A capacity compensation information generation method and system

Abstract

This application provides a method and system for generating capacity compensation information. The method, after obtaining power system information within the compensation range, constructs an optimization model based on the power system information, and then uses a mixed-integer algorithm to solve for the optimal parameters using the optimization model. A capacity cost reference system is established according to the electricity cost and capacity cost of the target generating units, and capacity compensation information, including the capacity compensation amount, is generated based on the optimal parameters and the capacity cost reference system. This method can solve for the optimal generating unit configuration that meets reliability requirements through the optimization model and form the capacity cost reference system. By comparing the continuous electricity price curve over the entire cycle with the capacity cost reference system, the capacity compensation amount can be obtained, accurately estimating the capacity compensation amount under fluctuating market electricity prices, thus solving the problem of inaccurate capacity compensation information generation results.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method and system for generating capacity compensation information. Background Technology

[0002] In an electricity market environment, large-scale renewable energy sources have low marginal costs and exhibit significant volatility and randomness, leading to a situation where declining electricity prices and reduced power generation result in insufficient returns for thermal power units based on market prices. To ensure sufficient power system capacity, a capacity market can be established outside the electricity market to compensate thermal power units for their capacity, thereby guaranteeing adequate power system capacity.

[0003] Capacity compensation is an economic compensation mechanism provided by the market to recover the fixed costs of power generation or energy storage resources and ensure the long-term capacity adequacy and operational safety of the power system. The guiding information used to implement capacity compensation is called capacity compensation information. Capacity compensation information can be assessed based on the difference between the revenue that thermal power units should receive and the revenue they actually receive from the market.

[0004] To obtain capacity compensation information, an administratively determined method can be used. This involves estimating the cost of generating units and comparing it with the determined electricity sales price of thermal power units to determine the capacity fees to be compensated, and then compensating according to the installed capacity of the thermal power units. However, the administratively determined capacity compensation method estimates market revenue by multiplying the average electricity sales cost price by the amount of electricity generated. This method is difficult to accurately estimate the actual revenue of thermal power units and cannot effectively assess the characteristics of electricity market price fluctuations, resulting in inaccurate capacity compensation information. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method and system for generating capacity compensation information to solve the problem of inaccurate capacity compensation information generation results.

[0006] According to one aspect of this application, a method for generating capacity compensation information is provided, the method comprising:

[0007] Obtain power system information within the compensation scope, including load data, power supply data, and security data;

[0008] An optimization model is constructed based on the power system information. The optimization model is a mixed integer programming model constructed based on a mixed integer solution algorithm.

[0009] The optimal parameters are solved based on the optimization model. The optimal parameters include the optimal unit composition and the optimal unit power generation that meet the preset safety requirements.

[0010] Set a capacity cost reference system based on the electricity cost and capacity cost of the target unit;

[0011] Capacity compensation information is generated based on the optimal parameters and the capacity cost reference system, and the capacity compensation information includes the capacity compensation amount.

[0012] According to another aspect of this application, a capacity compensation information generation system is provided, the system comprising:

[0013] The information acquisition module is used to acquire power system information within the compensation range, including load data, power supply data, and security data.

[0014] The model building module is used to build an optimization model based on the power system information. The optimization model is a mixed integer programming model built based on a mixed integer solution algorithm.

[0015] The solution module is used to solve for the optimal parameters based on the optimization model. The optimal parameters include the optimal unit configuration and the optimal unit power generation that meet the preset safety requirements.

[0016] The reference system setting module is used to set the capacity cost reference system according to the target unit's electricity cost and capacity cost.

[0017] The information generation module is used to generate capacity compensation information based on the optimal parameters and the capacity cost reference system, wherein the capacity compensation information includes the capacity compensation amount.

[0018] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described capacity compensation information generation method.

[0019] According to another aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described capacity compensation information generation method.

[0020] By employing the above technical solutions, embodiments of this application provide a method and system for generating capacity compensation information. The method, after obtaining power system information within the compensation range, constructs an optimization model based on the power system information, and then uses a mixed-integer algorithm to solve for the optimal parameters using the optimization model. A capacity cost reference system is set according to the electricity cost and capacity cost of the target generating units, and capacity compensation information, including the capacity compensation amount, is generated based on the optimal parameters and the capacity cost reference system. This method can solve for the optimal generating unit configuration that meets reliability requirements through the optimization model and form a capacity cost reference system. By comparing the continuous electricity price curve over the entire cycle with the capacity cost reference system, the capacity compensation amount can be obtained, accurately estimating the capacity compensation amount under fluctuating market electricity prices, thus solving the problem of inaccurate capacity compensation information generation results.

[0021] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0022] 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:

[0023] Figure 1 This is a schematic diagram of the capacity compensation information generation method provided in the embodiments of this application;

[0024] Figure 2 This is a schematic diagram of load zoning provided in an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of the capacity compensation provided in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the capacity compensation information generation system provided in the embodiments of this application. Detailed Implementation

[0027] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0028] In this embodiment, capacity compensation is an economic compensation mechanism provided by the market to recover the fixed costs of power generation or energy storage resources and ensure the long-term capacity adequacy and operational safety of the power system. Because large-scale renewable energy has low marginal costs and exhibits significant volatility and randomness in the electricity market environment, thermal power units face a situation where declining electricity prices lead to reduced power generation, resulting in insufficient returns for thermal power units based on market prices. Therefore, to ensure the adequacy of power system capacity, a capacity market can be established outside the electricity market to provide capacity compensation for thermal power units, thereby guaranteeing the adequacy of the power system's capacity.

[0029] To implement capacity compensation, capacity compensation information can be generated. This information, serving as guidance, can be assessed based on the difference between the revenue that a thermal power unit should receive and the revenue it actually receives from the market. In some embodiments, the capacity compensation amount can be determined based on an administratively determined method, by comparing the cost estimate of the generating unit with the determined electricity sales price of the thermal power unit, and then compensation can be made according to the installed capacity of the thermal power unit.

[0030] However, when determining capacity compensation information through administrative means, it is difficult to accurately estimate the actual revenue of thermal power units. Simply estimating market revenue by multiplying the average electricity sales cost by the generated electricity volume cannot effectively assess the characteristics of electricity market price fluctuations. In other words, it is difficult to select representative units as the benchmark for capacity compensation, leading to arbitrary designations lacking scientific basis. Furthermore, assessing the revenue of units in the market is difficult; using the average market electricity price to represent market revenue differs significantly from the actual revenue of the units, potentially causing over-compensation or under-compensation.

[0031] In some embodiments, capacity compensation information can also be generated based on the capacity market. The capacity market approach is implemented by a designated independent electricity market operator, such as the International Organization for Standardization (ISO). The operator first determines the demand curve for the capacity market. Then, power generators submit their declared capacity compensation prices and capacities. These declared capacities and prices are then sorted by price from highest to lowest to form a cumulative capacity supply curve. Finally, the compensation capacity and price are determined based on the intersection of the demand and supply curves.

[0032] However, in the process of generating capacity compensation information based on the capacity market, both the capacity market and the scarcity price require specifying demand curves. Since the capacity market's demand curve only uses a portion of the net load and does not perform year-round calculations, the computational workload is substantial. The demand curve for the scarcity price only considers the uncertainty of renewable energy sources, not the uncertainty of generating units. Furthermore, the capacity demand curve requires a Value of Lost Load (VOLL), which is difficult to obtain accurately. Even if a specific unit type's capacity cost is specified as a substitute VOLL value, there is a lack of theoretical basis for choosing which type. In addition, in practice, the capacity demand curve is based on empirical estimates, making it difficult for the demand curve to accurately reflect the actual situation and reducing the accuracy of the generated capacity compensation information.

[0033] In some embodiments, capacity compensation information can also be generated based on a scarcity pricing method. This method involves adding a scarcity price to the day-ahead market to compensate for capacity based on all participating generating units. The added scarcity price can be determined by a designated independent system operator based on the reserve capacity demand curve. Then, all declared capacity in the reserve market is summed, and the added scarcity price is determined by the price corresponding to the summed capacity on the reserve capacity demand curve. Based on a power planning model, and considering the constraint of sufficient power revenue, the organic power structure is solved under the condition of minimizing compensation costs, thereby generating capacity compensation information based on the organic power structure. Furthermore, constraints on the amount of capacity compensation can be added to the power planning to ensure sufficient power revenue by minimizing the compensation amount.

[0034] However, when the income adequacy constraint is added to the power planning model, it will change the organic composition of the power source. Since the power planning model is derived from a two-level model, it is computationally complex and difficult to apply to systems of actual scale, resulting in computational difficulties and inaccurate capacity compensation information.

[0035] To address the issue of inaccurate capacity compensation information generation, some embodiments of this application provide a method for generating capacity compensation information. This method can be applied to electronic devices with data processing capabilities. These electronic devices include, but are not limited to, computers, servers, mobile terminals, smart wearable devices, and industrial control machines. For ease of description, this application uses an electronic device as the executing entity for the method. It should be understood that the method can also be applied to other types of executing entities, which are not illustrated in all embodiments of this application. Figure 1 As shown, the method includes:

[0036] S101. Obtain power system information within the compensation range.

[0037] To generate capacity compensation information, electronic devices can first acquire power system information within the compensation range. The compensation range can be determined based on the actual application area and policy documents of the power system. The power system information refers to the necessary information about the power system within the compensation range during the capacity compensation determination process. Power system information may include load data, power supply data, and security data.

[0038] Load data refers to the net load in the target year. Net load characterizes the load faced by thermal power units requiring compensation in the target year and can be represented at preset time intervals. For example, net load values ​​can be given at hourly intervals. While dividing the target year into hourly intervals, time can also be divided into time periods, such as dividing a year into 52 weeks, and then dividing each time period... b The time interval is further divided into hours. t Therefore, the net load for the target year can be recorded as follows: l b,t , b ∈ B , t ∈ T b .

[0039] Power source data refers to data related to power sources collected from power systems. Power sources in a power system can be categorized into generating unit types based on similar characteristics such as installed capacity and operating features, using symbols... g It is indicated that its overall set is G .

[0040] Therefore, in some embodiments, when acquiring power system information within the compensation range, the electronic device can first traverse the unit information of the power generation units within the compensation range and query characteristic parameters based on the unit information. These characteristic parameters include at least one of the installed capacity and operating characteristics of the power generation units. Then, based on the principle of similarity in characteristics, the power generation units within the compensation range are divided into at least one unit class according to the characteristic parameters. Finally, the power data is calculated according to the unit class, and the power data includes at least one of the following: unit number information, capacity information, operating information, and economic-related data for each unit class.

[0041] Power data can include unit type g Information on the number of generating units. For each generating unit category, it is necessary to further collect the current number of generating units in that category. n g exist The amount of new investment that can be made in the future n g new The maximum number of available units of this type ng max The number of power generation units is shown in Table 1:

[0042] Table 1. Number of power generation units;

[0043]

[0044] Power data can also include unit type. g Capacity information. Unit type. g Power generation capacity block set K g Unit type g Power generation capacity block k corresponding capacity P g,k MAX The first part refers to the minimum generating capacity limit corresponding to this type of unit. P g,1 MAX = P g MIN The sum of the capacity blocks of all units of this type equals the maximum allowable generating capacity limit of that unit, i.e.:

[0045] ;

[0046] Therefore, the capacity information for power supplies can be shown in Table 2:

[0047] Table 2. Capacity information for power supplies;

[0048]

[0049] Power data can also include unit type. g Operational information. Unit type. g Maximum output limit of a single machine P g MAX Units g Minimum output limit of a single unit P g MIN Units g Maximum downhill ramp capacity per unit p g downmax and uphill capacity p g upmax Units g Minimum continuous downtime m g downand minimum continuous power-on time m g up .

[0050] Therefore, unit type g The operating information can be represented as the operating characteristic data of the power supply type as shown in Table 3:

[0051] Table 3. Operating characteristics data of power supplies;

[0052]

[0053] Power supply data may also include economic data related to the generating unit. Economic data may include unit type. g Capacity investment cost c g capital In order to discount the investment cost to the target year, an average interest rate is also needed. ω Units g Economic lifespan a g life Parameters, etc. Unit type g Repair costs c g fixO&M Units g The proportion of pre-repair costs to total maintenance costs a g MaintFract , a g maint Units g Number of weeks required for pre-maintenance. (Unit type) g The k Marginal generation cost of block capacity c g,k MC The start-up cost of unit type g. c g SU Penalty cost for not meeting load requirements c l Penalty costs for failing to meet ancillary service reserve capacity requirements c as .

[0054] Therefore, the relevant economic data can be presented as shown in Table 4:

[0055] Table 4. Economic-related data;

[0056]

[0057] Security data broadly refers to data on reliability and security requirements in power systems. Specifically, security data can include the proportion of planned reserve requirements in the system. a PlanReserve The system adjusts the frequency reserve load ratio upwards. r 1,up Downward adjustment of the reserve load ratio r 1,down ; r 2,up and r 2,down These are, respectively, the upward and downward load tracking reserve ratio; and the system non-spinning reserve requirement ratio. r outage Non-rotating standby equipment ratio x nonsync Units g They participate in upward frequency regulation separately; the ratio of spinning reserve to non-spinning reserve is limited. a g 1,up , a g 2,up , a g 3,up Units g They participate in downward frequency modulation separately; the ratio of spinning reserve to non-spinning reserve is limited. a g 1,down , a g 2,down , a g 3,down .

[0058] Therefore, the security data can be seen in Table 5:

[0059] Table 5. Data on system reliability and security requirements;

[0060]

[0061] S102. Construct an optimization model based on the power system information.

[0062] Based on the collected power system information, electronic equipment can be programmed using an optimization model to obtain the optimal unit configuration and optimal unit power generation that meet the system reliability and security requirements. Before programming, an optimization model needs to be constructed based on the power system information. This optimization model is a mixed-integer programming model constructed using a mixed-integer algorithm.

[0063] In order to construct an optimization model, electronic devices need to set objective functions, decision variables, and optimization conditions. Therefore, in some embodiments, when electronic devices construct an optimization model based on the power system information, they can first set the decision variables of the optimization model based on the power system information.

[0064] Decision variables are used to make decisions about generator sets according to their type. The decision variables in the optimization model can be divided into five categories: the number of newly invested generator sets, the operating status of the generator sets, variables related to generator set operation, variables related to generator set maintenance, and variables related to generator set reliability. Among these, the variable representing the number of newly invested generator sets indicates the total number of new generator sets invested in. N g new The unit operating status variable represents the unit class. g In time block b time t Number of units in operation U g,b,t .

[0065] Variables related to generator unit operation may include: generator set class g In time block b time t Electricity generation P g,b,t,k DA Generator set type g In time block b time t Upstream and downstream service capacity of the FM market R g,k,b,t 1,up , R g,k,b,t 1,down Generator set type g In time block b time t The service capacity of the spinning reserve market both upward and downward R g,k,b,t 2,up , R g,k,b,t 2,down Generator set type g In time block b time t Service capacity of the non-spinning spare market R g,k,b,t 3 .

[0066] Variables related to generator set maintenance can include generator set class g In time block b Number of units under maintenance Mg,b Number of units M g,b It is an integer variable representing the unit class. g The number of units under maintenance.

[0067] Variables related to unit reliability may include: time blocks b time t The system does not provide power. p b,t unmet Time block b time t The demand for both up- and down-frequency modulation is insufficient. r b,t 1upunmet , r b,t 1downunmet Time block b time t The upward and downward rotation of the spare capacity is insufficient to meet demand. r b,t 2upunmet , r b,t 2downunmet Time block b time t Insufficient non-spinning spare capacity r b,t 3unmet .

[0068] The decision variables are represented as follows:

[0069] X 3={ P g,b,t,k DA , R g,k,b,t 1,up , R g,k,b,t 1,down , R g,k,b,t 2,up , R g,k,b,t 2,down , R g,k,b,t 3 , N g new , U g,b,t , M g,b , p b,t unmet ,r b,t 1upunmet , r b,t 1downunmet , r b,t 2downunmet , r b,t 3unmet , r b,t 2upunme};

[0070] By setting decision variables, the optimization model can make decisions based on generator sets according to categories during power planning. For example, generator set type. g The investment decision variable is the amount of investment. N g new This variable is an integer, representing the number of newly invested units of this type. Unit Type g The number of devices that are currently powered on is U g,b,t , which is also an integer variable, represents the number of units in this type that are in an active state. Since the active state is not determined for each individual unit, but rather for the number of active states for each type of unit, the size of the model can be greatly reduced, thus reducing computation time.

[0071] After setting the decision variables, the objective function of the optimization model can be constructed based on the decision variables. The objective function is the sum of multiple cost items; these cost items include the investment cost of new units in each unit class, the maintenance cost of all units throughout the entire lifecycle, the power generation and start-up cost of all units throughout the entire lifecycle, and the penalty costs for unmet load and unmet ancillary service reserve capacity.

[0072] For example, as a power planning model, the objective function of an optimization model can include four cost terms: the first term is the investment cost of new generating units; the second term is the annual maintenance cost of all units; the third term is the annual generation and start-up cost of all units; and the fourth term is the penalty cost for unmet load and unmet ancillary service reserve capacity. Therefore, the objective function of the optimization model can be expressed as:

[0073] (1);

[0074] in, G For generator clusters; B A collection of time blocks in weeks; T b For time blocks b The collection of time periods in hours; K The generator capacity is divided into blocks;N g new The number of newly invested generating units in unit category g; ω This represents the weighted average cost of capital. a g life The lifespan of cluster g units is represented in years; c g capital Represents unit class g The cost of capital investment is expressed in yuan / MW. c g fixO&M Clustering g Fixed operation and maintenance costs of the generating unit, expressed in yuan / MW; a g MaintFract Clustering g The proportion of maintenance costs in the fixed operation and maintenance costs of the generating unit; a g maint This indicates the number of maintenance weeks required per year for cluster g units; l b duration Represents time block b The number of weeks it lasts; c g,k MC Clustering g The first unit of the middle generation k Marginal generation cost per MW of block capacity; P g,b,t,k DA Clustering g The first of all units k Block capacity in time block b of t Electricity generation at any given moment, in MW; S g,b,t Clustering g Unit in time block b of t The total number of power-on events at any given time is an integer variable. c g SU Clustering g The start-up cost of medium-sized generating units is expressed in yuan / MW. c l This represents the penalty cost for unmet load, expressed in yuan / MW. p b,t unmet Indicates in time block b of t Unmet load at any given time, in MW; cas This represents the penalty cost for failing to meet ancillary service reserve capacity requirements, expressed in yuan / MW. r b,t 1upunmet , r b,t 1downunmet These represent time blocks respectively. b of t The reserve capacity for upward and downward frequency regulation is not always met. r b,t 2upunmet , r b,t 2downunmet These represent time blocks respectively. b of t Unmet upward and downward load tracking and spinning emergency reserve capacity. r b,t 3unmet Indicates in time block b of t The amount of alternative reserves that are not yet met, in MW.

[0075] While setting the objective function, the electronic equipment also needs to set the constraints of the optimization model. These constraints include reliability constraints, ancillary service constraints, power supply state constraints, load balancing constraints, unit operation constraints, and preventative maintenance constraints.

[0076] In some embodiments, when setting reliability constraints, the electronic equipment may first obtain planned reserve parameters and operational reserve parameters before executing the constraint conditions for setting the optimization model. The planned reserve parameters include planned reserve margin and net load demand; the operational reserve parameters include the number of units for each unit type and the maximum generating capacity of a single unit.

[0077] Next, the maximum net load demand is extracted from the planned reserve parameters, and the demand factor is calculated based on the planned reserve margin. Then, the planned quantity is calculated based on the demand factor and the maximum net load demand, and the operating quantity is calculated based on the operating reserve parameters. The planned quantity is the product of the demand factor and the maximum net load demand, and the operating quantity is the sum of the products of the number of units corresponding to multiple unit types and the maximum generating capacity of a single unit. Finally, the reliability constraint is constructed, which is a constraint function used to characterize whether the operating quantity is greater than or equal to the planned quantity.

[0078] For example, the reliability constraints of a power system can be described by planned reserve, which also includes the overall requirement for operational reserve. This requires that the total installed capacity of thermal power generating units reach a certain reserve ratio based on the maximum net load. This ratio could be 15%. Therefore, the planned reserve constraint can be expressed as:

[0079]

[0080] in, P g MAX Represents unit class g Maximum generating capacity of a single unit, in MW; a PlanReserve Indicates the planned reserve margin; l b,t Represents time block b time t Net load demand, in MW.

[0081] In some embodiments, when setting ancillary service constraints, the electronic device may first extract ancillary service data from the decision variables. The ancillary service data includes frequency regulation reserve capacity, spinning reserve capacity, and non-spinning reserve capacity.

[0082] Next, intermediate variables are calculated based on the ancillary service data. These intermediate variables include the total frequency regulation reserve capacity of the generating units, the total spinning reserve capacity of the generating units, and the total non-spinning reserve capacity of the generating units. By obtaining a preset maximum ratio limit, ancillary service constraints for the optimization model are set based on the intermediate variables and the preset maximum ratio limit. These ancillary service constraints include frequency regulation constraints, spinning reserve constraints, and non-spinning reserve constraints.

[0083] For example, ancillary services of a power system can include three parts: frequency regulation, spinning reserve, and non-spinning reserve. To ensure the reliability of system operation, it is also necessary to ensure that the reserve capacity of these three types of power systems meets the requirements. That is, for the capacity constraint of the frequency regulation service that the generating unit needs to provide, the demand for frequency regulation capacity is given according to the proportion of net load. Therefore, the capacity of frequency regulation upward service provided by unit g can be obtained from the decision variable according to the following formula:

[0084]

[0085] in, For unit type g All the k Block capacity in time block b of t Upward frequency regulation reserve capacity at any given time, in MW; Clustering g Unit in time block b of t The total up-frequency reserve capacity at any given time, in MW.

[0086] The capacity of frequency regulation downlink service provided by unit g is obtained from the decision variables according to the following formula:

[0087]

[0088] in, R g,k,b,t 1,down For unit type g All the k Block capacity in time block b of t Downward frequency modulation reserve capacity at all times; R g,b,t 1,down Clustering g Unit in time block b of t Total standby capacity for downward frequency modulation at any given time.

[0089] Correspondingly, the upward frequency modulation constraint is:

[0090]

[0091] in, r b,t 1_up_unmeet The unmet upward frequency modulation capacity is expressed in MW. r 1,up This indicates the ratio of up-regulation reserve demand to load.

[0092] The downward frequency modulation constraint is:

[0093]

[0094] in, r b,t 1_down_unmeet The unmet down-modulation capacity is expressed in MW. r 1,down This indicates the ratio of down-regulation reserve demand to load.

[0095] For spinning reserve constraints, upward spinning reserve demand equals the ratio of load demand to the maximum unit capacity in the system, while downward spinning reserve demand is given as a certain ratio of load size. It can be calculated from the decision variable (computer unit) using the following formula. g The capacity of the provided spin-off standby up service:

[0096]

[0097] in, For unit type g The first of all the groups k Block capacity in time block b of t The upward rotation reserve capacity at any given time is a decision variable, and its unit is MW; Represents unit class g Unit in time block b of t The upward rotation reserve capacity at any given time is an intermediate variable, and its unit is GW.

[0098] The capacity of the spinning reserve service provided by the decision variable computer group g is calculated using the following formula:

[0099]

[0100] in, For unit type g The first of all the groups k Block capacity in time block b of t The downward rotation reserve capacity at any given time is a decision variable, and the unit is MW; Represents unit class g Unit in time block b of t The downward rotation reserve capacity at any given time is an intermediate variable, and its unit is GW.

[0101] The upward rotation spare requirement constraint can then be expressed as:

[0102]

[0103] in, r 2,up The load proportion for rotational reserve demand is a given value. r outage This represents the maximum single-machine capacity in the system. The unmet spin-up reserve capacity, in MW; r 2,up This indicates the ratio of standby demand to load.

[0104] The downward rotation spare requirement constraint can be expressed as:

[0105]

[0106] in, r 2,down The load proportion for downward rotation reserve demand is a given value; The unmet rolling-down reserve capacity, in MW; r 2,down This indicates the ratio of reserve demand to load during the downward rotation.

[0107] For non-rotating spare constraints, the following constraints can be set:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] in, They are respectively unit type g Maximum ratio limit for providing up- and down-frequency modulation services; They are respectively unit type g Maximum percentage limits for providing spin-up and spin-down backup services; For unit type g Provides a maximum percentage limit on non-spinning reserve capacity.

[0114] It is evident that the planned reserve requirements in an annual sense are given to the reliability requirements of the power system, while the requirements for ancillary services express the need for handling random changes. Therefore, these two types of capacity requirements can reflect the need for reliability.

[0115] To set power supply state constraints, in some embodiments, when setting constraints for the optimization model, the electronic device can first statistically analyze the number of generating units from the power system information. This number of generating units includes the number of units in operation, the number of units under maintenance, the total number of generating units, and the number of available generating units. Then, the difference between the number of units in operation and the number of units under maintenance is calculated, and a first state constraint is set based on this difference. This first state constraint is a constraint function that characterizes the difference as being less than or equal to the total number of generating units and less than or equal to the number of available generating units.

[0116] For example, for power supply state constraints, the number of generating units in a power supply class must satisfy the condition that the number of units in the "on" state minus the number of units in the "under maintenance" state is less than or equal to the total number of generating units, and less than or equal to the total number of available generating units. The corresponding first state constraint formula is:

[0117]

[0118] in, U g,b,t Represents unit clustering g In time block b of t The number of generating units that are always in operation is an integer variable and is a decision variable; M g,b Represents unit class g In time block bThe number of generating units under maintenance is an integer variable. N g exist Represents unit class g The number of existing generating units, a predetermined number; N g new Represents unit class g The number of newly commissioned units is a decision variable; n g max Represents unit clustering g The maximum total number of units is a pre-specified number.

[0119] All decision variables are integers, meaning the constraint formula is:

[0120]

[0121] The number of power-on events and the number of power-off events are then calculated based on the decision variables. A second state constraint is set based on the number of power-on events, and a third state constraint is set based on the number of power-off events. The second state constraint represents minimizing the number of power-on events occurring at the target time of the target time block, and the third state constraint represents minimizing the number of power-off events occurring at the target time of the target time block. The power supply class state constraint is then set by combining the first, second, and third state constraints.

[0122] For example, unit type g In time block b of t The number of power-on events occurring at any given time can be calculated using the following formula, i.e., the second state constraint is:

[0123]

[0124] Units g In time block b of t The number of shutdown events occurring at any given time can be calculated using the following formula, i.e., the third state constraint is:

[0125]

[0126] in, S g,b,t Units g In time block b of t The number of power-on events occurring at any given time is an intermediate variable, determined by the decision variable. U g,b,t Calculated; Dg,b,t Units g In time block b of t The number of downtime events occurring at any given time is an intermediate variable, determined by the decision variable. U g,b,t Calculated.

[0127] In some embodiments, when setting load balancing constraints, the sum of the total generating capacity of all units of type g plus the non-serviced power at time t of time block b can equal the net load. Therefore, the load balancing constraint can be expressed as:

[0128]

[0129]

[0130] in, P g,k,b,t DA Represents unit class g All unit capacity blocks k in time blocks b time t The output is a decision variable; P g,b,t Represents unit class g All units in the time block b time t The output is determined by the decision variables. P g,k,b,t DA The intermediate variables obtained from the calculation; p b,t unmet Indicates in time block b time t Unmet load, also known as unserved electricity, is a decision variable.

[0131] In some embodiments, when setting unit operation constraints, maximum and minimum constraints on unit output, upward and downward ramp constraints, and start-up and shutdown time constraints can be set separately. The maximum and minimum constraints on unit output represent the unit type. g Capacity blocks for all units k In time block b The moment t The sum of the power generation capacity, the upward frequency regulation capacity, and the upward spinning reserve capacity must not exceed the maximum output of the capacity block. This constraint is expressed as:

[0132]

[0133] Units g The sum of the generating capacity, upward frequency regulation capacity, and upward spinning reserve capacity shall not exceed the unit's capacity. gThe maximum output force, constrained as follows:

[0134]

[0135] The power output of unit class g minus its downward frequency regulation capacity and downward spinning reserve capacity must be greater than or equal to its minimum capacity, as constrained as follows:

[0136]

[0137] in, p g min This represents the minimum power output of cluster g units, in MW. p g max Clustering g The unit's maximum power output, measured in MW; P g,k MAX Clustering g The unit's first k Maximum power output per block capacity, measured in MW.

[0138] The unit's upward and downward ramp constraints can be expressed as:

[0139]

[0140]

[0141] in, p g downmax and p g upmax Clustering g Maximum downhill and uphill capacity limits for the unit, in MW / h.

[0142] The start-up and shutdown durations of the unit must meet the minimum continuous start-up and shutdown time, as expressed by the constraint:

[0143]

[0144]

[0145] in: m g down and m g up Clustering g The minimum continuous shutdown and startup duration of the unit, in hours (h).

[0146] In some embodiments, when setting preventive maintenance constraints, in order to ensure that each cluster... All units undergo necessary annual maintenance, by limiting each time block. The sum of maintenance time and the number of maintenance units is used to achieve this, and the constraint can be expressed as:

[0147]

[0148] Preventive maintenance constraints can represent each cluster g In all time blocks b The total maintenance time scheduled must meet or exceed the annual maintenance requirements of the cluster.

[0149] Continuous maintenance means that once a unit begins maintenance, it will remain offline for the entire maintenance cycle until maintenance is completed. Similar to a minimum uptime constraint, this ensures that the unit's maintenance cycle is not interrupted and is arranged in continuous time blocks. The following constraints can be set to provide specific constraints based on decision variables. The number of units in unit class g that underwent preventive maintenance upon startup and the number of units that underwent maintenance upon shutdown were calculated within the time block:

[0150]

[0151]

[0152] Limitations on continuous maintenance can also be given by defining the following constraints:

[0153]

[0154] The following constraints are used to restrict these variables to integers:

[0155]

[0156] The following constraint limits the number of units that can be maintained simultaneously in unit class g.

[0157]

[0158] in, M g,b begin Represent each cluster g In time block b The number of units that have started maintenance is an integer variable. M g,b end Represent each cluster g In time block bThe number of units that completed maintenance within the specified time is an integer variable. w g maintfract The minimum allowable maintenance ratio is a constant.

[0159] S103. Solve for the optimal parameters based on the optimization model.

[0160] After constructing an optimization model based on power system information, electronic equipment can solve for optimal parameters based on the optimization model. The optimal parameters include the optimal unit composition and the optimal unit power generation that meet preset safety requirements.

[0161] In some embodiments, during the solution process, the electronic device may first invoke a mixed-integer algorithm application, then set the solution parameters of the mixed-integer algorithm application based on the optimization model, and run the mixed-integer algorithm application based on the solution parameters to obtain the optimal parameters output by the mixed-integer algorithm application. The optimal parameters include the optimal unit configuration and optimal unit power generation under the conditions of satisfying net load demand and power system constraints; the optimal unit configuration includes the installed capacity and number of all thermal power units.

[0162] For example, by combining formulas (1) to (34) above, a mixed-integer programming model can be constructed. This model can be solved directly using a mixed-integer algorithm, which can be optimized through its application. Data from a real-world power system can be solved on an Intel i7-13700F CPU using the default options. The model runs for 37.33 hours, which is sufficient in terms of time.

[0163] The solution from this model provides the optimal unit configuration and optimal unit generation for the power system under planned reserve, frequency regulation service, spinning reserve, and non-spinning reserve conditions, satisfying net load demand and power system reliability requirements. Specifically, it includes:

[0164] Total number of installed units in unit category g N g exist + N g new Each unit type g Maximum capacity of a single machine p g max This is a given value, therefore the installed capacity of all thermal power units in the power system p g max , g ∈ G and the number of installed unitsN g exist + N g new The generator sets that make up a power system.

[0165] Units g capacity block k In each time block b The moment t Electricity generation P g,k,b,t DA This is also the optimal result quantity for the model.

[0166] S104. Set a capacity cost reference system based on the electricity cost and capacity cost of the target unit.

[0167] Based on the optimization model to find the optimal parameters, these optimal parameters can be used as input parameters to further calculate the capacity cost reference system, i.e., setting the capacity cost reference system according to the electricity cost and capacity cost of the target unit. In some embodiments, the capacity cost reference system is a price line that satisfies the capacity return of thermal power units. When the electronic equipment executes the setting of the capacity cost reference system according to the electricity cost and capacity cost of the target unit, it can first divide the time-series load into multiple load regions based on a preset number of regions, and obtain the duration of the load regions. Then, it calculates the power generation ratio and power supply ratio of the target unit in the load region for the power generation blocks of the computer group. Then, it calculates the power generation capacity of the target unit based on the power generation ratio and the power supply ratio. And it calculates the electricity cost and capacity cost based on the power generation capacity of the target unit, and generates a capacity cost reference system based on the electricity cost and the capacity cost, which includes a minimum capacity cost line.

[0168] The capacity cost reference system is a price curve that satisfies the capacity return of thermal power units. When setting the capacity cost reference system, electronic equipment can be constructed using the electricity cost and capacity cost of a typical unit. Therefore, determining the capacity cost reference system involves obtaining the electricity cost and capacity cost of a typical (target) unit. The process of obtaining the capacity cost reference system is based on the information shown in Table 6.

[0169] Table 6. Information needed to establish a capacity cost reference system and the sources of that information;

[0170]

[0171] Therefore, when constructing a capacity cost reference system, we can first divide the region into zones based on load levels and determine the duration of each zone. t a This means dividing the load into different zones based on load levels by selecting the number of zones. For example... Figure 2 As shown, by selecting four regions, the number and size of the regions and the method of selecting load regions can be chosen to make each region representative, for example, by dividing them according to peak load, mid-load, and base load. The left side of the graph shows the time-series load, with the horizontal axis representing time and the vertical axis representing load. If the observation period is the whole year, then this region represents the time-series load for the entire year. The right side of the graph shows the continuous load for the corresponding observation period, with the horizontal axis representing time and the vertical axis representing load.

[0172] The duration of a region can then be determined using the load duration curve. The duration of each region can be determined by the time corresponding to the intersection of the region's minimum load and the load duration curve. For example, the duration of region VI in the figure. t VI Just a point c The indicated time. And so on, for regions... I It is the base load, duration t I It is 8760, the duration of Zone II. t II It is point a, the duration of region III. t III The point is b .

[0173] Recomputer group class g Power generation blocks k In the region a Typical (target) unit power generation ratio r a,g,k G :

[0174]

[0175] in, r a,g,k G Indicates in the region a Internal, unit type g Power generation blocks k In time block b time carve t The proportion of electricity generated in total electricity generation; T b Indicates the time block during the investigation period b The set of all moments; a This is a region identifier, which can be any region in the system. a ∈ A ; P g,k,b,t a It is a region a In the middle, unit type g Power generation blocks k In time block btime t The power generation capacity; t It is a time interval, and without loss of generality, it can be taken as an hour. t =1; G It is a collection of all generator set types; K g Is it the type of generator set? g A set of capacity blocks; B It is a collection of time blocks; A It is a set of regions.

[0176] Recomputer group class g capacity block k In the region a The proportion of electricity supply r a,g,k P To quantify the power share of each generating unit within a region and its contribution to that region, the following formula can be used to classify the generating units. g capacity block k In the region a The proportion of electricity supply:

[0177] (36);

[0178] in, r a,g,k,b,t P Indicates at a point in time t ,area a Medium-sized generating units g capacity block k In time block b The moment t The proportion of the region's total power generation capacity; L at max It is a region a In the moment t The maximum load level; L at min Then it is a region a In the moment t The minimum load level; r a,g,k P Representative area a Internal generator set g capacity block k The proportion of the region's total power generation capacity to the total power generation capacity of [the region's power generation capacity]; B | Indicates the number of elements in the time block set; | T b| Indicates a time block b The number of time sets in the time series.

[0179] Then determine typical units a The generating capacity. The generating blocks of each load area are represented as a single equivalent generating unit. The generating capacity of the equivalent generating unit in each area can be determined by the difference between the area's maximum load capacity and minimum load capacity:

[0180]

[0181] in,: P a e Representative area a Equivalent generator set capacity; L a max It is a region a The maximum load value in; L a min It is a region a The minimum load value in the system.

[0182] Then determine typical units a The electricity cost, and the equivalent generator capacity, can represent the combined output capacity of all power generation units within a region, and also provide a foundation for constructing a power system capacity reference system. Through optimization models, the power system can more accurately assess the power generation demand of each region and its relationship with the overall power system capacity, i.e.:

[0183]

[0184] in, c g,k MC Represents unit class g Capacity block k The marginal cost of generating electricity, expressed in yuan / MW; b a e For the region a Operating costs of a typical generator set; r a,g,k G It is a region a Medium-sized generating units g Capacity block k The percentage of electricity generated.

[0185] And determine typical units a The capacity cost, i.e., the electricity cost of a typical generator unit in a region, is equal to the weighted average of the marginal costs of all generator blocks in that region. The capacity cost, on the other hand, is the weighted average of the capacity costs of all generator blocks in that region.

[0186]

[0187] in, c a e For the region a Capacity cost of a typical generator set; r a,g,k P Representative area a Indoor unit type g The capacity percentage of capacity block k; c g capital Represents unit class g The capital investment cost is expressed in yuan / MW.

[0188] Therefore, a capacity cost reference system can be obtained. If the regions are numbered starting from the lowest load, the energy cost of the corresponding typical generating units in each region obtained above can be calculated. b a e Capacity cost c a e The following relationship must be satisfied:

[0189]

[0190] Electronic devices can first generate a minimum capacity cost breakline, that is, in Figure 3 On the price plane, starting from the origin, the electricity cost of the highest typical unit... b Na e Draw a straight line from the slope to the duration of that region. t Na Location, and then at the second highest electricity cost. b Na-1 e Draw a straight line from the endpoint of the previous broken line to the duration of the slope in that region. t Na-1 Then, using the third highest electricity cost as the slope, continue drawing a straight line until the duration of that region. t Na-2 And so on, until the last area is loaded.

[0191] By selecting the unit with the lowest capacity cost among typical units, the capacity cost is determined. c Na e Shift the minimum capacity cost line upwards. c Na e .like Figure 3As shown in the figure, the thick broken line represents the overall capacity cost reference system.

[0192] S105. Generate capacity compensation information based on the optimal parameters and the capacity cost reference system.

[0193] After setting a capacity cost reference system based on the target unit's power cost and capacity cost, the electronic equipment can generate capacity compensation information based on the optimal parameters and the capacity cost reference system, wherein the capacity compensation information includes the capacity compensation amount.

[0194] In some embodiments, to generate capacity compensation information, the electronic device, when generating capacity compensation information based on the optimal parameters and the capacity cost reference system, may first acquire electricity market data, which includes market prices at multiple recorded times within a preset period. The electricity market data is then sorted according to the market prices from high to low, and a price continuity curve is generated based on the duration of the recorded times. The capacity cost reference system and the price continuity curve are then merged into the same coordinate system to generate a capacity compensation map. The target to be compensated is then identified from the capacity compensation map. The target to be compensated is the capacity of the thermal power unit when the price continuity curve is below the capacity cost reference system in the capacity compensation map. The capacity compensation amount is then calculated as the amount of translation when the price continuity curve is shifted to a position tangent to the capacity cost reference system.

[0195] When determining capacity compensation, electronic devices can first obtain the electricity market price continuity curve, for example, by collecting a year's worth of market prices from the day-ahead market of the electricity market and recording the time. The current market electricity price ρ t Then sort them from highest to lowest electricity price, and then by duration. t, plot the continuous electricity price curve on the price plane, such as Figure 3 As shown. For ease of description, let's assume... t =1, use ρ k , k =1, 2, ..., 8760 represent the sorted market electricity prices, where all electricity prices satisfy... ρ 1≥ ρ 2≥…≥ ρ 8760 .exist Figure 3 On the price plane, draw a straight line from the origin with the first electricity price (the highest electricity price) as the slope to the time axis. t, draw a straight line from the end of the previous straight line to 2 with the second price as the slope. t, and so on, until all the straight lines are completed, ending at the 8760-hour mark. Therefore, the electricity price sustainability curve in the electricity market is as follows: Figure 3 As shown.

[0196] Furthermore, by plotting the capacity cost reference system and the electricity market price continuity curve on the same coordinate system, such as... Figure 3 As shown. When the market price continuation curve is below the capacity cost reference system, it indicates that the capacity of thermal power units needs to be compensated. The amount of compensation is determined by shifting the market price continuation curve upwards to a position tangent to the capacity cost reference system. The corresponding increase in capacity price is the capacity compensation price, such as... Figure 3 As shown.

[0197] The electricity price sustainability curve is described using a series of time points and their corresponding prices, denoted as ρ_k, k=1,…,8760. The capacity cost reference system is also represented using a series of points, denoted as ρ_k. k R If k = 1, 2, ..., 8760, then the capacity compensation price is:

[0198]

[0199] in: ρ k , k =1,…,8760 The value of the electricity price continuity curve at point k; ρ k R , k =1, 2, ..., 8760 capacity cost reference system k The value that a point can take.

[0200] By applying the technical solutions of the above embodiments, the capacity compensation information generation method described in the above embodiments can evaluate the capacity compensation of thermal power units based on the optimal power supply configuration to meet future net load requirements. The capacity compensation amount uses a power planning model that includes not only planned reserve ratio requirements but also ancillary service requirements such as maintenance plans, short-term frequency regulation, spinning reserve, and non-spinning reserve, thus making the description of capacity usage more accurate. Furthermore, because power planning is based on power sources, the power planning solution method is faster and can be used for calculations on actual-scale power systems.

[0201] By establishing a capacity cost reference system based on the optimal power source composition of a power system that meets reliability requirements, the capacity cost estimation of generating units required by the power system can be achieved. Using a capacity cost reference system under the optimal power source composition can improve upon the difficulties encountered in administrative methods, capacity market methods, and scarcity pricing methods. By comparing the annual electricity price curve with the capacity cost reference system to obtain the capacity compensation amount, the problem of estimating capacity compensation under fluctuating market electricity prices can be solved.

[0202] In some embodiments, as a specific implementation of the capacity compensation information generation method described in the above embodiments, some embodiments of this application also provide a capacity compensation information generation system, such as... Figure 4 As shown, the system includes:

[0203] The information acquisition module is used to acquire power system information within the compensation range, including load data, power supply data, and security data.

[0204] The model building module is used to build an optimization model based on the power system information. The optimization model is a mixed integer programming model built based on a mixed integer solution algorithm.

[0205] The solution module is used to solve for the optimal parameters based on the optimization model. The optimal parameters include the optimal unit configuration and the optimal unit power generation that meet the preset safety requirements.

[0206] The reference system setting module is used to set the capacity cost reference system according to the target unit's electricity cost and capacity cost.

[0207] The information generation module is used to generate capacity compensation information based on the optimal parameters and the capacity cost reference system, wherein the capacity compensation information includes the capacity compensation amount.

[0208] By applying the technical solutions of the above embodiments, this application provides a capacity compensation information generation system. The system can acquire power system information within the compensation range via an information acquisition module, construct an optimization model based on the power system information via a model building module, and then use a solution module to solve for the optimal parameters using a mixed-integer solution algorithm. A reference system setting module sets a capacity cost reference system according to the target unit's electricity cost and capacity cost, and generates capacity compensation information including the capacity compensation amount based on the optimal parameters and the capacity cost reference system. The system can solve for the optimal unit configuration that meets reliability requirements through the optimization model and form a capacity cost reference system. By comparing the continuous electricity price curve over the entire cycle with the capacity cost reference system, the capacity compensation amount can be obtained, accurately estimating the capacity compensation amount under fluctuating market electricity prices, thus solving the problem of inaccurate capacity compensation information generation results.

[0209] It should be noted that other corresponding descriptions of the functional units involved in the capacity compensation information generation system provided in this application embodiment can be found in the corresponding descriptions in the capacity compensation information generation method provided in the above embodiments, and will not be repeated here.

[0210] This application also provides a computer device, specifically a personal computer, server, network device, etc. The computer device includes a bus, processor, memory, and communication interface, and may also include input / output interfaces and a display device. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores location information. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0211] Those skilled in the art will understand that the structure of the computer device described above is only a partial structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components, or combine certain components, or have different component arrangements.

[0212] In one embodiment, a computer-readable storage medium is also provided, which may be non-volatile or volatile, and a computer program is stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0213] In one embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0214] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0215] Those skilled in the art will understand that all or part of the processes in 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. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0216] Any references to memory, database, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc.

[0217] Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0218] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0219] 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.

[0220] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating capacity compensation information, characterized in that, The method includes: Obtain power system information within the compensation scope, including load data, power supply data, and security data; An optimization model is constructed based on the power system information. The optimization model is a mixed integer programming model constructed based on a mixed integer solution algorithm. The optimal parameters are solved based on the optimization model. The optimal parameters include the optimal unit composition and the optimal unit power generation that meet the preset safety requirements. A capacity cost reference system is established based on the electricity cost and capacity cost of the target unit; the capacity cost reference system is a price curve that satisfies the capacity return of thermal power units; establishing the capacity cost reference system based on the electricity cost and capacity cost of the target unit includes: dividing the time-series load into multiple load regions based on a preset number of regions, and obtaining the duration of the load regions; the power generation ratio and power supply ratio of the target unit in the load region for computer group-type power generation blocks; calculating the power generation capacity of the target unit based on the power generation ratio and the power supply ratio; calculating the electricity cost and capacity cost based on the power generation capacity of the target unit; generating a capacity cost reference system based on the electricity cost and the capacity cost, the capacity cost reference system including a minimum capacity cost curve; Generate capacity compensation information based on the optimal parameters and the capacity cost reference system, the capacity compensation information including the capacity compensation amount; generating capacity compensation information based on the optimal parameters and the capacity cost reference system includes: acquiring electricity market data, the electricity market data including market prices at multiple recorded times within a preset period; sorting the electricity market data according to the market prices from high to low, and generating a price continuity curve according to the duration of the recorded times; merging the capacity cost reference system and the price continuity curve into the same coordinate system to generate a capacity compensation map; identifying the target to be compensated from the capacity compensation map, the target to be compensated being the capacity of thermal power units in the capacity compensation map when the price continuity curve is below the capacity cost reference system; calculating the capacity compensation amount, the capacity compensation amount being the amount of translation when the price continuity curve is translated to a position tangent to the capacity cost reference system.

2. The method according to claim 1, characterized in that, Obtain information about the power system within the compensation scope, including: Iterate through the unit information of power generating units within the compensation range; Based on the unit information, query the property parameters, which include at least one of the installed capacity and operating characteristics of the power unit; Based on the principle of similar properties, the power units within the compensation range are divided into at least one unit class according to the property parameters. The power data is calculated according to the unit type, and the power data includes at least one of the following: unit number information, capacity information, operation information, and economic-related data for the unit type.

3. The method according to claim 1, characterized in that, An optimization model is constructed based on the power system information, including: The decision variables of the optimization model are set based on the power system information. The decision variables are used to make decisions about generator sets according to their unit types. The decision variables include variables related to the number of newly invested generator sets, unit operating status, unit operation, unit maintenance, and unit reliability. The objective function of the optimization model is constructed based on the decision variables. The objective function is the sum of multiple cost items. The multiple cost items include the investment cost of new investment units for each unit class, the maintenance cost of all units throughout the entire cycle, the power generation and start-up cost of all units throughout the entire cycle, and the penalty cost for unmet load and unmet ancillary service reserve capacity. The constraints of the optimization model are set, including reliability constraints, ancillary service constraints, power supply status constraints, load balancing constraints, unit operation constraints, and preventive maintenance constraints.

4. The method according to claim 3, characterized in that, The constraints of the optimization model are set, including: Obtain planned reserve parameters and operational reserve parameters. The planned reserve parameters include planned reserve margin and net load demand. The operational reserve parameters include the number of units for each unit type and the maximum generating capacity of a single unit. Extract the maximum net load demand from the planned reserve parameters, and calculate the demand factor based on the planned reserve margin; The planned quantity is calculated based on the demand coefficient and the maximum net load demand, wherein the planned quantity is the product of the demand coefficient and the maximum net load demand; The operating volume is calculated based on the operating reserve parameters, whereby the operating volume is the sum of the products of the number of units corresponding to multiple unit types and the maximum generating capacity of a single unit; Construct the reliability constraint, which is a constraint function used to characterize that the operating quantity is greater than or equal to the planned quantity.

5. The method according to claim 4, characterized in that, The constraints of the optimization model are set, including: Ancillary service data is extracted from the decision variables, and the ancillary service data includes frequency regulation reserve capacity, spinning reserve capacity and non-spinning reserve capacity; The intermediate variables are calculated based on the ancillary service data. The intermediate variables include the total frequency regulation reserve capacity of the unit type, the total spinning reserve capacity of the unit type, and the total non-spinning reserve capacity of the unit type. Get the preset maximum ratio limit; Based on the intermediate variables and the preset maximum ratio limit, auxiliary service constraints are set for the optimization model. The auxiliary service constraints include frequency modulation constraints, spinning reserve constraints, and non-spinning reserve constraints.

6. The method according to claim 5, characterized in that, The constraints of the optimization model are set, including: The number of generating units is statistically analyzed from the power system information. The number of generating units includes the number of generating units in operation, the number of generating units under maintenance, the total number of generating units, and the number of available generating units. Calculate the difference between the number of units in operation and the number of units under maintenance; A first state constraint is set based on the difference, and the first state constraint is a constraint function used to characterize that the difference is less than or equal to the total number of units and less than or equal to the number of available units; The number of power-on events and the number of power-off events are calculated based on the decision variables. A second state constraint is set based on the number of power-on events, and the second state constraint is used to characterize that the number of power-on events occurring in the unit class at the target time of the target time block is minimized. A third state constraint is set based on the number of shutdown events, which is used to characterize the minimum number of shutdown events occurring in the unit class at the target time of the target time block; The power supply class state constraints are set by combining the first state constraint, the second state constraint, and the third state constraint.

7. The method according to claim 1, characterized in that, Solving for the optimal parameters based on the aforementioned optimization model includes: Call the mixed integer solving algorithm; The solution parameters for the mixed integer solution algorithm are set based on the optimization model. The mixed integer solving algorithm is run based on the solution parameters to obtain the optimal parameters output by the mixed integer solving algorithm. The optimal parameters include the optimal unit configuration and the optimal unit power generation under the conditions of satisfying net load demand and power system constraints. The optimal unit configuration includes the installed capacity and number of all thermal power units.

8. A capacity compensation information generation system, characterized in that, The system includes: The information acquisition module is used to acquire power system information within the compensation range, including load data, power supply data, and security data. The model building module is used to build an optimization model based on the power system information. The optimization model is a mixed integer programming model built based on a mixed integer solution algorithm. The solution module is used to solve for the optimal parameters based on the optimization model. The optimal parameters include the optimal unit configuration and the optimal unit power generation that meet the preset safety requirements. A reference system setting module is used to set a capacity cost reference system according to the electricity cost and capacity cost of the target unit; the capacity cost reference system is a price curve that satisfies the capacity return of thermal power units; setting the capacity cost reference system according to the electricity cost and capacity cost of the target unit includes: dividing the time-series load into multiple load regions based on a preset number of regions, and obtaining the duration of the load regions; the power generation ratio and power supply ratio of the target unit in the load region for computer group-type power generation blocks; calculating the power generation capacity of the target unit based on the power generation ratio and the power supply ratio; calculating the electricity cost and capacity cost based on the power generation capacity of the target unit; generating a capacity cost reference system based on the electricity cost and the capacity cost, the capacity cost reference system including a minimum capacity cost curve; An information generation module is used to generate capacity compensation information based on the optimal parameters and the capacity cost reference system, the capacity compensation information including a capacity compensation amount; generating capacity compensation information based on the optimal parameters and the capacity cost reference system includes: acquiring electricity market data, the electricity market data including market prices at multiple recorded times within a preset period; sorting the electricity market data according to the market prices from high to low, and generating a price continuity curve according to the duration of the recorded times; merging the capacity cost reference system and the price continuity curve in the same coordinate system to generate a capacity compensation map; identifying the target to be compensated from the capacity compensation map, the target to be compensated being the capacity of thermal power units when the price continuity curve is below the capacity cost reference system in the capacity compensation map; and calculating the capacity compensation amount, the capacity compensation amount being the amount of translation when the price continuity curve is shifted to a position tangent to the capacity cost reference system.

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