Could coal-fired power plants with CCS be an effective way for carbon neutrality in China?

Headnote

ABSTRACT

The transition toward carbon neutrality in China necessitates integrating more renewable energy sources (RES) into the power grid. However, a high share of RES can destabilize the grid, making it crucial to add clean, flexible power sources, such as battery energy storage systems (BESS) and flexible coal power combined with carbon capture and storage (CCS). This study shifted focus from traditional power plants to flexible power solutions, including BESS and CCS, and adopted a macro perspective. An hourly basis simulation model of the power system was developed to assess the cost-effectiveness of these flexible power options. The results revealed that BESS is more cost-effective when RES penetration rates are low, whereas CCS becomes more advantageous as RES constitutes a larger portion of the power supply. Combining BESS and CCS can ensure grid stability and maximize RES utilization. Sensitivity analysis indicated the need to control power demand growth to support this transition effectively.

Keywords:

Renewable energy resources

Flexible power

Battery energy storage system

Carbon capture and storage

(ProQuest: ... denotes formulae omitted.)

1. Introduction

China faces significant challenges in transitioning its electricity system toward a low-carbon economy. To achieve a cost-effective low-carbon transition of the electricity system, it is crucial to address the challenges of integrating renewable energy sources (RES) while ensuring grid stability.

Currently, China's energy structure is heavily reliant on fossil fuels, especially coal power, which accounted for 58% of the country's total electricity production in 2023. The growing demand for electricity has primarily been met by widely dispersed coal power generators. However, to meet the ambitious "carbon neutrality" goal, it is essential to reduce dependency on coal power.

To facilitate this transition, the government has enacted numerous policies to promote the development of wind and photovoltaic (PV) power (Chai et al., 2023; Li et al., 2022). Wind and PV power are expected to become the cornerstone of China's future power supply (He et al., 2020, 2022; Liu et al., 2017).

Although wind power and PV power are clean and renewable energy sources, their inherent volatility introduces risks and additional costs for the power grid (Engel-Cox and Chapman, 2023; Wang et al., 2022). Previous studies have explored the effects of high penetration rates of RES on the power grid (Heptonstall and Gross, 2020; Makarov et al., 2009). These studies have demonstrated that a substantial share of RES complicates balancing power supply and demand, thereby increasing operational costs. Due to these challenges, the importance of flexible power sources is steadily increasing (Kopiske et al., 2017; Lin and Liu, 2024; Mikkola and Lund, 2016).

The increase in the penetration rate of RES has led to a marked decline in the proportion of coal power in the overall power supply. However, given the widespread presence of young coal power units across the nation, an immediate phase-out of coal power is not advisable (Jiang et al., 2023). Instead, transitioning coal power to a flexible role during the phase-out process offers a viable pathway (Kubik et al., 2015). Previous studies have shown that modifications to enhance coal power flexibility can be cost-effective (Brouwer et al., 2015; Yoshiba et al., 2021). Moreover, the advent of carbon capture and storage (CCS) technology has introduced new opportunities for coal power, especially in light of rising carbon emission costs (Wu et al., 2022). Coal power integrated with CCS could effectively reduce carbon emissions while remaining cost-effective (Hu and Zhai, 2017; Liu et al., 2022; Wei et al., 2021a; Yang et al., 2021). Discussions on coal power equipped with CCS indicate its competitiveness, and the performance of CCS is affected by emission trading systems (Abadie and Chamorro, 2008; Fan et al., 2019; Kemp and Kasim, 2008; Mo et al., 2015). Furthermore, studies have highlighted significant potential for CCS implementation in China (Cai et al., 2017; Fan et al., 2018, 2021a, 2021b;Wei et al., 2021b). Flexible coal power integrated with CCS represents a suitable solution for mitigating the risks associated with a high share of RES in the energy mix.

The battery energy storage system (BESS) is a viable option for a flexible power solution. The BESS can be charged using outputs from RES during periods of low power demand and discharged during peak demand times. Many studies have confirmed the feasibility of the BESS within the power system (Gandhok and Manthri, 2023; Lai et al., 2021; Maeyaert et al., 2020), highlighting its ability to enhance grid flexibility and support tasks, such as peak-valley regulation, frequency regulation, and other ancillary services. Moreover, economic studies have demonstrated the cost-effectiveness of the BESS, particularly when supported by appropriate policies and market mechanisms (Berrada et al., 2016; Lin and Wu, 2017; Loisel et al., 2010; Wu and Lin, 2018).

RES, particularly wind and PV power, are set to become the primary contributors to China's power supply. This shift has raised concerns regarding volatility risks associated with these energy sources. Thus, flexible power solutions are urgently needed. In addition, the transformation of coal power requires thorough examination. Coal power integrated with CCS and the BESS are two promising flexible power solutions that merit attention. Although existing studies have largely focused on these solutions from a micro perspective, examining individual power plants, it is essential to evaluate and compare them from a macro perspective, considering their broader impacts on the power grid and governmental policies. Such comprehensive evaluations will facilitate the development of recommendations for optimal flexible power deployment strategies, especially in addressing the imbalance between power supply and demand caused by high RES penetration rates.

Building on previous research, this study conducted a detailed evaluation and comparison of flexible coal power with CCS and BESS from a macro perspective, providing policy implications for the optimal deployment of flexible power solutions. A power balance simulation model was developed on the basis of hourly load curves, which incorporates dynamic factors, such as power demand growth and cost reductions resulting from technological advancements.

The novelties and potential contributions of this study are multifaceted. First, it shifts focus from traditional power generation to flexible power solutions, evaluating the roles of flexible coal power with CCS and BESS in accommodating increasing RES penetration rates. Second, the study conducts detailed comparisons between BESS and CCS, delineating their distinct advantages and limitations over both short and long term periods, helping to clarify when each technology might be most beneficial within the energy mix. Third, instead of immediate decommissioning, which can be disruptive, the study proposes a stepwise phase-out of coal power. This approach includes reducing operational hours and transitioning to more flexible power configurations, supplemented by flexibility enhancements and CCS integration, providing a more practical alternative. Fourth, the findings lead to specific policy implications for the deployment of flexible power solutions, recommending optimal choices for integrating and scaling these technologies across different timelines, tailored to maximize their effectiveness and efficiency in the transition to a lower-carbon energy system.

2. Methodology

This study examined flexible power options from a broad perspective, particularly focusing on the use of flexible coal power with CCS and BESS to bridge the gap in power supply caused by the variability of RES. To achieve this, a power balance simulation model is constructed using hourly load curves, to maintain a balance between power demand and supply. Subsequently the costs associated with these two flexible power solutions are meticulously computed. The analysis includes dynamic factors from 2020 to 2050, covering different growth rates in power demand and gradual reductions in the costs of flexible power units over time.

2.1. Framework

The study's framework is illustrated in Fig. 1. It builds on existing research about China's low-carbon transition, predicting that RES, especially wind and PV power, will increasingly dominate the power supply. In this study, the share of RES in the power supply is expected to linearly increase from 10% to 90% during the research period. On the demand side, three growth rate scenariosdlow, medium, and highdare considered, with power consumption expected to rise at varying rates from 2020 to 2050. Power consumption for any given year "n" is calculated using linear interpolation. Similarly, RES production for year "n" is determined using the same method.

Given the established power demand and RES supply curves, coal power demand curves can be derived. To align with the goal of carbon neutrality, coal power usage must comply with the guidelines from the Intergovernmental Panel on Climate Change (IPCC). The maximum coal power production is set based on the IPCC's target to limit global warming to 1.5C. Supply gap curves are determined by comparing these coal power demand curves with the established limits on coal power production. These gaps indicate the need for flexible power solutions. Flexible coal power with CCS and BESS are designed to respond to these gaps. The effectiveness of these two flexible power solutions will be assessed based on their operational performance.

Table 1 presents the model's parameters along with their explanations for clarity and reference.

2.2. Power balance

On the demand side, power demand curves are derived using the hourly load curve from the initial year and the total power consumption for each subsequent year, as outlined in Equation (1). Here, loadi;n denotes the power demand for hour i in year n, while consumptionn represents the total power consumption for that particular year. The power consumption for each year is computed based on the specified scenario settings.

... (1)

On the supply side, power demand is met through a mix of RES, traditional coal power, and flexible power solutions. A detailed analysis of the current state of RES in China reveals that while hydro power has developed significantly, its future growth is limited by space constraints and rising marginal costs. Nuclear power, although stable, faces limitations in installed capacities and public perception issues, which hinder its expansion as a major power source. In light of the goal for carbon neutrality, wind and PV power are recognized as crucial to future power generation strategies. Therefore, this study primarily focused on wind and PV power, maintaining hydro power at current capacity levels and excluding nuclear power from consideration.

In summary, RES in this study included wind power, PV power, and hydro power. Traditional coal power was defined as coal-based electricity generation that conforms to the IPCC's 1.5C warming limit. Flexible power solutions in this analysis include BESS and coal power enhanced with CCS. Considering both demand and supply dynamics, the load curves are balanced as follows:

load_i,n = wind_i,n + PV_i,n + hydro_i,n + coalpower_i,n (2)

The variable windi;n denotes the wind power output for hour i in year n, alongside PVi;n, hydroi;n and coalpoweri;n. In instances of high RES penetration rates, there are occasions where RES alone can satisfactorily meet the power demand, resulting in a negative value for coalpoweri;n. Thus, the actual coal power demand curves can be determined as follows:

... (3)

2.3. RES outputs

RES outputs are significantly affected by weather conditions. Hydropower exhibits distinct seasonal fluctuations, whereas wind and PV power demonstrate fluctuations both seasonally and hourly. In this study, RES outputs were calculated using typical hourly output curves, monthly output proportions, and total RES production as follows:

... (4)

... (5)

... (6)

Using historical statistical data, including daily output curves and monthly production figures, we can accurately derive hourly load curves for wind power, effectively capturing its fluctuation patterns. The term windi;n represents the wind production during hour i in year n. The parameter tcwind i denotes the proportion of wind power output during hour i of the day. Furthermore, windmonth;2020 represents wind power production for the specific month in 2020, whereas daysmonth indicates the number of days in that month. The total RES production for year n is denoted as RESn. Similarly, hourly output curves for PV power can be computed using identical methodologies as those employed for wind power. For hydro power, hourly output curves are determined using monthly output statistics through averaging. Hydro power outputs are maintained at stable levels in accordance with the principles outlined in Section 2.2.

2.4. Coal power

In cases where the output from RES is insufficient to meet power demand, coal power generation is necessary to fill the shortfall. However, this study sets limits on the share of traditional coal power within the overall power supply, aiming for a maximum of 4% by 2050. This target is in line with the IPCC 1.5C goal and is supported by findings from recent research (Zhang et al., 2021). If traditional coal power production stays within these limits and can meet the demand, there is no need for additional flexible power generation. However, if the demand exceeds what traditional coal power can provide within these constraints, the deployment of flexible power sources becomes essential to ensure a balanced and effective power supply.

... (8)

... (9)

TDCn signifies the total demand for coal power in year n, and Gapn represents the cumulative demand for flexible power. Flexible coal power integrated with CCS and BESS will be deployed to address this gap.

CCS technology is pivotal in mitigating carbon emissions from coal power generation, allowing coal power units to operate flexibly without the constraints typically associated with traditional coal power. Given that this study focuses on assessing flexible power solutions, the fixed costs of coal power units are considered negligible. Additionally, the coal power units in question are relatively young and would likely be decommissioned without the integration of CCS. Therefore, the analysis only factors in the costs of coal, disregarding other fixed costs.

cost^ccs_n = Gap_n x (ccs_n+coal_n) (10)

2.5. BESS

BESS is crucial for managing fluctuations between electricity supply and demand. BESS primarily functions by discharging to bridge any gaps between power supply and demand. With detailed calculations of power consumption and coal power utilization, there are significant opportunities for BESS to charge during periods when there is a surplus of electricity supply. Consequently, this study primarily focuses on the discharge phase of BESS operations. When a discrepancy between supply and demand occurs, BESS discharges to stabilize the system. This operation incurs costs associated with both discharging and the subsequent recharging of the system. The cost of BESS is computed as follow:

cost^BESS_n = Gap_n + BESS_n (11)

Considering the escalating surplus in RES outputs during periods of high RES penetration, the study opts not to include the cost of BESS charging. This decision is made to present flexible power costs in a more straightforward and intuitive manner.

3. Data

This study focused on Anhui Province in China as a case study, primarily because of the availability of hourly load curve data. Anhui's power infrastructure and dynamics closely reflect those of China as a whole, both in terms of the demand and supply of electricity as well as the evolution of its power system.

Table 2 illustrates that Anhui's secondary industry accounts for the majority of power demand, mirroring the national trend. The distribution of power consumption across industries in Anhui closely resembles that of China. Moreover, from a trend perspective, Anhui exhibits changes similar to those observed nationally: a gradual decrease in the proportion of power consumption by the secondary industry, stable power consumption by the primary industry, and steady growth in power consumption by the tertiary industry and residential sectors. Thus, Anhui serves as a suitable representative for understanding power demand fluctuations and trends in China.

Table 3 and Fig. 2 illustrate the power supply structures of Anhui and China. The structure in Anhui closely mirrors the national pattern, with thermal power playing a dominant role and significant growth in wind and PV power. While Anhui's proportion of hydro power is lower than the national average and its share of thermal power is higher, these differences are considered acceptable due to the limited potential for expanding hydro power in China's future energy framework. The national power transition strategy aims to reduce reliance on thermal power and increase the generation of wind and PV power. Furthermore, because the evaluation of flexible power in this study focused on enhancing the capabilities of wind, PV, and existing coal power plants, the relatively stable contribution of hydro power in Anhui exerts a minimal impact on assessing flexible power options. Thus, Anhui is a suitable example for analyzing the supply side in this study.

3.1. Power consumption

On the basis of historical data and studies (He et al., 2020, 2022; Liu et al., 2017) on the development of China's power system, there is a clear trend of steady annual increases in power consumption. In the baseline scenario, power consumption by 2050 is projected to double from current levels, following a linear growth trajectory. To explore the potential effects on power demand, scenarios with both low and high growth rates are examined. In the low growth rate scenario, power consumption in 2050 is expected to increase to 1.5 times the current level, whereas in the high growth rate scenario, it could reach 2.5 times the current level.

Fig. 3 illustrates the derivation of the hourly load curve, which is based on the average of actual hourly load data from Anhui Province over an eight-year period. Using the initial hourly load curve and the projected annual power consumption figures, the hourly load curves for subsequent years are calculated using Equation (1).

3.2. RES outputs

According to historical data and existing studies (Heinermann and Kramer, 2016; Markvart et al., 2006; Moharil and Kulkarni, 2010), monthly output curves and hourly output curves of RES are shown in Fig. 4.

3.3. Flexible power

The cost parameters for the two flexible power solutions primarily include the CCS cost, coal cost, and BESS cost, with explanations and sources as follows:

CCS cost is determined by the unit cost of CCS per ton of CO2 processed and the emission coefficient of coal power.

E^coalpower = emission^coal × C^coalpower (12)

ccs_n = p^ccs_n × E^coalpower (13)

Ecoalpower denotes the CO2 emission coefficient per kilowatt of coal power, emissioncoal represents the CO2 emission coefficient per ton of coal, and Ccoalpower represents the coal consumption per kilowatt of coal power. Based on existing studies (Wu et al., 2022; Zhang et al., 2021) and data from the China Electricity Council, Ecoalpower is established at 0.8835 kgCO2/kW$h. The cost parameter pccs n reflects the cost of CCS dealing with per ton of CO2 processed. According to existing studies (Fan et al., 2018, 2019; Hu and Zhai, 2017; Liu et al., 2022; Wei et al., 2021a) and the parameters set in this study, pccs n is projected to decrease from 1020 CNY/kW$h to 240 CNY/kW$h over the course of 30 years.

Coal power fuel costs are calculated on the basis of the levelized cost of electricity of coal power. According to data from the China National Coal Association and the China Electricity Council, coaln is set at 0.2 CNY/kW·h.

BESS cost in this study is derived from the operational cost per kilowatt of electricity over the lifespan of BESS. Market data and existing studies (Berrada et al., 2016; Lai et al., 2021; Lin and Wu, 2017; Wu and Lin, 2018) inform the setting of BESSn which is projected to decrease from 0.7 CNY/kW$h to 0.5 CNY/kW$h.

4. Results and discussions

4.1. Costs

Fig. 5 displays the total costs associated with BESS and flexible coal power with CCS, as well as the cost of CCS alone, excluding coal costs. In the short term, when the RES penetration rate in the power gridwas low, BESSwas more cost-effective than coal power with CCS. However, as RES penetration rate increased over time, CCS began to exhibit a cost advantage. Specifically, the costs associated with CCS became lower than those of BESS once the RES penetration rate reached approximately 45%. Furthermore, when the costs of coal were included, flexible coal power with CCS became less expensive than BESS at an RES penetration rate of approximately 75%.

The marginal cost of CCS exhibited a decreasing trend after the RES penetration rate surpassed approximately 55%, whereas the marginal cost of BESS continued to rise. Over the long term, a high penetration rate of RES led to increasing supply shortages in the power grid, primarily due to the intermittent nature of wind and PV power. CCS technology has reached maturity, providing significant opportunities for cost reduction as the industry chain evolves and market conditions improve. By contrast, the cost reductions for BESS are limited by advancements in battery technology, making substantial decreases in costs difficult to achieve.

Fig. 6 demonstrates the additional cost per kilowatt of electricity incurred by BESS or CCS. The levelized cost represents the incremental expense associated with implementing BESS or CCS to counteract the volatility induced by RES. When the RES penetration rate in the power grid was low, the levelized cost was minimal, typically less than 0.05 CNY/kW·h, which is negligible compared to the overall cost of power generation. However, as the RES penetration rate increased, the levelized cost associated with BESS increased markedly, whereas the cost for CCS remained consistently below 0.1 CNY/kW·h, making CCS a more economically viable option than BESS.

Fig. 7 presents the additional costs incurred by BESS and CCS under various power demand growth scenarios. Higher demand growth rates led to increased costs for flexible power solutions. Notably, the costs associated with CCS were more sensitive to changes in power demand growth rates compared with BESS. Additionally, higher rates of power demand growth delayed the point at which CCS becomes more cost-effective than BESS.

From a cost perspective, BESS are more economical in the short term, whereas flexible coal power with CCS is a more costeffective option in the long term. In scenarios where RES penetration rates in the power grid were low, the overall cost of BESS remained below that of CCS. However, when the RES penetration rate reached approximately 45%, the total cost associated with CCS became more favorable compared with BESS. Therefore, it is advisable to prioritize the deployment of BESS in the short term while also beginning to implement CCS to take advantage of its cost benefits as RES penetration increases. This strategy ensures that as RES penetration rates rise, flexible coal power with CCS can effectively manage the volatility induced by RES at a lower overall cost over time.

4.2. Power supply

Fig. 8 visualizes the power supply throughout the year. The columns in the figure, from top to bottom, display the production from wind and PV power, a gap indicating a deficit in supply, and the production from traditional coal power within specified limits. The top line shows the total power demand for the year, whereas the line directly beneath it illustrates the cumulative gap. The figure clearly shows that the cumulative gap increases rapidly as the penetration rate of RES rises.

Initially, the power supply is predominantly met by traditional coal power. However, as production from wind and PV power increases, the gapdwhich represents the need for flexible power productiondalso increases. This is due to the growth in RES and the corresponding reduction in traditional coal power production. This trend highlights the growing importance of flexible power solutions, especially in scenarios with high RES penetration rates. When RES penetration is high, there is a risk of surplus production from RES, which could be wasted without appropriate storage solutions. This situation indicates the necessity of deploying BESS, despite their economic considerations.

Fig. 9 illustrates the cumulative surplus power production from wind and PV power. The green line indicates the total hours of "RES only" operation, which signifies periods when only RES contribute to power supply. These "RES only" hours mark times when the power grid relies solely on RES generation. Given the weather-dependent nature of wind and PV power, an increase in "RES only" hours presents greater risks to grid stability.

Despite the associated costs, the value and necessity of BESS and CCS are significant. BESS helps mitigate the risk of surplus electricity production from wind and PV power going to waste. As more RES are integrated into the grid, volatility increases, demanding additional ancillary services. BESS is particularly valuable in this context due to its rapid response capabilities. In scenarios where the power system relies entirely on RES, grid reliability becomes precarious due to the inherent volatility of RES. Wind and PV power, as primary components of RES, are especially vulnerable to weather conditions, including extreme weather events that can disrupt their operation. In such situations, flexible coal power with CCS provides a reliable backup power source, ensuring a stable power supply even during adverse weather conditions.

4.3. Coal power utilization

Fig. 10 depicts the utilization hours of coal power units throughout the year under various penetration rates of RES. When BESS is used to address the gap resulting from increased RES integration, the utilization hours of coal power units decline sharply with higher RES penetration rates. By contrast, CCS helps mitigate this decrease, providing crucial support for coal power in China.

The data in Fig. 10 highlights the potential of CCS to prolong the viability of coal power in China by moderating the reduction in coal power utilization hours. While coal power is currently a primary energy source in China, the nation's goal to achieve "carbon neutrality" necessitates a gradual phase-out of coal power. This transition requires a strategic and effective approach to decrease reliance on coal power. Implementing CCS allows certain coal power units to continue operating by addressing carbon emissions, making it a practical solution in the broader strategy to reduce dependency on coal power given the extensive coal industry in China.

Fig.10 emphasizes that higher power demand growth rates complicate efforts to reduce dependency on coal power within the power grid. Consequently, managing both the demand and supply sides is crucial for successfully transitioning to a lowcarbon power system. Effectively controlling power demand growth rates is especially crucial to align with the objective of achieving "carbon neutrality."

5. Conclusion and policy implications

This study evaluated the cost-effectiveness of BESS and flexible coal power with CCS on an hourly basis from 2020 to 2050, taking into account various penetration rates of RES on the supply side and different power demand growth rates. The conclusions of the study are summarized as follows:

(i) In the short term, flexible coal power with CCS experiences slightly higher costs than BESS as RES penetration rates increase. However, in the long term, coal power with CCS displays a clear cost advantage over BESS, particularly as RES becomes the dominant component of the power supply.

(ii) The growth of RES leads to an increase in surplus power and more frequent "RES only" hours. BESS plays a crucial role in preventing the wastage of this surplus power, whereas flexible coal power with CCS provides a backup to ensure the safety and stability of the power grid.

(iii) CCS offers a gradual pathway for the phase-out of coal power plants in China, facilitating a stepwise reduction in their utilization hours.

(iv) The growth rate of power demand significantly influences the costs of transitioning to a low-carbon system, with CCS showing more sensitivity to changes in power demand compared with BESS. Effectively managing power demand growth is crucial for navigating the transition to a low-carbon energy system.

Achieving carbon neutrality requires a transition to a low-carbon power system that balances cost-effectiveness with efficiency. Currently, coal power is the backbone of China's power system. However, given the constraints on carbon emissions, a gradual phase-out of coal power is inevitable. RES, particularly wind and PV power, are set to play a leading role in future power generation. Nevertheless, the increased volatility associated with a high share of RES highlights the importance of incorporating flexible power solutions.

Based on our assessments, flexible coal power with CCS offers superior long-term cost-effectiveness, whereas the BESS provides affordability in the short term. However, the cost of BESS escalates rapidly with increasing RES penetration rates. Given the imperatives of power system efficiency and safety, both CCS and BESS warrant large-scale deployment. It is crucial to tailor flexible power strategies judiciously to the unique characteristics of each solution. Coal-fired power plants equipped with CCS could significantly contribute to China's carbon neutrality goals, especially when integrated into optimal flexible power strategies.

Several key policy implications emerge for policymakers from the findings of this study.

First, prioritize attention to CCS and introduce supportive policies promptly. Encourage flexibility modifications in coalfired power plants and incentivize the adoption of CCS technology.

Second, acknowledge that CCS may require more subsidies than BESS in the short term, while the cost of BESS may escalate over time. Implement appropriate market mechanisms and subsidy strategies, subject to timely adjustments.

Third, refrain from immediate discontinuation of large numbers of young coal-fired power plants. Gradual phase-out through a stepwise reduction in utilization hours, facilitated by flexibility modifications and CCS integration, presents a more cost-effective and feasible approach.

Fourth, emphasize the importance of the demand side in achieving carbon neutrality objectives. High power demand growth rates elevate total costs and impede efforts to reduce reliance on coal power. Thus, effective power demand management assumes paramount importance.

Ethics statements

Not applicable because this work does not involve the use of animal or human subjects.

CRediT authorship contribution statement

Boqiang Lin: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Zhiwei Liu: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization,Writing - original draft, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This paper is supported by National Natural Science Foundation of China (Key Program， No 72133003).

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ARTICLE INFO

Article history:

Received 24 May 2023

Received in revised form 29 March 2024

Accepted 21 May 2024

Available online 27 June 2024

Footnote

* Corresponding author.

E-mail addresses: [email protected], [email protected] (B. Lin), [email protected] (Z. Liu).

References

Abadie, L. M., & Chamorro, J. M. (2008). European CO2 prices and carbon capture investments. Energy Econ., 30(6), 2992e3015. https://doi.org/10.1016/j.eneco.2008.03.008

Berrada, A., Loudiyi, K., & Zorkani, I. (2016). Valuation of energy storage in energy and regulation markets. Energy, 115, 1109e1118. https://doi.org/10.1016/j.energy.2016.09.093

Brouwer, A. S., van den Broek, M., Seebregts, A., & Faaij, A. (2015). Operational flexibility and economics of power plants in future low-carbon power systems. Appl. Energy, 156, 107e128. https://doi.org/10.1016/j.apenergy.2015.06.065

Cai, B., Li, Q., Liu, G., Liu, L., Jin, T., & Shi, H. (2017). Environmental concern-based site screening of carbon dioxide geological storage in China. Sci. Rep., 7(1), 7598. https://doi.org/10.1038/s41598-017-07881-7

Chai, J., Zhang, X., Zhang, X., &Wang, Y. (2023). Effects of scenario-based carbon pricing policies on China's dual climate change mitigation goals: does policy design matter? Journal of Management Science and Engineering, 8(2), 167e175. https://doi.org/10.1016/j.jmse.2022.10.002

Engel-Cox, J. A., & Chapman, A. (2023). Accomplishments and challenges of metrics for sustainable energy, population, and economics as illustrated through three countries. Frontiers in Sustainable Energy Policy, 2. https://doi.org/10.3389/fsuep.2023.1203520

Fan, J.-L.,Wei, S., Shen, S., Xu, M., & Zhang, X. (2021a). Geological storage potential of CO2 emissions for China's coal-fired power plants: a city-level analysis. Int. J. Greenh. Gas Control, 106, Article 103278. https://doi.org/10.1016/j.ijggc.2021.103278

Fan, J.-L., Wei, S., Yang, L., Wang, H., Zhong, P., & Zhang, X. (2019). Comparison of the LCOE between coal-fired power plants with CCS and main low-carbon generation technologies: Evidence from China. Energy, 176, 143e155. https://doi.org/10.1016/j.energy.2019.04.003

Fan, J.-L., Xu, M., Li, F., Yang, L., & Zhang, X. (2018). Carbon capture and storage (CCS) retrofit potential of coal-fired power plants in China: the technology lock-in and cost optimization perspective. Appl. Energy, 229, 326e334. https://doi.org/10.1016/j.apenergy.2018.07.117

Fan, J.-L., Xu, M., Wei, S., Shen, S., Diao, Y., & Zhang, X. (2021b). Carbon reduction potential of China's coal-fired power plants based on a CCUS source-sink matching model. Resour. Conserv. Recycl., 168, Article 105320. https://doi.org/10.1016/j.resconrec.2020.105320

Gandhok, T., & Manthri, P. (2023). Public policy and strategic business recommendations to accelerate adoption of stationary battery energy storage systems (BESS) in India. Manag. Environ. Qual. Int. J., 34(6), 1516e1533. https://doi.org/10.1108/meq-02-2023-0050

He, J., Li, Z., Zhang, X., Wang, H., Dong, W., Chang, S., Ou, X., Guo, S., Tian, Z., Gu, A., Teng, F., Yang, X., Chen, S., Yao, M., Yuan, Z., Zhou, L., & Zhao, X. (2020). Comprehensive report on China's long-term low-carbon development strategies and pathways. Chinese Journal of Population, Resources and Environment, 18(4), 263e295. https://doi.org/10.1016/j.cjpre.2021.04.004

He, J., Li, Z., Zhang, X.,Wang, H., Dong,W., Du, E., Chang, S., Ou, X., Guo, S., Tian, Z., Gu, A., Teng, F., Hu, B., Yang, X., Chen, S., Yao, M., Yuan, Z., Zhou, L., Zhao, X., ... Zhang, D. (2022). Towards carbon neutrality: a study on China's long-term low-carbon transition pathways and strategies. Environ Sci Ecotechnol, 9, Article 100134. https://doi.org/10.1016/j.ese.2021.100134

Heinermann, J., & Kramer, O. (2016). Machine learning ensembles for wind power prediction. Renew. Energy, 89, 671e679. https://doi.org/10.1016/j.renene.2015.11.073

Heptonstall, P. J., & Gross, R. J. K. (2020). A systematic review of the costs and impacts of integrating variable renewables into power grids. Nat. Energy, 6(1), 72e83. https://doi.org/10.1038/s41560-020-00695-4

Hu, B., & Zhai, H. (2017). The cost of carbon capture and storage for coal-fired power plants in China. Int. J. Greenh. Gas Control, 65, 23e31. https://doi.org/10.1016/j.ijggc.2017.08.009

Jiang, T., Zhang, R., Zhang, F., Shi, G., &Wang, C. (2023). Assessing provincial coal reliance for just low-carbon transition in China. Environ. Impact Assess. Rev., 102. https://doi.org/10.1016/j.eiar.2023.107198

Kemp, A. G., & Kasim, A. S. (2008). A Least-cost Optimisation model of CO2 capture applied to major UK power plants within the EU-ETS framework. Energy J., 99e134.

Kopiske, J., Spieker, S., & Tsatsaronis, G. (2017). Value of power plant flexibility in power systems with high shares of variable renewables: a scenario outlook for Germany 2035. Energy, 137, 823e833. https://doi.org/10.1016/j.energy.2017.04.138

Kubik, M. L., Coker, P. J., & Barlow, J. F. (2015). Increasing thermal plant flexibility in a high renewables power system. Appl. Energy, 154, 102e111. https://doi.org/10.1016/j.apenergy.2015.04.063

Lai, C. S., Locatelli, G., Pimm, A., Wu, X., & Lai, L. L. (2021). A review on long-term electrical power system modeling with energy storage. J. Clean. Prod., 280, Article 124298. https://doi.org/10.1016/j.jclepro.2020.124298

Li, S., Jia, N., Chen, Z., Du, H., Zhang, Z., & Bian, B. (2022). Multi-objective optimization of environmental tax for mitigating air pollution and greenhouse gas. Journal of Management Science and Engineering, 7(3), 473e488. https://doi.org/10.1016/j.jmse.2022.02.001

Lin, B., & Liu, Z. (2024). Assessment of China's flexible power investment value in the emission trading system. Appl. Energy, 359. https://doi.org/10.1016/j.apenergy.2024.122663

Lin, B., & Wu, W. (2017). Economic viability of battery energy storage and grid strategy: a special case of China electricity market. Energy, 124, 423e434. https://doi.org/10.1016/j.energy.2017.02.086

Liu, Q., Chen, Y., Teng, F., Tian, C., Zheng, X., & Zhao, X. (2017). Pathway and policy analysis to China's deep decarbonization. Chinese Journal of Population Resources and Environment, 15(1), 39e49. https://doi.org/10.1080/10042857.2017.1286753

Liu, S., Li, H., Zhang, K., & Lau, H. C. (2022). Techno-economic analysis of using carbon capture and storage (CCS) in decarbonizing China's coal-fired power plants. J. Clean. Prod., 351, Article 131384. https://doi.org/10.1016/j.jclepro.2022.131384

Loisel, R., Mercier, A., Gatzen, C., Elms, N., & Petric, H. (2010). Valuation framework for large scale electricity storage in a case with wind curtailment. Energy Pol., 38(11), 7323e7337. https://doi.org/10.1016/j.enpol.2010.08.007

Maeyaert, L., Vandevelde, L., & D€oring, T. (2020). Battery storage for ancillary services in Smart distribution grids. J. Energy Storage, 30, Article 101524. https://doi.org/10.1016/j.est.2020.101524

Makarov, Y. V., Loutan, C., Jian, M., & de Mello, P. (2009). Operational impacts of wind generation on California power systems. IEEE Trans. Power Syst., 24(2), 1039e1050. https://doi.org/10.1109/tpwrs.2009.2016364

Markvart, T., Fragaki, A., & Ross, J. N. (2006). PV system sizing using observed time series of solar radiation. Sol. Energy, 80(1), 46e50. https://doi.org/10.1016/j.solener.2005.08.011

Mikkola, J., & Lund, P. D. (2016). Modeling flexibility and optimal use of existing power plants with large-scale variable renewable power schemes. Energy, 112, 364e375. https://doi.org/10.1016/j.energy.2016.06.082

Mo, J.-L., Schleich, J., Zhu, L., & Fan, Y. (2015). Delaying the introduction of emissions trading systemsdimplications for power plant investment and operation from a multi-stage decision model. Energy Econ., 52, 255e264. https://doi.org/10.1016/j.eneco.2015.11.009

Moharil, R. M., & Kulkarni, P. S. (2010). Reliability analysis of solar photovoltaic system using hourly mean solar radiation data. Sol. Energy, 84(4), 691e702. https://doi.org/10.1016/j.solener.2010.01.022

Wang, Y., Mao, J., Chen, F., & Wang, D. (2022). Uncovering the dynamics and uncertainties of substituting coal power with renewable energy resources. Renew. Energy, 193, 669e686. https://doi.org/10.1016/j.renene.2022.04.164

Wei, N., Jiao, Z., Ellett, K., Ku, A. Y., Liu, S., Middleton, R., & Li, X. (2021a). Decarbonizing the coal-fired power sector in China via carbon capture, geological utilization, and storage technology. Environ. Sci. Technol., 55(19), 13164e13173. https://doi.org/10.1021/acs.est.1c01144

Wei, Y.-M., Kang, J.-N., Liu, L.-C., Li, Q., Wang, P.-T., Hou, J.-J., Liang, Q.-M., Liao, H., Huang, S.-F., & Yu, B. (2021b). A proposed global layout of carbon capture and storage in line with a 2C climate target. Nat. Clim. Change, 11(2), 112e118. https://doi.org/10.1038/s41558-020-00960-0

Wu, F., Ji, D.-J., Zha, D.-L., Zhou, D.-Q., & Zhou, P. (2022). A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO2 emissions. Journal of Management Science and Engineering, 7(2), 330e345. https://doi.org/10.1016/j.jmse.2021.12.001

Wu,W., & Lin, B. (2018). Application value of energy storage in power grid: a special case of China electricity market. Energy, 165, 1191e1199. https://doi.org/10.1016/j.energy.2018.09.202

Yang, B., Wei, Y.-M., Liu, L.-C., Hou, Y.-B., Zhang, K., Yang, L., & Feng, Y. (2021). Life cycle cost assessment of biomass co-firing power plants with CO2 capture and storage considering multiple incentives. Energy Econ., 96, Article 105173. https://doi.org/10.1016/j.eneco.2021.105173

Yoshiba, F., Hanai, Y., Watanabe, I., & Shirai, H. (2021). Methodology to evaluate contribution of thermal power plant flexibility to power system stability when increasing share of renewable energies: Classification and additional fuel cost of flexible operation. Fuel, 292, Article 120352. https://doi.org/10.1016/j.fuel.2021.120352

Zhang, W., Zhou, Y., Gong, Z., Kang, J., Zhao, C., Meng, Z., Zhang, J., Zhang, T., & Yuan, J. (2021). Quantifying stranded assets of the coal-fired power in China under the Paris Agreement target. Clim. Pol., 23(1), 11e24. https://doi.org/10.1080/14693062.2021.1953433