Carbon economy optimization scheduling method and system of thermal power and molten salt heat storage coupling system

Abstract

The application discloses a carbon economy optimization scheduling method and system of a thermal power and molten salt heat storage coupling system, combines variable load thermal power and molten salt heat storage to form a coupling system, and performs modification, establishes a variable load thermal power and molten salt heat storage coupling system operation model, quantifies dynamic carbon emission right transaction costs of each period of the operation model, linearizes a nonlinear peak shaving correction coefficient in a carbon emission right quota, establishes a thermal power carbon emission right transaction cost quantification model, adds the carbon emission right transaction cost into a thermal power coupling system economic scheduling model, takes maximum daily comprehensive operation income as an objective, establishes a variable load thermal power and molten salt heat storage coupling system carbon economy optimization scheduling model, solves the variable load thermal power and molten salt heat storage coupling system carbon economy model, and obtains a thermal power day-ahead optimization scheduling scheme and optimal operation income. The method can improve the comprehensive income and carbon economy of the overall system, and improve the peak shaving capacity and operation flexibility.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization and dispatching technology. It relates to a carbon economy optimization and dispatching method and system for a coupled thermal power and molten salt thermal storage system. Background Technology

[0002] Upgrading thermal power plants to improve their flexibility and enable them to adjust rapidly under varying loads is a crucial means of promoting renewable energy integration and participating in deep peak shaving of the power grid. However, on the one hand, the high penetration rate of renewable energy places increasingly higher demands on the flexibility of thermal power plants, making it difficult for them to simultaneously achieve a wide peak shaving range and low coal consumption and emissions under rapidly changing load conditions. On the other hand, carbon economic optimization scheduling models are still incomplete, failing to address the overall carbon economic quantification and assessment of the coordinated operation of upgraded thermal power units and renewable energy units, as well as the impact of optimized scheduling schemes. To further explore the low-carbon coordinated operation capabilities of thermal power plants under different flexibility upgrade schemes, researching the carbon economic optimization scheduling of rapidly changing load thermal power plants coupled with molten salt thermal energy storage systems is of practical significance.

[0003] Upgrading thermal power plants to improve their flexibility is fundamental to achieving rapid and deep peak and frequency regulation across the power system. Exploring more effective flexibility upgrades and accurately describing the physical characteristics and mathematical models of the upgraded thermal power units are crucial for system operation under conditions of high renewable energy penetration. In scenarios where upgraded thermal power units operate in conjunction with renewable energy sources, quantifying and evaluating the carbon economy of thermal power units and strengthening their planning and deployment in the carbon trading market are important pathways to achieving energy conservation, emission reduction, and dual-carbon goals.

[0004] Currently, flexible retrofitting schemes for thermal power plants mainly focus on three aspects: stable combustion under low load, rapid start-up and shutdown, and rapid ramp-up. Rapid start-up and shutdown, and rapid ramp-up, can adjust thermal power output in real time, but increase unit lifespan and additional coal consumption emissions. For stable combustion under low load, thermal power plant thermal storage retrofitting is widely used. Retrofitting with molten salt thermal storage can effectively improve the peak-shaving depth of the unit, while simultaneously achieving rapid load changes on the power output side. However, due to limitations in energy storage capacity, it is difficult to achieve rapid load changes over a wide range. Furthermore, research on carbon economics mainly focuses on carbon trading mechanisms and low-carbon economic dispatching in thermal power plants or multi-energy industrial parks. Studies show that the introduction of carbon economics can effectively balance the environmental and economic aspects of integrated energy system dispatching. However, research on the carbon economics of coupled systems combining rapidly load-changing thermal power units with molten salt thermal storage retrofitting is limited. This invention addresses this need. Summary of the Invention

[0005] The purpose of this invention is to provide a carbon economy optimization scheduling method and system for a coupled thermal power and molten salt thermal energy storage system. This system combines rapidly changing thermal power with molten salt thermal energy storage to form a coupled system, thereby solving the problems of unit and carbon loss and economy, and further improving the flexibility retrofit performance. The invention also studies the carbon economy quantification and evaluation method based on the flexible retrofit thermal power coupled system, and derives an optimized scheduling strategy for the coupled system of thermal power and molten salt thermal energy storage.

[0006] The technical solution to achieve the purpose of this invention is as follows:

[0007] A carbon-economic optimization scheduling method for a coupled thermal power and molten salt thermal energy storage system includes the following steps:

[0008] The variable load thermal power and molten salt thermal storage are combined to form a coupled system, and the system is modified to establish an operation model for the variable load thermal power and molten salt thermal storage coupled system.

[0009] The dynamic carbon emission trading cost of the quantitative operation model in each period is linearized by linearizing the nonlinear peak-shaving correction coefficient in the carbon emission quota and establishing a quantitative model of the carbon emission trading cost of thermal power.

[0010] Based on the established cost quantification model for trading carbon emission rights of thermal power, and with the goal of maximizing daily comprehensive operating revenue, a carbon economic optimization scheduling model for a variable load thermal power and molten salt thermal storage coupled system is established.

[0011] Solve the carbon economy model of the thermal power and molten salt thermal storage coupled system under variable load to obtain the day-ahead optimal dispatch scheme and optimal operating benefits of thermal power.

[0012] In the preferred technical solution, the variable load thermal power and molten salt thermal storage are combined to form a coupled system, and the modifications include:

[0013] The molten salt thermal energy storage system consists of storage tanks, heat exchangers, pumps, and piping systems. The storage tanks store high-temperature molten salt, the heat exchangers transfer heat to the working fluid, and the pumps and piping systems circulate the molten salt. In actual operation, the storage tanks are divided into high-temperature and low-temperature dual-circulation tanks. The high-temperature storage tank is located between the boiler and the high-pressure tank of the steam turbine, while the low-temperature storage tank is located between the steam turbine's working cylinder and the boiler. The charging process occurs during periods of low load or excess power generation in the thermal power unit. The high-temperature and low-temperature storage tanks absorb excess heat power from the boiler and steam turbine's working cylinder, transferring it to the molten salt through the heat exchangers and storing it in the storage tanks, thus reducing the minimum or excess output of the thermal power plant. The heat release process occurs during peak load periods or when renewable energy output is insufficient. The storage tanks release the stored heat energy. The high-temperature storage tank increases the steam intake of the steam turbine, while the low-temperature storage tank heats liquid water to form high-temperature feedwater that enters the boiler, increasing the thermal power output. Without adjustment, change its external power output. .

[0014] In the preferred technical solution, when the thermal power plant needs to change load to meet fluctuating demand, the output is first adjusted through the molten salt thermal storage system. The coupled system operation model is shown in the following equation:

[0015]

[0016] In the formula, T For time intervals; / 、 / These represent the power stored / released by the high-temperature and low-temperature thermal storage systems during time period t, respectively. This represents the heat power transferred from the steam turbine to the generator during time interval t; , These represent the heat storage capacity of the high-temperature and low-temperature thermal storage systems at time t, respectively. or H , or L These are the heat loss rates of high-temperature and low-temperature thermal storage systems, respectively. or HC / or HD , or LC / or LD These refer to the thermal efficiency stored / released by high-temperature and low-temperature thermal storage systems, respectively. or coal For coal combustion thermal efficiency; for The amount of coal consumed at any given time; The calorific value of pulverized coal combustion; or ST This refers to the thermoelectric conversion efficiency.

[0017] In the preferred technical solution, the molten salt thermal storage system includes capacity constraints and operational constraints, as shown below:

[0018] Capacity constraints of high and low temperature thermal storage systems:

[0019]

[0020] In the formula, , Let represent the minimum and maximum heat storage capacities of the high-temperature system at the time scale t, respectively. , Let represent the minimum and maximum thermal storage capacities of the cryogenic system at the time scale t, respectively.

[0021] Operating constraints of high and low temperature thermal storage systems:

[0022]

[0023] In the formula, 、 and 、 These are 0-1 variables representing whether the high and low thermal storage systems are storing / releasing thermal energy. If the system is in operation, the value is 1; otherwise, the value is 0. i HD and i LD The maximum heat transfer coefficient of high and low temperature thermal storage systems; and These are the maximum heat release power of the high- and low-temperature thermal storage systems, respectively.

[0024] When the molten salt thermal storage capacity or charge / discharge efficiency does not meet the real-time load requirements, the thermal power boiler side performs rapid load adjustment. Under this operating condition, the coupled system must meet the thermal power step ramp rate constraint under rapid load change conditions, as shown in the following formula:

[0025]

[0026] In the formula, a This represents the range of the load factor. b This represents the range where the maximum load factor is located. c This represents the range where the minimum load factor is located. Let be the load factor of the thermal power unit at time t. and These represent the upper and lower limits of the load factor of thermal power units on the time scale t; m a , m b and m c These are the lower limit benchmarks of the peak-shaving range where the load rate of thermal power units is located on the time scale t; This represents the lower limit of the x-th output range; r a This represents the upper limit of the ramp rate corresponding to the load factor; r b This represents the upper limit of the ramp rate corresponding to the maximum load rate; r c This represents the upper limit of the ramp rate corresponding to the minimum load rate; r x This represents the upper limit of the ramp rate for the x-th power output interval.

[0027] In the preferred technical solution, the established cost quantification model for trading carbon emission rights of thermal power includes:

[0028] Carbon emission trading costs of thermal power units for:

[0029]

[0030] In the formula, T Indicates the total scheduling period. For carbon prices in the carbon emissions trading market, The carbon emissions of a thermal power unit at time t are given by the following formula:

[0031]

[0032] In the formula, Carbon emission factor for standard coal used in thermal power generation; This represents the standard coal consumption generated over a time scale of t.

[0033] The carbon emission allowance for thermal power units at time scale t is given by the following formula:

[0034]

[0035] In the formula, d The prescribed baseline value for carbon emissions from thermal power plants; For the current thermal combustion output of thermal power units, The load factor of the thermal power unit at the time scale t; m C The load factor benchmark needs to be adjusted for peak shaving; , These are the correction coefficients corresponding to different benchmark intervals;

[0036] when > At that time, carbon emission differentials are purchased from the carbon emission trading market; when < At that time, the remaining balance can be traded in the carbon emission trading market to obtain additional profits.

[0037] In the preferred technical solution, linearizing the nonlinear peak-shaving correction coefficient in carbon emission allowances includes:

[0038] Peak shaving correction coefficient of thermal power units The values ​​differ under different load rates, and are as follows:

[0039]

[0040] When peak shaving correction coefficient When the behavior is nonlinear, linearize it:

[0041] Substitute into this interval Taking the value and simplifying, we get:

[0042]

[0043] In the formula, This is the upper limit of the output of thermal power units;

[0044] right A direct linear fit is performed to obtain the linear fitting function for the carbon emission baseline value.

[0045] In the preferred technical solution, the optimization objective function of the carbon economy optimization scheduling model for the variable load thermal power and molten salt thermal energy storage coupled system is: to maximize the comprehensive operating benefits of the coupled system and the new energy unit within one day. f A As shown in the following formula:

[0046]

[0047] In the formula, u Numbering of thermal power units within the coupling system; U The set of the number of thermal power units within the coupled system; P UN This is the difference between the actual total power generation and the dispatch demand; C GU As a penalty factor; The comprehensive power generation revenue of a single thermal power plant is calculated as follows:

[0048]

[0049] In the formula, This is the basic revenue from the sale of electricity by thermal power units; For thermal power units participating in deep peak shaving compensation benefits; The operating cost of thermal power units; For the flexibility and backup costs of thermal power units; The cost of pollution emissions from thermal power units; The cost of trading carbon emission rights for thermal power units; The comprehensive operating cost of molten salt thermal energy storage for thermal power units;

[0050] The constraints are divided into power balance, thermal power step ramp rate constraints under rapid load changes, thermal power unit power generation constraints, new energy power generation constraints, and molten salt thermal storage system constraints.

[0051] In the preferred technical solution, the power generation constraints for thermal power units include upper and lower limits of unit output constraints and flexible reserve constraints, wherein the flexible reserve constraints are as follows:

[0052]

[0053] In the formula, This is the maximum thermal combustion output of the thermal power unit; This represents the minimum value of the u-th thermal power unit in the current output range; and These refer to the upward and downward flexibility reserves of thermal power units at the time scale t. and These refer to the upward and downward flexibility reserves of thermal power units at the t-1 time scale; This represents the lower limit of the x-th output range of the u-th thermal power unit;

[0054] Regarding constraints on renewable energy power generation, the reduction in renewable energy output during each time period and within a day cannot exceed the specified value:

[0055]

[0056] In the formula, To actually contribute to the power generation of new energy units m RE Minimum utilization rate for new energy generating units; m RE,d The proportion of reduced output of new energy generating units to the predicted output per day; It is predicted to contribute to the power output of new energy generating units.

[0057] This invention also discloses a carbon economy optimization scheduling system for a coupled thermal power and molten salt thermal energy storage system, comprising:

[0058] The module for constructing an operation model of a variable load thermal power and molten salt thermal storage coupled system combines a variable load thermal power and molten salt thermal storage system to form a coupled system, and then modifies it to establish an operation model of the variable load thermal power and molten salt thermal storage coupled system.

[0059] The module for constructing a quantitative model of carbon emission trading costs for thermal power plants quantifies the dynamic carbon emission trading costs of the operating model at different time periods, linearizes the nonlinear peak-shaving correction coefficient in the carbon emission quota, and establishes a quantitative model of carbon emission trading costs for thermal power plants.

[0060] The carbon economic optimization scheduling model construction module establishes a carbon economic optimization scheduling model for a variable load thermal power and molten salt thermal storage coupled system based on the established thermal power carbon emission rights trading cost quantification model and with the goal of maximizing daily comprehensive operating benefits.

[0061] The optimization scheduling module solves the carbon economy model of the thermal power and molten salt thermal storage coupled system with variable load, and obtains the day-ahead optimized scheduling scheme and optimal operating benefits of thermal power.

[0062] The present invention also discloses a computer storage medium storing a computer program, wherein when the computer executes the computer program, it implements the carbon economy optimization scheduling method for the coupled thermal power and molten salt thermal energy storage system described in any of the above claims.

[0063] The present invention also discloses an electronic device, including a memory and a processor, wherein the memory stores a computer program, the processor runs the computer program stored in the memory, and the computer program, when executed, implements the carbon economy optimization scheduling method for the coupled thermal power and molten salt thermal storage system described in any of the above claims.

[0064] Compared with the prior art, the significant advantages of this invention are:

[0065] 1. This invention proposes a collaborative scheme for rapid load change thermal power and molten salt thermal storage retrofit, forming a coupled system of thermal power and molten salt thermal storage retrofit, establishing its operation model, giving full play to the advantages of rapid load change and molten salt thermal storage retrofit, and further improving its peak-shaving capacity, operational flexibility and carbon economy.

[0066] 2. This invention calculates the carbon difference for thermal power units by subtracting their carbon emissions from the prescribed carbon emission quota at each time scale during thermal power dispatch. The difference is then used to buy or sell carbon trading rights on the carbon trading market to cover the costs or profits associated with these rights. The peak-shaving correction coefficient for thermal power units is a non-linear coefficient. This quantitative model linearizes the solution for the carbon emission quota, resulting in a linear function with high goodness of fit that meets the linearization requirements of the optimization model.

[0067] 3. This invention establishes a carbon economy model for a thermal power and molten salt thermal energy storage coupled system that takes into account rapid load changes. Using the coordinated operation of a thermal power and molten salt thermal energy storage coupled system with new energy units, which includes rapid load changes, as the research object, the model maximizes the overall comprehensive operating benefits of the thermal power coupled system within a single day, while considering the carbon emission trading costs / benefits. Attached Figure Description

[0068] Figure 1 Flowchart of carbon economy optimization scheduling method for coupled thermal power and molten salt thermal storage system;

[0069] Figure 2 Linear fitting function and its deviation plot;

[0070] Figure 3 Flowchart for solving the optimal scheduling model of the coupled system after variable load and molten salt thermal storage modification. Detailed Implementation

[0071] The principle of this invention is to introduce an operation model of a rapidly changing thermal power and molten salt thermal energy storage coupled system, a cost quantification model for thermal power carbon emission trading, and a carbon emission quota that considers a nonlinear peak-shaving correction coefficient, and to quantify and evaluate its impact on day-ahead optimized scheduling.

[0072] Example:

[0073] like Figure 1 As shown, a carbon economy optimization scheduling method for a coupled thermal power and molten salt thermal energy storage system includes the following steps:

[0074] The variable load thermal power and molten salt thermal storage are combined to form a coupled system, and the system is modified to establish an operation model for the variable load thermal power and molten salt thermal storage coupled system.

[0075] The dynamic carbon emission trading cost of the quantitative operation model in each period is linearized by linearizing the nonlinear peak-shaving correction coefficient in the carbon emission quota and establishing a quantitative model of the carbon emission trading cost of thermal power.

[0076] Based on the established cost quantification model for trading carbon emission rights of thermal power, and with the goal of maximizing daily comprehensive operating revenue, a carbon economic optimization scheduling model for a variable load thermal power and molten salt thermal storage coupled system is established.

[0077] Solve the carbon economy model of the thermal power and molten salt thermal storage coupled system under variable load to obtain the day-ahead optimal dispatch scheme and optimal operating benefits of thermal power.

[0078] In another embodiment, a carbon economy optimization scheduling system for a coupled thermal power and molten salt thermal energy storage system includes:

[0079] The module for constructing an operation model of a variable load thermal power and molten salt thermal storage coupled system combines a variable load thermal power and molten salt thermal storage system to form a coupled system, and then modifies it to establish an operation model of the variable load thermal power and molten salt thermal storage coupled system.

[0080] The module for constructing a quantitative model of carbon emission trading costs for thermal power plants quantifies the dynamic carbon emission trading costs of the operating model at different time periods, linearizes the nonlinear peak-shaving correction coefficient in the carbon emission quota, and establishes a quantitative model of carbon emission trading costs for thermal power plants.

[0081] The carbon economic optimization scheduling model construction module establishes a carbon economic optimization scheduling model for a variable load thermal power and molten salt thermal storage coupled system based on the established thermal power carbon emission rights trading cost quantification model and with the goal of maximizing daily comprehensive operating benefits.

[0082] The optimization scheduling module solves the carbon economy model of the thermal power and molten salt thermal storage coupled system with variable load, and obtains the day-ahead optimized scheduling scheme and optimal operating benefits of thermal power.

[0083] The following example illustrates the workflow of a carbon economy optimization scheduling system for a coupled thermal power and molten salt thermal energy storage system, including the following steps:

[0084] S1. Establish an operation model for a coupled thermal power and molten salt thermal storage system with rapid load changes;

[0085] The molten salt thermal energy storage system consists of storage tanks, heat exchangers, pumps, and piping systems. The storage tanks store high-temperature molten salt; the heat exchangers transfer heat to the working fluid (such as water or steam); and the pumps and piping systems circulate the molten salt. In actual operation, the storage tanks are divided into high-temperature and low-temperature dual-circulation storage tanks. The high-temperature storage tank is located between the boiler and the high-pressure tank of the steam turbine, while the low-temperature storage tank is located between the steam turbine's working cylinder and the boiler. The heat charging process occurs during periods of low load or excess power generation in the thermal power unit. The high-temperature and low-temperature storage tanks absorb excess heat power from the boiler and the steam turbine's working cylinder, transferring it to the molten salt through the heat exchangers and storing it in the storage tanks, thus reducing the minimum or excess output of the thermal power plant. The heat release process occurs during peak load periods or when renewable energy output is insufficient. The storage tanks release the stored heat energy; the high-temperature storage tank increases the steam intake of the steam turbine, while the low-temperature storage tank heats liquid water to form high-temperature feedwater that enters the boiler, increasing the thermal power output. This achieves the goal of maximizing thermal power output. Without adjustment, change its external power output. .

[0086] To further reduce coal consumption and carbon emissions, when thermal power plants need to adjust loads to meet fluctuating demand, the output is first regulated by molten salt thermal storage. The operating model of the coupled system at this time is shown in the following equation:

[0087]

[0088] In the formula, T For time intervals; / 、 / These represent the power stored / released by the high-temperature and low-temperature thermal storage systems during time period t, respectively. This represents the heat power transferred from the steam turbine to the generator during time interval t; , These represent the heat storage capacity of the high-temperature and low-temperature thermal storage systems at time t, respectively. or H , or L These are the heat loss rates of high-temperature and low-temperature thermal storage systems, respectively. or HC / or HD , or LC / or LD These refer to the thermal efficiency stored / released by high-temperature and low-temperature thermal storage systems, respectively. or coal For coal combustion thermal efficiency; for The amount of coal consumed at any given time; The calorific value of pulverized coal combustion; or ST This refers to the thermoelectric conversion efficiency.

[0089] Molten salt thermal storage systems include capacity constraints and operational constraints, as shown below:

[0090] (1) Capacity constraints of high and low temperature thermal storage systems

[0091]

[0092] In the formula, , Let represent the minimum and maximum heat storage capacities of the high-temperature system at the time scale t, respectively. , and represent the minimum and maximum thermal storage capacity of the cryogenic system at the time scale t, respectively.

[0093] (2) Operational constraints of high and low temperature thermal storage systems

[0094]

[0095] In the formula, 、 and 、 These are 0-1 variables representing whether the high and low thermal storage systems are storing / releasing thermal energy. If the system is in operation, the value is 1; otherwise, the value is 0. i HD and i LD The maximum heat transfer coefficient of high and low temperature thermal storage systems; and These represent the maximum heat release power of the high- and low-temperature thermal storage systems, respectively.

[0096] To improve peak-shaving capacity, when the molten salt thermal storage capacity or charge / discharge efficiency does not meet real-time load demands, the primary objective is to mitigate wind and solar power fluctuations in real time, requiring rapid load adjustments at the thermal power boiler side. Under this operating condition, the coupled system must meet the thermal power plant's stepped ramp rate constraint under rapid load changes, as shown in the following equation:

[0097]

[0098] In the formula, a This represents the range of the load factor. b This represents the range where the maximum load factor is located. c This represents the range where the minimum load factor is located. Let be the load factor of the thermal power unit at time t. and These represent the upper and lower limits of the load factor of thermal power units on the time scale t; m a , m b and m c These are the lower limit benchmarks of the peak-shaving range where the load rate of thermal power units is located on the time scale t; This represents the lower limit of the x-th output range; r a This represents the upper limit of the ramp rate corresponding to the load factor; r b This represents the upper limit of the ramp rate corresponding to the maximum load rate; r c This represents the upper limit of the ramp rate corresponding to the minimum load rate; r x This represents the upper limit of the ramp rate for the x-th power output interval.

[0099] S2. Quantify the dynamic carbon emission trading costs for each period, linearize the nonlinear peak-shaving correction coefficient in the carbon emission quota, and establish a quantitative model for the carbon emission trading costs of thermal power plants.

[0100] According to the national carbon emission trading scheme, the difference between actual carbon emissions and quotas is quantified, and the difference is invested in the carbon emission trading market to obtain the carbon emission trading cost. The carbon economics assessment of the coupled system is then incorporated into the economic scheduling model.

[0101] In thermal power units, their overall carbon economics can be measured by calculating their carbon emission trading costs. The carbon emission trading costs of thermal power units. As shown in the following formula:

[0102]

[0103] In the formula, T Indicates the total scheduling period. For carbon prices in the carbon emissions trading market, The carbon emissions of a thermal power unit at time t are given by the following formula:

[0104]

[0105] In the formula, The carbon emission factor for standard coal equivalent of thermal power is taken as 2.67 tC. / tce; This represents the standard coal consumption generated at a time scale of t.

[0106] The carbon emission allowance for thermal power units at time scale t is calculated using the following formula:

[0107]

[0108] In the formula, d The prescribed baseline value for carbon emissions from thermal power plants; For the current thermal combustion output of thermal power units, The load factor of the thermal power unit at the time scale t; m C The load factor benchmark needs to be adjusted for peak shaving; , These are the correction coefficients corresponding to different benchmark intervals;

[0109] when > At this time, the carbon emissions of thermal power units exceed their quotas, and they need to purchase the carbon emission difference from the carbon emission trading market; when < At this time, the carbon emissions of thermal power units are below the quota, and the remaining amount can be traded in the carbon emission trading market to obtain additional profits.

[0110] Due to the peak-shaving correction coefficient of thermal power units The values ​​differ under different load rates. Referring to the allocation scheme, the values ​​are as follows:

[0111]

[0112] In the formula, This refers to the thermal output load rate of a thermal power unit after undergoing molten salt thermal storage retrofit.

[0113] From this formula, we can see that... When the unit load rate is within the range of 30%-65%, the peak-shaving correction factor It exhibits nonlinearity, therefore it needs to be linearized.

[0114] Substitute into this interval Taking the value and simplifying, we get:

[0115]

[0116] In the formula, This is the upper limit of the output of thermal power units.

[0117] To improve the solution efficiency of the carbon economic optimization scheduling model for variable load thermal power and molten salt thermal storage coupled systems, a direct linear fit is performed on the above equation. Taking a conventional coal-fired power unit with a rated capacity of 600 MW as an example, when the load rate is within the range of 30%-65%, the linear fitting function of the carbon emission baseline value and its deviation are obtained as follows: Figure 2 As shown, the goodness of fit of the linear fitting function is 0.9125, which meets the linearization requirements of the optimization model. Therefore, it can be substituted into the carbon economy optimization scheduling model of the variable load thermal power and molten salt thermal storage coupled system.

[0118] S3. Incorporate carbon emission trading costs into the carbon economic optimization scheduling model of thermal power and molten salt thermal energy storage coupled systems under variable load, and establish a carbon economic optimization scheduling model for thermal power and molten salt thermal energy storage coupled systems under variable load with the goal of maximizing daily comprehensive operating benefits.

[0119] The objective function of the optimization model is to maximize the combined daily operating benefits of the coupled system and the new energy unit. f A As shown in the following formula:

[0120]

[0121] In the formula, u Numbering of thermal power units within the coupling system; U The set of the number of thermal power units within the coupled system; P UN This is the difference between the actual total power generation and the dispatch demand; C GU As a penalty factor; The comprehensive power generation revenue of a single thermal power plant is calculated as follows:

[0122]

[0123] In the formula, This is the basic revenue from the sale of electricity by thermal power units; For thermal power units participating in deep peak shaving compensation benefits; The operating cost of thermal power units; For the flexibility and backup costs of thermal power units; The cost of pollution emissions from thermal power units; The cost of trading carbon emission rights for thermal power units; This refers to the comprehensive operating cost of molten salt thermal energy storage for thermal power units.

[0124] (1) The comprehensive electricity sales revenue of thermal power units can be expressed as: The sum is in the form of a sum. When the generating capacity of the unit exceeds the compensation benchmark, only the basic revenue remains from electricity sales. , can be represented as:

[0125]

[0126] In the formula, C The electricity price is based on a unit of electricity generation, and thermal power units can obtain this revenue regardless of their power generation status.

[0127] When the generating capacity of the unit is less than the compensation benchmark, the revenue from the sale of electricity by the thermal power unit is divided by the basic revenue. In addition, it also includes compensation revenue from participating in peak-shaving ancillary services. , It can be represented as:

[0128]

[0129]

[0130]

[0131] In the formula, This represents the maximum electrical output of the thermal power unit. C 1 and C 2 represents the unit electricity sales price based on different compensation benchmarks. If the generating capacity of the unit is less than the corresponding compensation benchmark, it can obtain this additional income. The power output load rate of thermal power units; m 1, m 2 represents the load factor benchmark corresponding to the first and second tiers of peak-shaving compensation; This is a seasonally related earnings adjustment factor.

[0132] (2) Operating costs of thermal power units Two scenarios were considered for thermal power plants: regular peak regulation (RPR) and deep peak regulation (DPR). In the regular peak regulation scenario, operating costs only considered the coal consumption for power generation; in the deep peak regulation scenario, operating costs included not only the coal consumption for power generation but also the additional losses incurred by the thermal power units due to reduced output. Calculated using the following formula:

[0133]

[0134]

[0135] In the formula, C coal This refers to the price per ton of coal. , b RPR , c RPR The coefficients are the fitting function coefficients for RPR loss; , b DPR These are the coefficients of the DPR loss fitting function.

[0136] (3) Flexibility standby cost of thermal power units The calculation formula is as follows:

[0137]

[0138] In the formula, , These represent the costs required for a thermal power unit to provide upward and downward flexibility reserves to the system at time t. Since the unit's rapid load ramp-up rate is divided into multiple stages, the corresponding flexibility reserves have different costs at different output stages.

[0139] (4) Pollution emission costs of thermal power units as follows:

[0140]

[0141] In the formula, , , For each pollutant in a thermal power unit, the unit treatment cost, emission coefficient per unit power generation, and pollutant conversion coefficient are calculated. and The unit's pollution fitting coefficient; h For pollutant types, H The total number of pollutants considered in this model is particulate matter, SO2, and NO. x .

[0142] (5) Comprehensive operating cost of molten salt thermal energy storage for thermal power units as follows:

[0143] Thermal power units retrofitted with molten salt thermal energy storage include high / low temperature thermal energy storage systems, resulting in comprehensive operating costs, including operation and maintenance costs. and lifespan depreciation costs .

[0144]

[0145] The thermal energy storage system incurs certain operation and maintenance costs during the charging and discharging process, calculated using the following formula:

[0146]

[0147] In the formula, / 、 / These represent the power stored / released by the high-temperature and low-temperature thermal energy storage systems of the u-th thermal power unit during time period t, respectively. This refers to the operation and maintenance costs of the thermal storage system.

[0148] Thermal storage systems experience losses during charging and discharging, resulting in a reduced lifespan.

[0149]

[0150] In the formula, The investment and construction costs of the thermal storage system; The cycle life of the thermal storage system; , These are 0-1 variables representing whether the high-temperature and low-temperature thermal storage systems undergo a charge / discharge state transition at time t. If a charge / discharge state transition occurs, the value is 1; otherwise, it is 0.

[0151] The constraints of the coordinated economic operation model of the coupled system and the new energy unit can be divided into power balance, thermal power step ramp rate constraint under rapid load change, thermal power unit power generation constraint, new energy power generation constraint, and molten salt thermal storage system constraint.

[0152] Constraints for thermal power generation include upper and lower limits for unit output and flexible reserve constraints, among which the flexible reserve constraints are as follows:

[0153]

[0154] In the formula, This is the maximum thermal combustion output of the thermal power unit; This represents the minimum value of the u-th thermal power unit in the current output range; and These refer to the upward and downward flexibility reserves of thermal power units at the time scale t. and These refer to the upward and downward flexibility reserves of thermal power units at the t-1 time scale; This represents the lower limit of the x-th output range of the u-th thermal power unit.

[0155] Regarding constraints on renewable energy power generation, the reduction in renewable energy output during each time period and within a day cannot exceed the specified value:

[0156]

[0157] In the formula, To contribute to the actual power output of new energy units m RE Minimum utilization rate for new energy generating units; m RE,dThe proportion of reduced output of new energy generating units to the predicted output per day; It is predicted to contribute to the power output of new energy generating units.

[0158] S4. Using the MATLAB environment and the Yalmip platform, employ the Gurobi solver to calculate and solve the optimal scheduling model of the coupled system after rapid load changes and molten salt thermal storage modification. Figure 3 As shown;

[0159] (1) Initialization and data input: Import the system load forecast curve, the day-ahead wind power and photovoltaic maximum output curve. These curves are the relevant data obtained by the dispatch center on the day-ahead, which are used to optimize the scheduling of each output of the coupled system.

[0160] (2) Set operating constraint parameters: After completing the data input, set constraints such as power balance, thermal power step ramp rate under rapid load change, thermal power unit power generation constraint, new energy power generation constraint, and molten salt thermal storage system constraint.

[0161] (3) Set the solution objective: Set the optimization scheduling solution objective as maximizing daily operating benefits.

[0162] (4) Solve the coupled system operation model and carbon economy quantification model: Establish the operation model of the coupled system of thermal power and molten salt thermal storage with rapid load change and the carbon emission trading cost quantification model of thermal power unit, and call the gurobi solver to calculate.

[0163] (5) Record the output results: obtain the day-ahead optimized scheduling scheme of thermal power and the optimal system operation benefits.

[0164] The present invention provides a carbon economy optimization scheduling strategy for a coupled thermal power and molten salt thermal energy storage system. This system can form a coupled system of thermal power and molten salt thermal energy storage retrofit, establish its operation model, give full play to the advantages of rapid load change and molten salt thermal energy storage retrofit, and further improve its peak-shaving capacity, operational flexibility and carbon economy.

[0165] In another embodiment, a computer storage medium stores a computer program thereon, wherein when the computer executes the computer program, it implements the carbon economy optimization scheduling method for the coupled thermal power and molten salt thermal energy storage system described in any of the above embodiments.

[0166] In another embodiment, an electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor runs the computer program stored in the memory, wherein when the computer program is executed, it implements the carbon economy optimization scheduling method for the coupled thermal power and molten salt thermal storage system described in any of the preceding embodiments.

[0167] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A carbon-economic optimization scheduling method for a coupled thermal power and molten salt thermal energy storage system, characterized in that, Includes the following steps: A coupled system is formed by combining variable-load thermal power with molten salt thermal storage, and modifications are made to establish an operational model for this system. The molten salt thermal storage system consists of storage tanks, heat exchangers, pumps, and piping systems. The storage tanks store high-temperature molten salt, the heat exchangers transfer heat to the working fluid, and the pumps and piping systems circulate the molten salt. In actual operation, the storage tanks are divided into high-temperature and low-temperature dual-circulation tanks. The high-temperature tank is located between the boiler and the high-pressure tank of the steam turbine, while the low-temperature tank is located in the steam turbine's working cylinder. Between the thermal power unit and the boiler, the heat charging process occurs during periods of low load or excess power generation. High-temperature and low-temperature thermal storage tanks absorb excess heat power from the boiler and steam turbine's working cylinders, transferring it to molten salt via heat exchangers and storing it in the storage tanks, thus reducing the minimum or excess output of the thermal power unit. The heat release process occurs during peak load periods or when renewable energy output is insufficient. The storage tanks release the stored heat energy; the high-temperature storage tank increases the steam intake of the steam turbine, while the low-temperature storage tank heats the liquid water to form high-temperature feedwater that enters the boiler, increasing the thermal power output. Without adjustment, change its external power output. ; When thermal power plants need to adjust loads to meet fluctuating demand, the output is first regulated through a molten salt thermal storage system. The coupled system operation model is shown in the following equation: In the formula, T For time intervals; / 、 / These represent the power stored / released by the high-temperature and low-temperature thermal storage systems during time period t, respectively. This represents the heat power transferred from the steam turbine to the generator during time interval t; , These represent the heat storage capacity of the high-temperature and low-temperature thermal storage systems at time t, respectively. η H , η L These are the heat loss rates of high-temperature and low-temperature thermal storage systems, respectively. η HC / η HD , η LC / η LD These refer to the thermal efficiency stored / released by high-temperature and low-temperature thermal storage systems, respectively. η coal For coal combustion thermal efficiency; for The amount of coal consumed at any given time; The calorific value of pulverized coal combustion; η ST For thermoelectric conversion efficiency; The dynamic carbon emission trading costs of the quantitative operation model at different time periods are linearized by linearizing the nonlinear peak-shaving correction coefficient in the carbon emission quota, thus establishing a quantitative model for the carbon emission trading costs of thermal power plants. The established quantitative model for the carbon emission trading costs of thermal power plants includes: Carbon emission trading costs of thermal power units for: In the formula, T Indicates the total scheduling period. For carbon prices in the carbon emissions trading market, This represents the carbon emissions of thermal power units over a time scale of t. This represents the carbon emission quota for thermal power units on the time scale t. The calculation formula is shown below: In the formula, Carbon emission factor for standard coal used in thermal power generation; This represents the standard coal consumption generated over a time scale of t. The calculation formula is shown below: In the formula, δ The prescribed baseline value for carbon emissions from thermal power plants; For the current thermal combustion output of thermal power units, The load factor of the thermal power unit at the time scale t; μ C The load factor benchmark needs to be adjusted for peak shaving; , These are the correction coefficients corresponding to different benchmark intervals; Linearization of the nonlinear peak-shaving correction coefficient in carbon emission allowances includes: Peak shaving correction coefficient of thermal power units The values ​​differ under different load rates, and are as follows: When peak shaving correction coefficient When the behavior is nonlinear, linearize it: Substitute into this interval Taking the value and simplifying, we get: In the formula, This is the upper limit of the output of thermal power units; right A direct linear fit is performed to obtain a linear fitting function for the carbon emission baseline value; Based on the established cost quantification model for carbon emission trading of thermal power plants, and with the objective of maximizing the daily comprehensive operating revenue, a carbon economic optimization scheduling model for a variable load thermal power and molten salt thermal energy storage coupled system is established. The optimization objective function of the carbon economic optimization scheduling model is: to maximize the comprehensive operating revenue of the coupled system and the new energy units within one day. f A As shown in the following formula: In the formula, u Numbering of thermal power units within the coupling system; U The set of the number of thermal power units within the coupled system; P UN This is the difference between the actual total power generation and the dispatch demand; C GU As a penalty factor; The comprehensive power generation revenue of a single thermal power plant is calculated as follows: In the formula, This is the basic revenue from the sale of electricity by thermal power units; For thermal power units participating in deep peak shaving compensation benefits; The operating cost of thermal power units; For the flexibility and backup costs of thermal power units; The cost of pollution emissions from thermal power units; The cost of trading carbon emission rights for thermal power units; The comprehensive operating cost of molten salt thermal energy storage for thermal power units; The constraints are divided into power balance, thermal power step ramp rate constraints under rapid load changes, thermal power unit power generation constraints, new energy power generation constraints, and molten salt thermal storage system constraints. Solve the carbon economy model of the thermal power and molten salt thermal storage coupled system under variable load to obtain the day-ahead optimal dispatch scheme and optimal operating benefits of thermal power.

2. The carbon economy optimization scheduling method for a coupled thermal power and molten salt thermal energy storage system according to claim 1, characterized in that, Molten salt thermal storage systems include capacity constraints and operational constraints, as shown below: Capacity constraints of high and low temperature thermal storage systems: In the formula, , Let represent the minimum and maximum heat storage capacities of the high-temperature system at the time scale t, respectively. , Let represent the minimum and maximum thermal storage capacities of the cryogenic system at the time scale t, respectively. Operating constraints of high and low temperature thermal storage systems: In the formula, 、 and 、 These are 0-1 variables representing whether the high and low thermal storage systems are storing / releasing thermal energy. If the system is in operation, the value is 1; otherwise, the value is 0. θ HD and θ LD The maximum heat transfer coefficient of high and low temperature thermal storage systems; and These are the maximum heat release power of the high- and low-temperature thermal storage systems, respectively. When the molten salt thermal storage capacity or charge / discharge efficiency does not meet the real-time load requirements, the thermal power boiler side performs rapid load adjustment. Under this operating condition, the coupled system must meet the thermal power step ramp rate constraint under rapid load change conditions, as shown in the following formula: In the formula, a This represents the range of the load factor; b This represents the range where the maximum load factor is located. c This represents the range where the minimum load factor is located. Let be the load factor of the thermal power unit at time t. and These represent the upper and lower limits of the load factor of thermal power units on the time scale t; μ a , μ b and μ c These are the lower limit benchmarks of the peak-shaving range where the load rate of thermal power units is located on the time scale t; This represents the lower limit of the x-th output range; r a This represents the upper limit of the ramp rate corresponding to the load factor; r b This represents the upper limit of the ramp rate corresponding to the maximum load rate; r c This represents the upper limit of the ramp rate corresponding to the minimum load rate; r x This represents the upper limit of the ramp rate for the x-th power output interval.

3. The carbon economy optimization scheduling method for a coupled thermal power and molten salt thermal energy storage system according to claim 1, characterized in that, The constraints on thermal power unit generation include upper and lower limits of unit output constraints and flexible reserve constraints, among which the flexible reserve constraints are as follows: In the formula, This is the maximum thermal combustion output of the thermal power unit; This represents the minimum value of the u-th thermal power unit in the current output range; and These refer to the upward and downward flexibility reserves of thermal power units at the time scale t. and These refer to the upward and downward flexibility reserves of thermal power units at the t-1 time scale; This represents the lower limit of the x-th output range of the u-th thermal power unit; Regarding constraints on renewable energy power generation, the reduction in renewable energy output during each time period and within a day cannot exceed the specified value: In the formula, To actually contribute to the power generation of new energy units μ RE Minimum utilization rate for new energy generating units; μ RE,d The proportion of reduced output of new energy generating units to the predicted output per day; It is predicted to contribute to the power output of new energy generating units.

4. A carbon economy optimization scheduling system for a coupled thermal power and molten salt thermal energy storage system, used to implement the carbon economy optimization scheduling method for the coupled thermal power and molten salt thermal energy storage system as described in any one of claims 1-3, characterized in that, include: The module for constructing an operation model of a variable load thermal power and molten salt thermal storage coupled system combines a variable load thermal power and molten salt thermal storage system to form a coupled system, and then modifies it to establish an operation model of the variable load thermal power and molten salt thermal storage coupled system. The module for constructing a quantitative model of carbon emission trading costs for thermal power plants quantifies the dynamic carbon emission trading costs of the operating model at different time periods, linearizes the nonlinear peak-shaving correction coefficient in the carbon emission quota, and establishes a quantitative model of carbon emission trading costs for thermal power plants. The carbon economic optimization scheduling model construction module establishes a carbon economic optimization scheduling model for a variable load thermal power and molten salt thermal storage coupled system based on the established thermal power carbon emission rights trading cost quantification model and with the goal of maximizing daily comprehensive operating benefits. The optimization scheduling module solves the carbon economy model of the thermal power and molten salt thermal storage coupled system with variable load, and obtains the day-ahead optimized scheduling scheme and optimal operating benefits of thermal power.

5. A computer storage medium having a computer program stored thereon, characterized in that, When the computer executes the computer program, it implements the carbon economy optimization scheduling method for the coupled thermal power and molten salt thermal storage system as described in any one of claims 1-3.

6. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor runs the computer program stored in the memory. When the computer program is executed, it implements the carbon economy optimization scheduling method for the coupled thermal power and molten salt thermal storage system as described in any one of claims 1-3.

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