Optimal Energy Storage System and CCS Accelerate Energy Decarbonization : A Case Study of Inner Mongolia, China

Abstract

Abstract: One of the crucial prerequisites to realize the goal of carbon neutrality is the integrated energy system (IES) to achieve a net zero emissions target. Energy storage systems(ESSs) provide an effective way to address the volatility and instability of renewable energy systems (RESs). Carbon dioxide (CO₂) capture and storage (CCS) technology is an efficient method to reduce carbon emissions from coal-fired power plants (CFPPs). In terms of IES, the total costs, emission reduction potential and marginal abatement cost (MAC) are considered as significant indicators. This study aims to explore the emission reduction effects and economic analysis of future IES from the perspective of life-cycle carbon emissions and costs through the deployment of optimized ESSs (including short-term and long-term energy storage) and different CCS retrofitting rates. Taking the energy system of Inner Mongolia, China, as an example, based on future energy goal and emission reduction potential, our results indicate that the deployment of CCS and EESs could reduce emission by approximately 26 – 128 million tons in 2030 under current energy plans, power generation cost may increase by 8 billion to 46 billion CNY, this is mainly because that ESSs have higher cost to compare CCS retrofitting cost, which significantly increased the total IES cost. Moreover, research results also show that the MAC value of CCS retrofitting is the lowest under different energy scenarios, CCS retrofitting should be prioritized under the current energy structure. The synergy between ESSs and CCS retrofitting not only accelerates energy carbon emission reduction but also the MAC of synergy is between ESSs installation and CCS retrofitting.

1.Introduction

Green energy transitions have become the global consensus for solving environmental problems. As the largest carbon emitter in the world, China made a sole commitment at the 75^th United Nations General Assembly to achieve carbon peak by 2030 and carbon neutrality by 2060. Approximately 43% of greenhouse gas emissions originate from coal-fired power generation systems, which provide over 60% of China’s electricity. Conventional energy structures should be transformed into RESs alternatives, such as wind and solar resources. A hybrid wind-solar complementary system combined with ESSs can significantly increase the penetration and stability of RESs. ESSs can also serve as backup sources to supply continuous and high-quality power when renewable energy plants are suddenly interrupted¹⁾. To achieve net-zero carbon emissions power system, it is essential to implement effective abatement methods for CFPPs. Among the available abatement methods, CCS is the only technology that allows existing CFPPs to continue operating with a significant reduction in emissions. Thus, CCS technology is a more feasible technical means to reduce emissions from CFPPs²⁾.

To achieve the carbon peak in IES by 2030, this paper presents an optimization model, aiming to minimize lifecycle CO₂ emissions, this model goal is to store excess renewable energy through ESSs, thereby reducing coal-fired power generation. In addition, we consider the future emission reduction potential for CCS retrofit in CFPPs, further to explore the synergy effect of ESSs and CCS in terms of carbon reduction and economic cost. Finally, taking China’s key renewable energy base, Inner Mongolia, as a case study, this is mainly because it has ranked first in coal production, electricity generation, coal transportation, and power transmission since 2021. As a result, the electricity sector is the largest source of emission in the province, accounting for 63% of the total provincial emission, CO₂ emission from the electricity sector are expected to rise from 400 million tons in 2020 to 430 million tons in 2025, with further grow to approximately 460 million tons by 2030; the carbon emissions from the electricity sector pose a significant threat to the achievement of carbon peak in the region.

Hanak et al. analyzed potential links between CCS with energy storage and renewable energy could reduce the efficiency penalty associated with the integration of CO₂ capture to fossil fuel power plants, and at the same time, increase the profitability of the entire system³⁾. Sean et al. examined the role that power generation with flexible capture systems could play in a future European power system,CO₂ price affect the deployment of CCS with high shares of renewable energy⁴⁾. Li et al. proposed a two-stage multi-objective optimal scheduling model of virtual power plant considering flexible low-carbon retrofit and virtual hybrid energy storage expansion⁵⁾. Bruce and Bandyopadhyay analyzed the performance of a hybrid electricity system, which consists of wind power plant, energy storage, CFPP and CCS in Britain and eastern region of the U.S.⁶⁾⁷⁾. Lan et al. proposed a long-term equilibrium in multi-energy systems with CCS and power to gas, to verify this system can reduce carbon emissions while increasing the consumption of renewable energy⁸⁾. Lilliestam et al. compared CFPPs with CCS facility and concentrating solar power, their costs will decrease with large-scale deployment⁹⁾. Sgouridis et al. showed the accomplishment of power-sector CO₂ emission reduction by investing in renewable technologies generally provide a better energetic return than CCS¹⁰⁾. Qiu and Al‐Ghussain constructed a wind-PV‑ energy storage electricity system, which integrated both long-term and short-term energy storage considerations^{11) 12)}.

Through a review of the existing literature, we found a lack of comprehensive evaluation on the combination of the ESSs of short-term and long-term energy storage, as well as CCS retrofitting. Previous studies have considered the arbitrage principles for energy storage in the context of renewable energy, and some have explored the net present value of different CCS projects. Most studies have less focused on the synergistic effect of ESSs and CCS retrofitting under rapid renewable energy penetration, and do not conduct an in-depth exploration about Inner Mongolia, an important energy base in China, as a case study. To address these issues, we explored the emission reduction of IES through the deployment of ESSs and CCS project, established an evaluation model to analyze the carbon reduction pathway of IES. The main contributions of this study are as follows:

1.We used an optimization model to evaluate the emission reduction potential of combining ESSs with short-term and long-term energy storage and RESs, comprehensively assessing the impact of ESSs introduction on emission reduction and cost variation in the IES.

2.We discussed the cost benefits in CCS retrofitting technologies for CFPPs carbon reduction, with a focus on the economy and environmental effect of different CCS retrofitting scenarios.

3.Taking the Inner Mongolia, China, as a case study, we explore the impact of emission reductions and cost change in combining the ESSs employment and CCS retrofitting, to analyze the emission reduction pathways of future energy system under different scenarios.

2. Theory and Methodology

2-1 Study Area

Inner Mongolia is one of the autonomous regions of China, located in the north of the country, it constitutes 12% of China's total land area, making it the third-largest Chinese subdivision. According to the current reaearch¹³⁾, Inner Mongolia coal reserves are 466.01 billion tons, which is 27.12% of China’s total. Because of the heavy reliance on coal for economic development, carbon emissions from the power sector show a consistent upward trend. Carbon emissions in 2020 were twice as high as those in 2010. Regarding renewable energy, the total wind energy reserves in the region reach 89,800 kW, and the annual utilization rate of wind energy resources is 60% − 90%. Moreover, the annual sunshine duration of solar energy is 2517 – 3277h, and the amount of solar radiation per square meter is 4800 – 6400 J. Given the abundant solar and wind resources, ESSs are integrated with the RESs to optimize the amount of clean energy used. Figure 1 shows the geographical location of Inner Mongolia in China.

Figure 1. Location of the study area in China

Based on Inner Mongolia geological, oil and gas resource, and coalbed methane resource data for Inner Mongolia, CO₂ deep saline storage has the greatest potential, reaching 5,938 million tons, and CO₂ enhanced coalbed methane and CO₂ enhanced oil recovery can achieve 3,445 million tons and 1,858 million tons of CO₂ geological storage, respectively. CO₂ geological storage technology can help achieve the goal of carbon peak and carbon neutralization in Inner Mongolia. The stored CO₂ using geological storage technology is currently traded on the carbon market to obtain the ecological compensation and realize the value of ecological products¹⁴⁾. The abundant renewable energy, coal resources and CO₂ storage conditions of the region provide favorable conditions for a low-carbon energy transition.

2-2 Data Collection

According to the relevant reports and future plan issued by

the government and the power systems, the report of “Low-Carbon Development of Inner Mongolia”¹⁵⁾ includes the targets of power generation and renewable energy share for 2030(BAU: business as usual plan, LC: low carbon plan). In this study, we utilized data from Inner Mongolia’s hourly electricity load in 2019¹⁶⁾, average hourly wind speed at a height of 10m, and hourly solar radiation intensity for the entire region in 2022¹⁷⁾. We analyze the average wind speed and solar radiation intensity in Inner Mongolia. This is mainly because it is difficult to obtain data from different regions within Inner Mongolia and some regions are not representative. The provincial data could comprehensively represent the level of renewable energy in the entire region.

This paper assumes that the hourly electricity load trend in 2019 is consistent with the trend in 2030, combining with predicted electricity load in 2030 from report, we simulate the hourly electricity load in 2030. As predicting wind speed and solar radiation intensity using existing models and methods is difficult, we also assumed that the trend of their variations in 2030 are similar with 2022, to simulate hourly electricity generation for 2030 based on the predicated data of electricity generation of renewable energy power.

Table 1. The target of IES in 2030 (Billion kWh)

Item	2030BAU	2030LC
Electricity generation	963.4	968.5
Coal-fired power	576.6	484.2
Wind power	260.0	310.0
Solar power	110.5	153.0
Other energy	21.3	21.3

Table 1 shows the target of IES in 2030. The share of coal-fired power includes electricity transmission, this paper only focuses on electricity consumed within the province. According to the future energy goals of the report in 2030, as electricity generation from other energy only accounts for around 2%, this paper only focuses on wind, solar and coal-fired power.

2-3 Workflow and Scenario Setting

This paper constructs an energy optimization model from a provincial perspective. It is conductive to compare with other province energy carbon reduction scenario and evaluate its contribution to energy emission reduction to national level.

Figure 2. Research flowchart

We focus on the impact of different power structures on

the carbon emissions and cost of IES. Figure 2 shows the

Figure 3. System boundary in the different scenarios

research flowchart of this paper.

Table 2 lists descriptions of the different energy scenarios. S1 is the baseline scenario that reflects the energy structure under the current policy. S2 − S4 are comparative scenarios that consider the introduction of ESSs, CCS retrofitting and combination of both, respectively.

Table 2. Descriptions of different scenarios

Scenario	Description
S1	Current 2030BAU and 2030LC plan
S2	Optimized ESSs into 2030BAU and 2030LC plan
S3	Different CCS retrofitting scenarios (10%, 20% and 30%) into 2030BAU and 2030LC plan
S4	Optimized ESSs and different CCS retrofitting scenarios (10%, 20% and 30%) into 2030BAU and 2030LC plan

Based on the China CCUS Report 2023¹⁸⁾, the emission reduction demand by CCUS is expected to grow in nearly 100 million tons of CO₂ by 2030, considering the Inner Mongolia’s share of coal-fired power generation in China by 2030. If 20% of the electricity from coal-fired power from CFPPs with CCS technology, it meets the national allocation requirements. We assume that 20% of the electricity from CFPPs with CCS projects is classified as medium carbon

reduction scenario, 10% as low carbon reduction scenario, and 30% as high carbon reduction scenario. Furthermore, different CCS retrofitting scenarios are set to compare with optimized ESSs scenario in carbon reduction and cost change.

In this study, the unit carbon emission and unit power generation cost were analyzed from a life-cycle perspective, to estimate the total carbon emission and total cost under different energy scenarios. Figure 3 shows the system boundary of different energy scenarios. We mainly consider wind power, solar power, coal-fired power, CCS retrofitting technology and ESSs installation. According to this system boundary, we refer to some parameters and they involved two sectors: one is directly selected from existing research, and the other is derived from existing research to further perform the result of carbon emission and cost analysis.

2-4 Renewable Energy Model

The wind power strongly depends on wind speed and wind turbine parameter. The wind speed at hub elevation is obtained by Eq.(1). The wind power generation model refers to the National Standard¹⁹⁾ and is performed by Eq.(2). In this study, the wind speed at a height of 10m from the original dataset was converted to that at a height of 100m²⁰⁾.

V_10, V₁₀₀: The wind speed in 10m and 100m height (m/s)

H_10，H₁₀₀: The height of 10m and100m (m).

ɑ: The shear coefficient (0.19).

P_W: The real-time wind power output (kW).

C_P: The power factor of the rotor (0.59).

A: The swept area of the rotor (6358.5m²).

ρ: The air density (1.29kg/m³).

P₀: The rated power of the turbine (kW).

The energy generated by the solar system depends on many factors. The solar power generation model refers to the National Standard²¹⁾ and is performed by Eq.(3).

E_p: The electricity from solar system (kW).

H_A: The solar irradiance (kW/m²).

P_AZ: The capacity of the solar system (kW).

E_S: The irradiance constant (1kW/m²).

K: The overall efficiency of solar PV system (0.85).

2-5 Energy Storage System Model

Energy storage equipment can solve the mismatch problem between renewable power output and electricity load. A hybrid energy storage system (hydrogen energy storage + battery storage) can effectively consume renewable energy, reduce the load of the electricity system using a superior grid power supply ratio, and decrease the operating costs of the operation process²²⁾. A co-optimization method for planning and operating a hybrid renewable energy system is proposed, which contains wind farms, photovoltaic farms, batteries and hydrogen storage systems, the proposed methodology and optimization framework reduce the levelized cost of storage by 13.7% compared with the rule-based operation strategy²³⁾.

Based on the characteristics and operation of ESSs and optimization configuration model of hybrid storage system, this paper considers the cooperative operation of ESSs which includes long-term storage and short-term storage. We focused on two types of energy storage technologies: battery storage and hydrogen storage. When power generation from RESs is sufficient, the excess electricity is stored to ESSs, the power generation from RESs cannot meet electricity load, power will be released from the ESSs.

(1)Battery energy storage model

The battery energy storage system (BESS) is repeatedly charged and discharged during operation, which was selected as a short-term energy storage system. The model of the battery is referred to current research²⁴⁾ and is expressed by Eqs.(4) − (5):

E_batt: The stored electricity in battery (kWh).

σ: The hourly self-discharge efficiency (0.05).

η_cha, η_dis: The charge and discharge efficiency (95%).

E_batt,in, E_batt,out: The input and output power in battery (kWh).

(2)Hydrogen energy storage model

Hydrogen energy storage (HES) is considered an alternative fuel energy storage technology based on reversable electrochemical reactions. Therefore, it is suitable for meeting long-term storage and has high power rates and seasonal energy storage features. The HES comprises electrolyzer (EL), H₂ storage tanks, and hydrogen fuel cell(FC). The EL converts water into hydrogen energy by excess electricity from RESs. H₂ storage tanks is used to store the produced hydrogen by EL. FC is applied for converting hydrogen energy to electrical power.

The model of the HES is referred to current research²⁴⁾.The output power converted from the excess electricity by EL is calculated in Eq.(6). The hydrogen energy storage charge and discharge state of the H₂ tank is expressed by Eqs.(7) – (8). The mass of H₂ in the tanks is calculated by Eq.(9). The output power of FC is expressed by Eq.(10).

P_EL,out: The output power of EL (kWh).

P_EL,in: The input power of EL (kWh).

η_EL: The efficiency of EL (75%).

E_H2: The stored power of the H₂ tank (kWh).

P_{FC in}, P_FC,out: The input and output power of FC (kWh).

η_HS: The efficiency of hydrogen energy storage (95%).

M_H2: The mass of hydrogen in H₂ tank (kg).

HHV_H2: The higher heating value of H₂ (39.7 kWh/kg).

η_FC: The efficiency of FC (70%).

2-6 Optimization Model Design

To solve this optimization problem, an objective function was formulated to minimize lifecycle CO₂ emissions from RESs, ESSs, and electricity from other energy. The optimal method is a linear programming algorithm in python to determine the hourly charge & discharge amount and coal-fired power generation amount in a year²⁵⁾²⁶⁾. These optimal solutions (decision variables) include coal-fired power supply, RESs supply, charge and discharge amounts from BESS and HES in per hour.

Eq.(11) is the objective function on the minimum lifecycle CO₂ emissions of power generation of the IES including RESs, ESSs and other energy. About constraint condition, Eqs.(12) – (14) are carbon emission amount from RESs, ESSs and other energy. The carbon emission of ESSs includes BESS and HES carbon emission. Eq.(15) is the hourly supply–demand balance relationship constraint. In the constraint of charge and discharge of ESSs, we set the charge duration time of BESS is not more than two hours, and the charge duration time of HES is not less than two hours. The sequence of charge and discharge is as follows: BESS is charged first and then to HES; when discharge, BESS also discharge first and then the HES.

RESs_c，ESSs_c，and OE_c : The CO₂ emission from RESs, ESSs and other energy sources(g), the result is converted to ton (t).

RESs_c,i : The electricity from RESs types (kWh).

E_{RESs, i}: The CO₂ emission factor of RESs types (g/kWh).

ESSs_c,i : The electricity from ESSs types (kWh).

E_ESSs,i : The CO₂ emission factor of ESSs types (g/kWh).

OE_c,i :The electricity from other energy types (kWh).

E_c,i :The CO₂ emission factor of other energy types (g/kWh).

D_load :The electricity load (kWh).

About the parameter in optimization model, the hourly wind power generation and solar power generation are evaluated by renewable energy model Eqs.(1) - (3). Coal-fired power generation and long-term and short-term energy storage amount are determined based on optimization model and energy storage model. The carbon emission factor parameters are summarized in Table 3.

As for the installed capacity of BESS and HES, they are determined based on the maximum energy storage states of the BESS and HES by per hour, Because the minimum and maximum energy storage states of the BESS and HES are 10 % and 90 % of the installed capacity²⁴⁾. According to the optimal result in the state of energy storage, this paper calculates the installed capacity of BESS and HES, Eqs.(16) - (19) show the installed capacity of BESS and HES.

IC_B: The battery installation capacity (kW).

IC_EL: The EL installation capacity (kW).

IC_H2: The H₂ tank installation capacity (kg).

IC_FC: The FC installation capacity (kW).

△t: The time interval (1 hour h).

2-7 CCS Retrofitting Model

CFPPs are a main source of carbon emissions, and the CCS technology installation can capture the CO₂ generated during power generation, significantly reducing the carbon footprint. CCS projects involve capturing CO₂ from emission sources and transporting it for subsequent utilization or storage, thereby preventing its release into the atmosphere.

Currently, post-combustion capture has emerged as the primary method for carbon capture retrofits in China’s CFPPs, owing to its mature technology, high adaptability and minimal modifications to the existing power generation process, 90% of total carbon emissions are set to be captured by CCS equipment²⁷⁾. According to the different carbon capture locations, principles, and carbon emission sources, selecting the appropriate CCS retrofitting technology is crucial for CFPPs carbon reduction.

Through reviewing current research^28)29)30)31), we analyzed the geographical distribution of CFPPs and CO₂ storage region in Inner Mongolia, the abundance of the CFPPs is located in the near the region with CO₂ storage potential. It should be noted that the IEA³²⁾ recommends CFPPs with installed capacity more than 300 MW as CCS retrofitting target to meet energy requirements. This paper first considers implementing CCS retrofitting for CFPPs more than 300MW, which are located near CO₂ storage region, so we assume that CO₂ transport distance is 100km. In addition, to ensure that the CCS retrofitting has sufficient operating time, we selected coal-fired power plants with more than 20 years lifetime as targets for CCS retrofitting.

In this study, the potential of carbon reduction after installing CCS projects was considered from a lifecycle perspective. The model of carbon emissions after and before retrofitting CFPPs with CCS are referred to current research³³⁾ is showed by Eqs.(20) – (23).

CE_c: The carbon emission of CFPPs before CCS retrofitting (g), the finial result is converted to ton (t).

EP: The power generation from CFPPs (kWh).

CEF: The CO₂ emission factor of coal-fired power (g/kWh).

CE_post: The carbon emission of CFPPs after CCS retrofitting (t).

CR: The CO₂ capture rate after CCS retrofitting (90%).

CERc: The carbon emission reduction (t).

CE_CCS:The total carbon emission of CFPPs after CCS retrofitting (t).

F: The CO₂ emission from power loss by per ton CO₂ capture (t/t).

T: The CO₂ emission of per ton CO₂ transportation (t/t).

S: The CO₂ emission of per ton CO₂ storage (t/t).

The lifecycle CO₂ emission analysis was conducted by the capture, transportation, and store by per ton CO₂, the CCS retrofitting parameters are shown in Table3.

2-8 Carbon Emission Model

To explore the total carbon emissions after integrating the optimized ESSs and different CCS retrofit rates into the IESs, the sum of the carbon emissions from different power generation types is defined as the IES total carbon emissions, which are calculated in accordance with the electricity consumption and CO₂ emission factors.

Combining the carbon emission for RESs and other energy sources by Eqs.(12) - (14) (coal-fired power without CCS retrofitting), and carbon emission from coal-fired power generation with CCS retrofitting by Eqs.(20) - (23), Eq.(24) shows the IES total carbon emissions.

2-9 Economic Assessment Model

An economic model of the levelized cost of energy (LCOE) and levelized cost of storage (LCOS) was formulated to analyze total IES costs. LCOE indicates the unit cost of electricity generation over the full life cycle, which is calculated by dividing the lifecycle cost by the lifetime electricity production. The LCOS indicates the unit costs of electricity storage or discharge over the full life cycle, which are calculated by dividing the lifecycle cost by the lifetime electricity storage or discharge. Therefore, LCOE and LCOS measure the techno-economic feasibility of electricity generation and storage. We mainly refer to some formulas of LCOE and LCOS from current research³⁴⁾³⁵⁾ and appropriately adjust some contents of these formulas.

(1) LCOE in wind, solar and coal-fired power

Regarding the LCOE of renewable energy generation and coal-fired power generation, we reviewed recent research^36)37)38)39) on the distribution of wind plant and solar plant in Inner Mongolia, the most of wind plant mainly distributed in grasslands or gently undulating mountainous areas, while solar plant were mainly in the Gobi and desert regions. There is less variation in the power generation cost within the region. Therefore, this paper selects some data of the prediction LCOE value of renewable power generation in Inner Mongolia in 2030 from the current research, more details in Table 4.

(2) BESS and HES cost model

The LCOS analyzed per kWh of power discharge cost in BESS by Eq.(25). It includes initial installation cost, maintenance and operation cost, and replacement costs throughout the lifetime of the BESS divided by the total amount of discharge power. The yearly power output and costs are discounted by a discount rate.

B_B: The initial installation cost of battery (CNY/kW).

B_OM: The operation and maintenance cost of battery (CNY/kW).

B_R: The replacement cost of BESS (CNY/kW).

EP_E: The purchase electricity price in ESSs (CNY/kWh).

E_BC: The annual charge amount in lifetime (kWh).

E_BD: The annual discharge amount in lifetime (kWh).

r: The discount rate (%).

N: The life of the system in years (y).

The LCOS analyzed per kWh of power discharge cost in HES by Eq.(26). It includes initial installation cost, maintenance and operation cost, and replacement costs throughout the lifetime of the HES divided by the total amount of discharge power. The yearly power output and costs are discounted considering a discount rate.

H_CAPES : The initial installation cost of HES (CNY).

H_OM: The operation and maintenance cost of HES (CNY).

H_R: The replacement cost of HES (CNY).

E_HD: The annual discharge amount in lifetime (kWh).

r: The discount rate (%).

N: The life of the system in years (y).

C_EL: The initial installation cost of EL (CNY/kW).

C_H2: The initial installation cost of H₂ tank (CNY/kg).

C_FC: The initial installation cost of FC (CNY/kW).

OM_EL: The operation and maintenance cost of EL (CNY/kW).

OM_H2: The operation and maintenance cost of H₂ tank (CNY/kg).

OM_FC: The operation and maintenance cost of FC (CNY/kW).

E_HC: The annual charge amount in lifetime (kWh).

R_EL: The replacement cost of EL (CNY/kW).

R_H2: The replacement cost of H₂ tank (CNY/kg).

R_FC: The replacement cost of FC (CNY/kW).

This paper set the ESSs lifetime as 20 years(N=20 year), so some facilities of BESS and HES need to be replaced during this period. It is worth that we also consider the purchase electricity cost from the RESs to BESS and HES in maintenance and operation cost, we assume that the unit purchase electricity price is average renewable energy power cost. The BESS and HES of economic parameters to perform the LCOE model were listed in Table 4.

(3) CCS retrofitting cost model

The life cycle cost of CCS retrofitting was calculated using the LCOE, which provides a systematic evaluation of the economic viability of the CCS in power generation using Eq. (28). We assume that the plant will start investment in year y, the construction period is one year, and the operation starts in year y+2, the remaining operating life of the equipment is T.

CS_CCS: The initial construction cost of the CCS retrofitting (CNY).

CS_rest : The annual remaining life cycle cost (CNY).

CS_U: The unit initial infrastructure cost (CNY/t).

e: The CCS retrofitting improvement index (3.17).

a:The factor of investment cost of the CCS which is affected by technological progress (2.02).

r: The discount rate (%).

CS_OM: The unit CCS operation and maintenance cost (CNY /t).

CS_T: The unit CO₂ transportation cost (CNY / t∙km).

K_CO2:The CO₂ transportation distant (km).

CS_S: The unit CO₂ storage cost (CNY /t).

CS_Q: The cost of additional energy loss by the CO₂ capture (CNY).

EP_CCS: The purchase electricity price in CCS (CNY/kWh).

CS_p: The additional power loss by capturing per ton CO₂ (kWh/t).

Q_e: The annual coal-fired power generation in lifetime (kWh).

This paper sets the CFPPs after CCS retrofitting lifetime as 20 years. The CO₂ capture and storage process consumes additional electricity, we assume that this electricity is purchased at the price of coal-fired power generation cost. Table 4 provides the economic parameters of CCS retrofitting LCOE model.

(4) IES cost model

The total cost of the IES is the sum of all power generation type costs. By incorporating the LCOE of CCS retrofitting and LCOS of ESSs into the IES, and applying the above equations, the total cost is calculated by Eq.(30).

C_total : The total cost of IES (CNY).

P_E,i: The power generation of different power generation types (kWh)

L_E,i: The LCOE of different power generation types (CNY/kWh)

P_S,i: The energy storage amount of different storage types (kWh)

L_S,i: The LCOS of different storage types (CNY/kWh)

2-10 Marginal Abatement Cost Model

The MAC is the cost assessment of energy project implementation and reflects the emission reduction price. This paper utilized MAC to evaluate the extra cost in reducing 1 ton of CO₂ emissions by CCS retrofit and installing ESSs while maintaining a constant power supply. The MAC is the difference between the electricity generation cost in the policy scenario and baseline scenario divided by the difference between the GHG emissions in the baseline scenario and policy scenario⁴⁰⁾. The MAC value in this study was calculated by Eq.(31).

MAC_IES: The MAC of electricity generation in IES (CNY/t).

C_B: The electricity cost in baseline scenario (CNY).

C_L: The electricity cost in comparative scenario (CNY).

CE_B: The CO₂ emission in the baseline scenario (t).

CE_L: The CO₂ emission in the comparative scenario (t).

The MAC analysis was used to compare different emission reduction technologies, and the optimal emission reduction path was determined under different scenarios. Therefore, estimates of MAC can provide valuable information about the economic potential of carbon emission reduction.

2-11 Parameters Summary

The model parameters include coal-fired power, wind power, solar power, energy storage, and CCS retrofitting. They were quantitatively analyzed to evaluate them in the most detailed and accurate way possible. Some parameters were derived from current research of Inner Mongolia, the other parameters were selected from regions near Inner Mongolia and other region within China, they have a high similarity, to be close to energy system emission reduction development in Inner Mongolia as possible. The carbon emission parameters used to perform the model are shown in Table 3, and the economic parameters are shown in Table 4.

Table 3. The related carbon emission parameters

Item	Unit	Value	Source
Power generation type
Solar	g/kWh	81	(41)
Wind	g/kWh	6.6	(42)
Coal-fired	g/kWh	820	(43)
BESS	g/kWh	72	(44)
HES	g/kWh	61	(44)
CCS retrofitting
F	t/t	0.05	(33)
T	t/t	0.02	(43)
S	t/t	0.06	(43)

Table 4. The related economic parameters

Item	Indicator	Unit	Value	Source
Battery	BB	CNY/kW	1167	(45)
	BOM	CNY/kW	15	(45)
	BR	CNY/kW	1167	(45)
	Lifetime	yr	5	(24)
EL	CEL	CNY/ kW	9750	(24)
	OMEL	CNY/ kW	130	(24)
	REL	CNY/ kW	9750	(24)
	Lifetime	yr	15	(24)
H2 tank	CH2	CNY/ kg	3900	(24)
	OMH2	CNY/ kg	75	(24)
	RH2	CNY/ kg	3900	(24)
	Lifetime	yr	20	(24)
FC	CFC	CNY/ kW	10000	(46)
	OMFC	CNY/ kW	175	(47)
	RFC	CNY/ kW	10000	(46)
	Lifetime	yr	10	(46)
ESSs	r	%	5	(47)
	EPE	CNY/ kWh	0.325	(48)
CCS retrofitting	CSU	CNY/t	290	(49)
	CSOM	CNY/t	28	(30)
	CST	CNY/t∙km	0.8	(30)
	CSS	CNY/t	20	(49)
	CSP	kWh/t	196	(30)
	EPCCS	CNY/kWh	0.3	(48)
	r	%	5	(30)
Other energy cost	Coal-fired	CNY/kWh	0.3	(48)
	Wind	CNY/kWh	0.32	(48)
	Solar	CNY/kWh	0.33	(48)

3. Result and Analysis

3-1 Optimal Result Analysis

As mentioned above, based on the optimal solutions, we evaluated the power generation of different energy types in the future scenario. Figure 4 shows the electricity supply amount percentage of different power generation types by optimization model under two energy scenarios of 2030. Comparing the BAU, the LC increases the storage share by 3%, with hydrogen storage increasing by 1.9% and battery storage by 0.8%, due to the 3.4% and 3.2% increases in wind and solar energy, respectively. The increasing energy storage offsets 56 – 78 billion kWh of coal-fired power generation.

Figure 4. The optimal proportion of different types of power generation amounts in 2030BAU and 2030LC.

Figure 5. Different power generation type proportions on a typical day and month. a: Different power generation type proportions every month in 2030LC. b: Different power generation type proportions on a typical day in 2030LC.

Based on the optimized results, Figure 5 (a) shows the monthly power generation share for the 2030LC scenario. Because from March to May and from September to December are windy seasons, HES as long-term storage can store excess electricity from RESs, highlighting the characteristics of seasonal storage. The BESS as short-term storage plays a supplementary role in ESSs. Figure 5b reveals the daily electricity share for a typical day. The ESSs can store electricity during the peak period of renewable energy generation and release it during the electricity demand peak at night, which not only reduces coal-fired generation, but also helps balance supply and demand by peak-load shifting.

3-2 Carbon Reduction Analysis

From carbon emission perspective, the optimized ESSs and different CCS retrofit rates were integrated into the IES. According to the scenario setting in Section 2 – 3, the total carbon emission of the different scenarios was calculated by Eq.(22) and parameters in Table 4. In the 2030 BAU scenario, the emission reduction potential of the IES is 28 million to 120 million tons, and in the 2030 LC, the emission reduction potential of the IES is 26 million to 128 million tons.

Figure 6. The total carbon emission of different scenarios

Figure 6 shows the total carbon emission under different scenarios(the brackets in S3 and S4 indicate different CCUS retrofit rates). Compared with S1, the optimized ESSs can reduce carbon emissions by 15% in S2. Based on the S2 and S3 analysis, in the 2030 LC scenario, when 30% of the electricity comes from CFPPs with CCS retrofit, the emission reduction effect is equivalent to the optimized ESSs emission reduction. However, in the 2030 BAU scenario, when 20% of the electricity comes from CFPPs with CCS retrofit, the emission reduction effect is equivalent to the optimized ESSs emission reduction, because power generation from RESs increase by 7% in 2030LC, providing more renewable electricity for ESSs. Compared with other scenarios, S4 has the highest emission reduction potential because it incorporates ESSs and CCS project into the IES. Generally, integrating ESSs and CCS is a viable alternative for reducing the carbon emission on the grid.

3-3 Total Cost Analysis

In terms of energy storage costs, considering the differences in maintenance cost, lifetime, efficiency and operational characteristics of the BESS and HES. Based on the result of charge-discharge in optimization model, installed capacity, and parameters in Table 4, the LCOS of the BESS and HES are approximately 0.74 and 0.67 CNY/kWh by Eq.(25) and (26), respectively. In the LCOE analysis of the CCS retrofitting, based on CCS retrofitting cost model and parameters in Table 4. The unit cost of power generation for CFPPs with CCS retrofitting may increase by about 0.24 CNY/kWh by Eq.(28).

Table 5. The total cost in different scenarios (Billion CNY)

Scenario	2030BAU	2030LC
S1	216.93	227.55
S2	237.81	255.61
S3(10%)	225.75	235.77
S3(20%)	234.59	243.78
S3(30%)	243.62	251.89
S4(10%)	245.09	263.23
S4(20%)	252.58	269.04
S4(30%)	260.34	275.41

Table 5 lists the total IES cost under different scenarios. Compared with S1, the optimized ESSs in S2 increase IES costs by 13% in the 2030 BAU and 16% in the 2030 LC, owing to the higher LCOS and the increase in storage capacity with the increase in renewable energy generation. In S3, the cost increase in the 2030 BAU(CCS retrofitting percentage from 10% to 30%) is slightly higher than that in the 2030 LC, mainly because of the higher share of coal-fired power in the BAU scenario, leading to an increase in the total cost of CCS retrofit. Comparing S3 with S4 reveals that the cost increase in the 2030 BAU scenario is significantly lower than that in the 2030 LC, mainly because the increase in the storage capacity in 2030 LC leads to a noticeable rise in total IES costs. In summary, in a relatively high proportion of coal-fired power scenarios, the cost increase in IES is mainly caused by the increase in the overall cost of CCS retrofit, whereas in scenario with higher renewable energy generation, it is the energy storage cost that drives the increase in IES total cost.

3-4 Marginal Abatement Cost Analysis

Based on the analysis of total carbon emissions and costs of IES under the different scenarios, the MAC values under the

2030 BAU and 2030 LC were compared by Eq (28), baseline scenario S1and comparative scenarios S2 – S4.

Figure 7. The MAC value of different scenarios.

Figure 7 shows the MAC values for different scenarios. The MAC results show that S3 has the best performance because the LCOE of coal-fired power with CCS retrofit is lower than the LCOS of ESSs, and the MAC value of the CCS retrofit decreases with increasing CCS retrofit rates. Since MAC value increases with increasing storage capacity and higher LCOS, the MAC values of 2030LC are generally higher than those of the 2030BAU in S2 and S4, However, the results are reversed in S3 without ESSs, the overall CCS retrofit cost is higher for 2030BAU than in 2030LC, due to relatively higher share of coal-fired power in 2030BAU. Compared with S2 and S3, the MAC values of S4 are between the MAC values of S2 and S3, indicating that the MAC of the synergy between ESSs and CCS retrofit can reduce the MAC of the ESSs.

Considering Figure 6, compared to S2 and S3, carbon emission from S2 is between S3 (10%) and S3 (20%) in the BAU scenario, but the MAC values for S3 (10%) and S3 (20%) are about 80% and 77% of S2, respectively. In the LC scenario, carbon emission from S2 is between S3 (20%) and S3 (30%), but the MAC values for S3(10%) and S3 (20%) are about 76% and 72% of S2, respectively. In addition, compared to S3, S4 accelerates the emission reduction process. In the BAU scenario, S4 reduces carbon emissions by about 13% and increases the MAC value by 15%. In the LC scenario, S4 reduces carbon emissions by about 21% and increases the MAC value by approximately 19%. The result implies that we should first consider CCS retrofitting to reduce emission in current energy system of Inner Mongolia. With share of renewable energy power generation increases and accelerate the energy system carbon reduction, combining with carbon reduction efficiency and the MAC value, it is important to focus on the synergistic abatement effect of CCS retrofitting and ESSs installation.

3-5 Sensitivity analysis

The mentioned cost estimate for ESSs and CCS retrofitting are determined based on current research operation parameters and regional characteristics. However, these assumptions are associated with certain levels of uncertainty. This study further conducts a sensitivity analysis using a control variable approach, the initial cost, the operation and maintenance cost (OM cost), the replacement cost, the electricity cost and so on, they are selected as the influencing factors. Specifically, the change percentage of selected parameters is ±10%, ±20% and ±30%.

Figure 8. The result of sensitivity analysis in BESS and HES.

Figure 8 (a) and (b) show the LCOE results of BESS and HES from sensitivity analysis, it seems that capital cost is the most sensitive parameter, followed by the replacement cost. For BESS, the replacement cost has a higher influence on the LCOS due to the short lifetime of battery and they need to replace several times during the 20-year period. Secondly, we found that OM cost is sensitive to change in the HES more than BESS for OM cost. This is because the unit OM cost of HES is higher than that of BESS. For ESSs the change in LCOS is not linear to change in lifetime, which can be explained by the fact that lifetime is in the exponential function of LCOS equation (see Eqs.(25) and (26)). As for purchased electricity price, there were no significant variations for LCOS values due to the small share of this

parameter in LCOS calculation.

Figure 9. The result of sensitivity analysis in CCS retrofitting.

Figure 9 shows the results of the sensitivity analysis for CCS retrofitting; the capital cost is still the most sensitive parameter in the cost of CFPP with CCS retrofitting. The CO₂ storage cost and OM cost have similar sensitivity. The behavior of other parameters is similar to that for BESS and HES. Therefore, whether for ESSs or CCS retrofitting as carbon reduction technologies, we should give priority to reducing initial capital investment, and then these emission reduction technologies should be fully utilized over long lifetime.

3-6 Limitation and Future Research

There are some limitation to this study, we mainly analyze the emission reduction effect of energy system in Inner Mongolia in a specific year, we do not comprehensively consider the impact of technology upgrades and future cost changes for carbon reduction technologies over lifetime, the next research will integrate time models to conduct environmental and economic analysis of these technologies under different energy scenarios in more detail. Besides, due to the data limitation and provincial perspective as the start point in this paper, we will explore the differentiation of power generation and storage cost in different regions of Inner Mongolia to formulate a more accurate regional energy carbon reduction plan, thereby providing a more comprehensive suggestion for the long-term planning of low-carbon energy development in Inner Mongolia.

4. Conclusion

This paper mainly focuses on the carbon reduction goal of 2030 year (China's carbon peak target year), to evaluate the different carbon reduction scenarios of energy system in Inner Mongolia, to provide an important reference to achieve the region carbon peak. The impact of combination of ESSs and CCS retrofitting in future IES was analyzed in terms of carbon reduction and economic analysis, the introduction of optimized ESSs and different CCS retrofitting scenarios into the IES was assessed in terms of their emission reduction potential, total costs and MAC.

Combining the optimal energy storage capacity and the different CCS retrofit rates, according to the BAU and LC scenario analyses, emission reduction can reach 10% – 42%, and the integration of ESSs installation and CCS retrofit offers significant potential for IES emission reduction. However, this leads to a total IES cost increase ranging from 13% to 36%. Because the LCOE of CCS retrofit cost is slightly lower than the LCOS of ESSs, the total IES cost of the 2030LC scenario with a higher share of RESs is higher than that of 2030BAU with higher share of coal-fired power. Based on the result of total carbon emission and total cost under different scenarios to analyze the MAC value, the MAC value of CCS retrofit is lower than that of the ESSs.

The results also reveal that the emission reduction pathway of Inner Mongolia should focus on CCS retrofit in 2030-year power structure. If rapid improvement of emission reduction efficiency is needed in the future, the synergy effect of ESS and CCS retrofitting should be considered. The MAC value is higher than that of ESSs installation but lower than that of CCS retrofitting, while the total cost will increase significantly. According to the sensitivity analysis, the initial construction cost is still the main factor affecting the cost of emission reduction technologies in Inner Mongolia. Therefore, Inner Mongolia of future emission reduction pathways should focus on the initial investment costs of carbon reduction technologies.

This research provides benchmarks for power transition and low-carbon development in coal-dominant and renewable resources-abundant regions and implement carbon reduction technology in different energy scenarios.

Corresponding Author

Boyi LI

Graduate School of Environmental Engineering,

The University of Kitakyushu,

Wakamatsu-ku,Hibikino1-1,Kitakyushu 808-0135.

E-mail: [e-mail]

Received: 21 October 2024 Accepted: 18 March 2025

©日本環境共生学会(JAHES) 2025

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