Analysis of China’s Low-Carbon Power Transition Path Considering Low-Carbon Energy Technology Innovation

Abstract

Innovation in key low-carbon technologies plays a supporting role in achieving a high-quality low-carbon transition in the power sector. This paper aims to integrate research on the power transition pathway under the “dual carbon” goals with key technological innovation layouts. First, it deeply analyzes the development trends of three key low-carbon technologies in the power sector—new energy storage, CCUS, and hydrogen energy—and establishes a quantitative model for their technological support in the low-carbon transition of the power sector. On this basis, the objective function and constraints of traditional power planning models are improved to create an integrated optimization model for the power transition pathway and key low-carbon technologies. Finally, a simulation analysis is conducted using China’s power industry “dual carbon” pathway as a case study. The optimization results include the power generation capacity structure, power generation mix, carbon reduction pathway, and key low-carbon technology development path for China from 2020 to 2060. Additionally, the impact of uncertainties in breakthroughs in new energy storage, CCUS, and hydrogen technologies on the power “dual carbon” pathway is analyzed, providing technological and decision-making support for the low-carbon transition of the power sector.

1. Introduction

In recent years, global warming has intensified, with extreme weather events occurring more frequently, highlighting the negative impact of climate change on human survival. Reducing greenhouse gas emissions has become a growing international consensus [1]. The EU has been committed to promoting global carbon emission reduction and sustainable development. In 2018, the EU adopted the “EU 2050 Strategic Long-term Vision”, which proposed the goal of achieving carbon neutrality by 2050. In 2021, the European Commission proposed a package of emission reduction plans, requiring member states to achieve a 55% reduction by 2030 within the energy industry, finance, and other fields to help build an EU sustainable development model [2,3]. In 2020, China pledged to peak its carbon dioxide emissions by 2030, strive to achieve carbon neutrality by 2060, and gradually develop and improve a set of “1 + N” policy systems, which are of great significance for tackling global climate change. Low-carbon technological innovations in the power sector provide crucial support for intensive carbon reduction efforts [4]. Therefore, considering the development trends of key low-carbon technologies and optimizing the integration of the power transition pathway with energy technology innovation are essential for realizing the “dual carbon” targets in the energy and power industries in a high-quality manner [5].

The most direct manifestation of the low-carbon transition in the power sector is the shift from the dominance of coal-fired power plants to a more diverse energy mix, with a focus on renewable energy, such as new energy sources. This entails long-term, macro-scale low-carbon power planning [6,7,8]. Currently, China is working to build a new power system centered around renewable energy, characterized by a renewable energy-dominant power structure; a highly flexible, digitalized, and intelligent grid; interactive systems involving power generation, grids, load, and storage; and a comprehensive energy service system focused on electricity. Through a unified, efficient, and well-coordinated electricity market, these elements are expected to be closely linked and operate in a stable and orderly manner across all aspects of the power system [9,10]. From a power planning perspective, constructing a new power system requires the coordinated optimization of power generation, grids, load, and storage, aiming to enhance the flexible and reliable operation of a high proportion of renewable energy. Renewable energy units are expected to play a primary role in supporting system operations [11]. Several studies have explored China’s energy and power transformation trends leading up to 2050, providing reference points for medium- and long-term pathways [12]. Further research has elaborated on specific strategies for promoting the transition of the power sector toward renewable energy generation [13]. Other studies have discussed the integration of high proportions of intermittent renewable energy into the grid and analyzed how to effectively combine various options to enhance system flexibility [14]. The transition’s near- and medium-term characteristics, as well as the future shape of the power system, have been examined, with energy storage identified as a key factor in determining the pace and form of the power system’s transformation [15]. Additionally, grid development plays an important role in the low-carbon transition of the power sector. Some studies have pointed out that the new power system will deeply integrate digital and physical systems, with digital grids becoming the best platform to support the new power system [16,17]. In addition, relevant European scholars have examined the needs of key users for energy demand modeling in the context of climate-neutral goals, and proposed the direction of improvement of energy demand models [18]. However, most of the existing research primarily focuses on projecting future power systems based on current technologies, with less emphasis on the strategic application of low-carbon technologies within the “dual carbon” pathway.

In fact, the low-carbon transition of the power sector is inseparable from the support of low-carbon technologies, particularly relying on comprehensive, transformative, and even disruptive technological innovations. These include high-efficiency, low-cost solar and wind power generation technologies, advanced energy storage technologies, advanced transmission line technologies, CCUS (carbon capture, utilization, and storage), hydrogen energy, large models, and artificial intelligence [10]. To explore technological pathways for achieving significant emission reductions in the power sector, many studies have been conducted by scholars both domestically and internationally. The effects of green technology innovation, renewable energy use, and financial development on CO₂ emissions in 21 European countries have been studied. The results of a cross-sectional autoregressive distributed lag model show that there is a two-way causal relationship between green technology innovation, renewable energy use, and CO₂ emissions [19,20]. MacDonald et al., for instance, used high-resolution meteorological data to simulate wind and solar resources in the U.S., employing their developed National Energy with Weather System (NEWS) model to calculate the least-cost power technology mix to meet future electricity demand and carbon reduction requirements [21]. Clack et al. found that while wind and solar have significant potential in the low-carbon transition, achieving zero emissions is extremely challenging, and a broad portfolio of technologies is more conducive to a successful transition [6]. Sepulveda et al. generated 912 scenarios considering various low-carbon technologies and the uncertainty of cost parameters to comprehensively analyze the cost of achieving zero-carbon electricity in the U.S. However, existing studies rarely provide quantitative modeling and analysis of the key characteristics of these low-carbon technologies, and few have integrated the optimization of power transition pathways and the application scale of key low-carbon technologies from a system planning perspective.

In view of this, as detailed in this paper, we conduct an in-depth analysis of the development trends and supporting roles of three key low-carbon technologies in the energy and power sector—new energy storage, CCUS, and hydrogen energy. We establish technical characteristic models suitable for power planning. On this basis, taking into account the impact of key low-carbon technologies, the traditional power planning model’s objective function and constraints are improved, and an integrated optimization model for the power “dual carbon” pathway and key low-carbon technologies is developed. This model enables the comprehensive optimization of power generation capacity structure, power generation mix, carbon reduction pathways, and key low-carbon technology pathways over the long-term period from 2020 to 2060, providing technical support for the low-carbon transition of the power sector.

2. Development Trends and Characteristics Modeling Analysis of Key Low-Carbon Technologies in the Energy and Power Sector

2.1. New Energy Storage Technology

Various types of new energy storage technologies will show a trend of diversified development in the future. According to relevant research, electrochemical energy storage is expected to reach a large-scale application level by 2025, while the economic efficiency of new energy storage technologies such as compressed air and flywheels will significantly improve by 2030, leading to their large-scale application. Due to their cost advantages in power or energy, they will have a comparative advantage in specific application scenarios and become important supplementary elements in energy storage systems.

Table 1. Trends in different types of energy storage technologies.

Type of Energy Storage	Energy Conversion Efficiency (%)			Discharge Duration	Response Speed	Unit Capacity Cost			Expected Year of Large-Scale Application
	2025	2030	2060			2025	2030	2060
Compressed air	40~60	50~65	50~70	Hour level	Minute level	1120 USD/kW	980 USD/kW	910 USD/kw	2030
Phase change thermal storage	-	-	-	Several hours	Minute level	23.8–28 USD/kWh	22.4–26.6 USD/kWh	21–25.2 USD/kWh	2030
Flywheel energy storage	-	-	-	Minute level	<2 milliseconds	420–560 USD/kW·min	280–350 USD/kW·min	210–280 USD/kW·min	2030
Supercapacitor	95	95	95	Second-to-minute level	Millisecond level	11,200 USD/kWh	9800 USD/kWh	8400 USD/kWh	2030
Lithium battery	88–90	90–91	92–94	Hour level	Millisecond level	126–154 USD/kWh	70–98 USD/kWh	42–56 USD/kWh	2025
Vanadium redox flow battery	65–70	70–72	72–75	Hour level	Millisecond level	350–420 USD/kWh	1500–2000 USD/kWh	1000–1500 USD/kWh	2030
Sodium-ion battery	88–90	90–91	92–94	Hour level	Millisecond level	252–350 USD/kWh	112–140 USD/kWh	35–42 USD/kWh	2035

illustrates the future economic trends of different types of energy storage technologies.

As an important means to enhance the flexibility of the power system and address the integration of renewable energy in the future, new energy storage technologies can be used in various application scenarios, including high-proportion renewable energy integration, inertia support and primary frequency regulation, emergency power support, peak load power supply, and the coordinated operation of generation, grid, load, and storage. Among these, electrochemical energy storage will become the fastest growing and most widely applicable energy storage technology in the power sector during the process of achieving a carbon peak. The support capability of new energy storage for the power system is primarily constrained by parameters such as installed storage capacity, storage duration, and regulation rate. The related mathematical model is shown in the following equation:

$$\left\{\begin{array}{l}{P}_{stor,0}^{\min }\le P_{stor,t}\le P_{stor,1}^{\max }\\ \left|P_{stor,t}-P_{stor,t-1}\right|\le \mathrm{\Delta }{P}_{stor}^{\max }\\ S_{stor.t}=S_{stor.t-1}+P_{stor,t}\bullet \mathrm{\Delta }T\\ S_{stor}^{\min }\le S_{stor,t}\le S_{stor}^{\max }\\ S_{stor}^{\max }-S_{stor}^{\min }=P_{stor,1}^{\max }\bullet T_{\max }\end{array}\right.$$

(1)

In the equation, $P_{stor,t}$ represents the charging/discharging power of the new energy storage technology at time t. A $P_{stor,t}$ greater than 0 represents discharging, and a $P_{stor,t}$ less than 0 represents charging; $P_{stor,0}^{\min }$, $P_{stor,1}^{\max }$ represents the maximum charging and discharging power of the new energy storage technology; $\mathrm{\Delta }{P}_{stor}^{\max }$ represents the upper limit of the charging and discharging rate of the energy storage technology; $S_{stor.t}$ represents the remaining energy of the new energy storage technology at time t; $S_{stor}^{\min }$, $S_{stor}^{\max }$ represents the upper and lower limits of the energy storage capacity; $T_{\max }$ represents the maximum sustainable charging and discharging duration of the new energy storage technology.

2.2. CCUS Technology

As one of the strategic technological options for the large-scale mitigation of greenhouse gas emissions, the technological maturity and economic affordability of CCUS (carbon capture, utilization, and storage) are crucial prerequisites for its large-scale application. Taking China’s thermal power CCUS retrofit as an example, under current technological conditions, the cost of CCUS is approximately 70–140 USD per ton of CO₂, which increases the cost of electricity generation by 0.036–0.056 USD per kWh and raises energy consumption levels by 14–25%. Additionally, there is a risk of leakage during geological storage. Strengthening research on the fundamental theory and key technologies of low-energy large-scale CO₂ capture, CO₂ utilization, and safe and reliable CO₂ transport and storage is essential. Achieving breakthroughs in next-generation CCUS technology, characterized by low-cost capture, large-scale emission reduction, high safety, and comprehensive benefits, will be a major technological innovation demand in the future. The future trends in the technical and economic aspects of CCUS across different stages, according to relevant studies, are shown in

Table 2. Future development trends of technical costs in various stages of CCUS.

Year		2025	2030	2035	2040	2050	2060
Capture cost (USD/ton)	Pre-combustion	14~25.2	12.6~18.2	9.8~11.2	7~9.8	4.2~7	2.8~5.6
	Post-combustion	32.2~43.4	26.6~39.2	22.4~30.8	15.4~25.2	11.2~21	9.8~16.8
	Oxy-fuel combustion	42~67.2	22.4~54.6	18.2~44.8	15.4~32.2	12.6~21	11.2~18.2
Transportation cost (USD/(ton·km))	Tank truck transportation	0.126~0.196	0.112~0.182	0.098~0.168	0.084~0.154	0.07~0.14	0.07~0.14
	Pipeline transportation	0.112	0.098	0.084	0.07	0.063	0.056
Sequestration cost (USD/ton)		7~8.4	5.6~7	4.9~5.6	4.2~4.9	3.5~4.2	2.8~3.5

.

More than half of China’s existing coal-fired power units were built between 2005 and 2015. As their service life is 30 to 40 years, the coal power industry will usher in a peak period of unit renewal between 2035 and 2045. Considering the development laws of the coal power industry and the development trend of capture technologies, the transformation of existing coal-fired power units into ones first-generation capture technology should be the main focus before 2035, and the construction of new coal-fired power units with second-generation capture technology for capacity replacement should be the main focus after 2035. The period around 2035 will be a key time for the intergenerational upgrade of capture technology. The scale of future coal power CCUS upgrading will be mainly guided by the economic viability of CCUS technology and constrained by coal power development plans and electricity carbon emission space, as shown in the following mathematical model:

$$\left(\begin{array}{l}{P}_{CCUS}^{N,t}=P_{CCUS}^{coal,t}+P_{CCUS}^{gas,t}+P_{CCUS}^{bio,t}\\ P_{N,\min }^{coal,t}\le P_{CCUS}^{coal,t}\le P_{N,\max }^{coal,t}\\ P_{N,\min }^{gas,t}\le P_{CCUS}^{gas,t}\le P_{N,\max }^{gas,t}\\ P_{N,\min }^{bio,t}\le P_{CCUS}^{bio,t}\le P_{N,\max }^{bio,t}\\ {\lambda }_t\left(P_{CCUS}^{coal,t}\bullet T_{CCUS}^{coal,t}+P_{CCUS}^{gas,t}\bullet T_{CCUS}^{gas,t}+P_{CCUS}^{bio,t}\bullet T_{CCUS}^{bio,t}\right)=S_{CCUS}^{CO2,t}\end{array}\right.$$

(2)

In the formula, $P_{CCUS}^{N,t}$ represents the installed capacity of CCUS for the planning horizon year t. $P_{CCUS}^{coal,t}$, $P_{CCUS}^{gas,t}$, and $P_{CCUS}^{bio,t}$ represent the CCUS retrofitting and upgrading scales for coal power, gas power, and biomass power generation, respectively, for the planning horizon year t. $P_{N,\min }^{coal,t}$, $P_{N,\max }^{coal,t}$ represents the upper and lower limits of the coal power scale that can be upgraded and retrofitted with CCUS by the planning horizon year t; $P_{N,\min }^{gas,t}$, $P_{N,\max }^{gas,t}$ represents the upper and lower limits of the gas power scale that can be retrofitted with CCUS. $P_{N,\min }^{bio,t}$, $P_{N,\max }^{bio,t}$ represents the upper and lower limits of the biomass power generation scale that can be retrofitted with CCUS. $T_{CCUS}^{coal,t}$, $T_{CCUS}^{gas,t}$, and $T_{CCUS}^{bio,t}$ represent the annual utilization hours of coal power, gas power, and biomass power that have undergone CCUS retrofitting and upgrading by the planning horizon year t. ${\lambda }_t$ represents the carbon dioxide capture rate of CCUS equipment by the planning horizon year t. $S_{CCUS}^{CO2,t}$ represents the required amount of carbon dioxide capture for CCUS in the planning horizon year t.

2.3. Hydrogen Energy Technology

Hydrogen energy, with its green and carbon-free characteristics, is well-suited for long-term storage and is a key medium for building a low-carbon and efficient modern energy system. Hydrogen energy technology has the potential to participate in the flexible interaction and regulation of the power grid at multiple stages, including hydrogen production, storage, and utilization. Electrolytic hydrogen production can act as a highly adjustable load, effectively enhancing the flexibility and security of the power system and promoting the consumption and utilization of new energy sources. It can also be combined with hydrogen storage equipment, hydrogen combustion units, or fuel cells to achieve the large-scale, long-duration storage and conversion of new energy, effectively ensuring the sustainable and large-scale development and utilization of new energy sources. In terms of hydrogen storage, high-pressure gaseous storage has low energy consumption but a relatively low storage efficiency of about 3–38 kg/m³. Low-temperature liquid storage has an extremely high efficiency of 71–120 kg/m³, but its energy consumption is 1–2 times that of the former. In terms of hydrogen utilization, fuel cells have a high power generation efficiency of approximately 60–80% and excellent flexible regulation performance. However, they are currently constrained by technological breakthroughs and high costs. In the long term, they have the broadest potential to participate in flexible grid regulation. The development trends of hydrogen energy technology in the future are shown in

Table 3. Future economic development trends of hydrogen energy technology.

Year		2025	2030	2060
Electrolytic hydrogen production	Cost	≤2.8 USD/H2	≤USD 2.1/H2	≤USD 1.4/H2
	Alkaline	Current density: 0.8 A/cm2 Single-cell capacity: 2 MW	Electricity density: 1 A/cm2 Single-cell scale: 3 MW	Electricity density: 2 A/cm2 Single-cell scale: 10 MW
	PEM	Single-cell scale: 1 MW Energy consumption: 4.5 kWh/Nm3 H2 Adjustment range: 5~120% Lifespan: 50,000 h	Single-cell scale: 2 MW Energy consumption: 4.3 kilowatt-hours/Nm3 H2 Adjustment range: 5~150% Lifespan: 80,000 h	Single-cell scale: 10 MW Energy consumption: 4 kilowatt-hours/Nm3 H2 Lifespan: 120,000 h
Hydrogen storage	Hydrogen storage density	4.0 wt%	5 wt%	7 wt%
Fuel Cell	Cost	560 USD/kilowatt	112 USD/kilowatt	42 USD/kilowatt
	Lifespan	20,000 h fixed power station	50,000 h fixed power station	100,000 h fixed power station
	Cold start temperature	−30 °C	−30 °C	−40 °C

, where proton exchange membrane (PEM) electrolysis for hydrogen production, high-pressure gaseous hydrogen storage, and proton exchange membrane fuel cell technology are all key development directions for hydrogen energy technology.

In the future low-carbon transformation of the power sector, the development scale of hydrogen energy technology will mainly be determined by the demand for hydrogen production electricity and the flexibility regulation needs of the power system. The specific mathematical model is shown in the following formula:

$$\left\{\begin{array}{l}{S}^{P2H}={\displaystyle \sum _{t=1}^{8760}{P}_t^{P2H}}\bullet \mathrm{\Delta }T\\ P_{\min }^{P2H}\le P_t^{P2H}\le P_{\max }^{P2H}\\ \left|P_t^{P2H}-P_{t-1}^{P2H}\right|\le \mathrm{\Delta }{P}^{P2H}\\ P_{\mathrm{stor},\min }^{P2H}\le P_{\mathrm{stor},t}^{P2H}\le P_{\mathrm{stor},\max }^{P2H}\\ \left|P_{\mathrm{stor},t}^{P2H}-P_{\mathrm{stor},t-1}^{P2H}\right|\le \mathrm{\Delta }{P}_{\mathrm{stor}}^{P2H}\\ P_{\mathrm{stor},t}^{P2H}\le P_t^{P2H}\\ S_{\mathrm{stor},t}^{P2H}=S_{\mathrm{stor},t-1}^{P2H}+P_{\mathrm{stor},t}^{P2H}\bullet \mathrm{\Delta }T\\ S_{\mathrm{stor},\min }^{P2H}\le S_{\mathrm{stor},t}^{P2H}\le S_{\mathrm{stor},\max }^{P2H}\end{array}\right.$$

(3)

In the formula, $S^{P2H}$ represents the annual electricity demand for hydrogen production. $P_t^{P2H}$ represents the power of electrolytic hydrogen production at time t. $P_{\min }^{P2H}$, $P_{\max }^{P2H}$ represent the upper and lower limits of the electrolytic hydrogen production power variation at time t. $\mathrm{\Delta }{P}^{P2H}$ represents the maximum rate of change of the electrolytic hydrogen production power. $P_{\mathrm{stor},t}^{P2H}$ represents the charging and discharging power of the hydrogen energy storage at time t. If $P_{\mathrm{stor},t}^{P2H}$ is less than 0, it indicates charging, and if greater than 0, it indicates discharging. $P_{\mathrm{stor},\min }^{P2H}$, $P_{\mathrm{stor},\max }^{P2H}$ represent the maximum charging and discharging power limits of hydrogen energy storage. $\mathrm{\Delta }{P}_{\mathrm{stor}}^{P2H}$ represents the maximum rate of change of hydrogen energy storage power. $S_{\mathrm{stor},t}^{P2H}$ represents the amount of hydrogen energy stored at time t. $S_{\mathrm{stor},\min }^{P2H}$, $S_{\mathrm{stor},\max }^{P2H}$ represent the upper and lower limits of the hydrogen energy storage capacity.

3. Optimization Model for the “Dual Carbon” Path in the Power Industry, Taking into Account Key Low-Carbon Technology Support

Addressing the optimization issue of the power sector’s “dual carbon” path supported by key low-carbon technologies, this paper aims to minimize the power supply cost from 2020 to 2060. The optimization objectives include various types of power generation installations, electricity generation, and the scale of key low-carbon technology applications. Meanwhile, considering constraints such as power balance, carbon emissions, renewable energy generation resources, and characteristics of key low-carbon technologies, an optimization model for the power sector’s “dual carbon” path supported by key low-carbon technologies is established.

3.1. Objective Function

Under the premise of a given carbon quota budget for the power industry at each planning horizon year, and relying on key low-carbon technology support, it is assumed that the carbon emissions of the power industry in each planning horizon year are exactly equal to the power carbon quota, achieving a supply–demand balance of carbon quotas between coal-fired enterprises within the power industry. Overall, the power industry does not need to purchase or sell carbon quotas to other industries. Therefore, the cost objective function of the power industry’s “dual carbon” path optimization model mainly includes three parts: power generation investment cost I, key low-carbon technology investment cost D, and operational cost F, as shown in the following formula:

Min C = I + D + F

(4)

In the formula, C represents the total cost of electricity supply within the planning period.

(1)

Power Generation Investment Cost

The power generation investment cost I mainly includes the investment and construction costs of typical power sources such as coal power, gas power, nuclear power, biomass power generation, wind power, solar power, hydropower, and pumped storage.

$$\mathrm{I}=\sum _{t\in \mathrm{T}}\sum _{i\in \mathrm{\Phi }}{K}_{i,t}·\mathrm{\Delta }{R}_{i,t}·Fi{n}_t$$

(5)

In the formula, $K_{i,t}$ represents the unit capacity investment cost of power source $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $\mathrm{\Delta }{R}_{i,t}$ represents the newly added installed capacity of power source $\mathit{I}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. Φ represents the set of generator groups for eight types of typical power sources. $T=\left\{2025,2030\dots \left.2060\right\}\right.$ represents the set of planning horizon years. $\mathrm{\Delta }{R}_{i,t}>0$ represents the newly added generator groups. $Fi{n}_t$ represents the discount factor for the $\mathit{t}-\mathit{t}\mathit{h}$ year.

(2)

Key Low-Carbon Technology Investment Cost

The key low-carbon technology investment cost $\mathit{D}$ mainly includes the investment and construction costs of new energy storage, CCUS retrofitting and upgrading, and hydrogen energy technology.

$$D={\displaystyle \sum _{t\in T}\left({\displaystyle \sum _{i=1}^{N1}{K}_{i,t}^{stor}\mathrm{\Delta }{R}_{i,t}^{stor}}+{\displaystyle \sum _{i=1}^{N2}{\displaystyle \sum _{j=1}^3{K}_{i,j,t}^{CCUS}\mathrm{\Delta }{R}_{i,j,t}^{stor}}}+{\displaystyle \sum _{i=1}^{N3}{\displaystyle \sum _{j=1}^2{K}_{i,j,t}^{P2H}\mathrm{\Delta }{R}_{i,j,t}^{P2H}}}\right)}Fi{n}_t$$

(6)

In the formula, $K_{i,t}^{stor}$, $K_{i,j,t}^{CCUS}$, and $K_{i,j,t}^{P2H}$ represent the unit capacity investment costs of new energy storage power station i, the $\mathit{j}-\mathit{t}\mathit{h}$ type of CCUS upgrading station $\mathit{i}$, and the $\mathit{j}-\mathit{t}\mathit{h}$ type of hydrogen energy application station $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning horizon year, respectively.

In the formula, the subscript $\mathit{j}$ = 1, 2, 3 in $K_{i,j,t}^{CCUS}$, respectively, indicates that the types of thermal power units undergoing CCUS retrofitting and upgrading are coal-fired power, gas-fired power, and biomass power generation. The subscript $\mathit{j}$ = 1, 2, in $K_{i,j,t}^{P2H}$, respectively, indicates that the technologies are electrolytic hydrogen production and hydrogen energy storage. $\mathrm{\Delta }{R}_{i,t}^{stor}$, $\mathrm{\Delta }{R}_{i,t}^{stor}$, and $\mathrm{\Delta }{R}_{i,t}^{P2H}$, respectively, represent the newly added installed capacities of new energy storage, CCUS, and hydrogen energy technology application stations $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $\mathit{N}1$, $\mathit{N}2$, and $\mathit{N}3$ represent the number of new energy storage, CCUS, and hydrogen energy technology application stations, respectively.

(3)

Operating Cost

The operating cost $\mathit{F}$ mainly includes the following: fuel and operation and maintenance (O&M) costs for various types of power sources, among which the fuel cost for renewable energy generation is zero, and the operation and maintenance costs for key low-carbon technology application stations such as new energy storage, CCUS, and hydrogen energy.

$$\begin{array}{ll}\mathrm{F}=& {\displaystyle \sum _{t\in T}{\displaystyle \sum _{i\in \mathrm{\Phi }}{\displaystyle \sum _{k=1}^{8760}\left(C{f}_{i,t}+C{v}_{i,t}\right)P_{i,t,k}\mathrm{\Delta }T}\cdot Fi{n}_t}}+{\displaystyle \sum _{i\in \mathrm{\Phi }}C{g}_{i,t}\left(R_{i,t}+{\displaystyle \sum _{t\in T}\left(\mathrm{\Delta }{R}_{i,t}-\mathrm{\Delta }{R}_{i,t}^{dw}\right)Fi{n}_t}\right)}\\ & \begin{array}{l}+{\displaystyle \sum _{t\in T}{\displaystyle \sum _{k=1}^{8760}\left({\displaystyle \sum _{i=1}^{N1}C{v}_{i,t}^{stor}\left|P_{i,t,k}^{stor}\right|\mathrm{\Delta }T}+{\displaystyle \sum _{i=1}^{N2}{\displaystyle \sum _{j=1}^{3}C{v}_{i,j,t}^{CCUS}{P}_{i,j,t,k}^{CCUS}\mathrm{\Delta }T}}+{\displaystyle \sum _{i=1}^{N3}{\displaystyle \sum _{j=1}^{2}C{v}_{i,j,t}^{P2H}{P}_{i,j,t,k}^{P2H}\mathrm{\Delta }T}}\right)Fi{n}_t}}\\ +{\displaystyle \sum _{i=1}^{N1}C{g}_{i,t}^{stor}\left(R_i^{stor}+{\displaystyle \sum _{t\in T}\mathrm{\Delta }{R}_{i,t}^{stor}}\right)}Fi{n}_t\\ +{\displaystyle \sum _{i=1}^{N2}{\displaystyle \sum _{j=1}^{3}C{g}_{i,j,t}^{CCUS}\left(R_{i,j}^{CCUS}+{\displaystyle \sum _{t\in T}\mathrm{\Delta }{R}_{i,j,t}^{CCUS}}\right)}}Fi{n}_t\\ +{\displaystyle \sum _{i=1}^{N3}{\displaystyle \sum _{j=1}^{2}C{g}_{i,j,t}^{P2H}\left(R_{i,j}^{P2H}+{\displaystyle \sum _{t\in T}\mathrm{\Delta }{R}_{i,j,t}^{P2H}}\right)}}Fi{n}_t\end{array}\end{array}$$

(7)

$C{f}_{i,t}$, $C{v}_{i,t}$ represent the fuel cost coefficient and variable operation and maintenance cost coefficient per unit of electricity generation for power source $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, respectively; $P_{i,t,k}$ represents the power generation of power source $\mathit{i}$ at time $\mathit{k}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $\mathrm{\Delta }T$ represents the time step length, $C{g}_{i,t}$ represents the fixed operation and maintenance cost coefficient for power source $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, $R_{i,t}$ represents the initial installed capacity of power source i in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, and $\mathrm{\Delta }{R}_{i,t}^{dw}$ represents the retired installed capacity of power source i in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $C{v}_{i,t}^{stor}$, $C{g}_{i,t}^{stor}$, $P_{i,t,k}^{stor}$, $\mathrm{\Delta }{R}_{i,t}^{stor}$ represent the variable operation and maintenance cost coefficient, fixed operation and maintenance cost coefficient, charging and discharging power at moment k, and newly added installed capacity of new energy storage $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $C{v}_{i,j,t}^{CCUS}$, $C{g}_{i,j,t}^{CCUS}$, $P_{i,j,t,k}^{CCUS}$, $\mathrm{\Delta }{R}_{i,j,t}^{CCUS}$ represent the variable operation and maintenance cost coefficient, fixed operation and maintenance cost coefficient, charging and discharging power at moment k, and newly added installed capacity of the $\mathit{j}-\mathit{t}\mathit{h}$ type of CCUS technology application station i in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, respectively. $C{v}_{i,j,t}^{P2H}$, $C{g}_{i,j,t}^{P2H}$, $P_{i,j,t,k}^{P2H}$, $\mathrm{\Delta }{R}_{i,j,t}^{P2H}$ represents the variable operation and maintenance cost coefficient, fixed operation and maintenance cost coefficient, charging and discharging power at moment $\mathit{k}$, and newly added installed capacity of the $\mathit{j}-\mathit{t}\mathit{h}$ type of hydrogen energy technology station $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $R_i^{stor}$, $R_{i,j}^{CCUS}$, and $R_{i,j}^{P2H}$ represent the initial installed capacity of new energy storage i, the $\mathit{j}-\mathit{t}\mathit{h}$ type of CCUS technology application station $\mathit{i}$, and the $\mathit{j}-\mathit{t}\mathit{h}$ type of hydrogen energy technology station $\mathit{i}$ at the initial level year, respectively.

3.2. Constraints

3.2.1. Planning Capacity Constraints

The future expansion of hydropower and nuclear power is mainly constrained by the availability of suitable sites, and existing units will continue to operate beyond their economic life cycle without unit retirement being considered. The scale of new renewable energy generation is primarily constrained by national policies and industry production capacity, and existing renewable energy stations will be replaced with an equivalent capacity after reaching their economic life cycle, with unit retirement effectively not being considered. The scale of new gas-fired power installations is influenced by local policies and gas prices, while the scale of new coal-fired power installations is mainly constrained by national policy planning. For existing coal-fired power units, some will continue to operate after reaching their economic life cycle through life extension or equivalent capacity replacement, while others will be retired as planned. Overall, the new installed capacity and retirement capacity of various types of power sources should meet the constraints as shown in the following formula:

$$\left\{\begin{array}{l}\mathrm{\Delta }{R}_{i,t}^{\min }\le \mathrm{\Delta }{R}_{t,t}\le \mathrm{\Delta }{R}_{i,t}^{\max }\\ \mathrm{\Delta }{R}_{i,t}^{dw,\min }\le \mathrm{\Delta }{R}_{t,t}^{dw}\le \mathrm{\Delta }{R}_{i,t}^{dw,\max }\end{array}\right.$$

(8)

In the formula, $\mathrm{\Delta }{R}_{i,t}^{\max }$, $\mathrm{\Delta }{R}_{i,t}^{\min }$ represent the lower and upper limits of the newly added installed capacity for power source $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, respectively.$\mathrm{\Delta }{R}_{i,t}^{dw,\max }$, $\mathrm{\Delta }{R}_{i,t}^{dw,\min }$ represent the upper and lower limits of the retired installed capacity for power source $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, respectively.

To meet the system capacity adequacy requirements, especially electricity supply guarantee issues under extreme weather conditions, the installed capacities of various types of power sources and new energy storage technologies must meet the following constraints:

$${\displaystyle \sum _{i\in \mathrm{\Phi }}{\beta }_{i,t}\left(R_{i,t}+{\displaystyle \sum _{l=1}^t\left(\mathrm{\Delta }{R}_{i,l}-\mathrm{\Delta }{R}_{i,l}^{\mathrm{dw}}\right)}\right)+}{\displaystyle \sum _{i=1}^{N1}{\beta }_{i,t}^{stor}\left(R_i^{stor}+{\displaystyle \sum _{l=1}^t\mathrm{\Delta }{R}_{i,l}^{stor}}\right)}\le (1+{\alpha }_t)P_t^{\mathrm{load},\max }$$

(9)

In the formula, ${\beta }_{i,t}$, ${\beta }_{i,t}^{stor}$ represent the effective capacity coefficients of power source $\mathit{i}$ and new energy storage $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, respectively. ${\alpha }_t$ represents the system reserve capacity coefficient in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $P_t^{load,\max }$ represents the maximum load of all of society in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year.

The future scale of new low-carbon technologies such as new energy storage, CCUS, and hydrogen energy is mainly limited by factors such as the development status of technology, national policy planning, and industry production capacity. In addition, the scale of CCUS technology application is also closely related to the scale of existing thermal power units and the demand for electricity carbon emission reduction.

3.2.2. System Operational Constraints

(1)

Power Balance Constraint

To meet the demand for power balance, the generation power of various types of power sources, the charging and discharging power of new energy storage, and the charging and discharging power of hydrogen energy storage should meet the power demand of the electricity load at all times, as shown in the following formula:

$${\displaystyle \sum _{i\in \mathrm{\Phi }}{P}_{i,t,k}}+{\displaystyle \sum _{i=1}^{N1}{P}_{i,t,k}^{stor}}+{\displaystyle \sum _{i=1}^{N3}{P}_{i,2,t,k}^{P2H}}=F_{t,k}^{load}$$

(10)

In the formula, $F_{t,j}^{load}$ represents the total electricity load power of all of society at moment $\mathit{j}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year.

(2)

Energy Balance Constraint

The electricity generation of each type of power source in each planning year should meet the total electricity demand of the society, and the electricity consumption for hydrogen production should equal the green electricity required for hydrogen production. The specific formula is as follows:

$$\left\{\begin{array}{l}{\displaystyle \sum _{i\in \mathrm{\Phi }}{\displaystyle \sum _{k=1}^{8760}{P}_{i,t,k}\mathrm{\Delta }T}=}{Q}_t^{load}\\ {\displaystyle \sum _{k=1}^{8760}{\displaystyle \sum _{i=1}^{N3}{P}_{i,1,t,k}^{P2H}\mathrm{\Delta }T}}=Q_t^{\mathrm{P}2H}\end{array}\right.$$

(11)

In the formula, $Q_t^{load}$, $Q_t^{\mathrm{P}2H}$ represent the total electricity demand and the electricity demand for green hydrogen production in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, respectively.

(3)

Carbon Emission Constraint

The carbon emissions of the power industry in each planning year are equal to its carbon emission quota, with the specific constraint shown in the following formula:

$${\displaystyle \sum _{k=1}^{8760}\left({\displaystyle \sum _{i\in \mathrm{\Phi }}{\zeta }_{i,t}{P}_{i,t,k}-{\displaystyle \sum _{i=1}^{N2}{\displaystyle \sum _{j=1}^3{\lambda }_{i,j,t}{P}_{i,j,t,k}^{CCUS}}}}\right)\mathrm{\Delta }T=Q_{Car,t}^{power}}$$

(12)

In the formula, ${\zeta }_{i,t}$ represents the carbon emission intensity coefficient of power source $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year, where the carbon emission intensity coefficients for hydropower, nuclear power, biomass power generation, new energy generation, and pumped storage are 0. ${\lambda }_{i,j,t}$ represents the carbon dioxide capture rate of the $\mathit{j}-\mathit{t}\mathit{h}$ type of CCUS upgrading station $\mathit{i}$ in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year. $Q_{Car,t}^{power}$ represents the carbon emission quota of the power industry in the $\mathit{t}-\mathit{t}\mathit{h}$ planning year.

(4)

Power Source Technical Parameter Constraint

The technical parameter constraints for each type of power source are detailed in reference [21], and the characteristic constraints of new energy storage, CCUS, and hydrogen energy technologies are shown in Equations (1)–(3).

4. Case Study Analysis

4.1. Case Introduction

Taking the power source structure of China in 2020 as the basis for optimizing the “dual carbon” path of the power sector, with a 5-year optimization step and combining global research institutions’ assessments of hydropower and nuclear power site resources in China [8], a China power sector “dual carbon” path optimization model supported by key low-carbon technologies was constructed. By considering the investment and operational costs of various types of power sources and the changing trends in the economic efficiency of key low-carbon technologies [9], the model was solved to obtain the structure of power source installations and electricity generation, the carbon emission structure of the power industry, and the scale of various types of key low-carbon technologies from 2020 to 2060. The simulation calculation conditions for the power industry carbon quota and electricity demand are as follows:

(1)

Carbon Quota

Synthesizing domestic and international research findings, a cumulative carbon emission budget of 1200 billion tons for China’s power industry from 2020 to 2060 was set. The carbon emission quotas for the power industry in each planning year are shown in

Table 4. Carbon quotas for the power industry in each planning year.

Year	2020	2025	2030	2035	2040	2045	2050	2055	2060
Carbon quota (108 t)	38	45	50	53	45	32	16	6	0

, with the quotas for the power industry in 2030 and 2060 being 50 billion tons and 0 billion tons, respectively.

(2)

Electricity Demand

Taking into account the impact of future economic growth, industrial structure adjustment, energy conservation and electricity saving, electricity substitution, and hydrogen production through electrolysis in China, and referring to the outlook results of developed European and American countries for indicators such as future per-capita electricity consumption, the electricity demand forecasting method was used to obtain the future total electricity demand in China, as shown in Figure 1. It is expected that total electricity consumption will reach 13.3 trillion kilowatt-hours in 2030, will tend to saturate from 2040 to 2045 (with an average annual growth rate lower than 1%), and will reach 21.9 trillion kilowatt-hours in 2060, of which the electricity consumption for hydrogen production will be about 2 trillion kilowatt-hours.

4.2. Optimization Results of the Power Sector’s “Dual Carbon” Path

Overall, the low-carbon transformation path of the power sector shows a continuous optimization of the power source structure and a steady increase in the proportion of new energy installations and electricity generation. In terms of power installations, as shown in Figure 2, by 2030, the total installed capacity of the power system reaches 5.66 billion kilowatts, with the proportion of non-fossil energy installations increasing from 45% in 2020 to 68%; by 2060, the total installed capacity reaches 12.3 billion kilowatts, with the proportion of non-fossil energy installations increasing to 91%, becoming the main power source of the power system in terms of capacity. Additionally, by 2060, the installed capacity of new energy reaches 9 billion kilowatts, accounting for 74% of the total, and the randomness and uncertainty of new energy output will pose significant challenges to the power balance during peak load periods and the security of electricity supply under extreme weather conditions. In terms of electricity generation structure, as shown in Figure 3, by 2030, the total electricity generation of the power system is 13.3 trillion kilowatt-hours, with the proportion of electricity generation from non-fossil energy increasing from 34% in 2020 to 49%; by 2060, the total electricity generation of the power system reaches 21.8 trillion kilowatt-hours, with the proportion of electricity generation from non-fossil energy increasing to 91%, becoming the main power source of the power system in terms of electricity volume; and by 2060, the proportion of coal power generation drops to 6%.

From the optimization results of key low-carbon technology applications such as new energy storage, CCUS, and hydrogen energy, we can reach the following conclusions:

(1)

New Energy Storage

Considering the need to meet electricity balance and the demand for new energy consumption, new energy storage has a certain substitutability with pumped storage. However, taking into account factors such as site resources, technological maturity, and unit investment costs, new energy storage and pumped storage should be developed in tandem in the future, with new energy storage expected to see a leapfrog in development in the medium to long term. As shown in Figure 4, in 2030, the installed capacities of new energy storage and pumped storage will be 230 million and 190 million kilowatts, respectively; from 2030 to 2040 and from 2040 to 2050, the average annual installations will grow by 18 million and 4 million kilowatts, respectively. By 2060, the installations will reach over 800 million and 400 million kilowatts, respectively.

(2)

CCUS Technology

The support of CCUS technology for the transformation and upgrading of coal-fired, gas-fired, and biomass power generation units is shown in Figure 5. Coal-fired power units, based on whether they have undergone CCUS technology transformation and upgrading and their functional positioning differences, gradually evolve into three types of units: CCUS power and electricity units, which have completed CCUS transformation, retain rotational inertia for the system while capturing carbon dioxide, and can undertake a certain amount of electricity supply function, with an installed capacity of about 400 million kilowatts by 2060; flexible regulation units, which have not undergone CCUS transformation, do not bear electricity, and only operate for peak shaving, with an installed capacity of about 150 million kilowatts by 2060; and emergency backup units, which are basically out of operation and are only called upon in individual extreme weather or emergency conditions, with an installed capacity of about 250 million kilowatts by 2060.

From the perspective of the carbon emission reduction effect supported by CCUS technology, as the commercial application scale of CCUS technology expands in the medium to long term, the carbon emissions of the power industry rapidly decrease. As shown in Figure 6, the annual carbon emissions are projected to drop by 1.02 hundred million tons from 2030 to 2040, and by 2.74 hundred million tons from 2040 to 2050, facilitating deep carbon emission reductions in the power industry. In the long term, facing security constraints such as power balance and electricity supply under extreme scenarios, traditional coal and gas power still have a certain installation, electricity generation, and carbon emission space. The “biomass power generation + CCUS” technology route, an important negative-carbon approach for the power system, will help achieve the internal carbon neutrality goal of the power industry. The carbon emission structure of the power industry in 2060 is shown in

Table 5. Composition of carbon emissions and capture in the power sector in 2060.

	Coal Power	Gas Power	Biomass Power Generation
Installed capacity (billion kilowatts)	4	4	2
CCUS retrofitting (billion kilowatts)	1.5	1.2	0.8
Net emissions (billion tons)	2.1	1.3	−3.4
CCUS capture (billion tons)	3.2	1.2	3.4
Fuel consumption (billion tons of standard coal)	1.8	1.7	2.8

.

(3)

Hydrogen Energy Technology

In the medium to long term, green electricity in hydrogen production will help meet the desired hydrogen consumption demand in industries such as transportation, construction, and manufacturing. Hydrogen production power stations, equipped with hydrogen storage devices and hydrogen fuel cells, will play a flexible regulatory role in daily peak shaving, flexible ramping, peak load supply, and even cross-day/week/month/season energy balance. They will promote the consumption of new energy across systems. As shown in

Table 6. Scale of hydrogen energy technology development from 2020 to 2060.

Year	2020	2025	2030	2035	2040	2045	2050	2055	2060
Electrolytic hydrogen load/billion kilowatts	0.00	0.05	0.1	0.6	1.7	2.9	4.5	5.5	6
Hydrogen storage (thousand tons)	0	0	13	70	340	880	1900	2440	2900
Hydrogen fuel power Generation (billion kilowatts)	0.00	0.00	0.00	0.01	0.05	0.13	0.27	0.34	0.40

, it is calculated that in 2030, the rated capacity of the electrolytic hydrogen load will be about 9 million kilowatts, and in 2060, the electrolytic hydrogen load, hydrogen storage capacity, and hydrogen fuel cell installation will be 600 million kilowatts, 2.9 million tons, and 40 million kilowatts, respectively.

4.3. Analysis of the Impact of Key Low-Carbon Technology Breakthroughs on China’s Power Sector’s “Dual Carbon” Path

(1)

New Energy Storage Technology

Under the “dual carbon” goal, “new energy + energy storage” and “coal power + CCUS” are two feasible competitive technological routes. In particular, the breakthrough of long-duration energy storage technology and its deployment scale will have a profound impact on the power sector’s “dual carbon” path. In the base scenario (without considering the deployment of cross-season long-duration energy storage systems), it is assumed that cross-seasonal energy storage technology will achieve a breakthrough and begin commercial deployment in 2030. As shown in Figure 7, Quantitative calculations show the following: compared to the base scenario, the installation scale of new energy in the medium to long term is significantly increased, with a 380-million-kilowatt increase in new energy installation in 2060, and a 62-million-kilowatt-hour increase in electricity generation. The pace of coal power’s exit is accelerated, with a reduction of 38 million kilowatts in the installation scale of “coal power + CCUS” in 2060, and a reduction of 96 million tons in carbon capture.

(2)

CCUS Technology

China has high potential for retrofitting coal power with CCUS technology, and the economic viability of this technology is expected to significantly improve after 2035. The maturity and adoption timeline of future CCUS technology will deeply affect coal’s phase-out and the transformation methods of coal power. If CCUS does not achieve technological breakthroughs and large-scale commercial application, the future system will essentially be unable to retain traditional power sources such as coal and gas power that can provide inertia. From an economic perspective, if CCUS cannot be retained, the complete phase-out of coal power by 2060 will require an additional 500 million to 1.3 billion kilowatts of new energy installation and over 1 billion kilowatts of new energy storage in the long term, As shown in Figure 8.

(3)

Hydrogen Energy Technology

Low-cost water electrolysis for hydrogen production is key to achieving the sustainable development of hydrogen energy. If the economic development of hydrogen technology falls short of expectations, the extensive decarbonization processes in industries such as transportation and manufacturing will be hindered, leading to a greater reliance on electricity-based alternatives. This would result in an overall increase in the electrification level of society’s energy consumption. Moreover, a reduction in the scale of hydrogen production through electrolysis would lower the capacity to integrate renewable energy across systems, making it more difficult to achieve breakthroughs in renewable energy consumption across different periods. This would profoundly impact the “dual carbon” (carbon peak and carbon neutrality) pathway for the power sector. Compared to the baseline scenario, a hypothetical increase or decrease of 50% in future hydrogen production from electrolysis is used to delineate high and low scenarios for the development scale of electrolysis-based hydrogen production. As shown in Figure 9, quantitative analysis reveals that in the near to mid-term, the differences in the dual carbon pathway for the power sector are minimal across different scenarios due to the relatively small scale of hydrogen production. However, in the long term, by 2060, under the high and low hydrogen production scenarios, renewable energy capacity increases by 410 million kW and decreases by 370 million kW, respectively, compared to the baseline scenario, while coal power capacity increases by 30 million kW and decreases by 20 million kW, respectively.

5. Conclusions

To address the optimization of the power sector’s “dual carbon” pathway supported by key low-carbon technologies, this paper first establishes a quantitative support model for three critical low-carbon technologies: new energy storage, CCUS (carbon capture, utilization, and storage), and hydrogen energy, focusing on their role in the low-carbon transition of the power sector. Then, considering the impact of these key low-carbon technologies on traditional power planning objectives and constraints, an integrated optimization model of the power sector’s “dual carbon” pathway and key low-carbon technologies was developed. Based on this model, a simulation of the low-carbon transition pathway for China’s power industry under the dual carbon goals was conducted. The simulation results show that the collaborative development of new energy storage and pumped storage effectively supports the rapid growth of renewable energy capacity, enabling the safe and orderly replacement of both existing and new coal-fired power generation. CCUS technology effectively facilitates the functional transformation of traditional coal-fired units, shifting them toward net-zero decarbonization, flexible regulation, and emergency backup roles. In the long term, the “CCUS + biomass power generation” technology pathway supports the achievement of carbon neutrality within the power sector. Additionally, the application of hydrogen energy supports the integration of renewable energy across systems and provides the power system with multi-time-scale and multi-type flexible regulation capabilities.