage component consists of pumped hydro storage and battery storage. This study designs four distinct hydrogen development pathways, constructing scenarios for Guangzhou’s development in 2025 and 2030, as summarized in

Table 1. Summary of scenario design.

Scenario Name	System ID	Scenario Description
Baseline Scenario	S1	Business-as-usual development to 2030: moderate growth rate for renewables, fossil fuel capacity develops according to plans, low hydrogen usage (below planned values).
Renewable Energy Development Scenario	S2	Rapid growth rate for renewables, fossil fuel capacity growth lower than baseline, low hydrogen usage.
Hydrogen + Renewable Energy Development Scenario	S3	Rapid growth rate for renewables, fossil fuel capacity growth lower than baseline, high hydrogen usage.
Carbon Emission Cost + Hydrogen + Renewable Energy Development Scenario	S4	Rapid growth rate for renewables, fossil fuel capacity growth lower than baseline, high hydrogen usage, incorporates a CO2 penalty price.

.

The Baseline Scenario (S1) forecasts the development of fossil fuel installed capacity (coal power, natural gas power) based on recent trends in Guangzhou’s IES. From 2020 to 2025, due to the city’s push for natural gas power, coal power capacity does not increase, while natural gas power capacity grows significantly. By 2030, this growth trend gradually stabilizes. Considering the possibility that renewable energy development may fall short of expectations, the installed capacity of renewables (solar PV, biomass power) is designed to be slightly reduced from planned targets. Simultaneously, S1 scenario assumes hydrogen development does not meet target expectations, resulting in low hydrogen usage (50% of the planned hydrogen load for Guangzhou) and configures the hydrogen supply side accordingly.

The Renewable Energy Development scenario (S2) builds upon S1 by increasing the installed capacity of renewable generators: solar PV by 30% and biomass power by 50% compared to S1. Fossil fuel generator capacity is reduced, with coal power set at 90% and natural gas power at 70% of S1’s forecasted values. Hydrogen usage is set at the planned development level (100% of Guangzhou’s planned hydrogen load).

The Hydrogen + Renewable Energy Development scenario (S3) further expands the scale of hydrogen development on the basis of S2, setting hydrogen usage at 120% of Guangzhou’s planned hydrogen load. Scenarios S2 and S3 share the same assumptions regarding the expansion of renewable energy capacities by 2030, but they differ in the penetration of hydrogen in the transportation sector. In S2, hydrogen penetration is not considered, and the transportation sector continues to rely on conventional fuels; therefore, the hydrogen demand in S2 is set to zero. In contrast, S3 represents a joint development pathway of renewable energy and hydrogen, where hydrogen demand from fuel cell buses and logistics vehicles is explicitly accounted for based on the municipal hydrogen development plans. The hydrogen load in S3 is thus determined by the projected numbers of these vehicles, their annual mileage and the specific hydrogen consumption per 100 km, as described in Section 2.5. This setting allows us to isolate and compare the impact of hydrogen penetration by contrasting the S2 (renewable energy only) and S3 (renewable energy plus hydrogen) scenarios under otherwise similar conditions.

The Carbon Emission Cost + Hydrogen + Renewable Energy Development scenario (S4) introduces a CO₂ penalty price (representing an increased carbon price) on top of the S3 assumptions. The carbon price for the IES is set at 80 CNY/ton in 2025 and 140 CNY/ton in 2030 [34].

3.2. Parameter Settings

3.2.1. Supply Side

The installed capacity settings for the generation side in each scenario are shown in

Table 2. Summary of Scenario Installed Capacity (Unit: MW).

Scenario	2020	2025				2030
		S1	S2	S3	S4	S1	S2	S3	S4
Coal	3680	3680	3180	3180	3180	3680	3180	3180	3180
Gas	3707	10,262	7482	10,262	10,262	12,277	10,262	12,277	12,277
Hydro	153	168	168	168	168	168	168	168	168
Wind	0	15	15	15	15	15	15	15	15
Solar	602	900	1200	1200	1200	1959	2400	2400	2400
Biomass	394	600	930	930	930	1150	1500	1500	1500

. Based on the 2020 capacity, and after forecasting the 2025 power system capacity, coal power capacity is set to decrease, with variations between scenarios as per the assumptions above. The coal power capacity in scenarios S2–S4 is 90% of that in S1. Due to Guangzhou’s promotion of natural gas power, and according to the city’s development plan, the annual growth rate of natural gas power installed capacity from 2020 to 2025 is 35% for S1, reaching 10,262 MW by 2025. Since S2 is a renewable energy development scenario, the growth rate for natural gas power is reduced to 20%.

For hydropower and wind power units, development is considered saturated by 2025, so their installed capacity is assumed not to increase beyond that point.

PV power still has significant development potential and is a major focus for Guangzhou’s renewable energy. The base annual growth rate for PV installed capacity is set at 10%. In scenarios S2–S4, designed for rapid renewable development, the PV capacity growth rate is set to 20%.

The intermittency of PV and wind generation is represented by using hourly time-series profiles for a typical meteorological year in Guangzhou. The PV and wind resource data are obtained from local meteorological stations and processed into hourly capacity factors, which are then used by HOMER Pro to simulate the output of PV panels and wind turbines. In each hour, the available PV and wind power is determined by the installed capacity multiplied by the corresponding capacity factor, and the optimization algorithm decides how much of this available power is used to serve the loads, stored as hydrogen or curtailed. In this way, the variability of solar irradiance and wind speed is incorporated into the model without introducing additional stochastic processes.

This study also considers biomass power units in Guangzhou, which also have substantial potential. Their setting follows the same pattern as PV power. Specific data is shown in Table 2.

According to Guangzhou’s hydrogen infrastructure plan, the hydrogen supply capacity is projected to reach 14,500 tons/year by 2030 [35].

In the study system, hydrogen supply comes from hydrogen reforming units and electrolyzers. The reforming units are the primary supply source, while the electrolyzers participate in grid peak shaving, converting surplus electricity into hydrogen.

3.2.2. Electricity, Heat, and Hydrogen Loads

The load settings for each scenario are shown in

Table 3. Summary of various types of daily loads.

Year	Heat Load (MWh/d)	Electric Load		Scenario	Hydrogen Load (t/d)
		Commercial (kWh/d)	Resident (kWh/d)
2020	17,933.1	11,835.1	15,472.3	BASE	0
2025	16,447.4	16,448.8	20,095.3	S1	5
				S2	10
				S3	20
				S4	20
2030	16,447.4	24,834.2	24,335.1	S1	50
				S2	100
				S3	200
				S4	200

. The electrical load is divided into industrial and residential loads. The industrial load is relatively flat [36,37], with little variation over time. The residential load is designed based on the electricity consumption patterns of Guangzhou residents, with peak hours set from 9:00 to 21:00 [38]. Summer and winter are set as annual peak seasons, with electricity consumption 130% of that in other seasons. Based on Guangzhou’s annual electricity consumption data, the annual growth rate for the electrical load is set at 9%.

Since the demand for heat in Guangzhou primarily comes from industry, which is already saturated, the thermal load remains basically stable. Guangzhou’s daily heat consumption in 2020 was 17,933.1 MWh/day [38]. According to annual load fluctuations, the thermal load decreased from 2020 to 2021 with an annual decline rate of 4.5%, and then stabilized from 2022 to 2024.

This study considers the hydrogen load for transportation, based on the Guangzhou Hydrogen Energy Industry Development Plan (2019–2030). Targets include: fuel cell vehicles (FCVs) accounting for no less than 10% of new or replacement vehicles in the sanitation sector; no less than 3000 FCVs demonstrated in public transport, logistics, engineering services, warehousing, and ports; hundreds of FC passenger cars demonstrated in official vehicles, taxis, and shared mobility. Hydrogen and fuel cells are also to be demonstrated in power and heat applications. The annual growth rate for hydrogen demand is set at 100% [39,40]. By 2025, the baseline scenario (S1) hydrogen load reaches 5 tons/day, reaching 50 tons/day by 2030. For the renewable development scenario S2, the load reaches 10 tons/day in 2025 and 100 tons/day in 2030. For scenarios S3 and S4, it reaches 20 tons/day in 2025 and 200 tons/day in 2030 [41].

The techno-economic parameters of the electrolysis and hydrogen storage units are derived from recent literature and technology assessment reports on hydrogen energy systems. In this study, the capital cost of electrolyzers and hydrogen storage tanks, as well as their fixed and variable operation and maintenance costs, are assumed to remain constant in real terms over the planning horizon, representing near-term cost levels for large-scale demonstration projects in China. The adopted values are summarized in Table 2. Although further cost reductions are expected in the long term due to technological learning, such dynamic cost trajectories are not explicitly modeled in this paper. Instead, a conservative cost assumption is used so that the contribution of hydrogen to system planning is not overestimated.

The table below summarizes the various loads for the IES in different years and scenarios.

3.2.3. Hydrogen Demand Forecast and Validation

The hydrogen demand in 2025 and 2030 is forecast based on the planned deployment of fuel cell vehicles and hydrogen refueling stations in Guangzhou, as specified in the municipal hydrogen development roadmap and related policy documents. The base-year hydrogen demand for 2020 is calibrated using available statistics on hydrogen consumption at existing refueling stations and demonstration projects, ensuring that the modeled hydrogen load is consistent with the current scale of hydrogen use in the city. The future hydrogen loads for 2025 and 2030 are then obtained by applying the projected numbers of fuel cell vehicles, typical annual driving distances and specific hydrogen consumption per 100 km. This approach ensures that the forecasted hydrogen demand is anchored in official planning targets and realistic usage patterns, rather than being arbitrarily assumed.

3.3. Model Validation

Before applying the proposed planning model to future scenarios, the model outputs are validated against available data and existing studies. First, a baseline simulation is carried out for the reference year in the S1 scenario without additional renewable expansion or hydrogen penetration. The resulting total electricity generation, fuel mix and CO₂ emission intensity of the integrated energy system are of the same order of magnitude as the official statistics for Guangzhou’s power system and energy consumption, which indicates that the model configuration is consistent with the real system.

Second, the optimized levelized cost of energy (LCOE) and the shares of coal-fired, gas-fired and grid-supplied electricity in the baseline case are compared with values reported in previous studies on urban integrated energy systems and on Guangzhou’s low-carbon transition. The model produces LCOE and fuel-mix indicators within the typical ranges reported in the literature, suggesting that the economic performance of the simulated system is reasonable.

Although detailed time-series validation for each technology is limited by data availability, these comparisons at the level of annual energy balance, fuel structure and emission intensity provide a practical verification that the model is capable of reproducing realistic system behavior. Therefore, we consider the model sufficiently reliable for exploring alternative planning scenarios and carbon-pricing policies in subsequent sections.

4. Low-Carbon Planning Prediction and Analysis for Guangzhou’s Electricity–Heat–Hydrogen System

4.1. Energy Consumption

The fuel consumption of various power generation components and heat pumps for heating is shown in Figure 3 below. In 2020, natural gas power generation had not yet been expanded on a large scale, making coal power the primary form of electricity generation in Guangzhou’s urban area. Consequently, fuel consumption in 2020 was dominated by coal for coal power, followed by coal consumed by heat pumps for heat production; all natural gas consumption came from natural gas power generation. By 2025, the capacity of natural gas power generation units had increased by 200%. The figure shows that natural gas fuel consumption increased by 3.58 million tons, approximately a 140% rise. For the S2 scenario, which reduces the proportion of fossil fuel power generation (coal power capacity at 90% and natural gas power capacity at 70% of the forecasted development values for S1), coal and natural gas consumption decreased by about 5%. The S3 scenario, building on S2, increases natural gas power capacity while maintaining coal power capacity at 90% of S1 (same as S2). This results in natural gas consumption in S3 being 5% higher than in S1 and 11.7% higher than in S2. In the 2025 S4 scenario, which incorporates an 80 CNY/ton carbon emission penalty, the higher carbon cost for coal power relative to gas power leads to a 2% increase in natural gas consumption for power generation. In the 2030 S4 scenario, with the carbon penalty raised to 140 CNY/ton, the system’s optimization for minimizing the levelized cost of electricity (LCOE) leads to increased electricity purchases from the external grid to reduce carbon costs, consequently decreasing natural gas power generation by about 1%.

4.2. System Power Generation Output

The electricity–heat–hydrogen integrated energy model optimizes the output of different generation units based on economic optimality principles, as shown in Figure 4. In 2020, coal power was the dominant form of fossil fuel generation. By 2025, with increased installed capacity, natural gas power output also rose. Compared to the 2020 baseline, the proportion of electricity generated from natural gas in the S1 scenario increased by 134%. Figure 5 shows that the installed capacity of natural gas units in the 2025 S1 scenario increased by about 176% compared to 2020. This discrepancy arises because the annual operating hours for natural gas units (~2920 h) are lower than those for coal units (~5839 h). Thus, with increasing overall electrical load and unchanged capacity, the increase in output from coal power is greater than from natural gas, explaining why the significant capacity increase for gas doesn’t translate proportionally into output share. In the S2 scenario, with solar PV capacity increased by 30%, biomass capacity by 50%, and coal power capacity reduced to 90% of the forecast, the share of electricity from natural gas increases by 4% compared to S1. The S3 scenario assumes a further increase in natural gas power capacity compared to S2, leading to a corresponding rise in its output share. The S4 scenario introduces a carbon emission penalty on top of S3. Notably, compared to S3, the share of fossil fuel generation decreases in S4, while the share of purchased electricity increases, a point reflected in the system economics section.

4.3. Power Generation Cost

The total annualized cost considered in this study includes fuel costs, operation and maintenance (O&M) costs and carbon emission costs. The power generation costs under different scenarios are shown in Figure 6.

Compared with 2020, the fuel costs for natural gas and coal in the 2025 S1 scenario increase by about 40%, primarily due to the increased installed capacity of fossil fuel generators. By 2030, these fossil-fuel costs in S1 increase by a further ~10% relative to 2025, reflecting the continuous growth in electricity demand and the reliance on coal and gas units to meet the additional load.

A detailed comparison of the 2030 scenarios shows that in S2, fossil fuel power generation costs are reduced by approximately 10% compared to S1. This cost reduction is mainly driven by the increased share of renewables in S2 (solar +30%, biomass +50%), which reduces coal and natural gas consumption by around 20%, thereby lowering fuel expenditures. By contrast, fossil fuel power generation costs in S3 are about 7% higher than in S2 because S3 deploys additional natural gas capacity to support hydrogen production, which leads to higher O&M expenses and more natural gas consumption.

4.4. Carbon Emissions

Carbon emissions under different scenarios are shown in Figure 7. Based on the total CO₂ emissions of the integrated energy system, the average annual growth rate of CO₂ emissions from 2020 to 2025 is about 5.8%, which then slows to 2.9% from 2025 to 2030. The initially higher growth is mainly attributed to the 176% increase in natural-gas power generation between 2020 and 2025, which occurs as gas units are added to meet rising electricity demand.

Increasing renewable energy generation, as represented in the 2025 S2 scenario (solar capacity +30%, biomass capacity +50%), can reduce the system’s overall CO₂ emissions by about 1.3%, corresponding to a reduction of 1.04 million tons compared with S1. In 2030, the S3 scenario further increases hydrogen use and achieves an additional reduction of 1.2 million tons per year relative to S2, accounting for a 1.4% decrease in total emissions. Calculations show that the annual CO₂ emissions in S3 are 416,000 tons lower than in S2. These results indicate that both expanding renewable installed capacity and increasing hydrogen penetration contribute significantly to emission reduction, with hydrogen use providing an effect comparable to that of adjusting the fossil generation structure.

4.5. Sensitivity Analysis of Carbon Price

To further investigate the impact of carbon pricing on the planning and operation of the integrated energy system, a sensitivity analysis is performed around the benchmark carbon price levels adopted in the scenarios. Starting from the optimized configurations in S3 for 2025 and 2030, the carbon price is varied over a wider range that includes and extends beyond the values of 80 and 140 CNY/t CO₂ used in S4, and the resulting changes in system-level LCOE and CO₂ emission intensity are evaluated.

The sensitivity curves confirm the trade-off between economic cost and emission reduction. As the carbon price increases, the CO₂ emission intensity of the integrated energy system decreases monotonically, whereas the LCOE gradually rises. At moderate carbon price levels, substantial emission reductions can be achieved with relatively modest increases in LCOE. For example, compared with the case without carbon pricing, imposing a carbon price of 80 CNY/t CO₂ in 2025 (corresponding to S4) reduces annual CO₂ emissions by about 42,000 tons, or 0.06%, relative to S3. By 2030, raising the carbon price to 140 CNY/t CO₂ leads to an additional reduction of approximately 297,000 tons per year, equivalent to about 0.3% of total emissions, on top of the emission reduction achieved in S3.

From the perspective of system operation, the carbon price increases the effective operating cost of carbon-intensive units and thus encourages the model to reduce the utilization of coal- and gas-fired generators. In the 2030 S4 case, the optimization tends to purchase more electricity from the external grid and slightly decrease natural-gas generation in order to minimize the LCOE while avoiding high carbon costs. As the carbon price continues to rise beyond the benchmark values, the marginal emission reduction becomes smaller, and the increase in LCOE becomes more pronounced. This suggests that there exists a reasonable range of carbon price levels within which the system can obtain meaningful emission reductions at acceptable economic costs, whereas excessively high carbon prices may impose disproportionate cost burdens on end users.

5. Discussions and Policy Implications

This paper investigates the development planning of an electricity–heat–hydrogen integrated energy system for Guangzhou under different carbon-pricing and hydrogen penetration scenarios. Based on the modeling results, this section discusses the main policy implications for urban energy planning, including the coordinated expansion of renewable energy and hydrogen infrastructure, the design of carbon price levels, and the interaction between economic costs and emission reduction targets. The quantitative conclusions and key numerical results are summarized separately in Section 5.

(1)

From the optimization results of the four development scenarios, the operating costs of fossil fuel generators in the IES are related to fuel consumption, operating hours, and installed capacity. After implementing a carbon emission penalty on the system, the optimal solution for minimizing the LCOE results in a effective 5% reduction in the power output from fossil fuel units. Comparing scenarios, this reduction in fossil fuel output corresponds to a decrease in overall system carbon emissions, accounting for approximately 1.2% of total emissions.

(2)

In calculating the system’s carbon emissions, the incorporation of hydrogen demonstrates a notable impact, primarily manifested through its consumption not generating additional carbon emissions. This effect is particularly evident across the 2030 scenarios. Compared to the emission reduction effect achieved through carbon pricing, the addition of hydrogen loads according to the plan contributes more significantly to reducing the system’s overall carbon emissions. Furthermore, increasing the share of installed renewable energy capacity alters the power generation output mix, reducing carbon emissions from the IES generation side by 400,000 tons. This contrast can be observed in the comparison between scenarios S1 and S2. Within the overall emission reduction structure of the system, 42% of the carbon reduction originates from hydrogen use, while 48% stems from changes in the power generation structure.

(3)

The integration of hydrogen into the IES also significantly impacts overall carbon emission reduction. With continuous technological advancements, the costs associated with large-scale hydrogen production, storage, and transportation are expected to gradually decrease in the long term, positioning hydrogen as a major component for decarbonizing IES. This study provides empirical support for this through the calculation and analysis of carbon emissions in an IES considering hydrogen integration. However, further in-depth research is required on system dispatch optimization and how to effectively utilize electrolyzers combined with fuel cells for grid interaction, leveraging their potential for peak shaving and frequency regulation.

(4)

The scenario comparison also sheds light on the mechanism by which carbon pricing drives energy structure optimization in the integrated system. By increasing the effective operating cost of carbon-intensive units, the carbon price raises the marginal cost of coal-fired and, to a lesser extent, gas-fired generation. This encourages the system to rely more on renewable generation and, in the presence of hydrogen demand, to use surplus renewable electricity for hydrogen production instead of curtailment. As a result, the optimal configurations under higher carbon prices feature higher shares of renewable capacity, lower utilization hours of fossil-fired units and larger amounts of hydrogen produced from clean electricity, which jointly contribute to emission reductions. From a policy perspective, this implies that a well-designed carbon price not only provides an economic incentive for low-carbon technologies, but also reshapes the dispatch and planning decisions in a way that accelerates the transition of the urban energy structure.

6. Conclusions

This paper develops a city-scale planning model for an electricity–heat–hydrogen integrated energy system under explicit carbon-pricing constraints and applies it to Guangzhou using HOMER Pro. Four development scenarios (S1–S4) are designed to represent different combinations of renewable energy expansion, hydrogen penetration in the transportation sector and carbon price levels. Based on the simulation and optimization results, the main conclusions are as follows.

(1)

Coordinated development of renewable energy and hydrogen can significantly reduce fossil fuel consumption and CO₂ emissions while maintaining acceptable system costs. Compared with the 2020 baseline, the S2 and S3 scenarios for 2030 achieve substantial reductions in coal and natural gas consumption, leading to notable decreases in fuel costs and system emission intensity. For example, by 2030, the introduction of large-scale renewable energy and hydrogen (S2) is projected to reduce fossil fuel consumption by around 20% relative to the S1 scenario and lower the fuel cost of fossil units by about 5% compared to S3. At the same time, the levelized cost of energy remains within a reasonable range, suggesting that deep decarbonization of the urban energy system can be compatible with economic feasibility.

(2)

Hydrogen penetration makes an important contribution to city-level decarbonization, and its emission reduction effect is comparable to that of power sector restructuring. The decomposition of emission reduction sources shows that the reduction attributable to hydrogen use in the transportation sector already accounts for approximately 1.2% of total system emissions in the studied period. When the contribution of hydrogen substitution is combined with the impact of renewable energy expansion and fossil fuel substitution, around 52% of the total emission reduction is associated with changes in hydrogen utilization and the remaining 48% stems from adjustments in the power generation structure. This result highlights that city-level decarbonization efforts should treat hydrogen not merely as a marginal technology, but as a key pillar alongside renewable power expansion.

(3)

Carbon pricing further enhances emission reductions and steers the system towards a lower-carbon configuration, but the incremental effect depends on the price level. Under the S4 scenario, raising the carbon price from 80 CNY/t CO₂ to 140 CNY/t CO₂ leads to additional emission reductions compared with S3. By 2030, the annual CO₂ emissions in S3 are about 416,000 tons lower than in S2, and increasing the carbon price in S4 yields a further reduction of approximately 297,000 tons per year (about 0.3% of total emissions). This indicates that a sufficiently strong carbon-price signal can effectively promote the replacement of fossil fuels by renewable energy and hydrogen, and can encourage more efficient operation of the integrated energy system. However, overly high carbon prices may increase the economic burden on end users, so policy makers need to carefully balance emission reduction targets and cost impacts when designing carbon pricing schemes.

Overall, the proposed modeling framework provides a practical tool for supporting low-carbon energy planning at the city level under carbon-pricing constraints. Nevertheless, several limitations should be acknowledged. The present study mainly focuses on a deterministic planning horizon and does not explicitly model uncertainties in fuel prices, technology costs and load growth. In addition, the representation of hydrogen infrastructure and transportation demand is simplified, and sector-coupling interactions with industry and buildings could be further refined. Future research will extend the model by incorporating uncertainty and stochastic optimization, refining the characterization of hydrogen supply chains, and applying the framework to other cities or regions to test its generalizability.