A Review of Carbon Reduction Pathways and Policy–Market Mechanisms in Integrated Energy Systems in China

Abstract

Integrated energy systems are critical physical platforms for driving clean energy transitions and achieving carbon reduction targets. This paper systematically reviews carbon reduction pathways across generation, grid, load, and storage from the dual perspectives of technology and policy–market mechanisms. First, the review outlines a multi-tier integrated energy system architecture and evaluates crucial technologies, such as back-pressure modification, flexible direct current transmission, and virtual energy storage, in improving energy efficiency and carbon reduction. Second, it explores how policy–market mechanisms incentivize carbon reduction, focusing on green power, green certificates, and the carbon market to support integrated energy system transformation. This paper offers a comprehensive theoretical framework and practical basis for the low-carbon transition of integrated energy systems.

1. Introduction

Energy crises [1], global warming [2], and pollution emissions [3] have increasingly highlighted the urgency of reducing carbon emissions [4], a goal that has garnered broad societal consensus [5]. The Chinese government has placed significant emphasis on addressing climate change, integrating it into the core agenda of ecological civilization and high-quality development. China is committed to peaking carbon emissions before 2030 and achieving carbon neutrality by 2060 [6]. Driving the low-carbon energy transition and enhancing the efficient development of renewable energy are critical pathways for attaining the “dual carbon” goal [7]. This process necessitates the effective integration of advanced technological solutions and innovative policy–market mechanisms. The integrated energy system (IES) overcomes the technological barriers of single-source energy supply, enabling the complementary and coordinated optimization of multiple energy forms [8].

Within the framework of the IES, the achievement of low-carbon objectives depends on the deployment of advanced technologies and innovative policy–market mechanisms. To this end, the IES has incorporated a series of advanced low-carbon technologies, such as back-pressure modification, flexible direct current transmission (FDCT), multi-energy load complementarity, and virtual energy storage. The synergistic application of these technologies not only provides robust support for the IES in realizing its “dual carbon” goal but also significantly enhances the flexibility, resilience, stability, reliability, and economic efficiency of the system [9]. Meanwhile, the operational mechanisms of energy markets directly influence the optimization of the IES, indirectly affecting the financial and low-carbon benefits of the energy system. As the carbon emission trading (CET) market, green certificate trading (GCT) market, and energy-sharing market continue to develop, IES energy management and optimization will gradually shift from traditional technological approaches to a combined application of technology and policy–market mechanisms. The 14th Five-Year Plan for New Energy Storage Development Implementation issued by the National Development and Reform Commission in 2022 advocates for the exploration of energy-sharing mechanisms within the IES, encouraging the IES to allocate energy storage through either purchasing or self-built systems, thereby achieving mutual benefits across shared systems [10]. Simultaneously, the plan actively supports the IES in developing innovative business models such as shared storage, energy sharing, and cloud storage, fostering the diversification of IES markets. Furthermore, the Action Plan for Accelerating the Construction of a New Power System (2024–2027) issued by the National Energy Administration in 2024 underscores the need to expedite renewable energy development, optimize power dispatch, promote coal power upgrades, and strengthen demand-side responses to facilitate the advancement of the IES and the application of low-carbon technologies, ultimately achieving a highly efficient and flexible low-carbon power system [11].

By analyzing the incentives provided by technological solutions and policy–market mechanisms for the low-carbon economic operation of the IES, this review aims to leverage the effects of integrated regulation to support the “dual carbon” goal. Building on recent advancements in technological innovations and policy–market frameworks, it explores the low-carbon pathways of the IES across the generation, grid, load, and storage sectors, systematically synthesizing the application of key technologies and the incentivizing role of policy–market mechanisms. This provides a theoretical foundation and valuable reference for the efficient operation and low-carbon transition of the IES.

2. Typical Architecture of the IES

As the core of the modern energy system, the IES aims to achieve efficient and low-carbon energy utilization through the coordinated optimization of multiple energy sources and effective carbon emissions control. By integrating various forms of energy such as gas, power, heat, and cooling, the IES maximizes energy utilization efficiency and plays a critical role in addressing the challenges of carbon neutrality. The primary objective of the IES is to establish a highly flexible and robust energy network capable of responding to fluctuations in renewable energy output and variations in energy demand. In this system, carbon emission control is not solely dependent on advancements in individual energy technologies. However, it is instead achieved through the synergistic application of multiple technological pathways and the innovation of policy–market mechanisms, with the ultimate goal of realizing comprehensive low-carbon operations.

The typical architecture of the IES is illustrated in Figure 1 [12], encompassing various energy forms such as gas, power, heat, and cooling, with tightly coupled and synergistically complementary energy networks. On the generation side, it includes coal-fired power plants (CFPPs), gas-fired power plants (GFPPs), and distributed renewable energy sources such as photovoltaic (PV), wind turbine (WT), and hydropower. The system employs a range of energy conversion technologies, including compressors, power-to-gas (P2G) systems, and combined cooling heating and power (CCHP) units, to enable efficient conversion and utilization of multiple energy forms. Additionally, energy storage systems, fuel cells (FCs), and carbon capture utilization and storage (CCUS) technologies are incorporated to further enhance the system’s flexibility and low-carbon characteristics. Through the horizontal integration of multi-energy complementarity and the vertical coordination of generation, grid, load, and storage, these components enable integrated planning and operational scheduling across different energy subsystems, improving energy utilization efficiency and system flexibility, while effectively supporting renewable energy integration and low-carbon development.

3. Carbon Reduction Technologies in the IES

The effective operation of the IES relies on implementing advanced technological measures across the generation, grid, load, and storage sectors to achieve significant carbon reduction outcomes. The following section systematically analyzes the specific application of technologies at each level and their emission reduction potential. The primary technological measures are illustrated in Figure 2.

3.1. Carbon Reduction Technologies on the Generation Side

The carbon reduction technologies on the generation side encompass both conventional and renewable energy domains. Initially, emission reduction for traditional energy sources is achieved by enhancing the efficient use of fossil fuels, while further reductions are driven by promoting the consumption of renewable energy, facilitating a transition toward a cleaner energy structure.

3.1.1. Efficient Utilization of Fossil Fuels

Given China’s resource endowment of being “rich in coal, poor in oil, and scarce in gas”, coal, oil, and natural gas will continue to dominate the energy mix in the short term. Without seeking cleaner utilization methods, their high carbon emissions will remain a primary driver of environmental challenges [13]. Enhancing the environmental and economic efficiency of fossil fuel use is key to the country’s energy transition [14]. Coal-fired power plants, as the backbone of baseload power, offer significant potential for cleaner utilization of fossil energy in the near term through the adoption and promotion of advanced technologies. Figure 3 illustrates the key low-carbon and high-efficiency technologies applied to coal-fired power plants.

First, the selection of ultra-supercritical (USC) units plays a crucial role in improving coal utilization efficiency and reducing carbon emissions. USC technology increases steam temperature and pressure, reducing heat loss and boosting thermal efficiency to over 45%. Compared to traditional subcritical units, this substantially lowers the carbon emissions per unit of power generated. Second, flexibility retrofitting of coal-fired units is a critical measure to drive system decarbonization. Typical flexibility retrofitting pathways include the following technological approaches: Hot water thermal storage technology: by installing thermal storage systems, surplus heat is stored to address load fluctuations and enhance peak-shaving capabilities. Power boiler and thermal storage tank integration: adding power boilers and thermal storage tanks allows the conversion of power to heat during periods of low demand, reducing excessive power consumption on the generation side. Steam turbine modification technology: modifications to steam turbines enable faster load variations and quicker start-ups and shutdowns, meeting the flexible dispatch needs of the grid. Back-pressure modification technology: this technology improves the efficiency of CCHP units, particularly in regions with high heating demand. Moreover, flexibility retrofits for coal-fired units should focus on three key performance indicators: Rapid start-up and shutdown: reducing the time required for unit start-up and shutdown enables more flexible operational scheduling, critical for managing the uncertainty of renewable energy generation. Fast load changes: units must be capable of rapidly responding to grid load fluctuations, providing reliable peak-shaving capacity. Deep peaking regulation: this technology allows coal-fired units to operate stably at low loads, minimizing unnecessary fuel consumption and carbon emissions. Through the integration and application of these technologies, it is possible to significantly reduce the carbon intensity of coal-fired power without compromising its stability and safety, thereby promoting a cleaner and more efficient transition in China’s energy structure.

3.1.2. Promoting Renewable Energy Consumption

In recent years, the rapid expansion of renewable energy installations has led to their share in China’s total power generation capacity surpassing 50%, overtaking traditional coal-fired power as the dominant energy generation [15]. However, the intermittent and random nature of wind and photovoltaic generation has posed significant challenges to the grid’s load-balancing capacity, leading to frequent instances of wind and solar curtailment. These phenomena have raised the demand for enhancing renewable energy consumption capacity [16,17,18]. To address this issue, the IES utilizes multi-energy complementary technologies to deeply integrate wind and solar power with other clean energy generations. By coupling and optimizing various energy forms, such as gas, power, heat, and cooling, the IES significantly enhances the temporal and spatial complementarity among different energy forms, thereby mitigating the impact of renewable output fluctuations on the power grid. Ref. [19] indicates that China currently faces a severe renewable energy consumption problem, and the development of multi-energy systems can help overcome grid capacity limitations and provide greater space for renewable energy consumption. Ref. [20] highlights the significant role of multi-energy complementary technologies in enhancing clean energy consumption. Meanwhile, renewable energy generation forecasting technology, based on big data and machine learning models, accurately predicts renewable generation capabilities and optimizes dispatch strategies, thereby improving the system’s ability to accommodate fluctuating power generation. Furthermore, distributed energy systems facilitate the flexible deployment of small-scale clean energy at the user level, promoting regional energy self-sufficiency and improving overall system stability and renewable energy consumption rates, effectively advancing the carbon reduction goal at the energy generation side.

3.2. Carbon Reduction Technologies on the Grid Side

Within the framework of grid-side carbon reduction technologies, the overall structure is divided into three distinct levels. Firstly, active management of the power grid technologies serves as optimization measures for individual energy systems, enhancing carbon reduction efficiency through intelligent scheduling and real-time control. Furthermore, the multi-energy coupling technologies of energy networks facilitate the coordinated optimization of various energy forms, including gas, power, heat, and cooling, thereby expanding the scope of carbon reduction and exemplifying the complementarity and synergy among multiple energy types. Ultimately, the integration of energy, transportation, and information systems not only consolidates energy systems but also incorporates transportation and information systems, achieving a profound convergence across diverse domains and systems, thereby facilitating a comprehensive enhancement of grid-side carbon reduction efforts.

3.2.1. Active Management of the Power Grid

In the evolution of the IES, the power grid has progressively transformed from a traditional passive structure into an intelligent network system characterized by active sourcing and proactive capabilities [21]. Active sourcing refers to the incorporation of distributed energy resources such as wind turbines, photovoltaics, and energy storage within the active distribution network. On one hand, the volatility of intermittent distributed power sources introduces uncertainties in the flow direction and voltage distribution within the active distribution network. On the other hand, in the event of a fault, the active distribution network possesses the capability to operate in an island mode, thereby maintaining normal power supply to non-faulted areas of the network and reducing user outages. Proactive capabilities refer to the active management functionalities of the distribution network, enabling coordinated control and proactive management of abundant controllable resources within the network, thus facilitating the efficient utilization of renewable energy and optimizing network operation. Figure 4 illustrates the proactive nature of the power grid.

This transformation represents a critical technological pathway for enhancing renewable energy consumption and reducing carbon emissions. Active management of the power grid employs intelligent sensors and big data analytics to optimize power flow in real-time. Integrating flexible scheduling and dynamic topology reconfiguration techniques significantly improves the grid’s responsiveness and operational efficiency. Through the incorporation of advanced power electronic devices, such as flexible direct current transmission and static synchronous compensators (STATCOMs), alongside intelligent control strategies, active management systems can accurately balance loads and optimize the distribution of power flow across the grid. This approach further diminishes peak-to-valley differences, enhancing the consumption of renewable energy sources. Additionally, the autonomous control algorithms and self-healing capabilities of the grid, augmented by artificial intelligence and machine learning technologies, equip the system with rapid adaptive capabilities in the face of fluctuating outputs from renewable energy generation. This ensures the stability and resilience of the overall system. Ref. [22] demonstrates an agent-based self-healing framework that complements autonomous control and AI-enhanced grid management. Featuring feeder, zone, and switch agents, the system quickly isolates faults and reconfigures the network based on local data and operational constraints, thereby reinforcing grid resilience and supporting low-carbon renewable integration.

3.2.2. Multi-Energy Coupling in Energy Networks

The coupling and coordination of energy networks are critical for achieving efficient energy utilization and reducing carbon emissions [23]. By deeply integrating gas, power, heat, and cooling networks, various forms of energy within the system can be efficiently converted and shared across temporal and spatial dimensions. Energy conversion devices, such as CCHP and gas turbine–waste heat boiler systems, along with intelligent dispatch technologies like real-time optimization algorithms based on big data and artificial intelligence scheduling strategies, play key roles in this process. Wu et al. [24] constructed an urban region multi-energy system with multiple IESs that interact with each other through the grid and heat network and compared it in detail with the performance of isolated IESs, where carbon emissions and grid interaction were reduced by 1.35 kg/m² and 1.89 kWh/m², respectively. Han et al. [25] integrated three isolated IESs in the form of cooperative alliance. The study showed that the urban region multi-energy system could realize flexible resource complementation and improve the economy of each subject, the local renewable energy accommodation rate increased to 95.8%, and the carbon emission was reduced by 13.5 t/d. In addition, it has been confirmed that hydrogen and electricity have the potential to complement each other in terms of system operation, transmission network operation, and end demand. The electric–hydrogen coupling contributes to improving the energy utilization performance and achieving lower costs in the industry multi-energy system [26]. These technologies enable the flexible allocation of multiple energy forms based on demand fluctuations, minimizing system inefficiencies and significantly enhancing overall operational efficiency. Meanwhile, virtual power plant (VPP) technology further strengthens the system’s flexibility and coordination capabilities, and Figure 5 illustrates a schematic diagram of VPP technology.

By integrating distributed energy resources, energy storage systems, and flexible loads into a unified virtual power asset, VPP can optimize the allocation of energy resources through real-time forecasting and dispatch, improving the system’s ability to adapt to variable renewable energy outputs. This is particularly valuable in scenarios with a high penetration of renewable energy, greatly enhancing system stability and flexibility. Additionally, the energy management system (EMS), through real-time data collection and forecasting, applies dynamic optimization strategies to precisely coordinate the operational states of different subsystems, ensuring efficient collaboration and energy flow between networks. Ref. [27] indicates that the coupling and coordination of energy networks not only facilitate local energy supply–demand balancing but also unlock the adjustable potential of distributed resources, promoting cross-regional energy complementarity and sharing. This significantly boosts overall energy efficiency and contributes to the system’s carbon reduction goals, ultimately supporting the long-term development goals of the IES.

3.2.3. Integration of Energy, Information, and Transportation Systems

The integration of energy networks, information and communication technologies, and transportation systems aims to achieve multidimensional synergy and resource optimization in the IES; Figure 6 illustrates the energy–information–transportation system schematic diagram.

This is enabled by advanced intelligent technologies, including distributed energy management architectures based on the internet of things (IOT) and edge computing, real-time big data analytics platforms driven by cloud computing, and dynamic optimization mechanisms supported by artificial intelligence (AI) algorithms. Through the integrated application of these technologies, the system can perceive and analyze the complex dynamic behaviors of energy, information, and transportation flows in real-time. It precisely predicts the uncertainties in energy demand and supply, facilitating deep interaction and resource sharing across different systems through coordinated control. Additionally, the combination of 5G communication technology with high-performance edge computing nodes significantly enhances cross-domain data transmission speeds and computational efficiency. This strengthens the interconnectivity between energy and transportation systems and optimizes resource allocation, making it an effective pathway toward achieving the “dual carbon” goals [28]. Ref. [29] further emphasizes that the continued development of integrated energy, information, and transportation systems not only accelerates the replacement of fossil fuels with clean energy and power in the transportation sector but also provides highly efficient technical support for energy supply and demand-side management in smart cities. This fosters technological innovation and practical applications in the pursuit of “dual carbon” targets, ultimately driving the synergistic advancement of energy systems within urban infrastructures.

3.3. Carbon Reduction Technologies on the Load Side

Within the framework of load-side carbon reduction technologies, the overall structure is divided into three distinct levels. Firstly, load forecasting technology serves as the foundation by utilizing advanced algorithms to accurately predict load demands, ensuring reasonable scheduling and efficient operation of the IES. Furthermore, multi-energy load complementarity technology achieves coordinated optimization of different energy forms based on this foundation, enhancing the overall energy efficiency of the system. Finally, intelligent management and energy efficiency optimization leverage the deep integration of smart homes, smart devices, and energy management platforms to enable real-time monitoring and dynamic optimization of system operations, thereby forming an adaptive and efficient management framework that comprehensively enhances carbon reduction benefits on the load side.

3.3.1. Load Forecasting

Inaccurate load forecasting can lead to energy supply shortages or surpluses, increasing reliance on high-carbon-emitting energy sources, resulting in energy wastage and reduced system stability. To address these challenges, load-side uncertainty management applies high-precision load forecasting techniques, real-time monitoring systems, and dynamic adjustment strategies to significantly improve forecasting accuracy and mitigate the negative impacts of forecast errors; Figure 7 illustrates the application of load forecasting technology in the IES.

High-precision load forecasting techniques enhance reliability by integrating historical data, meteorological information, and socioeconomic factors. Real-time monitoring systems provide precise tracking of loads and promptly adjust load distribution, reducing dependence on carbon-intensive energy sources. Additionally, fault-tolerant mechanisms and optimized dispatch algorithms balance supply and demand through intelligent scheduling and energy storage management, enhancing system flexibility and stability. These combined measures not only improve the efficiency of energy systems but also effectively support carbon reduction targets, promoting the development of a low-carbon economy. Ref. [30] indicates that in low-carbon demand response mechanisms, users’ carbon reduction directly influences their economic benefits, and accurate carbon reduction accounting is crucial to enhancing user participation. Since baseline load curves prior to response events cannot be directly measured, load forecasting techniques become essential. Baseline load forecasting reduces prediction errors while ensuring accurate carbon reduction calculations, optimizing the implementation of low-carbon demand response mechanisms.

3.3.2. Multi-Energy Load Complementarity

Multi-energy load complementary optimization integrates various energy forms such as gas, power, heat, and cooling to significantly enhance the energy efficiency and carbon reduction outcomes of the IES. The temporal and spatial complementarity of loads enables the system to dynamically adjust and consolidate resources during peak energy demand periods, leveraging advanced energy conversion technologies (such as power-to-thermal conversion, gas-to-power conversion) to optimize the flexibility and efficiency of energy supply [31]. This optimization strategy not only reduces reliance on high-carbon energy sources but also decreases system losses, improves renewable energy utilization, and enhances the overall stability and economic performance of the system. Moreover, multi-energy load complementarity plays a critical role in balancing energy supply–demand fluctuations, improving system responsiveness and adaptability, and optimizing the operation of energy markets, thereby supporting low-carbon economic goals. The multi-energy load complementary elasticity curve model proposed in [32] optimizes energy procurement strategies on the load side, achieving significant reductions in energy procurement costs while enhancing user benefits. This model plays a crucial role in improving resource allocation efficiency and market welfare and effectively alleviates market pressure during system congestion, thereby optimizing the low-carbon operational efficiency of energy systems. The proposed electricity–thermal–cooling planning model, as outlined in [33], integrates load clustering to coordinate multi-user load demands, thereby enabling a complementary energy supply that minimizes resource waste and enhances operational efficiency.

3.3.3. Intelligent Management and Energy Efficiency Optimization

Integrating the smart energy management system, smart devices, energy efficiency management platforms, and power quality control plays a pivotal role in enhancing flexibility on the demand side and improving energy efficiency;

Table 1. Key features of intelligent management and energy efficiency optimization.

Feature	Function and Role	Technologies
Smart energy management system	Real-time monitoring and control of energy consumption, enabling intelligent energy management	Automation control
Smart devices	Recording and transmitting electricity consumption data, assisting users in optimizing energy consumption patterns, and reducing peak load	Robust regression, neural networks, weather forecasting, user behavior patterns
Energy efficiency management platform	Detailed assessment of energy efficiency and provision of optimization recommendations, addressing energy efficiency bottlenecks	Data analysis, machine learning, adaptive control
Power quality management	Precise identification and elimination of harmonics, fluctuations, and disturbances, enhancing system reliability and operational safety	Advanced monitoring and control

shows the key features of intelligent management and energy efficiency optimization.

The smart energy management system, through the incorporation of advanced automation control technologies, can control electrical equipment in real time. The smart home scheduling model proposed in [34] effectively reduces operational costs for power and significantly lowers carbon emissions through multi-objective optimization. Smart meters, as a critical component of intelligent devices, continuously record and transmit detailed power consumption data, allowing users to gain in-depth insights into their consumption patterns and make informed optimizations. By integrating real-time power price information, weather forecasts, and user behavior patterns, smart devices further optimize energy distribution, reducing peak power loads and minimizing reliance on high-carbon energy sources. The distribution network applications leveraging smart meter data, as outlined in [35], facilitate enhanced power system efficiency and contribute to low-carbon energy transitions through advanced data-driven insights. Energy efficiency management platforms establish comprehensive energy monitoring systems, employing data analytics, machine learning, and adaptive control techniques to assess equipment efficiency in detail and provide optimization recommendations for system operation. These platforms not only automatically identify efficiency bottlenecks but also implement refined scheduling strategies that reduce both energy consumption and operational costs. Power quality management, as a crucial aspect of demand-side optimization, utilizes advanced monitoring and control technologies to precisely identify and eliminate issues such as harmonics, fluctuations, and other disturbances during power transmission, thereby improving the reliability of the power system and ensuring the safe operation of equipment. This contributes significantly to the system’s low-carbon transition. The power quality management techniques employed in smart grids, as discussed in [36], have optimized issues related to harmonics and voltage fluctuations during power transmission, effectively enhancing system operational efficiency and reducing carbon emissions, thus providing critical support for achieving a low-carbon power system.

3.4. Carbon Reduction Technologies on the Storage Side

Carbon reduction technologies on the storage side can be categorized into two levels: physical energy storage and virtual energy storage. The former relies on physical devices for energy storage, while the latter achieves virtual energy storage and release through optimized scheduling. Together, these two approaches enhance the flexibility and efficiency of the energy storage system.

3.4.1. Physical Energy Storage

The deep integration of P2G and CCUS technologies is classified as a critical solution for energy storage and carbon reduction. P2G efficiently converts and stores power energy into gaseous energy by producing hydrogen through water electrolysis and synthesizing methane with carbon dioxide, playing a significant role in grid load regulation and renewable energy consumption. Recent analyses indicate that P2G systems can achieve overall energy conversion efficiencies of up to 70%, while reducing CO₂ emissions by approximately 25% relative to conventional energy storage methods [37]. Simultaneously, CCUS technology captures CO₂ from industrial processes and combustion, providing a high-quality carbon source for the P2G process, thereby enhancing the overall carbon utilization efficiency of the system. Notably, state-of-the-art CCUS implementations have demonstrated CO₂ capture efficiencies exceeding 90%, substantially contributing to carbon reduction targets [38]. Through the synergistic operation of these technologies, the bidirectional coupling of power and gas systems optimizes energy dispatch, peak shaving, and load leveling, significantly improving system flexibility while reducing carbon emissions. For example, the two-stage robust optimization model proposed in [39] reduced operational uncertainties by 15% and decreased overall system costs by 10%, evidencing the practical advantages of integrating CCUS with P2G. Furthermore, the dynamic pricing model proposed in [40], which incorporates both CCUS and P2G technologies, optimizes the economic benefits and operational strategies of the energy system, achieving greater system flexibility and cost efficiency.

Multifaceted energy storage systems, integrating technologies such as battery storage, compressed air energy storage, and gravity-based energy storage, significantly enhance the efficiency of renewable energy utilization [41].

Table 2. Comparison of core parameters of mainstream physical energy storage technologies.

	Technology	Battery	Compressed Air	Adiabatic Compressed Air	Pumped	Gravity
Parameter
Capacity (MWh)		Hundreds	Hundreds	Hundreds	Hundreds to thousands	Tens to thousands
Efficiency (%)		60~95	42~61	50~72	65~75	80~90
Geographical adaptability		Good	Poor	Good	Poor	Good
Response		Tens of seconds	Minute	Minute	Minute	Millisecond to minute
Lifetime		Thousands of cycles	30~50 years	30~50 years	30~60 years	35~50 years
Self-discharge rate		0.1~20%/day	≈0	0.75%/day	≈0	≈0
LCOE (¥/kWh)		0.6~0.9	0.75~0.8	0.2~0.3	0.21~0.25	0.15~0.18
Technology readiness level		High	Medium	Medium	High	Low

details the core parameters of mainstream energy storage technologies. These various storage systems can efficiently store surplus energy during periods of abundant and low-cost renewable energy supply, and then flexibly convert or release this energy during peak demand periods, thereby optimizing overall energy utilization strategies. Well-configured energy storage systems not only improve the reliability of energy supply but also provide robust technical support for the integration of renewable energy sources and flexible loads. Ref. [42] highlights that diversified distributed energy storage systems—including power, hydrogen, gas, thermal, and cold storage—effectively mitigate the intermittency and variability of renewable energy units on the distribution side, while simultaneously enhancing system flexibility and economic performance. Moreover, ref. [43] emphasizes that building an energy supply system centered around clean and low-carbon energy requires efficient and cost-effective energy storage technologies, which are crucial for improving the system’s carbon reduction capabilities.

3.4.2. Virtual Energy Storage

Virtual energy storage technology, as an innovative energy management strategy, enables the virtual storage and release of energy by optimizing the scheduling of devices with spatiotemporal transfer characteristics, such as electric vehicles, heating and cooling loads, and heating systems [44]. Unlike traditional physical energy storage systems (e.g., batteries, thermal storage tanks, and cold storage tanks), virtual storage does not require actual storage devices. Instead, it achieves balance by adjusting the spatiotemporal distribution of energy consumption and the forms of energy conversion. For instance, in smart buildings, intelligent control of heating, ventilation, and air conditioning systems can leverage thermal inertia to shift energy demand, effectively acting as a virtual storage mechanism [45]. At the microgrid level, virtual energy storage can be used to smooth tie-line power fluctuations and improve economic dispatch, and at the large scale, it can reduce the renewables uncertainties and boost the system reliability and stability [46].

Table 3. Comparison of indicators for virtual and physical energy storage.

Virtual Energy Storage Type	Charging/Dis-Charging Power	Capacity	Charging/Dis-Charging Time	State of Charge (SoC)
Physical energy storage	Adjustable power for dispatch	Determined by energy characteristics	Related to equipment parameters	Remaining energy to rated capacity ratio
Temperature controlled load	Difference between transient and steady power	Depends on temperature limits	Related to perceived temperature change	Ratio of temperature difference to comfort range
Electric vehicle	Participates in demand response	Depends on max charging duration	Related to user energy profile	Ratio of remaining to max response capacity
Rotating motor		Dependent on rotor speed limits	Related to rotor inertia	Ratio of rotor speed to rated speed squared
Heating network	Influenced by heat source power, losses, and demand	Depends on supply temperature limits	Related to heat medium temperature change	Ratio of medium temperature to pipeline limits

compares the relevant metrics of virtual and physical energy storage systems. By precisely controlling the mobile storage characteristics of electric vehicles, the two-dimensional controllability of heating and cooling loads, and the thermal inertia of heating systems, virtual storage enables optimized energy scheduling across different times and regions, reducing the dependence on physical storage devices. The distributed optimization control method proposed in [47] demonstrates enhanced system regulation capacity and flexibility without the need for additional physical storage capacity by coordinating distributed resources. Additionally, ref. [48] develops a three-stage optimization model for power–gas–thermal systems, further illustrating the broad applicability of virtual storage under the generalized energy storage concept. In China, the implementation of a virtual energy storage system for aggregated air conditioner control has successfully reduced summer peak loads by 30–40%, significantly lowering the investments required for peaker power plants and promoting more economical distribution system operations [49]. A study on spatiotemporal aggregation of hydropower in the EU shows that there is potential for virtual energy storage capacity up to four times the available actual energy storage capacity in the reservoirs [50]. This model significantly improves the response speed and regulatory capacity of energy systems. The application of virtual energy storage not only enhances the ability to absorb renewable energy but also alleviates grid pressure, optimizes energy flows, and efficiently achieves multi-energy synergy and low-carbon objectives.

4. Policy–Market Mechanisms for Carbon Reduction in the IES

Within the framework of the IES, the multidimensional influence of policy–market mechanisms is critical for achieving low-carbon goals and sustainable development. This section will conduct a systematic exploration across four key dimensions: generation, grid, load, and storage, to elucidate their role in promoting low-carbon energy systems.

4.1. Carbon Reduction Mechanisms on the Generation Side

In the IES, green power and the green certificate mechanism constitute the core framework of the generation side. Green power refers to the power produced from renewable energy generation such as wind, solar, and hydropower, which directly influences the carbon emission intensity of the power system and the process of low-carbon transformation. Green certificates serve as official certification for the production of green power, ensuring compliance with environmental standards. Furthermore, they promote market recognition and trading of green power by providing trading vouchers, thereby establishing a dual mechanism of policy-driven and market-based incentives.

Table 4. China’s green certificate trading market development experience.

Year	Policy	Details
2014	Renewable energy power quota assessment methods (trial) [51]	Basic and advanced quota indicators are proposed
2016	Guidance on the establishment of a target guidance system for the development and utilization of renewable energy resources [52]	Enterprises must generate over 9% of their electricity from non-hydro renewable sources.
2017	Notice on the trial certification of renewable energy green power certificates and voluntary subscription trading system [53]	Launched a green certificate trading system aligned with renewable energy quotas and voluntary subscriptions.
2018	Clean energy consumption action plan (2018–2020) [54]	Implement a long-term clean energy consumption framework.
2019	Notice on the establishment and improvement of renewable energy power consumption guarantee mechanisms [55]	Set renewable electricity consumption responsibility weights.
2020	Notice on the weighting of responsibility for renewable energy power consumption in 2020 in each provincial administrative region [56]	Encourage responsible market players to fulfill and achieve renewable consumption targets.
2021	Green power trading pilot program [57]	Develop independent green power trading products.
2023	Notice on doing a good job in the full coverage of renewable energy green power certificates to promote renewable energy power consumption [58]	Enhance the green power certificate system to drive renewable energy consumption.
2024	Notice on strengthening the interface between green power certificates and energy saving and carbon reduction policies to vigorously promote non-fossil energy consumption [59]	Align green certificates with energy and carbon policies, expanding their application.

summarizes the development of China’s green certificate trading market.

Green power markets can be categorized into mandatory and voluntary markets based on policy objectives. Mandatory markets aim to meet policy requirements such as the renewable portfolio standard (RPS), while voluntary markets respond to voluntary consumer demand for green power. The green power market usually adopts green certificates as the basis for certification and the carrier for transferring environmental rights and interests and can be subdivided into two categories of green power trading and green certificate trading in terms of market operation: green power trading realizes “certificate and power in one” through simultaneous trading of green power and green certificates, provides new energy power generation enterprises with environmental attributes and power value gains, and facilitates the purchase of green power by users. It also facilitates the purchase of green power by users; green certificate trading, on the other hand, trades green certificates independently, using them as proof of green power consumption, with a high degree of market flexibility, independent of geographic location and power balancing zones, to better meet user demand [60]. Figure 8 further illustrates the “bundled” and “unbundled” delivery models of green power in actual transactions.

In the “bundled” mode, green power is delivered in tandem with environmental benefits, while in the “unbundled” mode, power and environmental benefits can be traded separately. These two models provide more options for different types of green power consumption needs, and

Table 5. Comparative analysis of green power transactions and green certificate transactions.

Characteristic	Green Power Trading	Green Certificate Trading
Definition	Direct purchase and sale of actual green power (renewable energy)	Purchase and sale of certificates representing green power generation (not necessarily direct power purchase)
Trading object	Power	Green power certificates
Trading format	Suppliers provide green power directly to users	Users buy certificates to support green power, with power generated from various providers
Purpose	Ensure power supply comes from renewable energy	Certify that power consumption or production supports renewable energy, tracking usage or generation through certificates
Market	Primarily includes green power markets and direct power purchase agreements	Includes green certificate markets (e.g., green certificate market) and various certificates issued by certification bodies

clearly highlights the main differences between green power trading and green certificate trading. According to the draft of the Basic Rules for Mid to Long-Term Power Trading—Chapter on Green Power Trading (Consultation Draft) published in April 2024, there is a commitment to establish a market system conducive to the production and consumption of green energy, aimed at promoting the development of the green power market and optimizing the low-carbon energy system [61]. However, currently, the penetration rate and trading volume of tradable green certificates in China remain low, indicating that the market is still in an exploratory phase [62]. Ref. [63] considers the impact of generation strategies and power prices, establishing a two-stage market equilibrium model that encompasses the GCT market and the power retail market. The authors of [64] develop a joint trading optimization mechanism for the power, carbon, and green certificate markets, revealing the inherent mechanisms by which the CET and GCT markets influence the power market.

4.2. Carbon Reduction Mechanisms on the Grid Side

On the grid side of the IES, the optimization of electricity market operation rules has substantially propelled the low-carbon transition. The electricity market encompasses three primary components: the electric energy market, the ancillary services market, and the capacity market; Figure 9 illustrates the framework of the electric market. Firstly, the electric energy market encompasses establishing both long-term and spot electricity trading markets. The long-term trading market offers a stable contractual framework that fosters long-term investments in low-carbon energy and optimally allocates resources. In contrast, the spot market utilizes real-time price signals and dynamically balances supply and demand, thereby facilitating the consumption of renewable energy and enhancing the flexibility and responsiveness of the grid. Secondly, the ancillary services market incentivizes flexible resources to participate by offering services such as peak shaving, frequency regulation, and reserves. This participation helps stabilize the power system amid the volatility of renewable energy and promotes the efficient utilization of low-carbon electricity. Lastly, the capacity market employs a compensation mechanism that prioritizes clean energy, ensuring adequate low-carbon generation capacity during peak load periods. This approach enhances system reliability, reduces reliance on high-carbon resources, and provides critical support for achieving carbon reduction targets. Ref. [65] indicates that constructing a market system suitable for the new power system is not only a requisite for achieving the energy and power transition but also a vital pathway toward attaining the goals of carbon peak and carbon neutrality. Furthermore, ref. [66] presents a method for power spot trading that considers carbon emission rights utilizing blockchain technology, which not only effectively enhances the participation of renewable energy in the spot market but also significantly improves carbon reduction efficiency, thereby providing practical technological support for the development of the IES.

4.3. Carbon Reduction Mechanisms on the Load Side

4.3.1. Demand Response Mechanisms

The demand response (DR) mechanisms commonly explored both domestically and internationally and primarily include two forms: price-based DR and incentive-based DR. Price DR is defined as the alteration of consumer energy consumption in response to price fluctuations. Conversely, incentive-based DR typically involves incentive reward policies that modify the temporal and quantitative aspects of energy consumption for IES users by providing incentive bonuses when the reliability of the power system is at risk [67]. Within the framework of the IES, DR has evolved into integrated demand response (IDR), which focuses on the coupling and collaboration of multiple energy forms, such as gas, power, thermal energy, and cooling, to achieve comprehensive optimization on the demand side [68]. IDR encompasses adjustable loads, energy storage systems, and distributed energy resources while significantly enhancing the flexibility and responsiveness of energy systems through intelligent regulation and real-time feedback mechanisms. This integrative approach not only improves energy utilization efficiency but also plays a critical role in carbon reduction by optimizing energy consumption patterns and reducing peak loads, thereby effectively lowering overall carbon emissions and facilitating the development of the IES. Ref. [69] focuses on profit maximization for the IES and examines dynamic smart pricing strategies, resulting in a mixed-integer quadratic programming optimization problem. IDR, on the other hand, considers the energy consumption demands of users for various energy types and implements demand-side incentives, typically employing elastic demand pricing to stimulate consumer engagement in energy consumption. Ref. [70] establishes an IDR model for the IES by treating hierarchical peak-shaving and valley-filling directives as internal optimization objectives while incorporating a tiered carbon trading mechanism. This approach provides diverse response strategies for users under different operational scenarios, effectively enhancing the adaptability and efficiency of DR initiatives within the IES.

4.3.2. Carbon Market Mechanism

In the IES, the carbon market mechanism plays a critical role in optimizing carbon emission reduction efficiency and enhancing economic viability [71]. The domestic carbon market framework, established by the Administrative Measures for Carbon Emission Trading [72], outlines the allocation of carbon allowances, trading rules, and regulatory mechanisms. The core of this framework lies in the combination of an allowance system and a trading mechanism, encouraging enterprises to emit within government-assigned quotas while utilizing the market to buy and sell allowances to achieve emission reduction goals in a cost-efficient manner.

Table 6. China’s carbon trading market development experience.

Year	Policy	Details
2011	Requirement of the Outline of the Twelfth Five-Year Plan to gradually establish a carbon emissions trading market [73]	Carbon emission trading pilots launched in seven provinces and cities.
2017	National Carbon Emission Trading Market Construction Program (Power Sector) [74]	China’s carbon emission trading system finalized and officially launched.
2020	Administrative Measures for Carbon Emission Trading (Trial) [75]	The national carbon market’s first compliance cycle officially launched.
2020	Implementation Program for the Setting and Allocation of Total National Carbon Emission Trading Allowances for 2019–2020 (Power Generation Sector) [76]	Integrating carbon emission and energy use rights trading into public resource platforms.
2021	Peak Carbon Action Program by 2030 [77]	Setting and allocation of total national carbon emission trading allowances.
2023	Guidelines for Building a Carbon Peak Carbon Neutral Standard System [78]	Basic completion of the dual-carbon standard system.
2024	Interim Regulations on the Administration of Carbon Emission Trading [79]	Pioneering the rule of law in China’s carbon trading.

summarizes the development of China’s carbon trading market. Specifically, the domestic carbon market is mainly divided into the mandatory carbon market and the voluntary carbon market; the mandatory carbon market includes the allocation of carbon quotas and the carbon trading mechanism, while the voluntary carbon market is mainly composed of China-certified emission reduction (CCER). Figure 10 shows the schematic diagram of the domestic carbon market mechanism in detail.

The mandatory carbon market manages carbon emissions in key emitting industries (e.g., petrochemicals, power) through the “China emissions allowance” (CEA) system. These units emit according to the quotas allocated by the government and must fulfill their obligations to clear the quotas within a specified period. As there may be discrepancies between allowances and actual emissions, the market mechanism provides flexibility in allowance trading, allowing CEA to be bought and sold through the market to adjust emissions and achieve cost-effective emission reductions. At the same time, CCER, as the main component of the voluntary carbon market, can be a complementary mechanism to the mandatory carbon market. CCER represents the carbon emission reductions generated by certified emission reduction projects. Enterprises with insufficient quotas can use CCER to offset emissions to meet reduction requirements and avoid penalties. Figure 11 demonstrates in detail the role of CCER in the offsetting mechanism of the carbon market. Currently, the national carbon market allows companies to use no more than 5 percent of CCER to offset CEA, while some pilot regions can offset up to 10 percent. Although the cost of CCER is usually lower than that of CEA, their role as a supplementary mechanism in the market is significant, and therefore reasonable control of the proportion of their use is crucial to maintaining the fairness and effectiveness of the market. Ref. [80] points out that using the stepped CET mechanism to calculate the energy cost and rationally optimizing the clean energy market can effectively promote the low-carbon development of industrial systems. Ref. [81] considered the synergistic operation of the carbon capture system and P2G system and analyzed the impact of CET market price risk on the IES. Ref. [82], based on the current trading mechanism of the domestic power carbon market, shows that the introduction of the CCER mechanism can effectively incentivize power generation groups to build additional or expanded clean energy power plants, but the proportion of CCER in carbon quota offsets needs to be reasonably limited to ensure the effectiveness of the emission reduction effect.

4.4. Carbon Reduction Mechanisms on the Storage Side

In the IES, the design of policy–market mechanisms on the energy storage side is crucial to the carbon reduction effectiveness and economy of the system. The Guiding Opinions on Accelerating the Development of New Types of Energy Storage state that energy sharing, as an innovative mechanism, significantly improves the system’s low-carbon effectiveness by optimizing the market-based allocation of energy storage resources. By integrating and scheduling decentralized energy storage resources, energy sharing enhances the system’s ability to consume renewable energy and boosts the energy storage system’s role in reducing carbon reduction, effectively supporting the realization of low-carbon goals. Currently, the structure of energy-sharing trading mechanisms can be broadly categorized into two types, such as the distributed trading and centralized trading. Distributed trading refers to point-to-point energy trading among multiple energy participants and is usually applied when there are fewer participants. It emphasizes the exchange of energy and information through a distributed approach and is suitable for those with high volume demand over a short period. The distributed trading mechanism is typically implemented through digital platforms or blockchain technologies, enabling decentralized management. In contrast, centralized trading is coordinated and managed by one or more centralized energy platforms, often overseen by aggregators or storage operators. This model is suited for scenarios with a large number of participants, substantial trading volumes, and complex regulation requirements. It facilitates more efficient resource allocation and enhances the overall operational efficiency of the system.

The distributed energy sharing mechanism is a form of distributed trading that involves the collaborative sharing of energy storage resources among multiple participants, optimizing resource utilization and sharing the resulting benefits. It breaks down the barriers between information and energy flow [83,84,85], emphasizing autonomous trading and resource sharing among participants. Cloud energy storage is a novel concept and a specific application within the distributed energy-sharing mechanism. It integrates dispersed energy storage resources into a virtual platform, allowing users to share and manage storage through the “cloud”, thereby achieving flexible demand adjustment and optimized energy storage utilization. While cloud energy storage is inherently distributed, it is managed and dispatched via a centralized digital platform, which introduces a subtle difference from the decentralized structure of distributed trading. The authors of [86] propose the concept of “cloud energy storage”, enabling interactive sharing between prosumers and the grid. The authors of [87] examine the integration of distributed energy storage in residential and commercial sectors and establish a cloud energy storage operational model to facilitate the sharing of diverse energy resources. The authors of [88] explore the coordinated management of electricity, heat, and natural gas resources of various scales within the IES, constructing a flexible and efficient multi-energy sharing platform that enables interactive mechanisms between diverse energy storage systems and the IES. The distributed trading mechanism is suitable for energy transactions involving a limited number of participants, often applied in cases with high-capacity energy demands over a short period. As the number of participants in energy sharing increases, the complexity of the distributed trading mechanism grows exponentially, inevitably introducing greater risks for both trading parties. A centralized energy-sharing mechanism is typically managed and scheduled by aggregators or third-party energy storage operators, who oversee the energy storage demands of multiple users. While the concept of resource sharing is similar to that of a distributed energy-sharing mechanism, the structure is highly centralized, allowing for coordinated control over resource allocation to meet the diverse needs of different users [89]. Ref. [90] proposes an evaluation index system for assessing the operational performance of centralized energy-sharing operators within the IES. Ref. [91] introduces a shared operational model for an integrated wind–solar–storage system, considering user demand response to optimize capacity management and system operation.

Table 7. Carbon reduction pathways for energy storage under distributed/centralized trading mechanisms.

Path	Description	Performance	Viability	Advantages	Challenges
Distributed energy sharing	Optimizes decentralized storage through cooperation	Boosts renewable consumption	High	Increases flexibility, responsiveness	Complexity rises with participants
Cloud energy storage	Virtualizes storage using cloud technology	Enhances scheduling flexibility	High	Boosts grid interaction, intelligence	Needs advanced tech, data security risks
Centralized energy sharing	Centralized management of shared storage	Improves utilization	Low	Efficient centralized control	High upfront costs, management issues
Wind–solar–storage integration	Integrates wind, solar, and storage	Optimizes operation, carbon reduction	High	Enhances stability, capacity management	High system complexity
Collaborative storage	Multi-party shared storage capacity	Enhances multi-energy systems	High	Improves overall management	Requires extensive coordination

comprehensively reflects the carbon reduction pathways on the storage side driven by policies and market mechanisms.

5. Conclusions

Against the backdrop of rapid development of renewable energy in China, IESs are increasingly becoming a crucial force in driving the energy transition towards a clean and low-carbon future. This paper, grounded in the dual impetus of technology-driven and policy-market mechanisms, summarizes pathways for carbon reduction across the generation–grid–load–storage spectrum. Starting with a typical multi-layered IES architecture, it focuses on analyzing methods to enhance carbon reduction efficiency through technological means at each stage. Key technologies, such as intelligent dispatching, virtual power plants, and virtual storage, are discussed to explore their carbon reduction potential. Furthermore, the review systematically distills the core roles of policy–market mechanisms in carbon emission reduction and comprehensively evaluates key aspects such as green power and green certificate mechanisms, power market mechanisms, and carbon trading mechanisms, summarizing the effects of policy–market mechanisms on the promotion of low-carbon transition. This review provides a theoretical framework and practical direction for future researchers aiming to support the transition to a cleaner and more efficient energy system.