Configuration method of carbon reduction technology in power system

Abstract

The invention provides a configuration method of a carbon reduction technology in a power system. Relates to the technical field of low-carbon power system planning. The method comprises the following steps: taking cost minimization of configuration of a carbon reduction technology as an optimization target, taking a mode of flexible transformation of a thermal power generating unit, the number of expansion of a wind turbine generator and scales of added hydrogen energy storage systems and carbon capture, storage and utilization equipment as decision variables, and taking technical constraints of the carbon reduction technology as constraint conditions; constructing a system-level planning model of the target power system; and solving the system-level planning model to obtain an optimal configuration parameter which minimizes the cost. The method breaks through the limitation of isolated configuration of each carbon reduction technology in traditional planning, realizes dynamic adaptation of hydrogen energy storage scale, thermal power flexible transformation mode, wind power expansion rhythm and carbon capture storage and utilization equipment configuration, and realizes balance of economic cost and operation flexibility of an electric power system under carbon emission reduction rigid constraint.

Description

Technical Field

[0001] This invention relates to the field of low-carbon power system planning technology, and in particular to a method for configuring carbon reduction technologies in a power system. Background Technology

[0002] While thermal power units remain the primary force in ensuring the safe and stable operation of the power system and providing reliable and flexible regulation capabilities, under the dual-carbon context, simply relying on the self-modification of thermal power plants is no longer sufficient to meet the dual demands of deep peak shaving and low-carbon operation. Therefore, introducing low-carbon technologies such as wind power and hydrogen storage into the grid has become an inevitable choice for optimizing the energy structure. However, the randomness and volatility of large-scale grid connection can easily lead to system power imbalances. Therefore, how to scientifically configure and plan the capacity of these technologies is crucial to ensuring the achievement of carbon reduction targets while controlling overall costs and providing a basis for energy transition decisions.

[0003] Existing configuration planning methods for grid-connected multiple low-carbon technologies mainly adopt a decentralized planning and independent dispatch operation mode. This mode treats each low-carbon technology as an isolated unit for separate planning and dispatch, and regards each low-carbon technology as a parallel competing entity independent of thermal power units. This results in the fragmentation of material flow between technologies, making it impossible to achieve closed-loop optimal allocation of system resources. As a result, the system is forced to maintain high redundancy to ensure reliability, leading to significant investment redundancy and waste of operating resources, which restricts the economy and feasibility of the low-carbon transformation of the power system. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for configuring carbon reduction technologies in power systems to address the aforementioned technical problems.

[0005] The following technical solution is adopted in this specification: This manual provides a method for configuring carbon reduction technologies in a power system, including: With the optimization objective of minimizing the cost of configuring carbon reduction technologies in the target power system, using the configuration parameters of carbon reduction technologies as decision variables, and the technical constraints of carbon reduction technologies as constraints, a system-level planning model for the target power system is constructed; whereby... Configuration parameters include: the method of flexibility retrofitting thermal power units in the target power system, the number of wind turbine units expanded in the target power system, and the scale of adding hydrogen energy storage system and carbon capture, storage and utilization equipment in the target power system; when the method of flexibility retrofitting thermal power units is oxygen-enriched combustion retrofitting, the oxygen source required for oxygen-enriched combustion retrofitting includes the oxygen generated synchronously during the hydrogen production process of the hydrogen energy storage system. Costs include wind curtailment and load shedding costs as well as carbon trading costs; wind curtailment and load shedding costs are negatively correlated with the flexibility level of thermal power units; carbon trading costs are related to the net carbon dioxide emissions generated by thermal power units after carbon capture and storage and the carbon dioxide captured and treated by the equipment. Solve the system-level planning model to obtain the optimal configuration parameters that minimize cost.

[0006] Furthermore, the carbon reduction technologies include the flexibility modification of thermal power units in the target power system, the expansion of wind power units in the target power system, and the addition of hydrogen energy storage systems and carbon capture, storage and utilization equipment in the target power system. The construction of the system-level planning model for the target power system specifically includes: The optimization objective is to minimize the cost of implementing carbon reduction technologies in the target power system, including investment and operating costs. in, This represents the annual investment cost of implementing carbon reduction technologies in the target power system. This represents the investment cost for the flexibility retrofitting of thermal power units in the target power system. This represents the investment cost of configuring carbon capture, storage and utilization (CCS) equipment in the target power system. This indicates the investment cost of configuring a hydrogen energy storage system in the target power system. This represents the investment cost of expanding wind turbine capacity in the target power system. This indicates the annual operating cost of the target power system after implementing carbon reduction technologies. This indicates the operating cost of thermal power units. This indicates the start / stop cost of wind turbine units. This represents the costs of wind curtailment and load shedding. Indicates carbon trading costs; Indicates the capital recovery factor. dr This represents the discount rate. y Indicates the lifespan of the equipment; The technical constraints of carbon reduction technologies, the uncertainties of wind power in the target power system, the power balance of the target power system, and carbon emission reduction limits are used as constraints for the system-level planning model; among them, Technical constraints on carbon capture, storage and utilization include energy consumption constraints for CO2 capture and storage, capacity constraints for CO2 storage equipment, and constraints for CO2 transportation pipelines. Technical constraints on wind turbine expansion include constraints on the scope of wind curtailment; Technical constraints on the flexibility retrofitting of thermal power units also include the ability to climb slopes under different retrofitting methods.

[0007] Furthermore, the system-level planning model is solved, specifically including: Transform the system-level planning model into a two-stage scenario-oriented distributed robust optimization model: in, The objective term in the first stage of the two-stage scenario-oriented distributed robust optimization model is used to determine the investment decision of the scale of each carbon reduction technology configuration that minimizes the total investment cost of carbon reduction technologies. Indicates the investment decision variables in the first stage; Represents the feasible region of investment decision variables; This represents the investment cost coefficient, with the superscript T indicating transpose; This is the objective term for the second stage in the two-stage scenario-oriented distributed robust optimization model. The second stage is used to determine the operating strategy that minimizes the operating cost of the corresponding typical operating scenario, given investment decisions and typical operating scenarios. This represents the total number of clustering results obtained after clustering the historical operating data of the target power system. Each clustering result represents a typical operating scenario of the target power system. Typical operating scenarios s The probability of; The feasible region represents the uncertainty of the scenario probability, characterizing the probability fluctuation range of each typical operating scenario; These represent the operational decision variables for the second phase. Indicates the variables of investment decision and typical operating scenarios s actual probability The feasible region for constrained operational decisions; This represents the operating cost coefficient, with the superscript T indicating transpose; A two-stage scenario-oriented distributed robust optimization model is solved using a fuzzy column and constraint generation algorithm, including: The two-stage scenario-oriented distributed robust optimization model is decomposed into a main problem and sub-problems, where the main problem corresponds to the first stage and the sub-problems correspond to the second stage. Iterative interaction between the main problem and sub-problems: The main problem outputs investment decisions for carbon reduction technologies, the sub-problems verify the corresponding operating costs based on the investment decisions for carbon reduction technologies, and feed back the operating costs of the worst-case typical operating scenario to the main problem; By iteratively interacting between the main problem and sub-problems, the investment and operation plan is gradually optimized to obtain the optimal capacity configuration and operation strategy for configuring various carbon reduction technologies in the target power system.

[0008] Furthermore, the annual investment cost specifically includes: Investment costs for the flexibility retrofitting of thermal power units in the target power system : in, , and These represent the unit cost of increasing minimum technical output, downhill ramp rate, and uphill ramp rate, respectively. , and They represent thermal power units i The increase in minimum technical output, the increase in downhill climbing rate, and the increase in uphill climbing rate; , and These respectively represent thermal power units i The binary decision variables for not implementing modification, conventional technical modification, and oxygen-enriched combustion modification are obtained through... Apply constraints; , and These respectively represent thermal power units i Minimum technical output corresponding to no modification, conventional technical modification, and oxygen-enriched combustion modification; Indicates the minimum technical output after the decision; and These respectively represent thermal power units i The binary decision variables for not implementing slope modification and implementing slope modification are obtained through... Apply constraints; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb downhill after a decision is made; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb uphill after a decision is made; Investment cost of configuring hydrogen energy storage systems in the target power system : in, This indicates the unit capacity cost of a hydrogen energy storage system; Indicates hydrogen energy storage system e Maximum capacity; Investment costs for expanding wind turbine capacity in the target power system : in, This indicates the cost of a single wind turbine unit; Indicates the number of newly added wind turbine units; Investment cost of configuring carbon capture, storage and utilization equipment in the target power system : in, This indicates the unit cost of equipping a thermal power unit with a carbon dioxide capture device. Indicates thermal power unit i Rated capacity; This indicates the carbon dioxide capture device for thermal power units. i Capture rate of emitted carbon dioxide; This represents a nonlinear function relating carbon dioxide capture rate to cost.

[0009] Furthermore, the annual operating costs specifically include: Operating costs of thermal power units : in, Indicates thermal power unit i At any moment t Actual technical output; Indicates thermal power unit i The operating cost function; This represents the set of all thermal power units; , Indicates the total number of time steps; Thermal power unit start / stop costs : in, Indicates thermal power unit i The unit startup cost; A binary variable representing the operating status of a thermal power unit, where 1 indicates startup and 0 indicates non-start. Indicates thermal power unit i Unit downtime cost; A binary variable representing the shutdown of a thermal power unit, where 1 indicates shutdown and 0 indicates that the unit is not shut down; Costs of wind curtailment and load shedding : in, This represents the unit cost of wind curtailment penalty; Indicates wind turbine At any moment The wind curtailment power; This represents the unit load shedding penalty cost; Indicates load node At any moment The amount of load shedding; This represents the set of all wind turbine units; Represents the set of all load nodes within the target power system; Carbon trading costs : in, This indicates the unit price of CO2. Indicates thermal power unit i At any moment t CO2 emissions; Indicates thermal power unit i The carbon quota coefficient corresponding to a unit output.

[0010] Furthermore, the wind power uncertainty of the target power system is obtained by allocating the probability fluctuation range of typical operating scenarios of the target power system through norm constraints, specifically including: The historical operating data of the target power system are clustered to obtain several clustering results. Each clustering result represents a typical operating scenario of the target power system. Assign a scenario probability fluctuation range to each typical operating scenario using norm constraints: in, The feasible region represents the uncertainty of the scenario probability, characterizing the probability fluctuation range of each typical operating scenario; Typical operating scenarios s The probability is determined based on the weight in the clustering results; Represent the set of positive real numbers; N s This represents the total number of typical operating scenarios; Typical operating scenarios s The initial probability is obtained from the historical operating data of the target power system. θ 1 and θ ∞ These represent the allowed probability deviations for the 1-norm and ∞-norm, respectively. S This represents the sample size of historical operating data for the target power system; α 1 and α ∞ These represent the confidence levels corresponding to the 1-norm and ∞-norm, respectively.

[0011] Furthermore, the technical constraints on the flexibility retrofitting of the thermal power units specifically include: Minimum technical output constraints for thermal power units under different retrofitting methods: in, , and They represent thermal power units i At any moment t Binary variables representing the running status, power-on status, and shutdown status. Indicates thermal power unit i At any moment t -1 is a binary variable representing the running state; This represents the minimum technical output after the decision. , , and These respectively represent thermal power units i The binary decision variables for not implementing modification, conventional technical modification, and oxygen-enriched combustion modification are obtained through... Apply constraints; Indicates thermal power unit i At any moment t Actual output; Indicates thermal power unit i Rated output; Technical constraints on the flexibility retrofitting of thermal power units also include the ability to climb slopes under different retrofitting methods: in, Indicates the thermal power unit after the decision i Downhill climbing ability , and These respectively represent thermal power units i The binary decision variables for not implementing slope modification and implementing slope modification are obtained through... Apply constraints; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb uphill after making a decision. , and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates thermal power unit i At any moment Actual output; Indicates thermal power unit i At any moment t Boot time; Indicates thermal power unit i Minimum boot time; Indicates thermal power unit i At any moment t -1 downtime; Indicates thermal power unit i Minimum downtime.

[0012] Furthermore, the technical constraints of the hydrogen energy storage system specifically include: Hydrogen electrolyzer power constraints: in, This indicates the rated operating power of the hydrogen electrolyzer; Indicates hydrogen electrolyzer n At any moment t The power consumed in the electro-hydrogen conversion; , and These represent hydrogen electrolyzers. n At any moment t Binary variables in underload, normal, and off states are accessed via... as well as To impose constraints, and These represent hydrogen electrolyzers. n At any moment t and time t- 1. A binary variable that is in the running state; Hydrogen electrolyzer n At any moment t The overload binary variable, with a value of 1, indicates that the hydrogen electrolyzer is allowed to operate in any of the following states: underload, normal, or overload; with a value of 0, it indicates non-overload operation. and These represent hydrogen electrolyzers. n At any moment t Hydrogen production and hydrogen utilization; and These represent the electro-hydrogen conversion efficiency coefficient and the hydrogen-electric conversion efficiency coefficient of the hydrogen electrolyzer, respectively. Indicates hydrogen electrolyzer n At any moment t Power consumed in hydrogen-to-electric conversion; Indicates hydrogen electrolyzer n Minimum power supplied; Indicates hydrogen electrolyzer nMaximum power provided; Hydrogen storage capacity constraints: in, and These represent hydrogen storage tanks. n At any moment t and time t -1 gas storage capacity; This indicates the hydrogen storage efficiency of the hydrogen storage tank; These represent hydrogen storage tanks. n At any moment t The inflation and deflation volumes; These represent the upper and lower limits of tank storage, respectively.

[0013] Furthermore, the technical constraints of the aforementioned carbon capture, storage, and utilization specifically include: CO2 capture and storage energy consumption constraints: in, This refers to thermal power units in the target power system equipped with carbon dioxide capture devices. The actual internet power at time t; Indicates thermal power unit Total power emitted at time t; Indicates thermal power unit The power consumed by the corresponding carbon dioxide capture device at time t; Indicates thermal power unit The variable energy consumption of the corresponding carbon dioxide capture device during operation at time t; Indicates thermal power unit The corresponding carbon dioxide capture device operates on binary decision variables at time t. 1 indicates that the carbon dioxide capture device is in operation, and 0 indicates that the carbon dioxide capture device is out of operation; Indicates the unit The fixed energy consumption of the corresponding carbon dioxide capture device during operation; Indicates the proportional coefficient of carbon dioxide variable energy consumption; Indicates thermal power unit The amount of carbon dioxide captured by the corresponding carbon dioxide capture device at time t; CO2 storage device capacity constraints: in, Indicates thermal power unit The volume of carbon dioxide to be stored in the corresponding carbon dioxide capture device at time t; Indicates the density of the carbon dioxide absorbent; and They represent thermal power units The corresponding carbon dioxide capture device at a certain time and time The remaining volume of carbon dioxide that can be contained; and They represent thermal power units The corresponding carbon dioxide capture device at a certain time and time The volume of carbon dioxide already stored; Indicates the maximum carbon dioxide storage capacity; CO2 transport pipeline constraints: in, Represents pipe nodes At any moment The carbon dioxide injection gas flow rate; Indicates from the pipeline node To pipeline node In time Gas flow rate; This represents the set of all pipes and pipe nodes; Represents pipe nodes At any moment The outflow rate of carbon dioxide gas; Represents pipe nodes At any moment The outflow rate of carbon dioxide gas; Represents pipe nodes Maximum outflow gas flow rate; Represents pipe nodes With pipeline nodes The flow coefficient of the pipeline between them; and Representing pipe nodes and pipeline nodes In time The pressure; Represents pipe nodes The minimum pressure; Represents pipe nodes The maximum pressure.

[0014] Furthermore, the power balance of the target power system specifically includes: in, Indicates thermal power unit With nodes The power-related binary variable, with a value of 1 indicating a thermal power unit. With nodes Power supply, 0 indicates no direct power supply relationship; Indicates thermal power unit At any moment Actual internet access power; Indicates thermal power unit At any moment Total output power; Indicates thermal power unit Fixed basic energy consumption; Indicates thermal power unit Variable energy consumption during operation; Indicates wind turbine With nodes Power-related binary variables; Indicates wind turbine At any moment Theoretical output; Indicates wind turbine At any moment The amount of wind curtailment; Indicates hydrogen energy storage system With nodes Power-related binary variables; Indicates hydrogen energy storage system At any moment The discharge power; Indicates hydrogen energy storage system At any moment The charging power; Indicates the time step; Indicates transmission line With nodes Power-related binary variables; Indicates transmission line At any moment The actual transmission power; Indicates load node At any moment Total load demand; Indicates load node At any moment The shear load.

[0015] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This invention establishes a system-level optimization model for integrated planning of carbon reduction technologies. By quantifying the coupling relationship between the flexible retrofitting of thermal power units, wind power expansion, hydrogen energy storage configuration, and carbon capture equipment configuration, it achieves collaborative planning that minimizes the overall system cost. Specifically: By adapting thermal power units to wind power fluctuations through flexible output adjustments, more wind power resources can be absorbed, reducing wind power curtailment and lowering the probability of system overload due to power imbalances. This achieves dual control over the costs of wind curtailment and load shedding. Oxygen, a byproduct of hydrogen production in hydrogen storage systems, can be directly used as the oxygen source for oxygen-enriched combustion in thermal power units, eliminating the need for additional oxygen preparation and storage equipment and effectively reducing costs in this area. Furthermore, oxygen-enriched combustion is highly efficient, improving combustion efficiency, reducing operating costs, and decreasing total carbon emissions. This reduction in total carbon emissions further reduces carbon trading costs, enabling cost control. This material flow coupling mechanism dynamically links hydrogen storage configuration with the flexibility of thermal power unit retrofitting and carbon capture, storage, and utilization equipment.

[0016] By integrating the aforementioned physical coupling mechanism into the model optimization process, the limitations of the isolated configuration of various carbon reduction technologies in traditional planning are broken. This enables dynamic adaptation of hydrogen energy storage scale, thermal power flexibility transformation methods, wind power expansion pace, and carbon capture, storage and utilization equipment configuration. The optimal configuration parameters obtained by the solution can achieve the coordinated operation of various carbon reduction technologies, and achieve a dynamic balance between economic cost and operational flexibility of the power system under carbon emission reduction constraints. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a flowchart illustrating a method for configuring carbon reduction technology in a power system, as provided in this specification. Figure 2 This is a schematic diagram illustrating the joint planning of multiple carbon reduction technology solutions provided in this specification. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0020] The technical solution provided by this invention can be applied to multi-technology synergy scenarios integrating the flexibility enhancement of thermal power units, carbon capture, utilization and storage, hydrogen energy storage, and new energy expansion, solving the problem of balancing system cost and carbon emission reduction under the uncertainty of wind power. As core renewable energy sources, wind power and other low-carbon technologies are prone to system power imbalance and wind curtailment due to the randomness and volatility of large-scale grid connection, requiring technical means to improve system regulation capabilities. Current low-carbon technologies mainly include carbon dioxide capture, storage and utilization, and flexibility enhancement to promote the consumption and expansion of new energy (flexibility enhancement of thermal power units, hydrogen energy storage) to reduce system carbon emissions. However, existing solutions have significant limitations: first, they often focus on single-technology planning, neglecting the synergistic effects of multiple technologies, and the handling of wind power uncertainty easily leads to an imbalance between system economics and carbon emission reduction targets; second, the handling of wind power uncertainty is inadequate, and multiple flexibility resources are not effectively combined to balance cost and carbon emission reduction constraints. In summary, there is an urgent need to develop a system planning method that integrates multiple technologies and takes into account both robustness and economics.

[0021] Based on this, this invention provides a multi-technology collaborative sub-Brussels bar planning method oriented towards carbon emission reduction constraints. First, based on multi-technology retrofit models for thermal power units, hydrogen energy storage models, and wind turbine expansion models, a total model of system retrofit and expansion options is obtained. Next, considering the annual amortized cost, a planning model oriented towards carbon emission reduction constraints is established to minimize system investment and operating costs. Based on a two-stage sub-Brussels bar optimization problem, the cost of multi-technology configuration is characterized, and the optimal configuration of different carbon reduction technologies is obtained. Subsequent numerical examples show that, compared with other carbon reduction technology methods, this optimization model can bring greater benefits and superior carbon emission reduction capabilities than individual configurations, proving the effectiveness of the method.

[0022] The following is combined Figures 1-2 The present invention describes a method for configuring carbon reduction technology in a power system.

[0023] Figure 1 This is a flowchart illustrating a method for configuring carbon reduction technology in a power system, as provided in this specification. Figure 1 As shown, the method includes: S1. With the optimization objective of minimizing the cost of configuring carbon reduction technologies in the target power system, and using the configuration parameters of carbon reduction technologies as decision variables and the technical constraints of carbon reduction technologies as constraints, a system-level planning model for the target power system is constructed. The configuration parameters include: the method of flexibility retrofitting thermal power units in the target power system, the number of wind turbine expansions in the target power system, and the scale of adding hydrogen energy storage systems and carbon capture, storage, and utilization (CVC) equipment in the target power system. When the flexibility retrofitting method for thermal power units is oxy-fuel combustion retrofitting, the oxygen source required for oxy-fuel combustion retrofitting includes the oxygen generated synchronously during the hydrogen production process of the hydrogen energy storage system. Costs include wind curtailment and load shedding costs, as well as carbon trading costs. Wind curtailment and load shedding costs are negatively correlated with the flexibility level of thermal power units. Carbon trading costs are related to the net carbon dioxide emissions after the carbon dioxide produced by thermal power units is captured and treated by CVC equipment.

[0024] S2. Solve the system-level planning model to obtain the optimal configuration parameters that minimize cost.

[0025] This invention establishes a system-level optimization model for integrated planning of carbon reduction technologies. By quantifying the coupling relationship between the flexibility retrofitting of thermal power units, wind power expansion, hydrogen energy storage configuration, and carbon capture equipment configuration, it achieves collaborative planning that minimizes the overall system cost. Specifically, through flexibility retrofitting, thermal power units can flexibly adjust their output to adapt to the volatility of wind power, thereby absorbing more wind power resources, reducing wind power curtailment, and simultaneously reducing the probability of the system being overloaded due to power imbalance, achieving dual control over wind curtailment and load shedding costs. The oxygen produced as a byproduct in the hydrogen production process of the hydrogen energy storage system can be directly used as the oxygen source for oxygen-enriched combustion in thermal power units, eliminating the need for additional oxygen preparation and storage equipment, effectively reducing costs in this area. Furthermore, oxygen-enriched combustion has high efficiency, improving the combustion efficiency of thermal power units, reducing operating costs, and reducing total carbon emissions. The reduction in total carbon emissions can further reduce carbon trading costs, achieving control over carbon trading costs. This material flow coupling mechanism dynamically links hydrogen energy storage configuration with the flexibility retrofitting of thermal power units and carbon capture, storage, and utilization equipment. By integrating the aforementioned physical coupling mechanism into the model optimization process, the limitations of the isolated configuration of various carbon reduction technologies in traditional planning are broken. This enables dynamic adaptation of hydrogen energy storage scale, thermal power flexibility transformation methods, wind power expansion pace, and carbon capture, storage and utilization equipment configuration. The optimal configuration parameters obtained by the solution can achieve the coordinated operation of various carbon reduction technologies, and achieve a dynamic balance between economic cost and operational flexibility of the power system under carbon emission reduction constraints.

[0026] Based on the above Figure 1In the illustrated embodiment, for example, in S1 above, the target power system is specifically a comprehensive power system based on thermal power generation and gradually replaced by new energy power generation. Its core carbon reduction goal is to gradually reduce the carbon emission intensity per unit of power generation while ensuring the stability and reliability of power supply. Figure 2 This specification provides a schematic diagram of a combined planning approach for multiple carbon reduction technologies, such as... Figure 2 As shown, the carbon reduction technologies employed include Hydrogen Energy Storage System (HESS), Flexibility Improvement of Thermal Power Units (Flex), Wind Turbine Generator Expansion (WTG), and Carbon Capture, Utilization and Storage (CCUS). Among these, Flexibility Improvement of Thermal Power Units includes reducing the minimum technical output of the units and improving their ramp-up capability. Lowering the minimum technical output effectively expands the load regulation range of thermal power units, enabling them to operate stably under low-load conditions. This frees up more grid connection space for intermittent renewable energy generation such as wind and solar power, reducing the curtailment of renewable energy. Optimization of minimum technical output can be achieved through two paths: traditional retrofitting and oxy-fuel combustion retrofitting. Traditional retrofitting involves upgrading existing equipment through methods such as boiler combustion adjustment and turbine flow modification. Oxy-fuel combustion, on the other hand, introduces a high concentration of oxygen into the boiler for combustion, which not only significantly reduces the minimum technical output of the unit but also improves combustion efficiency, reduces emissions of pollutants such as nitrogen oxides, and facilitates subsequent carbon capture, storage, and utilization (CCS) for the enrichment and recovery of carbon dioxide from the flue gas. Improving the unit's ramp-up capability focuses on shortening the response time to load fluctuations, enabling it to respond quickly to grid load fluctuations and further enhancing the power system's adaptability to the volatility of renewable energy generation.

[0027] For example, the cost of configuring carbon reduction technologies in a target power system includes investment costs and operating costs. Investment costs refer to one-time expenditures incurred for building, upgrading, or expanding various carbon reduction technology facilities, such as the purchase of hydrogen storage tanks and electrolyzers for hydrogen energy storage systems, and equipment upgrade costs for the flexibility modification of thermal power units. Operating costs refer to the ongoing expenditures incurred after various carbon reduction technology facilities are put into use to maintain normal operation and ensure the stable performance of the technologies. Specifically:

[0028] ; in, This represents the annual investment cost of implementing carbon reduction technologies in the target power system. This indicates the investment cost for the flexibility retrofitting of thermal power units in the target power system (including both conventional and oxygen-enriched auxiliary systems). This represents the investment cost of configuring carbon capture, storage and utilization (CCS) equipment in the target power system. This indicates the investment cost of configuring a hydrogen energy storage system in the target power system. This represents the investment cost of expanding wind turbine capacity in the target power system. This indicates the annual operating cost of the target power system after implementing carbon reduction technologies. This indicates the operating cost of thermal power units. This indicates the start / stop cost of wind turbine units. This represents the costs of wind curtailment and load shedding. Indicates carbon trading costs; Indicates the capital recovery factor. dr This represents the discount rate. y This indicates the equipment's lifespan. A higher level of flexibility in thermal power units means better peak-shaving range and ramp-up rate regulation performance, stronger capacity to absorb and utilize wind power generated by wind turbines, and more efficient handling of the randomness and fluctuations in wind power output. This reduces wind power curtailment and lowers the probability of the system being overloaded due to power supply-demand imbalances, resulting in lower costs for wind curtailment and load shedding. Carbon trading costs are closely related to the net carbon dioxide emissions after capture and storage (CFS) and utilization equipment from thermal power units. The two are positively correlated; that is, the more carbon capture and storage equipment there is, the better the capture and treatment effect of carbon emissions from thermal power units, the lower the net carbon dioxide emissions, and the lower the corresponding carbon trading costs in the carbon trading market.

[0029] S11. Investment costs are as follows: S111. Investment cost for the flexibility retrofit of thermal power units in the target power system : ; ; ; ; in, , and These represent the unit cost of increasing minimum technical output, downhill ramp rate, and uphill ramp rate, respectively. , and They represent thermal power units i The increase in minimum technical output, the increase in downhill climbing rate, and the increase in uphill climbing rate; , and These respectively represent thermal power units i The binary decision variables for not implementing modification, conventional technical modification, and oxygen-enriched combustion modification are obtained through... Apply constraints; , and These respectively represent thermal power units i Minimum technical output corresponding to no modification, conventional technical modification, and oxygen-enriched combustion modification; Indicates the minimum technical output after the decision; and These respectively represent thermal power units i The binary decision variables for not implementing slope modification and implementing slope modification are obtained through... Apply constraints; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb downhill after a decision is made; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; This indicates the ability to climb uphill after making a decision.

[0030] S112. Investment cost of configuring a hydrogen energy storage system in the target power system : ; in, This indicates the unit capacity cost of a hydrogen energy storage system; Indicates hydrogen energy storage system e Maximum capacity.

[0031] S113. Investment costs for expanding wind turbine capacity in the target power system : ; in, This indicates the cost of a single wind turbine unit; This indicates the number of newly added wind turbine units.

[0032] S114. Investment cost of configuring carbon capture, storage and utilization equipment in the target power system : ; in, This indicates the unit cost of equipping a thermal power unit with a carbon dioxide capture device. Indicates thermal power unit i Rated capacity; This indicates the carbon dioxide capture device for thermal power units. i Capture rate of emitted carbon dioxide; The nonlinear function representing the carbon dioxide capture rate versus cost can be obtained through pre-training.

[0033] S12. Operating costs are as follows: S121. Operating costs of thermal power units : ; in, Indicates thermal power unit i At any moment t Actual technical output; Indicates thermal power unit i The operating cost function can be obtained through pre-training; This represents the set of all thermal power units; , This represents the total number of time steps.

[0034] S122. Start-up / shutdown costs of thermal power units : ; in, Indicates thermal power unit i The unit startup cost; A binary variable representing the operating status of a thermal power unit, where 1 indicates startup and 0 indicates non-start. Indicates thermal power unit i Unit downtime cost; A binary variable representing the shutdown of a thermal power unit, where 1 indicates shutdown and 0 indicates that the unit is not shut down.

[0035] S123. Costs of Wind Curtailment and Load Shedding : ; in, This represents the unit cost of wind curtailment penalty; Indicates wind turbine At any moment The wind curtailment power; This represents the unit load shedding penalty cost; Indicates load node At any moment The amount of load shedding; This represents the set of all wind turbine units; This represents the set of all load nodes within the target power system. The expansion of wind turbine units directly impacts wind curtailment and load shedding in the system: when the installed capacity of wind turbine units increases, if the system's absorption capacity (related to the flexibility of thermal power units) does not increase simultaneously, more wind power will be unable to be fully fed into the grid, thus increasing wind curtailment power; this increase in curtailment power further drives up the system's curtailment costs. Simultaneously, the increased power supply capacity brought about by wind turbine expansion can reduce the probability of the system being urgently loaded due to insufficient power supply, reducing load shedding and thus lowering the system's load shedding costs.

[0036] S124. Carbon Trading Costs : ; in, This indicates the unit price of CO2. Indicates thermal power unit i At any moment t CO2 emissions; Indicates thermal power unit i Carbon quota coefficient per unit of output This can represent the carbon emission allowance surplus or deficit of each thermal power unit at each operating moment. If the calculation result is positive, it means that the unit's actual carbon dioxide emissions exceed its free carbon emission allowance corresponding to its active power output. In this case, additional allowances need to be purchased in the carbon trading market, thus incurring costs. If the calculation result is negative, it means that the unit's actual carbon dioxide emissions are lower than the free allowance. In this case, the excess allowances can be sold on the market to generate revenue. Equipping thermal power units with carbon capture, storage, and utilization (CCS) equipment can affect their carbon dioxide emissions.

[0037] Based on any embodiment, for example, in S1 above, the optimization objective of the system-level planning model is to minimize the investment and operating costs of configuring carbon reduction technologies in the target power system. The constraints of the system-level planning model are the technical constraints of carbon reduction technologies, the uncertainty of wind power in the target power system, the power balance of the target power system, and carbon emission reduction limits. Specifically, this includes:

[0038] S21. The wind power uncertainty of the target power system is obtained by allocating the probability fluctuation range of typical operating scenarios of the target power system through norm constraints, specifically including: S211. Cluster the historical operating data of the target power system to obtain several clustering results. Each clustering result represents a typical operating scenario of the target power system.

[0039] S212. Assign a scenario probability fluctuation range to each typical operating scenario through norm constraints: ; ; ; ; in, The feasible region represents the uncertainty of the scenario probability, characterizing the probability fluctuation range of each typical operating scenario; Typical operating scenarios s The probability is determined based on the weight in the clustering results; Represent the set of positive real numbers; N s This represents the total number of typical operating scenarios; Typical operating scenarios s The initial probability is obtained from the historical operating data of the target power system. θ 1 and θ ∞ These represent the allowed probability deviations for the 1-norm and ∞-norm, respectively. S This represents the sample size of historical operating data for the target power system; α 1 and α ∞ These represent the confidence levels corresponding to the 1-norm and ∞-norm, respectively.

[0040] S22. Power balance of the target power system, specifically including: ; ; in, Indicates thermal power unit With nodes The power-related binary variable, with a value of 1 indicating a thermal power unit. With nodes Power supply, 0 indicates no direct power supply relationship; Indicates thermal power unit At any moment Actual internet access power; Indicates thermal power unit At any moment Total output power; Indicates thermal power unit Fixed basic energy consumption; Indicates thermal power unit Variable energy consumption during operation; Indicates wind turbine With nodes Power-related binary variables; Indicates wind turbine At any moment Theoretical output; Indicates wind turbine At any moment The amount of wind curtailment; Indicates hydrogen energy storage system With nodes Power-related binary variables; Indicates hydrogen energy storage system At any moment Hydrogen storage release power; Indicates hydrogen energy storage system At any moment Hydrogen energy storage absorption power; Indicates the time step; Indicates transmission line With nodes Power-related binary variables; Indicates transmission line At any moment Inter-line power flow; Indicates load node At any moment Total load demand; Indicates load node At any moment The shear load.

[0041] S23. Carbon emission reduction constraints, specifically including: ; in, Indicates thermal power unit At any moment CO2 emissions, This indicates the upper limit of the system's carbon emissions.

[0042] S24. Technical constraints on carbon capture, storage, and utilization include energy consumption constraints for CO2 capture and storage, capacity constraints for CO2 storage equipment, and constraints on CO2 transportation pipelines; technical constraints on wind turbine expansion include constraints on wind curtailment range; technical constraints on the flexibility retrofitting of thermal power units include: minimum technical output constraints for thermal power units under different retrofitting methods; and ramp-up capability constraints under different retrofitting methods; technical constraints on hydrogen energy storage systems include: power constraints for hydrogen electrolyzers and capacity constraints for hydrogen energy storage, specifically: S241. Regarding the upgrading and retrofitting technology of thermal power units, the key points of the retrofitting include reducing the minimum technical output of the unit and improving the unit's ramp-up capability. The minimum technical output includes both traditional retrofitting methods and oxygen-enriched combustion methods, specifically including: S2411. Minimum technical output constraints for thermal power units under different retrofitting methods: ; ; in, , and They represent thermal power units i At any moment t Binary variables representing the running status, power-on status, and shutdown status. Indicates thermal power unit i At any moment t -1 is a binary variable representing the running state; This represents the minimum technical output after the decision. , , and These respectively represent thermal power units i The binary decision variables for not implementing modification, conventional technical modification, and oxygen-enriched combustion modification are obtained through... Apply constraints; Indicates thermal power unit i At any moment t Actual output; Indicates thermal power unit i Rated output.

[0043] S2412. Technical constraints on the flexibility retrofitting of thermal power units also include the climbing ability under different retrofitting methods: ; ; in, Indicates the thermal power unit after the decision i Downhill climbing ability , and These respectively represent thermal power units i The binary decision variables for not implementing slope modification and implementing slope modification are obtained through... Apply constraints; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb uphill after making a decision. , and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates thermal power unit i At any moment Actual output; Indicates thermal power unit i At any moment tBoot time; Indicates thermal power unit i Minimum boot time; Indicates thermal power unit i At any moment t -1 downtime; Indicates thermal power unit i Minimum downtime.

[0044] S242. To meet the system's carbon reduction requirements, carbon dioxide capture and storage (CCS) devices need to be installed on some generating units. Since these devices consume some of the unit's power, the actual power supplied by the units to the grid needs to be described. Before describing the technical constraints of CCS, the operating parameters for CCS are first described: net output of the units after the modification. Carbon processing capacity .in This refers to the actual output / actual grid-connected power of thermal power units. Fixed basic energy consumption for equipment, For variable energy consumption during equipment operation, The amount of carbon dioxide emitted per unit of electricity generated by the generator unit. This refers to the dynamic change in storage device capacity. The specific technical constraints of carbon capture, storage, and utilization include:

[0045] S2421. Energy Constraints for CO2 Capture and Storage: ; ; ; in, This refers to thermal power units in the target power system equipped with carbon dioxide capture devices. The actual internet power at time t; Indicates thermal power unit Total power emitted at time t; Indicates thermal power unit The power consumed by the corresponding carbon dioxide capture device at time t; Indicates thermal power unit The variable energy consumption of the corresponding carbon dioxide capture device during operation at time t; Indicates thermal power unit The corresponding carbon dioxide capture device operates on binary decision variables at time t. 1 indicates that the carbon dioxide capture device is in operation, and 0 indicates that the carbon dioxide capture device is out of operation; Indicates the unit The fixed energy consumption of the corresponding carbon dioxide capture device during operation; Indicates the proportional coefficient of carbon dioxide variable energy consumption; Indicates thermal power unit The amount of carbon dioxide captured by the corresponding carbon dioxide capture device at time t.

[0046] S2422. The captured carbon dioxide is fed into the CCUS system gas storage tanks, including lean and rich liquid storage tanks. Assuming that one volume of solution can absorb 25 times its volume of CO2 gas, the capacity constraint of the CO2 storage equipment is: ; ; ; in, Indicates thermal power unit The volume of carbon dioxide to be stored in the corresponding carbon dioxide capture device at time t; Indicates the density of the carbon dioxide absorbent; and They represent thermal power units The corresponding carbon dioxide capture device at a certain time and time The remaining volume of carbon dioxide that can be contained; and They represent thermal power units The corresponding carbon dioxide capture device at a certain time and time The volume of carbon dioxide already stored; This indicates the maximum carbon dioxide storage capacity.

[0047] S2423. Constraints that must be met for gas transportation via pipelines include: gas material balance constraints, maximum receiving flow rate at the sink, node pressure constraints, and node pressure variation range. Constraints for CO2 transportation pipelines include: ； ； ； ； in, Represents pipe nodes At any moment The carbon dioxide injection gas flow rate; Indicates from the pipeline node To pipeline node In time Gas flow rate; This represents the set of all pipes and pipe nodes; Represents pipe nodes At any moment The outflow rate of carbon dioxide gas; Represents pipe nodes At any moment The outflow rate of carbon dioxide gas; Represents pipe nodes Maximum outflow gas flow rate; Represents pipe nodes With pipeline nodes The flow coefficient of the pipeline between them; and Representing pipe nodes and pipeline nodes In time The pressure; Represents pipe nodes The minimum pressure; Represents pipe nodes The maximum pressure.

[0048] S243. Hydrogen in the hydrogen energy storage device is generated through electrolysis in an electrolyzer. The electrolyzer operates in three states: underload, normal, and overload, which are limited by binary variables. The technical constraints of the hydrogen energy storage system include: S2431. Hydrogen electrolyzer power constraint: ; ; ; ; ; in, This indicates the rated operating power of the hydrogen electrolyzer; Indicates hydrogen electrolyzer n At any moment t The power consumed in the electro-hydrogen conversion; , and These represent hydrogen electrolyzers. n At any moment t Binary variables in underload, normal, and off states are accessed via... as well as To impose constraints, and These represent hydrogen electrolyzers. n At any moment t and time t- 1. A binary variable that is in the running state; Hydrogen electrolyzer n At any moment t The overload binary variable, with a value of 1, indicates that the hydrogen electrolyzer is allowed to operate in any of the following states: underload, normal, or overload; with a value of 0, it indicates non-overload operation. and These represent hydrogen electrolyzers. n At any moment t Hydrogen production and hydrogen utilization; and These represent the electro-hydrogen conversion efficiency coefficient and the hydrogen-electric conversion efficiency coefficient of the hydrogen electrolyzer, respectively. Indicates hydrogen electrolyzer n At any moment t The power consumed in the hydrogen-to-electric conversion; Indicates hydrogen electrolyzer n Minimum power supplied; Indicates hydrogen electrolyzer n Maximum power supplied.

[0049] S2432. Hydrogen storage must meet the constraints of dynamic gas changes. The hydrogen storage capacity constraints include: ; ; in, and These represent hydrogen storage tanks. n At any moment t and time t -1 gas storage capacity; This indicates the hydrogen storage efficiency of the hydrogen storage tank; These represent hydrogen storage tanks. n At any moment t The inflation and deflation volumes; These represent the upper and lower limits of tank storage, respectively.

[0050] S244. The scale of wind turbine units has been expanded to ,in, For the configured wind power rated capacity, For the unconfigured wind power rated capacity, Let represent the number of newly added wind turbine units. Then, the wind curtailment constraint for these wind turbine units is: ; in, Indicates wind turbine At any moment Theoretical output; Indicates wind turbine At any moment The amount of wind power that is forcibly diverted.

[0051] Based on any embodiment, for example, in S2 above, the system uncertainty problem is handled by solving the two-stage sub-Bruker optimization method, and a system configuration and planning scheme that is both cost-effective and robust is obtained, resulting in the best combination of technical solutions. The optimal configuration is used to allocate investment costs and optimize operating costs for the low-carbon system. Among them, the multi-technology collaborative sub-Bruker planning model adopts some key assumptions. Specifically: (1) Simplification of installation and modification costs, the cost expression of CCUS equipment is represented by a piecewise linearization method. At the same time, the nonlinear impact of flexibility improvement on the life and maintenance cycle of the modified unit is not considered. (2) It is assumed that traditional thermal power units can only be modified into flexible carbon capture power plants, excluding other carbon capture power plant schemes that may provide greater operational benefits. (3) The simulation part is represented by a typical day. This time clustering may not be able to fully capture extreme weather events and seasonal supply and demand changes, which may slightly affect the planning results. Based on the above assumptions, the steps for solving the system-level planning model include:

[0052] S31. Based on the two-stage bibliometric optimization method, the investment value is characterized, and the optimal configuration of different carbon reduction technology schemes is obtained. This optimal configuration is then used to allocate investment costs and optimize operating costs for the low-carbon system. The first-stage objective function is to obtain the system investment cost. The specific expression is:

[0053] ; The objective function for the second stage is to obtain the minimum operating cost of the system. The specific expression is:

[0054] ; By considering typical scenarios selected based on historical curves, the two-stage scenario-oriented distributed robust optimization model is simplified to the following form: ; in, The objective term in the first stage of the two-stage scenario-oriented distributed robust optimization model is used to determine the investment decision of the scale of each carbon reduction technology configuration that minimizes the total investment cost of carbon reduction technologies. Indicates the investment decision variables in the first stage; Represents the feasible region of investment decision variables; This represents the investment cost coefficient, with the superscript T indicating transpose; This is the objective term for the second stage in the two-stage scenario-oriented distributed robust optimization model. The second stage is used to determine the operating strategy that minimizes the operating cost of the corresponding typical operating scenario, given investment decisions and typical operating scenarios. This represents the total number of clustering results obtained after clustering the historical operating data of the target power system. Each clustering result represents a typical operating scenario of the target power system. Typical operating scenarios s The probability of; The feasible region represents the uncertainty of the scenario probability, characterizing the probability fluctuation range of each typical operating scenario; This represents the operational decision variables for the second stage in scenario S; Indicates the variables of investment decision and typical operating scenarios s actual probability The feasible region for constrained operational decisions; This represents the operating cost coefficient, with the superscript T indicating transpose; c , d , e , f , h These are the left-hand side coefficients in the constraints. C , D , G , H These are the right-hand side coefficients in the constraints.

[0055] S32. A two-stage scenario-oriented distributed robust optimization model is solved using a fuzzy column and constraint generation algorithm. This algorithm decomposes the model into a main problem and sub-problems, where the main problem corresponds to the first stage and the sub-problems to the second stage. The main problem and sub-problems then iteratively interact: the main problem outputs investment decisions for carbon reduction technologies, and the sub-problems verify the corresponding operating costs based on these decisions, feeding back the operating costs of the worst-case scenario to the main problem. Through this iterative interaction, the investment and operation schemes are gradually optimized to obtain the optimal capacity configuration and operation strategy for each carbon reduction technology in the target power system. Specifically, this includes:

[0056] S321. Initialize the algorithm parameters and set the lower bound of the solution. Upper Realm Global convergence threshold The main problem is the relative optimal gap. Iteration counter .

[0057] S322. Based on benchmark data, with the objective of minimizing total cost, within the relatively optimal gap. Solving the main problem yields the investment decision variables for the first stage. x Record the solution results of the main problem. And update the lower bound. The previous lower limit was .

[0058] S323. Investment Decision Variables in the First Stage Based on Solving the Main Problemx Solving for the optimal scheduling scheme and second-stage runtime variables in multiple typical scenarios. y s And the optimal scheduling cost for each typical scenario. Furthermore, within the probabilistically feasible region of the scenario... Under the constraints, calculate the distribution cost of the worst-case scenario. Update the upper boundary .

[0059] S324. Determine if convergence has occurred. If the algorithm converges, it outputs the optimal configuration result and investment plan. The surface currently has insufficient precision in the main problem; therefore, the relative gap should be updated to... Return to the main problem-solving steps and iterate again. If the above conditions are not met, it indicates that worst-case constraints need to be added, and the worst-case probability distribution needs to be adjusted. and corresponding second-stage variables Add this as a new constraint to the main problem, return to the main problem solution steps, iterate again, and update. Continue until the convergence condition is met.

[0060] This invention integrates four low-carbon technologies—unit flexibility enhancement, carbon capture, storage and utilization, hydrogen energy storage systems, and wind turbine expansion—to construct a planning model covering the entire lifecycle of technology investment and operation. This model achieves multi-technology synergistic optimization, overcomes the limitations of single-technology planning, integrates multiple carbon reduction solutions, and explores the interactive value between technologies, while simultaneously achieving cost control and carbon emission reduction targets. Based on historical data, typical operating scenarios are selected, and a two-stage scenario-oriented distributed robust optimization (DRO) model is established using norm-based joint confidence interval constraints on the probability distribution of wind power uncertainty. This model offers superior uncertainty handling. The two-stage scenario-oriented DRO model balances system economy and robustness while avoiding complex mathematical transformations, resulting in better practical engineering value. Furthermore, through fuzzy column and constraint generation (iC&C...)... G) The algorithm decomposes the main problem and sub-problems, iteratively solves them to obtain the optimal technical capacity configuration and operation strategy, and simultaneously realizes the coordinated scheduling among various technologies. This method is efficient in solving problems and accurate in configuration. By decomposing the optimization problem through the iC&CG algorithm, the iterative convergence speed is fast and the optimal configuration of each technology can be accurately output. At the same time, a scientific cost allocation mechanism is designed to ensure that the return on investment of each technology is reasonable. In addition, this invention clarifies the constraints such as output, capture rate, and flow rate of each technology operation stage. The operation constraints ensure implementation and ensure that the optimal configuration scheme can directly guide the actual engineering operation, avoiding the disconnect between theory and practice. Experimental results show that compared with a single emission reduction technology scheme, this invention can significantly reduce the total system cost and carbon emissions, taking into account the economy and robustness of the planning scheme, and providing technical support for the efficient planning of large-scale low-carbon power systems.

[0061] Furthermore, this application embodiment also provides an implementation scheme for obtaining comprehensive system benefits and achieving reasonable cost allocation and clear operational constraints. Specifically, it obtains the optimal capacity configuration of different carbon reduction technologies through a two-stage scenario-oriented distributed robust optimization model, and allocates the investment and operating costs of each technology within the system based on this optimal configuration, while clarifying the operational constraints of each technology to ensure the implementation of the scheme. First, it is necessary to obtain the basic parameters (including installed capacity, efficiency, cost coefficient, etc.) of each unit, hydrogen energy storage, and wind turbine units to be built within the system, as well as carbon trading prices and load demand data. Based on this, the objective function of the system-level planning model (minimizing the total cost of the system throughout its entire life cycle) is constructed, and wind power uncertainty constraints and technical operating parameters are embedded. Then, the comprehensive system benefits are calculated, and the calculation formula is: Comprehensive Benefit = (Sum of the total costs of each technology used individually - Total cost of the system throughout its entire life cycle for multiple technologies combined) + (Carbon emission reduction of multiple technologies combined × Carbon trading price), where carbon emission reduction = Baseline emissions (emissions without low-carbon technologies) - Actual emissions when multiple technologies are combined. Then, by comparing the comprehensive benefits of different technology combinations, the advantages of multiple technology combination are verified. Then, based on the optimal configuration, the cost allocation coefficient is obtained. Under the scenario set of optimal configurations, the allocation coefficient is obtained through the cost proportion of each technology:

[0062] Investment cost allocation coefficient: Similarly, the definition , , .

[0063] Operating cost allocation coefficient: Similarly, the definition , , , .

[0064] The operating and investment costs of each technology are allocated using various allocation coefficients. After allocation, a rationality verification is required to ensure that the sum of the allocated costs for all technologies equals the total cost of the system throughout its entire lifecycle. After allocating costs to each technology, the operational constraints for each type of technology must be clearly defined: For flexible thermal power units, output and ramp-up rate limits, modification strategies, and operational constraints on decision variables must be clearly defined; for CCUS units, capture rate and energy consumption limits, and storage tank operation and capacity constraints must be clearly defined; within the CCUS system, constraints on the carbon dioxide transport pipeline network, including material balance and pressure constraints, must be clearly defined; for hydrogen storage operations, the operational (overload, normal, low load) status limitations of the internal electrolyzer must be clearly defined; and for WTG operations, wind curtailment limits and capacity expansion options must be clearly defined.

[0065] Furthermore, this application embodiment also provides experimental verification results based on the above scheme. Table 1 shows different control scenario configuration schemes. Combining the scenario configuration schemes in Table 1, the effectiveness of the multi-technology collaborative sub-bar planning method oriented towards carbon emission reduction constraints is analyzed and verified.

[0066] Table 1 By considering multiple scenario configurations, simulations were conducted under the new energy curve generated from historical curves. The experiment was carried out based on the modified IEEE 118-node test system, which includes 54 thermal power units, wind turbine units with an initial installed capacity of 2000MW, and 186 transmission lines. The basic load demand is 10000MW, and the baseline carbon emissions (without new low-carbon technologies) are 19.26MtCO2 / year.

[0067] Considering the randomness and volatility of wind power output, based on the annual historical output data of a wind farm (time resolution of 1 hour, a total of 8760 data points), the k-means clustering algorithm was used to select 8 typical operating scenarios, covering high, medium, low, and sudden rise and fall fluctuations in wind power output. The initial probability of the scenario was determined by the proportion of clustered samples.

[0068] Based on the optimization results of Scenario 4, as shown in Table 2, the deployment capacities of hydrogen energy storage and newly added wind turbine units are 150MWh and 368MW, respectively. Among the thermal power units with retrofit potential, 5 units have completed minimum technical output (MTO) reduction retrofits, and 9 units have completed ramp rate improvement retrofits. 7 units with retrofit potential have completed CCUS retrofits. The above equipment deployment and retrofit measures work in synergy to fully meet the carbon emission reduction requirements of a low-carbon power system.

[0069] Table 2 As shown in Table 3, compared with Scenario 1 (the baseline case without any low-carbon technologies), Scenario 4 has the largest reduction in total cost, reaching US$37.43 million, which is significantly better than Scenario 2's US$23.52 million and Scenario 3's US$9.62 million.

[0070] Table 3 This advantage stems primarily from three synergistic effects: The investment configuration in Scenario 4 covers four types of low-carbon technologies, simultaneously improving system flexibility and carbon capture capabilities. It not only achieved a high carbon trading revenue of US$130.14 million, but also minimized wind curtailment and load shedding, reduced corresponding penalty costs, and achieved dual optimization of economic efficiency and operational stability. Compared to Scenario 2, Scenario 4 integrates CCUS equipment, giving the system real-time operational flexibility. CCUS equipment can control the energy consumption level of carbon capture power plants through start-stop control, while reducing the marginal cost of coal-fired power generation. This relatively weakens the economic competitiveness of hydrogen energy storage and WTG expansion, thereby reasonably reducing the investment scale of both and avoiding cost waste caused by over-allocation of a single flexible resource.

[0071] Compared to Scenario 3, Scenario 4 decouples CCUS operation from real-time scheduling requirements by integrating flexibility resources. When faced with load and wind power fluctuations, flexibility resources can respond and adjust quickly. CCUS equipment does not need to adapt to real-time power changes, but only needs to maximize carbon capture, significantly improving the cost-effectiveness of CCUS retrofitting. Therefore, more units completed CCUS retrofitting, and the system ultimately achieved the highest carbon trading revenue among all cases.

[0072] As shown in Table 4, with the tightening of carbon constraints (mild, moderate, and stringent), the system can meet carbon emission reduction requirements while minimizing cost increases (cost increase of only 8.2% under strict constraints) by adjusting the CCUS capture rate, WTG expansion scale, and the number of units flexibly modified. This verifies that the present invention can effectively balance economic efficiency and carbon emission reduction targets, avoiding excessive cost sacrifice in pursuit of emission reduction or failure to meet targets due to cost control.

[0073] Table 4 In summary, the experiment, through multi-scenario coverage, multi-case comparison, and multi-dimensional verification, proves that the multi-technology collaborative sub-bar planning method of the present invention can effectively reduce system costs, reduce carbon emissions, and improve wind power absorption capacity, and has good scenario adaptability and engineering practicality.

[0074] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0075] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for configuring carbon reduction technologies in a power system, characterized in that, include: With the optimization objective of minimizing the cost of configuring carbon reduction technologies in the target power system, using the configuration parameters of carbon reduction technologies as decision variables, and the technical constraints of carbon reduction technologies as constraints, a system-level planning model for the target power system is constructed; whereby... Configuration parameters include: the method of flexibility retrofitting thermal power units in the target power system, the number of wind turbine units expanded in the target power system, and the scale of adding hydrogen energy storage system and carbon capture, storage and utilization equipment in the target power system; when the method of flexibility retrofitting thermal power units is oxygen-enriched combustion retrofitting, the oxygen source required for oxygen-enriched combustion retrofitting includes the oxygen generated synchronously during the hydrogen production process of the hydrogen energy storage system. Costs include wind curtailment and load shedding costs as well as carbon trading costs; wind curtailment and load shedding costs are negatively correlated with the flexibility level of thermal power units; carbon trading costs are related to the net carbon dioxide emissions generated by thermal power units after carbon capture and storage and the carbon dioxide captured and treated by the equipment. Solve the system-level planning model to obtain the optimal configuration parameters that minimize cost.

2. The configuration method of carbon reduction technology in a power system as described in claim 1, characterized in that, The carbon reduction technologies include the flexibility modification of thermal power units in the target power system, the expansion of wind power units in the target power system, and the addition of hydrogen energy storage systems and carbon capture, storage and utilization equipment in the target power system. The construction of the system-level planning model for the target power system specifically includes: The optimization objective is to minimize the cost of implementing carbon reduction technologies in the target power system, including investment and operating costs. ； in, This represents the annual investment cost of implementing carbon reduction technologies in the target power system. This represents the investment cost for the flexibility retrofitting of thermal power units in the target power system. This represents the investment cost of configuring carbon capture, storage and utilization (CCS) equipment in the target power system. This indicates the investment cost of configuring a hydrogen energy storage system in the target power system. This represents the investment cost of expanding wind turbine capacity in the target power system. This indicates the annual operating cost of the target power system after implementing carbon reduction technologies. This indicates the operating cost of thermal power units. This indicates the start / stop cost of wind turbine units. This represents the costs of wind curtailment and load shedding. Indicates carbon trading costs; Indicates the capital recovery factor. dr This represents the discount rate. y Indicates the lifespan of the equipment; The technical constraints of carbon reduction technologies, the uncertainties of wind power in the target power system, the power balance of the target power system, and carbon emission reduction limits are used as constraints for the system-level planning model; among them, Technical constraints on carbon capture, storage and utilization include energy consumption constraints for CO2 capture and storage, capacity constraints for CO2 storage equipment, and constraints for CO2 transportation pipelines. Technical constraints on wind turbine expansion include constraints on the scope of wind curtailment; Technical constraints on the flexibility retrofitting of thermal power units also include the ability to climb slopes under different retrofitting methods.

3. The configuration method of carbon reduction technology in a power system as described in claim 2, characterized in that, Solving the system-level planning model specifically includes: Transform the system-level planning model into a two-stage scenario-oriented distributed robust optimization model: ； in, The objective term in the first stage of the two-stage scenario-oriented distributed robust optimization model is used to determine the investment decision of the scale of each carbon reduction technology configuration that minimizes the total investment cost of carbon reduction technologies. Indicates the investment decision variables in the first stage; Represents the feasible region of investment decision variables; This represents the investment cost coefficient, with the superscript T indicating transpose; This is the objective term for the second stage in the two-stage scenario-oriented distributed robust optimization model. The second stage is used to determine the operating strategy that minimizes the operating cost of the corresponding typical operating scenario, given investment decisions and typical operating scenarios. This represents the total number of clustering results obtained after clustering the historical operating data of the target power system. Each clustering result represents a typical operating scenario of the target power system. Typical operating scenarios are represented. s The probability of; The feasible region represents the uncertainty of the scenario probability, characterizing the probability fluctuation range of each typical operating scenario; These represent the operational decision variables for the second phase. Indicates the variables of investment decision and typical operating scenarios s actual probability The feasible region for operational decisions with constraints; This represents the operating cost coefficient; the superscript T indicates transpose. A two-stage scenario-oriented distributed robust optimization model is solved using a fuzzy column and constraint generation algorithm, including: The two-stage scenario-oriented distributed robust optimization model is decomposed into a main problem and sub-problems, where the main problem corresponds to the first stage and the sub-problems correspond to the second stage. Iterative interaction between the main problem and sub-problems: The main problem outputs investment decisions for carbon reduction technologies, the sub-problems verify the corresponding operating costs based on the investment decisions for carbon reduction technologies, and feed back the operating costs of the worst-case typical operating scenario to the main problem; By iteratively interacting between the main problem and sub-problems, the investment and operation plan is gradually optimized to obtain the optimal capacity configuration and operation strategy for configuring various carbon reduction technologies in the target power system.

4. The configuration method of carbon reduction technology in a power system as described in claim 2, characterized in that, The annual investment cost specifically includes: Investment costs for the flexibility retrofitting of thermal power units in the target power system : ； ； ； ； in, , and These represent the unit cost of increasing minimum technical output, downhill ramp rate, and uphill ramp rate, respectively. , and They represent thermal power units i The increase in minimum technical output, the increase in downhill climbing rate, and the increase in uphill climbing rate; , and These respectively represent thermal power units i The binary decision variables for not implementing modification, conventional technical modification, and oxygen-enriched combustion modification are obtained through... Apply constraints; , and These respectively represent thermal power units i Minimum technical output corresponding to no modification, conventional technical modification, and oxygen-enriched combustion modification; Indicates the minimum technical output after the decision; and These respectively represent thermal power units i The binary decision variables for not implementing slope modification and implementing slope modification are obtained through... Apply constraints; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb downhill after a decision is made; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb uphill after a decision is made; Investment cost of configuring hydrogen energy storage systems in the target power system : ； in, This indicates the unit capacity cost of a hydrogen energy storage system; Indicates hydrogen energy storage system e Maximum capacity; Investment costs for expanding wind turbine capacity in the target power system : ； in, This indicates the cost of a single wind turbine unit; Indicates the number of newly added wind turbine units; Investment cost of configuring carbon capture, storage and utilization equipment in the target power system : ； in, This indicates the unit cost of equipping a thermal power unit with a carbon dioxide capture device. Indicates thermal power unit i Rated capacity; This indicates the carbon dioxide capture device for thermal power units. i Capture rate of emitted carbon dioxide; This represents a nonlinear function relating carbon dioxide capture rate to cost.

5. The configuration method of carbon reduction technology in a power system as described in claim 2, characterized in that, The annual operating costs specifically include: Operating costs of thermal power units : ； in, Indicates thermal power unit i At any moment t Actual technical output; Indicates thermal power unit i The operating cost function; This represents the set of all thermal power units; , Indicates the total number of time steps; Thermal power unit start / stop costs : ； in, Indicates thermal power unit i The unit startup cost; A binary variable representing the operating status of a thermal power unit, where 1 indicates startup and 0 indicates non-start. Indicates thermal power unit i Unit downtime cost; A binary variable representing the shutdown of a thermal power unit, where 1 indicates shutdown and 0 indicates that the unit is not shut down; Costs of wind curtailment and load shedding : ； in, This represents the unit cost of wind curtailment penalty; Indicates wind turbine At any moment The wind curtailment power; This represents the unit load shedding penalty cost; Indicates load node At any moment The amount of load shedding; This represents the set of all wind turbine units; Represents the set of all load nodes within the target power system; Carbon trading costs : ； in, This indicates the unit price of CO2. Indicates thermal power unit i At any moment t CO2 emissions; Indicates thermal power unit i The carbon quota coefficient corresponding to a unit output.

6. The configuration method of carbon reduction technology in a power system as described in claim 2, characterized in that, The wind power uncertainty of the target power system is obtained by allocating the probability fluctuation range of typical operating scenarios of the target power system through norm constraints, specifically including: The historical operating data of the target power system are clustered to obtain several clustering results. Each clustering result represents a typical operating scenario of the target power system. Assign a scenario probability fluctuation range to each typical operating scenario using norm constraints: ； in, The feasible region represents the uncertainty of the scenario probability, characterizing the probability fluctuation range of each typical operating scenario; Typical operating scenarios are represented. s The probability is determined based on the weight in the clustering results; Represent the set of positive real numbers; N s This represents the total number of typical operating scenarios; Typical operating scenarios are represented. s The initial probability is obtained from the historical operating data of the target power system. θ 1 and θ ∞ These represent the allowed probability deviations for the 1-norm and ∞-norm, respectively. S This represents the sample size of historical operating data for the target power system; α 1 and α ∞ These represent the confidence levels corresponding to the 1-norm and ∞-norm, respectively.

7. The configuration method of carbon reduction technology in a power system as described in claim 4, characterized in that, The technical constraints on the flexibility retrofitting of thermal power units specifically include: Minimum technical output constraints for thermal power units under different retrofitting methods: ； ； in, , and They represent thermal power units i At any moment t Binary variables representing the running status, power-on status, and shutdown status. Indicates thermal power unit i At any moment t -1 is a binary variable representing the running state; This represents the minimum technical output after the decision is made. , , and These respectively represent thermal power units i The binary decision variables for not implementing modification, conventional technical modification, and oxygen-enriched combustion modification are obtained through... Apply constraints; Indicates thermal power unit i At any moment t Actual output; Indicates thermal power unit i Rated output; Technical constraints on the flexibility retrofitting of thermal power units also include the ability to climb slopes under different retrofitting methods: ； ； in, Indicates the thermal power unit after the decision i Downhill climbing ability , and These respectively represent thermal power units i The binary decision variables for not implementing slope modification and implementing slope modification are obtained through... Apply constraints; and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates the ability to climb uphill after making a decision. , and These respectively represent thermal power units i The downhill / climbing capacity corresponding to not implementing slope modification and implementing slope modification; Indicates thermal power unit i At any moment Actual output; Indicates thermal power unit i At any moment t Boot time; Indicates thermal power unit i Minimum boot time; Indicates thermal power unit i At any moment t -1 downtime; Indicates thermal power unit i Minimum downtime.

8. The configuration method of carbon reduction technology in a power system as described in claim 1, characterized in that, The technical constraints of the hydrogen energy storage system specifically include: Hydrogen electrolyzer power constraints: ； ； ； ； in, This indicates the rated operating power of the hydrogen electrolyzer; Indicates hydrogen electrolyzer n At any moment t The power consumed in the electro-hydrogen conversion; , and These represent hydrogen electrolyzers. n At any moment t Binary variables in underload, normal, and off states are accessed via... as well as To impose constraints, and These represent hydrogen electrolyzers. n At any moment t and time t- 1. A binary variable that is in the running state; Hydrogen electrolyzer n At any moment t The overload binary variable, with a value of 1, indicates that the hydrogen electrolyzer is allowed to operate in any of the following states: underload, normal, or overload; with a value of 0, it indicates non-overload operation. and These represent hydrogen electrolyzers. n At any moment t Hydrogen production and hydrogen utilization; and These represent the electro-hydrogen conversion efficiency coefficient and the hydrogen-electric conversion efficiency coefficient of the hydrogen electrolyzer, respectively. Indicates hydrogen electrolyzer n At any moment t Power consumed in hydrogen-to-electric conversion; Indicates hydrogen electrolyzer n Minimum power supplied; Indicates hydrogen electrolyzer n Maximum power provided; Hydrogen storage capacity constraints: ； ； in, and These represent hydrogen storage tanks. n At any moment t and time t -1 gas storage capacity; This indicates the hydrogen storage efficiency of the hydrogen storage tank; These represent hydrogen storage tanks. n At any moment t The inflation and deflation volumes; These represent the upper and lower limits of tank storage, respectively.

9. The configuration method of carbon reduction technology in a power system as described in claim 2, characterized in that, The technical constraints on carbon capture, storage and utilization specifically include: CO2 capture and storage energy consumption constraints: ； ； ； in, This refers to thermal power units in the target power system equipped with carbon dioxide capture devices. The actual internet power at time t; Indicates thermal power unit Total power emitted at time t; Indicates thermal power unit The power consumed by the corresponding carbon dioxide capture device at time t; Indicates thermal power unit The variable energy consumption of the corresponding carbon dioxide capture device during operation at time t; Indicates thermal power unit The corresponding carbon dioxide capture device operates on binary decision variables at time t. 1 indicates that the carbon dioxide capture device is in operation, and 0 indicates that the carbon dioxide capture device is out of operation; Indicates the unit The fixed energy consumption of the corresponding carbon dioxide capture device during operation; Indicates the proportional coefficient of carbon dioxide variable energy consumption; Indicates thermal power unit The amount of carbon dioxide captured by the corresponding carbon dioxide capture device at time t; CO2 storage device capacity constraints: ； ； ； in, Indicates thermal power unit The volume of carbon dioxide to be stored in the corresponding carbon dioxide capture device at time t; Indicates the density of the carbon dioxide absorbent; and They represent thermal power units The corresponding carbon dioxide capture device at a certain time and time The remaining volume of carbon dioxide that can be contained; and They represent thermal power units The corresponding carbon dioxide capture device at a certain time and time The volume of carbon dioxide already stored; Indicates the maximum carbon dioxide storage capacity; CO2 transport pipeline constraints: ； ； ； ； in, Represents pipe nodes At any moment The carbon dioxide injection gas flow rate; Indicates from the pipeline node To pipeline node In time Gas flow rate; This represents the set of all pipes and pipe nodes; Represents pipe nodes At any moment The outflow rate of carbon dioxide gas; Represents pipe nodes At any moment The outflow rate of carbon dioxide gas; Represents pipe nodes Maximum outflow gas flow rate; Represents pipe nodes With pipeline nodes The flow coefficient of the pipeline between them; and Representing pipe nodes and pipeline nodes In time The pressure; Represents pipe nodes The minimum pressure; Represents pipe nodes The maximum pressure.

10. The configuration method of carbon reduction technology in a power system as described in claim 2, characterized in that, The power balance of the target power system specifically includes: ； ； in, Indicates thermal power unit With nodes The power-related binary variable, with a value of 1 indicating a thermal power unit. With nodes Power supply, 0 indicates no direct power supply relationship; Indicates thermal power unit At any moment Actual internet access power; Indicates thermal power unit At any moment Total output power; Indicates thermal power unit Fixed basic energy consumption; Indicates thermal power unit Variable energy consumption during operation; Indicates wind turbine With nodes Power-related binary variables; Indicates wind turbine At any moment Theoretical output; Indicates wind turbine At any moment The amount of wind curtailment; Indicates hydrogen energy storage system With nodes Power-related binary variables; Indicates hydrogen energy storage system At any moment The discharge power; Indicates hydrogen energy storage system At any moment The charging power; Indicates the time step; Indicates transmission line With nodes Power-related binary variables; Indicates transmission line At any moment The actual transmission power; Indicates load node At any moment Total load demand; Indicates load node At any moment The shear load.

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