Low-carbon transition planning method for coal-fired power plants considering MTSU and flexible supply-demand balance

Abstract

The present application relates to the technical field of low-carbon planning of power systems, and discloses a low-carbon transformation planning method for coal-fired power plants considering MTSU and flexible supply-demand balance. First, basic data of a power system is obtained; a transformation decision framework of a coal-fired power plant is constructed, which is unit-level, multi-stage, mutually exclusive and irreversible; a multi-stage stochastic programming scenario tree is constructed considering multi-time scale uncertainty and unexpectedness constraints; the flexibility supply capacity and demand of each resource are quantified, and explicit flexibility supply-demand balance constraints are embedded in the planning model; the total cost including investment, operation and maintenance, carbon tax and penalty items is minimized as the target, the transformation path of the coal-fired power plant and the investment in the energy storage system are jointly optimized, a mixed integer linear programming model is constructed and solved, and the optimal collaborative planning scheme is obtained. The present application can reduce the total cost and carbon emissions of the low-carbon transformation of the power system, improve the renewable energy consumption capacity, and guarantee the operation safety and flexibility of the system under high proportion of renewable energy access.

Description

Technical Field

[0001] This invention relates to the field of low-carbon planning technology for power systems, specifically to a low-carbon transformation planning method for coal-fired power plants that considers MTSU (Multi-time-scale uncertainty) and flexible supply and demand balance. Background Technology

[0002] Global energy-related carbon dioxide emissions remain high, with the power sector being the primary source, mainly due to its continued reliance on coal-fired power plants. Accelerating the low-carbon transformation of the power system is crucial to achieving carbon neutrality. The decommissioning and retrofitting of coal-fired power plants is a core strategy for achieving deep decarbonization of the power sector.

[0003] Currently, research on the low-carbon transformation of coal-fired power plants mainly revolves around several paths: first, formulating decommissioning strategies for coal-fired power plants; second, carrying out low-carbon retrofitting, mainly including installing carbon capture and storage equipment, carrying out flexibility retrofitting to improve regulation capabilities, or retrofitting to Carnot batteries; and third, investing in flexible resources such as energy storage systems to support a high proportion of renewable energy grid connection.

[0004] However, existing research still has the following shortcomings: First, most studies consider decommissioning and various retrofitting paths in isolation, or limit retrofitting decisions to specific stages, lacking an integrated, unit-level, multi-stage, and mutually exclusive collaborative decision-making framework. This fails to provide a coherent and feasible low-carbon transition roadmap for coal-fired power plants. Second, in addressing uncertainty, existing models typically handle long-term carbon tax policy uncertainty or short-term renewable energy output fluctuations separately, failing to effectively couple these two uncertainties across different time scales. Furthermore, they often employ static decision-making frameworks such as two-stage stochastic programming, which cannot adapt to the dynamic decision-making process as information is gradually revealed. Third, current research on low-carbon strategic planning for power systems often neglects the system's flexibility and supply-demand balance. In scenarios with a high proportion of renewable energy penetration, this may jeopardize the system's operational safety and stability.

[0005] Therefore, there is an urgent need for a new planning method that can synergistically optimize the multi-path transformation of coal-fired power plants and the investment in energy storage systems, effectively handle the uncertainties of multi-timescale coupling, and ensure sufficient system flexibility, so as to promote the low-carbon transformation of the power system in an economical and reliable manner. Summary of the Invention

[0006] To address the aforementioned problems, the present invention aims to provide a low-carbon transition planning method for coal-fired power plants that considers the balance between MTSU (Medium-Terminal Utilization Unit) and flexible supply and demand. This method can reduce the total cost and carbon emissions of the power system's low-carbon transition, improve the renewable energy absorption capacity, and ensure the operational safety and flexibility of the system under a high proportion of renewable energy integration. The technical solution is as follows:

[0007] A planning approach for the low-carbon transition of coal-fired power plants that considers MTSU (Medium-Terminal Utilization Unit) and the balance between supply and demand for flexibility includes the following steps:

[0008] Step 1: Obtain basic power system data; the basic data includes parameters of coal-fired power plants, load, renewable energy and energy storage system data;

[0009] Step 2: Construct a unit-level, multi-stage, mutually exclusive and irreversible transformation decision framework for coal-fired power plants; the transformation decision framework includes four paths: Carnot battery retrofit, carbon capture retrofit, flexibility retrofit and decommissioning; for each coal-fired power plant, at most one path can be selected in each stage, and once selected, it is irreversible;

[0010] Step 3: To address the long-term uncertainty of carbon tax and the short-term fluctuations in renewable energy output, construct a multi-stage stochastic programming scenario tree that considers uncertainties at multiple time scales and unpredictable constraints. The interannual uncertainty of carbon tax is represented as a carbon tax instance, and the daily changes in wind and solar power generation are represented as wind and solar power generation scenarios. The unpredictable constraints ensure that the decision at each stage depends only on the information available at that stage and is independent of the future realization of carbon tax uncertainty.

[0011] Step 4: Quantify the flexibility supply capacity and demand of each resource, and construct a flexibility supply and demand balance constraint; the resources include coal-fired power plants, Carnot batteries and energy storage systems; the flexibility supply capacity includes upward flexibility supply capacity and downward flexibility supply capacity; the flexibility demand includes the flexibility demand caused by the fluctuation of renewable energy output and the flexibility demand caused by load fluctuation.

[0012] Step 5: With the goal of minimizing the total cost including investment cost, operation and maintenance cost, carbon tax cost and penalty items, jointly optimize the transformation path of coal-fired power plants and the investment of energy storage systems, construct a mixed integer linear programming model and solve it to obtain the optimal collaborative planning scheme.

[0013] The beneficial effects of this invention are:

[0014] 1) Significantly reduce the total cost and carbon emissions of the power system's low-carbon transition.

[0015] This invention constructs a unit-level, multi-stage, mutually exclusive and irreversible transformation decision-making framework for coal-fired power plants, and jointly optimizes four paths—Carnot battery retrofit, carbon capture retrofit, flexibility retrofit, and decommissioning—along with energy storage system investment. It can select the most suitable retrofit path based on the specific characteristics of each coal-fired power plant and the overall system requirements.

[0016] 2) Enhance the capacity for renewable energy absorption and reduce power curtailment.

[0017] This invention constructs an explicit flexibility supply and demand balance constraint by quantifying the upward and downward flexibility supply capacity of coal-fired power plants, Carnot batteries and energy storage systems, as well as the flexibility demand caused by fluctuations in renewable energy output and load, and embeds it into the planning model.

[0018] 3) Ensure the operational safety and flexibility of the system under a high proportion of renewable energy integration.

[0019] This invention employs a nested multi-stage stochastic programming scenario tree and unexpected constraints to simultaneously address long-term carbon tax uncertainty (interannual variations) and short-term renewable energy output fluctuations (intraday variations), enabling investment decisions to be dynamically adjusted based on the gradual disclosure of information.

[0020] 4) Provide a coherent and feasible low-carbon transition roadmap for coal-fired power plants.

[0021] The transformation decision-making framework constructed by this invention has the characteristics of being unit-level, multi-stage, mutually exclusive and irreversible, overcoming the shortcomings of existing technologies that separate decommissioning from various transformation paths or limit transformation decisions to specific stages. It can provide each coal-fired power plant with a coherent and implementable transformation roadmap throughout the entire planning cycle.

[0022] 5) Mitigating investment losses caused by carbon tax policy risks

[0023] This invention simulates the uncertainty of carbon tax (rising, stable, and falling) using a multi-stage stochastic programming model and introduces unexpected constraints so that investment decisions at each stage depend only on the carbon tax information disclosed up to that stage. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the low-carbon transformation planning method for coal-fired power plants that considers uncertainties and flexible supply-demand balance across multiple time scales, as described in this embodiment of the invention.

[0025] Figure 2 This is a schematic diagram of the planning costs for Examples 1 to 5 in the embodiments of the present invention.

[0026] Figure 3 This is a schematic diagram of carbon emissions in Examples 1 to 5 of the present invention.

[0027] Figure 4 This is a schematic diagram of the decommissioning and retrofitting and energy storage system construction capacity in Examples 1 to 5 of the present invention.

[0028] Figure 5 This is a schematic diagram of the planning results of Examples 5 and 6 in the embodiments of the present invention.

[0029] Figure 6 This is a schematic diagram of the planning results of Examples 6 and 7 in the embodiments of the present invention. Detailed Implementation

[0030] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0031] like Figure 1 The proposed low-carbon transition planning method for coal-fired power plants, which considers uncertainties and flexible supply-demand balance across multiple time scales, includes the following steps:

[0032] Step 1: Obtain basic power system data, including parameters of coal-fired power plants, load, and data on renewable energy and energy storage systems.

[0033] Step 2: Construct a unit-level, multi-stage, mutually exclusive and irreversible transformation decision framework for coal-fired power plants; the transformation decision framework includes four paths: Carnot battery retrofit, carbon capture retrofit, flexibility retrofit and decommissioning; for each coal-fired power plant, at most one path can be selected in each stage, and once selected, it is irreversible.

[0034] Carnot battery retrofitting significantly reduces carbon emissions from coal-fired power plants while retaining their flexibility. This technology stores electricity generated from renewable energy sources as heat, which is then converted back into electricity using the existing Rankine steam cycle. The constraints associated with the Carnot battery retrofitting are as follows:

[0035] ;

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[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] In the formula, and They represent stage Timecano Battery The charging and releasing of heat energy in the thermal storage tank; Indicates the conversion efficiency of the electric heating equipment; and They represent stage Timecano Battery The charging and discharging power; Indicates Carnot battery The capacity of the electric heating equipment; and They represent stage Timecano Battery The charging and discharging states are 0-1 variables; Indicates the efficiency of the Rankine cycle; and These represent Carnot batteries. The minimum and maximum output power; express Stage Carnot Battery The decision variables for the transformation are 0-1 variables; express Timecano Battery Thermal energy stored in thermal storage tanks; and These represent Carnot batteries. The heat storage and heat release efficiency of the thermal storage tank; and These represent Carnot batteries. The minimum and maximum heat storage capacity; and These represent Carnot batteries. The heat storage and release ramp-up rate of the thermal storage tank; Indicates a time interval; and These represent Carnot batteries. Minimum start and stop times. and These represent the initial and final thermal energy of a typical Nikkor power plant, respectively. and They represent stage Timecano Battery Continuous power-on and power-off time.

[0047] Carbon capture and treatment (CCD) retrofits enable coal-fired power plants to ensure a stable power supply while also facilitating their transition to low-carbon energy systems. The constraints related to these CCD retrofits are as follows:

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] In the formula, express stage Coal-fired power plants after carbon capture and storage retrofit Carbon capture amount; express stage Shike coal-fired power plant Maximum carbon capture capacity; express Phased coal-fired power plants The decision variables for carbon capture and storage transformation are 0-1 variables; Indicates coal-fired power plant Maximum capture rate; express stage Shike coal-fired power plant Carbon emissions; Indicates coal-fired power plant carbon emission intensity; express stage Shike coal-fired power plant ; output power; express stage Shike coal-fired power plant The power used to provide carbon capture and storage equipment; Indicates coal-fired power plant Carbon capture and storage (CCLS) reduces power consumption. Indicates coal-fired power plant Unit power consumption for carbon capture and storage; express stage Shike coal-fired power plant The net output power.

[0054] The flexibility retrofit of coal-fired power plants involves modifying boilers or turbines to reduce the minimum output level and increase the ramp-up rate. This method transforms coal-fired power plants from traditional primary energy generators into resources providing critical reliability support and flexible regulation services. This not only aligns with carbon reduction goals but also helps improve the power system's capacity to absorb renewable energy and its ability to cope with fluctuations in operational demand. The constraints related to the flexibility retrofit are as follows:

[0055] ;

[0056] ;

[0057] ;

[0058] In the formula, and They represent coal-fired power plants Minimum and maximum output power; express stage Shike coal-fired power plant The start / stop status is a 0-1 variable; express Phased coal-fired power plants The decision variables for flexible modification are 0-1 variables; Indicates coal-fired power plant Minimum output power after flexibility modification; express stage Shike coal-fired power plant ; output power; and They represent coal-fired power plants The upward and downward ramp rates; and They represent coal-fired power plants The upward and downward ramp rates after the flexibility modification.

[0059] Retiring coal-fired power plants is widely considered an effective strategy for promoting a low-carbon energy transition. Gradually phasing out outdated and inefficient coal-fired power units can significantly reduce carbon emissions from the power industry. The constraints related to the retirement are as follows:

[0060] ;

[0061] In the formula, express Phased coal-fired power plants The decision variable for retirement is a 0-1 variable.

[0062] Step 3: To address the long-term uncertainty of carbon tax and the short-term fluctuations in renewable energy output, a nested multi-stage stochastic programming scenario tree is constructed, and an unexpected constraint is introduced. The interannual uncertainty of carbon tax is represented as a carbon tax instance, and the daily changes in wind and solar power generation are represented as wind and solar power generation scenarios. The unexpected constraint ensures that the decision at each stage depends only on the information available at that stage and is independent of the future realization of carbon tax uncertainty.

[0063] The trajectory of carbon taxes is influenced by policy frameworks, technological advancements, economic conditions, and international dynamics, leading to long-term uncertainty. To ensure that investment decisions are independent of future actions and uncertainties, unforeseen constraints are applied to simulate carbon tax scenarios. These constraints ensure the practical feasibility of investment decisions under uncertainty. They guarantee that decisions at each stage depend solely on the information available at that stage, and are independent of the future realization of carbon tax uncertainty.

[0064] In contrast to the annual fluctuations in carbon taxes, the daily fluctuations in wind and solar output are much more significant and volatile. Wind power typically peaks in the morning and evening, then tends to decline in the afternoon. Solar production is affected by solar radiation, with its maximum output usually occurring around midday and decreasing rapidly as the sun's angle changes.

[0065] Recognizing the different timescales of carbon taxes (which vary annually) and wind and solar power generation (which varies daily), this invention proposes a multi-stage instance tree incorporating uncertainties across multiple timescales. In the proposed model, inactivity constraints are used to simulate the interannual uncertainty of carbon taxes, denoted as carbon tax instances. The daily variations in wind and solar power generation are represented by scenarios, referred to as wind and solar power generation scenarios. The decision for each carbon tax instance should be adapted to the daily fluctuations in wind and solar power generation across different scenarios, thus deriving a solution feasible for all scenarios.

[0066] Step 4: Quantify the flexibility supply capacity and demand of each resource, and construct a flexibility supply and demand balance constraint; the resources include coal-fired power plants, Carnot batteries and energy storage systems; the flexibility supply capacity includes upward flexibility supply capacity and downward flexibility supply capacity; the flexibility demand includes the flexibility demand generated by the fluctuation of renewable energy output and the flexibility demand generated by load fluctuation.

[0067] The supply and demand balance constraints for flexibility are as follows: ; ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] In the formula, and They represent stage Shike coal-fired power plant Upward and downward flexible supply capacity; This function represents the function that returns the smaller of two values. and They represent stage Timecano Battery Upward and downward flexible supply capacity; and They represent stage Real-time energy storage system Upward and downward flexible supply capacity; and They represent stage Upper and lower limits of fluctuations in renewable energy output at any given time; This represents the maximum prediction error coefficient for renewable energy units; and They represent stage The capacity required for upward and downward flexibility of renewable energy units at all times; and They represent stage Upper and lower limits of load fluctuation at any given time; This represents the maximum load forecast error coefficient; and They represent stage The capacity required for flexible load adjustment at all times, whether it is increasing or decreasing. Indicates energy storage system Maximum input and output power; and They represent stage Real-time energy storage system The charging and discharging power; express stage Real-time energy storage system The amount of electricity; and They represent energy storage systems. The minimum and maximum energy storage capacity; and They represent energy storage systems. The charging and discharging efficiency; express stage The output of renewable energy units at any given time; express stage The load size at any given time; , , These represent the number of coal-fired power plants, Carnot batteries, and energy storage systems, respectively.

[0073] Step 5: With the goal of minimizing the total cost including investment cost, operation and maintenance cost, carbon tax cost and penalty items, jointly optimize the transformation path of coal-fired power plants and the investment of energy storage systems, construct a mixed integer linear programming model and solve it to obtain the optimal collaborative planning scheme.

[0074] Total cost includes the cost of retrofitting and decommissioning coal-fired power plants. Maintenance costs of coal-fired power plants Investment costs of energy storage systems and the operating costs incurred at each stage. The costs of retrofitting and decommissioning coal-fired power plants include the costs of retrofitting Carnot batteries. Flexibility and transformation costs Carbon capture and transformation costs and the cost of decommissioning the unit Operating costs include fuel costs and start-up and shutdown costs for coal-fired power plants. Carbon tax costs The cost of abandoning load and the cost of penalties for curtailing renewable energy The objective function is expressed as:

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[0089] In the formula, This represents the total cost over the entire planning period; express Stage present value factor; This indicates the cost of retrofitting and decommissioning coal-fired power plants; This indicates the maintenance cost of a coal-fired power plant; This indicates the investment cost of the energy storage system; This represents the total operating cost during the entire planning period; Indicates the discount rate; express The number of years in a given period; , , and These represent the costs of Carnot battery retrofitting, flexibility retrofitting, carbon capture and storage retrofitting, and decommissioning of coal-fired power plants, respectively. , and These represent the investment costs per unit of electric heating equipment, thermal storage tank, and steam system, respectively. and These represent Carnot batteries. Coal-fired power plants Rated capacity; and These represent the unit costs for flexibility upgrades and carbon capture and storage upgrades, respectively. and These represent the unit value cost and disposal cost of waste from a coal-fired power plant, respectively. Indicates coal-fired power plant Maintenance costs in the reference year; This indicates the annual growth rate of maintenance costs; and These represent the unit capacity cost and power cost of the energy storage system, respectively. and They represent energy storage systems. Rated power, rated capacity; express Staged energy storage system The decision variables for investment and construction are 0-1 variables; , , and These represent the operating costs of coal-fired power plants, carbon tax costs, off-load penalty costs, and renewable energy curtailment costs, respectively. Indicates the number of days in a year; Indicates coal-fired power plant fuel prices; Indicates coal-fired power plant The heat dissipation curve; and They represent stage Shike coal-fired power plant Start-up and shutdown costs; express The price of carbon tax at different stages; This represents the unit loss-of-load penalty cost; express stage The magnitude of the load loss at any given moment; This represents the unit cost of penalties for curtailing renewable energy. and They represent stage Wind turbine Photovoltaic units The predicted value; and These represent wind turbine units. Photovoltaic units . output power. Indicates the total number of stages; A collection of Carnot batteries; Indicates Carnot battery The planned capacity of the thermal storage tank; A collection of coal-fired power plants; A collection of energy storage systems; This represents the total number of hours in a typical day; A collection of wind turbine units; A collection of photovoltaic units.

[0090] The constraints of the mixed-integer linear programming model include investment constraints and operational constraints.

[0091] Investment constraints are:

[0092] ;

[0093] ;

[0094] Operational constraints include power balance constraints, coal-fired power plant operation constraints, renewable energy unit operation constraints, and energy storage system operation constraints.

[0095] The power balance constraint is:

[0096] ;

[0097] ;

[0098] In the formula, and They represent stage Real-time energy storage system The charging and discharging power; express stage The load size at any given moment. , , These represent the number of wind turbine units, photovoltaic units, and energy storage systems, respectively.

[0099] The operating constraints for coal-fired power plants are:

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[0101] ;

[0102] ;

[0103] ;

[0104] In the formula, and They represent coal-fired power plants Start-up and shutdown fees; This indicates a function that returns the larger of two values. Indicates coal-fired power plant Continuous boot time, Indicates coal-fired power plant Continuous downtime, Indicates coal-fired power plant Minimum boot time Indicates coal-fired power plant Minimum downtime.

[0105] The operating constraints for renewable energy units are:

[0106] ;

[0107] The operating constraints of the energy storage system are:

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] In the formula, Indicates energy storage system Maximum input and output power; and They represent stage Real-time energy storage system The charging and discharging states are 0-1 variables; express stage Real-time energy storage system The amount of electricity; and They represent energy storage systems. The charging and discharging efficiency; and They represent energy storage systems. The minimum and maximum energy storage capacity; Indicates energy storage system The initial charge. and They represent energy storage systems. The amount of electricity at the initial and final moments.

[0114] exist In this phase, once uncertainty surrounding the carbon tax is observed, decisions will be made regarding the retrofitting and decommissioning of coal-fired power plants and the construction of energy storage systems. These decisions must also take into account the daily fluctuations in wind and solar power generation for each carbon tax instance. Represents an example of a carbon tax Wind and solar energy scenarios within Unexpectedness constraints ensure that decisions at each stage depend only on the uncertainties observed up to that point. With carbon tax examples... The gradual unfolding of the data allows for a series of adaptive decisions to be made sequentially. This approach effectively addresses the interannual variability of carbon taxes as well as the diurnal fluctuations in wind and solar power generation.

[0115] To rigorously characterize the aforementioned uncertainties across multiple time scales and sequential decision-making logic, a multi-stage stochastic programming model was constructed. Formally, the entire programming period is assumed to cover… The stages, namely The uncertainty of carbon tax stems from various scenarios. ( ) represents its corresponding probability satisfy .in, This represents the total amount of carbon tax. for Phase 1 A carbon tax scenario.

[0116] To describe the gradual revelation of information over time, the first... The stage scene tree node set is , From 1 to Phase 1 Each carbon tax scenario uniquely corresponds to a cutoff period. The specific historical trajectory of carbon tax implementation. Within this scenario tree framework, stages... The planning decision variables are defined as depending solely on the revealed historical trajectories. Regarding the random fluctuations in short-term renewable energy output... ( The model further introduces a pursuit decision vector. To characterize the adaptive operation and adjustment of the system, and the corresponding intraday scenario conditional probability. satisfy It is worth emphasizing that the stage Decision and node set Related, which means up to the stage Two complete carbon tax scenarios with the same history belong to the same node, and therefore must share exactly the same stage. Planning and decision-making.

[0117] The multi-stage stochastic programming model is shown below:

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[0120] ;

[0121] ;

[0122] ;

[0123] In the formula, , , , , It is an abstract form of vectors and matrices with appropriate dimensions; for Phased carbon tax scenario Planning decision variables under; express Phased carbon tax scenario ; express Phased carbon tax scenario Renewable energy scenarios ; Index for carbon tax scenarios.

[0124] The processed data are sequentially input into the mixed integer linear programming model, and the solver is used to solve the optimal collaborative programming scheme.

[0125] This embodiment provides a specific planning method, using an improved IEEE 24-bus power system as the case study. This system originally had 32 thermal power units (totaling 3650MW). To simplify calculations, the model sets each planning phase as 5 years. The construction of energy storage systems and the decommissioning and retrofitting of coal-fired power plants are assumed to be completed in the first year of each phase. The entire planning period is 15 years, with each phase represented by a typical 24-hour day. Relevant parameters for coal-fired power plant retrofitting and decommissioning, as well as candidate energy storage systems, are shown in Table 1. Table 2 shows the peak load and the planned installed capacity of wind and solar power in each phase. Table 1: Main Parameters of the Planning Model

[0126] .

[0127] Table 2 Peak Load and Renewable Energy Installed Capacity at Each Stage

[0128] .

[0129] The effectiveness of the proposed method was evaluated using five case studies:

[0130] Case 1: Coordinated planning of coal-fired power plant decommissioning and energy storage system investment.

[0131] Case 2: Based on Case 1, add the modification of Carnot battery.

[0132] Case 3: Based on Case 1, we will make more flexible modifications.

[0133] Case 4: Based on Case 1, carbon capture and storage (CCLS) modifications are added.

[0134] Case 5: Based on Case 1, integrate multi-path transformation solutions.

[0135] Case 6: Building on Case 5, incorporate the long-term uncertainty of carbon tax.

[0136] Case 7: Building on Case 6, incorporate the short-term uncertainties of renewable energy.

[0137] Case 1: As a baseline scenario, this case only considers the decommissioning of coal-fired power plants and investment in energy storage systems. For example... Figures 2 to 4 As shown, the total cost is US$5.1797 billion, carbon emissions are 1.8 billion tons, the decommissioned installed capacity is 2,074 megawatts, and the energy storage capacity is 2,710 megawatt-hours.

[0138] Case 2: This case study builds upon Case 1 by incorporating Carnot battery retrofitting. Total cost was reduced by 9.74%, and carbon emissions by 4.30%. This is attributed to the conversion of 861 MW of inefficient, aging coal-fired power units, originally slated for retirement, into Carnot batteries, thereby enhancing the system's renewable energy absorption capacity and flexible supply capabilities. Furthermore, Carnot batteries replaced energy storage requirements in this case, further reducing overall costs. However, due to the relatively low efficiency of Carnot batteries, their carbon reduction effect remains limited.

[0139] Case 3: This case study builds upon Case 1 by implementing a flexibility retrofit. Total costs were reduced by 5.17%, and carbon emissions by 7.1%. The flexibility retrofit capacity reached 1850 MW, targeting coal-fired units scheduled for retirement in the baseline scenario that had not yet reached their design life but had poor peak-shaving capabilities. This type of retrofit enhances the system's flexible supply capacity and improves the grid integration of renewable energy. However, to maintain system stability, coal-fired units must maintain minimum output levels, which limits the overall carbon reduction potential.

[0140] Case 4: This case applies carbon capture and storage (CCS) retrofitting to Case 1. Total cost was reduced by 3.75%, and carbon emissions were significantly reduced by 43.81%. The CCS retrofitting capacity was 1850 MW, with a supporting energy storage capacity of 1750 MWh. This retrofitting significantly reduced carbon emissions from continuously powered coal-fired units. Although CCS retrofitting is effective in reducing emissions, its high unit retrofitting cost makes it the least economical option among all retrofitting pathways.

[0141] Case 5: This case integrates a multi-path retrofit solution, including Carnot battery retrofit, flexibility retrofit, and carbon capture retrofit. Compared to Case 1, the proposed strategy reduces total costs by 11.53% and carbon emissions by 53.1%. Compared to Cases 2-4, this case demonstrates improvements in both economic efficiency and low-carbon performance. A total of 2414 MW of coal-fired units were retrofitted, including 564 MW of Carnot battery retrofit, 750 MW of flexibility retrofit, and 1100 MW of carbon capture retrofit. Each coal-fired power plant followed the most suitable retrofit path based on its specific characteristics and overall system requirements. Although retrofit and decommissioning costs increased, operating costs decreased by 20.3%. Overall, this strategy has proven to be significantly effective in reducing carbon emissions and system operating costs during the low-carbon transformation of the power system.

[0142] Case 6: This case study employs a multi-stage stochastic programming approach that considers unexpected constraints to address the uncertainty of carbon tax policy. By combining the impacts of policy, technology, and economic factors on the carbon tax, the carbon tax trajectory during the planning period is categorized into three types: increasing, stable, and decreasing. Cluster analysis is performed on each type to construct a scenario tree representing the uncertainty of the carbon tax.

[0143] Figure 5 The planning results of Case 5 and Case 6 were compared. In Case 6, the decommissioning and retrofitting decisions under carbon tax scenarios ①→③→⑦ are consistent with those of Case 5, but differences exist in other carbon tax scenarios. For example, under carbon tax scenarios ①→④→⑨, compared to Case 5, Case 6 increases the Carnot battery retrofitting capacity by 226 MW, the flexibility retrofitting capacity by 350 MW, the carbon capture retrofitting capacity by 700 MW, and adds 352 MW of decommissioning capacity. This indicates that the planning decisions in Case 5 may not always remain optimal or applicable under different carbon tax scenarios. Clearly, the uncertainties associated with carbon taxes are becoming increasingly prominent, and Case 6 can support a more flexible and dynamic decision-making process.

[0144] Case 7: For each carbon tax instance, wind and solar power scenarios were constructed at a 90% confidence level. To simulate the uncertainties of wind and solar power generation under each carbon tax instance, a total of 10,000 wind and solar power scenarios were generated. Subsequently, five typical scenarios were retained through scenario reduction techniques to achieve a balance between computational efficiency and accuracy.

[0145] In Case 7, the capacity for flexible retrofits and the capacity for retrofitting coal-fired boilers increased in all carbon tax examples. For example... Figure 6 As shown, taking carbon tax examples ①→③→⑦ as examples, compared to example 6, example 7 saw an increase of 185 MW in coal-fired boiler retrofit capacity and 200 MW in flexibility retrofit capacity. The introduction of uncertainties in wind and solar power generation further increased the system's demand for flexibility. The expansion of flexibility retrofit and coal-fired boiler retrofit capacity effectively promoted the grid connection and consumption of wind and solar power, and enhanced the system's flexible supply capacity.

Claims

1. A method for planning the low-carbon transition of coal-fired power plants, considering the balance between MTSU (Mechanical, Material, and Sustainability Units) and flexibility supply and demand, characterized in that: Includes the following steps: Step 1: Obtain basic power system data; the basic data includes parameters of coal-fired power plants, load, renewable energy and energy storage system data; Step 2: Construct a unit-level, multi-stage, mutually exclusive and irreversible transformation decision framework for coal-fired power plants; the transformation decision framework includes four paths: Carnot battery retrofit, carbon capture retrofit, flexibility retrofit and decommissioning; for each coal-fired power plant, at most one path can be selected in each stage, and once selected, it is irreversible; Step 3: To address the long-term uncertainty of carbon tax and the short-term fluctuations in renewable energy output, construct a multi-stage stochastic programming scenario tree that considers uncertainties at multiple time scales and unpredictable constraints. The interannual uncertainty of carbon tax is represented as a carbon tax instance, and the daily changes in wind and solar power generation are represented as wind and solar power generation scenarios. The unpredictable constraints ensure that the decision at each stage depends only on the information available at that stage and is independent of the future realization of carbon tax uncertainty. Step 4: Quantify the flexibility supply capacity and demand of each resource, and construct a flexibility supply and demand balance constraint; the resources include coal-fired power plants, Carnot batteries and energy storage systems; the flexibility supply capacity includes upward flexibility supply capacity and downward flexibility supply capacity; the flexibility demand includes the flexibility demand caused by the fluctuation of renewable energy output and the flexibility demand caused by load fluctuation. Step 5: With the goal of minimizing the total cost including investment cost, operation and maintenance cost, carbon tax cost and penalty items, jointly optimize the transformation path of coal-fired power plants and the investment of energy storage systems, construct a mixed integer linear programming model and solve it to obtain the optimal collaborative planning scheme.

2. The method for low-carbon transition planning of coal-fired power plants considering MTSU and flexibility supply and demand balance as described in claim 1, characterized in that, In step 2, the constraints related to the Carnot battery modification are as follows: ； ； ； ； ； ； ； ； ； ； ； In the formula, and They represent stage Timecano Battery The charging and releasing of heat energy in the thermal storage tank; Indicates the conversion efficiency of the electric heating equipment; and They represent stage Timecano Battery The charging and discharging power; Indicates Carnot battery The capacity of the electric heating equipment; and They represent stage Timecano Battery The charging and discharging states are 0-1 variables; Indicates the efficiency of the Rankine cycle; and These represent Carnot batteries. The minimum and maximum output power; express Stage Carnot Battery The decision variables for the transformation are 0-1 variables; express Timecano Battery Thermal energy stored in thermal storage tanks; and These represent Carnot batteries. The heat storage and heat release efficiency of the thermal storage tank; and These represent Carnot batteries. The minimum and maximum heat storage capacity; and These represent Carnot batteries. The heat storage and release ramp-up rate of the thermal storage tank; Indicates a time interval; and These represent Carnot batteries. Minimum start and stop times; and These represent the initial and final thermal energy of a typical Nikkor power plant, respectively. and They represent stage Timecano Battery Continuous power-on and power-off time; The constraints related to the carbon capture modification are as follows: ； ； ； ； ； In the formula, express stage Coal-fired power plants after carbon capture and storage retrofit Carbon capture amount; express stage Shike coal-fired power plant Maximum carbon capture capacity; express Phased coal-fired power plants The decision variables for carbon capture and storage transformation are 0-1 variables; Indicates coal-fired power plant Maximum capture rate; express stage Shike coal-fired power plant Carbon emissions; Indicates coal-fired power plant carbon emission intensity; express stage Shike coal-fired power plant ; output power; express stage Shike coal-fired power plant The power used to provide carbon capture and storage equipment; Indicates coal-fired power plant Carbon capture and storage (CCLS) reduces power consumption. Indicates coal-fired power plant Unit power consumption for carbon capture and storage; express stage Shike coal-fired power plant Net output power; The constraints related to the flexibility modification are as follows: ； ； ； In the formula, and They represent coal-fired power plants Minimum and maximum output power; express stage Shike coal-fired power plant The start / stop status is a 0-1 variable; express Phased coal-fired power plants The decision variables for flexible modification are 0-1 variables; Indicates coal-fired power plant Minimum output power after flexibility modification; and They represent coal-fired power plants The upward and downward ramp rates; and They represent coal-fired power plants The upward and downward ramp rates after the flexibility modification; The relevant constraints regarding retirement are as follows: ； In the formula, express Phased coal-fired power plants The decision variable for retirement is a 0-1 variable.

3. The low-carbon transition planning method for coal-fired power plants considering MTSU and flexibility supply and demand balance as described in claim 2, characterized in that, In step 4, the flexibility supply and demand balance constraint is as follows: ； ； ； ； ； ； In the formula, and They represent stage Shike coal-fired power plant Upward and downward flexible supply capacity; and These represent functions that return the smaller and larger of two values, respectively. and They represent stage Timecano Battery Upward and downward flexible supply capacity; and They represent stage Real-time energy storage system Upward and downward flexible supply capacity; and They represent stage Upper and lower limits of fluctuations in renewable energy output at any given time; This represents the maximum prediction error coefficient for renewable energy units; and They represent stage The capacity required for upward and downward flexibility of renewable energy units at all times; and They represent stage Upper and lower limits of load fluctuation at any given time; This represents the maximum load forecast error coefficient; and They represent stage Capacity requirements for flexibility in both upward and downward load conditions; Indicates energy storage system Maximum input and output power; and They represent stage Real-time energy storage system The charging and discharging power; express stage Real-time energy storage system The amount of electricity; and They represent energy storage systems. The minimum and maximum energy storage capacity; and They represent energy storage systems. The charging and discharging efficiency; express stage The output of renewable energy units at any given time; express stage The load size at any given time; , , These represent the number of coal-fired power plants, Carnot batteries, and energy storage systems, respectively.

4. The low-carbon transition planning method for coal-fired power plants considering MTSU and flexibility supply and demand balance as described in claim 3, characterized in that, In step 5, the mixed-integer linear programming model includes an objective function and constraints: The objective function is expressed as: ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the formula, This represents the total cost over the entire planning period; express Stage present value factor; This indicates the cost of retrofitting and decommissioning a coal-fired power plant; This indicates the maintenance cost of a coal-fired power plant; This indicates the investment cost of the energy storage system; This represents the total operating cost during the entire planning period; Indicates the discount rate; express The number of years in a given period; , , and These represent the costs of Carnot battery retrofitting, flexibility retrofitting, carbon capture and storage retrofitting, and decommissioning for coal-fired power plants, respectively. , and These represent the investment costs per unit of electric heating equipment, thermal storage tank, and steam system, respectively. and These represent Carnot batteries. Coal-fired power plants Rated capacity; and These represent the unit costs for flexibility upgrades and carbon capture and storage upgrades, respectively. and These represent the unit value cost and disposal cost of waste from a coal-fired power plant, respectively. express Maintenance costs in the reference year; This indicates the annual growth rate of maintenance costs; and These represent the unit capacity cost and power cost of the energy storage system, respectively. and They represent energy storage systems. Rated power and rated capacity; express Staged energy storage system The decision variables for investment and construction are 0-1 variables; , , and These represent the operating costs of coal-fired power plants, carbon tax costs, off-load penalty costs, and renewable energy curtailment costs, respectively. Indicates the number of days in a year; Indicates coal-fired power plant fuel prices; Indicates coal-fired power plant The heat dissipation curve; and They represent stage Shike coal-fired power plant Start-up and shutdown costs; express The price of carbon tax at different stages; This represents the unit loss of load penalty cost; express stage The magnitude of the load loss at any given moment; This represents the unit cost of penalties for curtailing renewable energy. and They represent stage Wind turbine Photovoltaic units The predicted value; and These represent wind turbine units. Photovoltaic units ; output power; Indicates the total number of stages; A collection of Carnot batteries; Indicates Carnot battery The planned capacity of the thermal storage tank; A collection of coal-fired power plants; A collection of energy storage systems; This represents the total number of hours in a typical day; A collection of wind turbine units; A collection of photovoltaic units; The constraints include investment constraints and operational constraints for coal-fired power plants; The investment constraints are as follows: ； ； in, express Phased coal-fired power plants The decision variables for the transformation are 0-1 variables; The operational constraints include power balance constraints, coal-fired power plant operational constraints, renewable energy unit operational constraints, and energy storage system operational constraints; specifically as follows: The power balance constraint is: ； ； In the formula, and They represent stage Real-time energy storage system The charging and discharging power; express stage The load size at any given time; and These represent the number of wind turbine units and photovoltaic units, respectively. The operating constraints of the coal-fired power plant are: ； ； ； ； In the formula, and They represent coal-fired power plants Start-up and shutdown fees; This function represents the function that returns the larger of two values. Indicates coal-fired power plant Continuous boot time, Indicates coal-fired power plant Continuous downtime, Indicates coal-fired power plant Minimum boot time Indicates coal-fired power plant Minimum downtime; The operating constraints for the renewable energy units are: ； The operating constraints of the energy storage system are: ； ； ； ； ； In the formula, Indicates energy storage system Maximum input and output power; and They represent stage Real-time energy storage system The charging and discharging states are 0-1 variables; express stage Real-time energy storage system The amount of electricity; and They represent energy storage systems. The charging and discharging efficiency; and They represent energy storage systems. The minimum and maximum energy storage capacity; Indicates energy storage system The initial charge; and They represent energy storage systems. The amount of electricity at the initial and final moments.

5. The method for low-carbon transition planning of coal-fired power plants considering MTSU and flexibility supply and demand balance as described in claim 4, characterized in that, Step 3 involves constructing a multi-stage stochastic programming scenario tree that considers uncertainties and unforeseen constraints across multiple time scales. Specifically, this includes: Construct a multi-stage stochastic programming model, assuming the planning period covers NT stages, and the uncertainty of carbon tax is determined by the scenario. express, , This represents the total amount of carbon tax. for Phase 1 Individual carbon tax scenarios; corresponding probabilities satisfy ; Define the first The stage scene tree node set is , From 1 to Phase 1 Each carbon tax scenario uniquely corresponds to a historical carbon tax implementation trajectory up to stage t; the planning and decision variables for stage t are defined as relying solely on the revealed historical trajectory. ; targeting the stochastic fluctuations in short-term renewable energy output , , Number of renewable energy scenarios; introduction of a trailing decision vector Its intraday scenario conditional probability satisfy Unexpected constraints ensure that two complete carbon tax scenarios with the same history up to stage t belong to the same node and must share the exact same planning decisions for stage t. The multi-stage stochastic programming model is as follows: ； ； ； ； ； In the formula, , , , , It is an abstract form of vectors and matrices with appropriate dimensions; for Phased carbon tax scenario Planning decision variables under; express Phased carbon tax scenario ; express Phased carbon tax scenario The following renewable energy scenarios ; Index for carbon tax scenarios; In step 5, the processed data are sequentially input into the mixed integer linear programming model and solved using a solver to obtain the optimal collaborative programming scheme.

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