Source-load standby coordinated rolling scheduling method and system

Abstract

The invention discloses a source-load standby coordinated rolling scheduling method and system. The method comprises the following steps: dividing standby resources into day-ahead standby resources, intra-day standby resources and real-time standby resources; obtaining a day-ahead clearing result, and according to the day-ahead clearing result, constructing a day-ahead standby optimization model taking the minimum comprehensive operation cost in the day-ahead stage as a target function; according to the intra-day-level standby resources, an intra-day standby optimization model with the minimum comprehensive operation cost and resource calling cost in the intra-day stage as a target function is established; according to the real-time level standby resources, a real-time standby optimization model with the minimum comprehensive operation cost, the minimum uncertainty risk cost and the minimum calling cost in the real-time stage as target functions is established; and establishing and solving a source-load standby coordinated rolling model to obtain a standby configuration result. By adopting the embodiment of the invention, the standby resources of different time scales can be integrated into the optimization framework for unified scheduling, and the multi-time scale advantage of the flexible resources is exerted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power resource scheduling, in particular to a rolling scheduling method and system for coordination of source-load-backup. BACKGROUND

[0002] The operation of modern power systems faces multiple uncertainties, for example, the intermittent nature of wind power leads to power prediction deviation, load prediction deviation and generator unit random failure threaten system stability, and the user response behavior of demand side resources in smart grids also brings uncertainty. Therefore, the system needs to be configured with sufficient backup capacity to ensure reliable operation.

[0003] Current research on backup focuses on capacity optimization configuration, which is limited to the coordination of traditional synchronous generators or a small number of backup resources, and lacks in-depth analysis of the priority scheduling order and other configuration principles of various backup resources. At the same time, the accuracy of load and wind power prediction improves as the time scale shortens, while the response capability of backup resources is related to time, and the existing technology cannot fully utilize the multi-time scale characteristics of backup resources through day-ahead scheduling. SUMMARY

[0004] The present application aims to provide a rolling scheduling method and system for coordination of source-load-backup, which can integrate backup resources of different time scales into an optimization framework for unified scheduling, better utilize the multi-time scale advantages of flexible resources compared to single day-ahead scheduling, reduce the overall operation cost of the power grid, and improve the safety and reliability of the power system.

[0005] The present application provides a rolling scheduling method for coordination of source-load-backup, comprising:

[0006] According to the time response characteristics of backup resources, the backup resources are divided into day-ahead backup resources, intra-day backup resources and real-time backup resources;

[0007] Obtain a day-ahead clearing result, and according to the day-ahead clearing result and the day-ahead backup resources, construct a day-ahead backup optimization model with a first cost minimization as the objective function; the first cost includes the comprehensive operation cost of the day-ahead backup resources in the day-ahead stage;

[0008] According to the intra-day backup resources, establish an intra-day backup optimization model with a second cost minimization as the objective function; the second cost includes the comprehensive operation cost of the intra-day backup resources in the intra-day stage and the calling cost of the day-ahead backup resources;

[0009] According to the real-time backup resources, establish a real-time backup optimization model with a third cost minimization as the objective function; the third cost includes the comprehensive operation cost of the real-time backup resources in the real-time stage, the uncertainty risk cost, and the calling cost of the day-ahead backup resources and the intra-day backup resources;

[0010] According to the day-ahead reserve optimization model, the intra-day reserve optimization model and the real-time reserve optimization model, a source-load reserve coordination rolling model is established, the source-load reserve coordination rolling model is solved, and a reserve configuration result is obtained.

[0011] As an improvement of the above scheme, according to the time response characteristics of the reserve resources, the reserve resources are divided into day-ahead level reserve resources, intra-day level reserve resources and real-time level reserve resources, which comprises:

[0012] The reserve resources are divided into power generation side reserve resources and demand side reserve resources;

[0013] According to the start-stop machine response capability of the power generation side reserve resources, the power generation side reserve resources are divided into slow machine units, fast machine units and automatic generation control units;

[0014] According to the advance notice time of the demand side reserve resources, the demand side reserve resources are divided into A-type IDR, B-type IDR and C-type IDR; wherein the advance notice time of the A-type IDR is the longest, and the advance notice time of the C-type IDR is the shortest;

[0015] The slow machine units and the A-type IDR are regarded as day-ahead level reserve resources, the fast machine units and the B-type IDR are regarded as intra-day level reserve resources, and the automatic generation control units and the C-type IDR are regarded as real-time level reserve resources.

[0016] As an improvement of the above scheme, the day-ahead clearing result is obtained, the day-ahead level reserve resources are constructed according to the day-ahead clearing result, and a day-ahead reserve optimization model with a first cost minimum as an objective function is constructed, which comprises:

[0017] The day-ahead clearing result is obtained, and the day-ahead clearing result comprises the start-stop machine plan of the day-ahead level reserve resources for the next 24 hours, the dispatching output plan, the spinning reserve and the purchase amount;

[0018] According to the day-ahead clearing result, the comprehensive operation cost of the day-ahead level reserve resources is calculated, and a first cost is obtained;

[0019] According to the day-ahead level reserve resources, a first constraint condition is established;

[0020] The day-ahead reserve optimization model is constructed with the first cost minimum as an objective function, and the constraint of the day-ahead reserve optimization model is the first constraint condition.

[0021] As an improvement of the above scheme, according to the day-ahead clearing result, the comprehensive operation cost of the day-ahead level reserve resources is calculated, and a first cost is obtained, which comprises:

[0022] The f DA = f fuel,DA+f SUSD,DA +f curt,DA +f ESS,DA +f Recap_s,DA +f A_IDRcap,DA Calculate the first cost f DA ;

[0023] Among them, f fuel,DA This represents the fuel cost of thermal power units in the current phase; f SUSD,DA The current stage of thermal power unit start-up and shutdown costs; f curt,DA The cost of wind curtailment penalties during the current phase; f ESS,DA The current stage ESS charge / discharge cost; f Recap_s,DA The cost of standby capacity for slow-moving units during the current phase; f A_IDRcap,DA This represents the cost of Class A IDR spare capacity for the current phase.

[0024] As an improvement to the above scheme, the establishment of the first constraint condition based on the day-ahead reserve resources includes:

[0025] pass Establish day-ahead power balance constraints; among which, For the unit output of thermal power unit i at time t, N Th This represents the total number of thermal power units. For the actual wind power generation in the current phase, N w This represents the total number of wind turbine units. The discharge power of the ESS at time t during the current phase. This represents the actual load power at the current date. The charging power of ESS at time t during the current phase;

[0026] pass Establish day-ahead unit output constraints; among which, and These are the maximum and minimum outputs of thermal power unit i, respectively. Let be the operating state variable of thermal power unit i at time t during the current phase. The unit output of thermal power unit i at time t;

[0027] pass Establish day-ahead ramp constraints; where P i Th ,RDmin P is the lower limit of the gradeability of thermal power unit i. i Th,RUmax This represents the upper limit of the ramp rate for thermal power unit i;

[0028] pass Establish daily minimum start-up and shutdown time constraints; among which, The number of hours of continuous start-up of the thermal power unit i at time t in the day-ahead stage, The number of hours of continuous shutdown of the thermal power unit i at time t in the day-ahead stage;

[0029] By Establishing a day-ahead energy storage system constraint; wherein, The state of charge of the ESS at time t in the day-ahead stage, The state of discharge of the ESS at time t in the day-ahead stage, The charging power of the energy storage system at time t in the day-ahead stage, P char,max The maximum charging power of the energy storage system, The discharging power of the energy storage system at time t in the day-ahead stage, P dis,max The maximum discharging power of the energy storage system, The energy storage capacity of the ESS at time t in the day-ahead stage, char The efficiency coefficient of the charging process of the energy storage system, dis The efficiency coefficient of the discharging process of the energy storage system, cap The maximum capacity of the energy storage system, and ε is a preset proportion parameter;

[0030] By Establishing a day-ahead reserve capacity constraint; wherein, The reserve capacity of the slow machine unit s at time t in the day-ahead stage, The power generation of the slow machine unit s at time t in the day-ahead stage, The maximum output of the slow machine unit s, The reserve capacity of the jth A-type IDR at time t, P j A_IDR,max The maximum reserve capacity of the jth A-type IDR, P Recap,DA The total reserve capacity demand in the day-ahead stage;

[0031] According to the day-ahead power balance constraint, the day-ahead unit output constraint, the day-ahead ramp constraint, the day-ahead minimum start-stop time constraint, the day-ahead energy storage system constraint and the day-ahead reserve capacity constraint, a first constraint condition is established.

[0032] As an improvement of the above scheme, the day-ahead reserve resource is used to establish an intraday reserve optimization model with a second cost minimum as an objective function, which comprises:

[0033] Based on the day-ahead clearing result, the capacity configuration of the intraday reserve resource is obtained;

[0034] According to the capacity configuration of the intraday-level backup resource, a comprehensive operation cost of the intraday-level backup resource and a calling cost of the day-ahead-level backup resource are calculated to obtain a second cost;

[0035] According to the intraday-level backup resource, a second constraint condition is established;

[0036] An intraday backup optimization model is constructed with the second cost being the minimum as an objective function, and the constraint of the intraday backup optimization model being the second constraint condition.

[0037] As an improvement of the above scheme, the second cost is obtained by calculating a comprehensive operation cost of the intraday-level backup resource and a calling cost of the day-ahead-level backup resource according to the capacity configuration of the intraday-level backup resource, and the calculation includes:

[0038] Through f ID =f fuel,ID +f SUSD,ID +f curt,ID +f ESS,ID +f Recap_f,ID +f B_IDRcap,ID +f Reinv_s,ID +f A_IDRinv,ID The second cost is calculated.

[0039] Wherein, f ID is a total cost of an intraday stage, f fuel,ID is a fuel cost of a thermal power unit in the intraday stage, f SUSD,ID is a start-stop cost of the thermal power unit in the intraday stage, f curt,ID is a wind curtailment penalty cost in the intraday stage, f ESS,ID is an ESS charging and discharging cost in the intraday stage, f Recap_f,ID is a backup capacity cost of a fast machine unit in the intraday stage, f B_IDRcap,ID is a backup capacity cost of a B-type IDR in the intraday stage, f Reinv_s,ID is a backup calling cost of a slow machine unit in the intraday stage, and f A_IDRinv,ID is a backup calling cost of an A-type IDR in the intraday stage.

[0040] As an improvement of the above scheme, the real-time backup optimization model with the third cost being the minimum as an objective function is established according to the real-time-level backup resource, and the establishment includes:

[0041] Real-time wind power, load prediction results and available states of power generating units are obtained, and the capacity configuration of the real-time-level backup resource is obtained in combination with the day-ahead clearing result;

[0042] According to the capacity configuration of the real-time-level backup resource, a comprehensive operation cost of the real-time-level backup resource, a calling cost of the intraday-level backup resource and an uncertain risk cost are calculated to obtain a third cost;

[0043] According to the real-time level backup resource, a third constraint condition is established;

[0044] A real-time backup optimization model is constructed with the third cost minimum as an objective function, and the constraint of the real-time backup optimization model is the third constraint condition.

[0045] As an improvement of the above scheme, the third cost is obtained by calculating the comprehensive operation cost of the real-time level backup resource, the calling cost of the intra-day level backup resource and the uncertain risk cost according to the capacity configuration of the real-time level backup resource, and the third cost includes:

[0046] Through f RT =f fuel,RT +f SUSD,RT +f curt,RT +f ESS,RT +f Recap_AGC,RT +f C_IDRcap,RT +f Reinv_s,RT +f A_IDRinv,RT +f B_IDRinv,RT +f risk,RT The third cost is calculated.

[0047] Wherein, f RT is the total cost of the real-time stage, f fuel,RT is the fuel cost of the thermal power unit in the real-time stage, f SUSD,RT is the start-stop cost of the thermal power unit in the real-time stage, f curt,RT is the wind curtailment penalty cost in the real-time stage, f ESS,RT is the ESS charging and discharging cost in the real-time stage, f Recap_AGC,RT is the AGC unit backup capacity cost in the real-time stage, f C_IDRcap,RT is the C-type IDR backup capacity cost in the real-time stage, f Reinv_s,RT is the slow machine unit backup calling cost in the real-time stage, f Reinv_f,RT is the fast machine unit backup calling cost in the real-time stage, f A_IDRinv,ID is the A-type IDR backup calling cost in the real-time stage, f B_IDRinv,RT is the B-type IDR backup calling cost in the real-time stage, and f risk,RT is the uncertainty risk cost in the real-time stage.

[0048] The embodiment of the application also provides a rolling scheduling system for source-load backup coordination, which comprises:

[0049] A resource division module is configured to divide the backup resource into a day-ahead level backup resource, an intra-day level backup resource and a real-time level backup resource according to the time response characteristics of the backup resource.

[0050] A day-ahead model construction module is configured to obtain a day-ahead clearing result, and construct a day-ahead reserve optimization model with a first cost minimization as an objective function according to the day-ahead clearing result and the day-ahead level reserve resource; the first cost includes a comprehensive operation cost of the day-ahead level reserve resource in a day-ahead stage.

[0051] An intra-day model construction module is configured to establish an intra-day reserve optimization model with a second cost minimization as an objective function according to the intra-day level reserve resource; the second cost includes a comprehensive operation cost of the intra-day level reserve resource in an intra-day stage and a calling cost of the day-ahead level reserve resource;

[0052] A real-time model construction module is configured to establish a real-time reserve optimization model with a third cost minimization as an objective function according to the real-time level reserve resource; the third cost includes a comprehensive operation cost of the real-time level reserve resource in a real-time stage, a calling cost of the intra-day level reserve resource and an uncertainty risk cost;

[0053] A rolling scheduling module is configured to establish a source-load reserve coordination rolling model according to the day-ahead reserve optimization model, the intra-day reserve optimization model and the real-time reserve optimization model, solve the source-load reserve coordination rolling model and obtain a reserve configuration result.

[0054] Compared with the prior art, the rolling scheduling method and system for source-load reserve coordination disclosed by the application divides the reserve resource into a day-ahead level reserve resource, an intra-day level reserve resource and a real-time level reserve resource according to the time response characteristics of the reserve resource; obtains a day-ahead clearing result, and constructs a day-ahead reserve optimization model with a first cost minimization as an objective function according to the day-ahead clearing result and the day-ahead level reserve resource; the first cost includes a comprehensive operation cost of the day-ahead level reserve resource in a day-ahead stage; establishes an intra-day reserve optimization model with a second cost minimization as an objective function according to the intra-day level reserve resource; the second cost includes a comprehensive operation cost of the intra-day level reserve resource in an intra-day stage and a calling cost of the day-ahead level reserve resource; establishes a real-time reserve optimization model with a third cost minimization as an objective function according to the real-time level reserve resource; the third cost includes a comprehensive operation cost of the real-time level reserve resource in a real-time stage, an uncertainty risk cost, a calling cost of the day-ahead level reserve resource and the intra-day level reserve resource; establishes a source-load reserve coordination rolling model according to the day-ahead reserve optimization model, the intra-day reserve optimization model and the real-time reserve optimization model, solves the source-load reserve coordination rolling model and obtains a reserve configuration result. The embodiment of the application can integrate the reserve resources of different time scales into an optimization framework for unified scheduling, exert the multi-time scale advantage of flexible resources, reduce the overall operation cost of the power grid and improve the safety and reliability of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a step flow diagram of a source-load backup coordinated rolling scheduling method provided by an embodiment of the present application;

[0056] Figure 2 is a structural diagram of a source-load backup coordinated rolling scheduling system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0058] In the description and claims of the specification, it is to be understood that the terms first, second, etc. are used only for the purpose of description and are not to be construed as indicating or implying relative importance or an indicated number of features. They are also not necessarily used to describe a sequence or an order, unless explicitly stated otherwise. The terms are interchangeable under appropriate circumstances. Thus, a feature described as“first” can be construed as comprising at least one of the feature.

[0059] The embodiments of the present application provide a source-load backup coordinated rolling scheduling method. Please refer to Figure 1 In the embodiments, the source-load backup coordinated rolling scheduling method is specifically executed through steps S1 to S5.

[0060] S1, according to the time response characteristics of backup resources, the backup resources are divided into day-ahead level backup resources, intra-day level backup resources and real-time level backup resources;

[0061] S2, a day-ahead clearing result is obtained, and a day-ahead backup optimization model with a first cost minimum as an objective function is constructed according to the day-ahead clearing result and the day-ahead level backup resources; the first cost includes a comprehensive operation cost of the day-ahead level backup resources in a day-ahead stage;

[0062] S3, according to the intra-day level backup resources, an intra-day backup optimization model with a second cost minimum as an objective function is established; the second cost includes a comprehensive operation cost of the intra-day level backup resources in an intra-day stage and a calling cost of the day-ahead level backup resources;

[0063] S4, according to the real-time level backup resources, a real-time backup optimization model with a third cost minimum as an objective function is established; the third cost includes a comprehensive operation cost of the real-time level backup resources in a real-time stage, an uncertainty risk cost, and calling costs of the day-ahead level backup resources and the intra-day level backup resources.

[0064] S5, establishing a source-load reserve coordination rolling model according to the day-ahead reserve optimization model, the intra-day reserve optimization model and the real-time reserve optimization model, solving the source-load reserve coordination rolling model to obtain a reserve configuration result.

[0065] Currently, the research on reserve mainly focuses on the optimal configuration of reserve capacity, is limited to the traditional synchronous generator or only focuses on the coordination and optimization of a few reserve resources, and the priority scheduling sequence of various reserve resources and other reserve configuration principles are not deeply researched and analyzed. In addition, the prediction accuracy of load and wind power improves as the time scale shortens and approaches the current time, and the multi-time scale coordination of reserve resources is the key to improve the flexibility of the power system, and the response ability and response time are different. The traditional day-ahead scheduling model cannot fully utilize the multi-time scale characteristics of flexible resources.

[0066] The embodiment of the application provides a source-load reserve coordination rolling model with a day-ahead-intra-day-real-time architecture, forms a rolling optimization mechanism by integrating multi-stage models, realizes the collaborative scheduling of multiple types of reserve resources on the source and load sides in different time scales, can fully utilize the prediction accuracy advantages and resource response characteristics of each time scale, solves the problem that a single scheduling model cannot take into account multiple uncertainties, and finally improves the overall safety, reliability and operation economy of the power system through the globally optimized reserve configuration result.

[0067] As a preferred embodiment, step S1, according to the time response characteristics of the reserve resources, the reserve resources are divided into day-ahead level reserve resources, intra-day level reserve resources and real-time level reserve resources, comprising:

[0068] The reserve resources are divided into power generation side reserve resources and demand side reserve resources;

[0069] According to the start-stop machine response ability of the power generation side reserve resources, the power generation side reserve resources are divided into slow machine units, fast machine units and automatic generation control units;

[0070] According to the advance notice time of the demand side reserve resources, the demand side reserve resources are divided into A-type IDR, B-type IDR and C-type IDR; wherein the advance notice time of the A-type IDR is the longest, and the advance notice time of the C-type IDR is the shortest;

[0071] The slow machine units and the A-type IDR are regarded as day-ahead level reserve resources, the fast machine units and the B-type IDR are regarded as intra-day level reserve resources, and the automatic generation control units and the C-type IDR are regarded as real-time level reserve resources.

[0072] For the generating side backup resource, the starting and stopping response ability of the unit is divided. In some preferred embodiments, the slow machine unit needs a longer starting and stopping time, the unit combination must be determined in the day-ahead stage, the response delay is ≥1h, and the ramping rate is ≤2% rated capacity / min. The starting time of the fast machine unit is shorter, and the start-stop decision can be made in the intra-day stage, 15min≤response delay<1h, ramping rate 2%~5% rated capacity / min. The starting time of the automatic generation control (AGC) unit is the shortest, and it can respond immediately in real time, response delay<15min, ramping rate≥5% rated capacity / min.

[0073] It should be noted that in the embodiments of the present application, the demand side backup resource is taken as an example of incentive-based demand response (IDR), which is divided according to the advance notice time. In some preferred embodiments, the A-type IDR is to inform the user one day in advance, the B-type IDR is to inform the user 15min~4h in advance, and the C-type IDR is to inform the user 5~15min in advance.

[0074] In order to enable various types of IDR resources to participate in power system dispatch, the load agent institution signs a contract with the user, classifies and integrates various users that can provide IDR services, and finally unifies the regulation and control by the dispatch center.

[0075] In the above scheme, by distinguishing the generating side and demand side backup resources and dividing them according to the different time response characteristics of the generating side and demand side backup resources, the responsibility boundary of each resource can be clearly defined, so that the system can call the corresponding resource according to the uncertain time characteristics.

[0076] As a preferred implementation, step S2, obtaining a day-ahead clearing result, constructing a day-ahead backup optimization model with a first cost minimum as an objective function according to the day-ahead backup resources in the day-ahead clearing result, and executing steps S21-S24:

[0077] S21, obtaining a day-ahead clearing result, the day-ahead clearing result including the starting and stopping plan of the day-ahead backup resources for the next day 24 hours, the dispatch output plan, the spinning reserve and the purchase amount;

[0078] S22, calculating the comprehensive operation cost of the day-ahead backup resources according to the day-ahead clearing result, and obtaining the first cost;

[0079] S23, establishing a first constraint condition according to the day-ahead backup resources;

[0080] S24, a day-ahead reserve optimization model is constructed with the first cost minimum as an objective function, and the constraint of the day-ahead reserve optimization model is the first constraint condition.

[0081] The day-ahead clearing result is a basic framework plan for the next day 24-hour operation in the day-ahead stage of the power system, and is a prerequisite for all subsequent dispatch steps. It directly defines the actual operation state of the day-ahead level reserve resource, can accurately screen the callable day-ahead level reserve resource, and ensures that the subsequent optimization model does not deviate from the actual operation condition.

[0082] In some preferred embodiments, the day-ahead clearing result is obtained by an existing unit combination and economic dispatch optimization model, and the clearing is realized by solving the model, which can determine the day-ahead slow machine unit start-stop plan, dispatch output plan, slow machine unit provided rotating reserve and purchase amount of A-type IDR reserve capacity for 24 hours of the next day, as the day-ahead clearing result.

[0083] Further, preferably, the first cost f DA in step S22 is expressed as:

[0084] f DA = f fuel,DA + f SUSD,DA + f curt,DA + f ESS,DA + f Recap_s,DA + f A_IDRcap,DA (1); wherein f fuel,DA is the fuel cost of the thermal power unit in the day-ahead stage; f SUSD,DA is the start-stop cost of the thermal power unit in the day-ahead stage; f curt,DA is the wind abandonment penalty cost in the day-ahead stage; f ESS,DA is the charging and discharging cost of the ESS in the day-ahead stage; f Recap_s,DA is the slow machine unit reserve capacity cost in the day-ahead stage; and f A_IDRcap,DA is the A-type IDR reserve capacity cost in the day-ahead stage.

[0085] Preferably, the first constraint condition in step S24 includes a day-ahead power balance constraint, a day-ahead unit output constraint, a day-ahead ramp constraint, a day-ahead minimum start-stop time constraint, a day-ahead energy storage system constraint, and a day-ahead reserve capacity constraint.

[0086] In some preferred embodiments, the day-ahead power balance constraint is expressed as:

[0087]

[0088] wherein P is the unit output of the thermal power unit i at time t, N Th is the total number of thermal power units, and P Pactual, day-ahead (t) is the actual wind power in the day-ahead stage at time t, w Ptotal is the total number of wind power generators, Pdischarge, day-ahead (t) is the discharging power of the ESS in the day-ahead stage at time t, Pload, day-ahead (t) is the actual load power in the day-ahead stage at time t, Pcharge, day-ahead (t) is the charging power of the ESS in the day-ahead stage at time t.

[0089] In some preferred embodiments, the day-ahead unit output constraint is expressed as:

[0090]

[0091] wherein P i Th,min and P i Th,max are the maximum output and the minimum output of the thermal power generator i respectively, Pstate, thermal (t) is the operating state variable of the thermal power generator i in the day-ahead stage at time t, Poutput, thermal (t) is the unit output of the thermal power generator i at time t.

[0092] In some preferred embodiments, the day-ahead ramping constraint is expressed as:

[0093]

[0094] wherein P i Th,RDmin is the lower limit of the ramping rate of the thermal power generator i, P i Th,RUmax is the upper limit of the ramping rate of the thermal power generator i.

[0095] In some preferred embodiments, the day-ahead minimum start-stop time constraint can be expressed as:

[0096]

[0097] wherein, Pstart, thermal (t) is the number of hours of continuous start-up of the thermal power generator i in the day-ahead stage at time t, Pstop, thermal (t) is the number of hours of continuous shut-down of the thermal power generator i in the day-ahead stage at time t, Pstartmin is the minimum number of continuous start-up hours of the thermal power generator, Pstopmin is the minimum number of continuous shut-down hours of the thermal power generator, Pstartstate, thermal (t) is the start-up state variable of the thermal power generator i in the day-ahead stage at time t, Pstopstate, thermal (t) is the shut-down state variable of the thermal power generator i in the day-ahead stage at time t;

[0098] Further, preferably, the day-ahead minimum start-stop time constraint is expressed as:

[0099]

[0100] In some preferred embodiments, the day-ahead energy storage system constraint is represented as:

[0101]

[0102] wherein, is the state of charge of the day-ahead stage ESS at time t, is the state of discharge of the day-ahead stage ESS at time t, is the charging power of the day-ahead stage energy storage system at time t, P char,max is the maximum charging power of the energy storage system, is the discharging power of the day-ahead stage energy storage system at time t, P dis,max is the maximum discharging power of the energy storage system, is the energy storage capacity of the day-ahead stage ESS at time t, η char is the efficiency coefficient of the energy storage system charging process, η dis is the efficiency coefficient of the energy storage system discharging process, E cap is the maximum capacity of the energy storage system, and ε is a preset proportion parameter.

[0103] In some preferred embodiments, the day-ahead reserve capacity constraint is represented as:

[0104]

[0105] wherein, is the reserve capacity of the day-ahead stage slow machine unit s at time t, is the power generation of the day-ahead stage slow machine unit s at time t, is the maximum output of the slow machine unit s, is the reserve capacity of the jth A-type IDR at time t, P j A_IDR,max is the maximum reserve capacity of the jth A-type IDR, P Recap,DA is the total reserve capacity demand of the day-ahead stage.

[0106] It should be noted that in the result of the day-ahead optimization, the unit combination and unit output of the slow machine unit, the contract purchase amount of the slow machine unit reserve, and the contract purchase amount of the A-type IDR reserve will be directly brought into the intraday and real-time reserve optimization model as input quantities to solve other unknown quantities.

[0107] In the above scheme, the day-ahead reserve optimization model can reduce the reserve configuration cost in the day-ahead stage, and the result of the day-ahead reserve optimization model will be input into the intraday reserve optimization model to ensure the data transmission consistency of the multi-stage model, and provide a reliable starting point for the global optimization of the source-load reserve coordination rolling model.

[0108] As a preferred embodiment, step S3, according to the intraday level reserve resource, establishes an intraday reserve optimization model with the second cost minimum as the objective function, which is executed through steps S31-S34:

[0109] S31, based on the day-ahead clearing result, obtains the capacity configuration of the intraday level reserve resource;

[0110] S32, according to the capacity configuration of the intraday level reserve resource, calculates the comprehensive operation cost of the intraday level reserve resource and the calling cost of the day-ahead level reserve resource, and obtains the second cost;

[0111] S33, according to the intraday level reserve resource, establishes a second constraint condition;

[0112] S34, an intraday reserve optimization model is constructed with the second cost minimum as the objective function, and the constraint of the intraday reserve optimization model is the second constraint condition.

[0113] It should be noted that the capacity configuration of the intraday level reserve resource is not an independent decision, but is based on the day-ahead clearing result. In the intraday stage, the state of the unit is adjusted based on the day-ahead unit commitment result. Some fast machine units will be called to deal with some emergency situations. The capacity of the fast machine unit and the B-type IDR reserve capacity purchase are determined in this stage. Among them, the unit commitment and unit output of the fast machine unit, the capacity of the fast machine unit reserve and the B-type IDR reserve will be equivalent to the input quantity directly substituted into the real-time reserve optimization model to solve other unknown quantities. Based on the day-ahead clearing result, the capacity upper limit of the intraday level reserve resource can be determined to provide an initial constraint range for the subsequent intraday optimization model, avoiding unreasonable capacity boundary in intraday optimization.

[0114] The second cost of the intraday stage and the first cost of the day-ahead stage have similarities, both of which are calculated based on the comprehensive operation cost. The difference is that the additional cost of calling slow machine units or A-type IDR due to intraday deviation is also considered in the calculation of the second cost. This cost coupling mechanism can guide the intraday optimization model to consider the comprehensive cost of using intraday resources and calling day-ahead resources when making decisions, avoid excessive calling of day-ahead resources to reduce intraday local cost, and achieve optimal economy in the whole cycle.

[0115] Preferably, the second cost f ID is expressed as:

[0116] f ID = f fuel,ID + f SUSD,ID + f curt,ID + f ESS,ID + f Recap_f,ID + f B_IDRcap,ID +

[0117] f Reinb_s,ID +f A_IDRinv,ID (9);

[0118] wherein, f ID is the total cost of the intra-day stage, f fuel,ID is the fuel cost of the intra-day stage thermal power unit, f SUSD,ID is the start-stop cost of the intra-day stage thermal power unit, f curt,ID is the wind curtailment penalty cost of the intra-day stage, f ESS,ID is the charging and discharging cost of the intra-day stage ESS, f Recap_f,ID is the reserve capacity cost of the intra-day stage fast machine unit, f B_IDRcap,ID is the reserve capacity cost of the intra-day stage B-type IDR, f Reinv_s,ID is the reserve calling cost of the intra-day stage slow machine unit, and f A_IDRinv,ID is the reserve calling cost of the intra-day stage A-type IDR.

[0119] In some preferred embodiments, the second constraint condition in step S33 also includes the intra-day power balance constraint, the intra-day unit output constraint, the intra-day ramp constraint, the intra-day minimum start-stop time constraint, the intra-day energy storage system constraint, and the intra-day reserve capacity constraint, which are similar to the first constraint condition. Those skilled in the art can easily know how to set the constraint conditions according to the physical limitations of the intra-day level reserve resources, and the details are not described here.

[0120] In the above scheme, the intra-day reserve optimization model can automatically balance the economy of using the intra-day level resources and calling the day-ahead level resources. This dynamic decision mechanism can maximize the resource utilization efficiency in the intra-day stage and reduce the economic loss of local scheduling. Furthermore, the results of the intra-day reserve optimization model will be used as the input of the real-time level reserve optimization, ensuring smooth connection of the reserve resources in the intra-day and real-time stages, providing detailed and feasible boundary conditions for subsequent real-time optimization, and promoting the whole-process collaboration of reserve rolling scheduling.

[0121] As a preferred embodiment, step S4 establishes a real-time reserve optimization model with a third cost minimum as the objective function according to the real-time level reserve resources, which is executed through steps S41-S44.

[0122] S41, obtaining real-time wind power, load prediction results and generator unit available states, and combining the day-ahead clearing results to obtain the capacity configuration of the real-time level reserve resources;

[0123] S42, calculating the comprehensive operation cost of the real-time level reserve resources and the calling cost and uncertainty risk cost of the intra-day level reserve resources according to the capacity configuration of the real-time level reserve resources, to obtain the third cost;

[0124] S43, establishing a third constraint condition according to the real-time level standby resource;

[0125] S44, constructing a real-time standby optimization model with the third cost minimum as an objective function, and the constraint of the real-time standby optimization model being the third constraint condition.

[0126] The start-stop mode of the slow machine and the fast machine in the real-time stage is the same as the unit combination result in the day-ahead stage. According to the latest wind power, load prediction results and the available state of the generator unit, economic dispatching is performed to optimize the standby capacity provided by the AGC and the C-type IDR standby capacity.

[0127] The optimization target of the real-time stage is the same as the first two stages, which is to minimize the total system cost. The difference is that the target function includes the power cost generated when the fast machine unit and the B-type IDR standby capacity determined in the day-ahead stage are called in the real-time stage, and the standby capacity cost of the AGC unit and the C-type IDR in the real-time stage, and the uncertainty risk cost is increased.

[0128] Preferably, the third cost f RT is expressed as:

[0129] f RT = f fuel,RT + f SUSD,RT + f curt,RT + f ESS,RT + f Recap_AGC,RT + f C_IDRcap,RT +

[0130] f Reinv_s,RT + f A_IDRinv,RT + f B_IDRinv,RT + f risk,RT (10);

[0131] wherein f RT is the total cost in the real-time stage, f fuel,RT is the fuel cost of the thermal power unit in the real-time stage, f SUSD,RT is the start-stop cost of the thermal power unit in the real-time stage, f curt,RT is the wind curtailment penalty cost in the real-time stage, f ESS,RT is the ESS charging and discharging cost in the real-time stage, f Recap_AGC,RT is the standby capacity cost of the AGC unit in the real-time stage, f C_IDRcap,RT is the standby capacity cost of the C-type IDR in the real-time stage, f Reinv_s,RT is the standby calling cost of the slow machine unit in the real-time stage, f Reinv_f,RT is the standby calling cost of the fast machine unit in the real-time stage, f A_IDRinv,ID is the standby calling cost of the A-type IDR in the real-time stage, f B_IDRinv,RT is the standby calling cost of the B-type IDR in the real-time stage, and f risk,RTTo determine the real-time stage uncertainty risk cost.

[0132] In some preferred embodiments, the third constraint condition in step S43 is similar to the first constraint condition and the second constraint condition in the previous two stages, including real-time power balance constraints, real-time unit output constraints, real-time ramping constraints, real-time minimum start-up and shut-down time constraints, real-time energy storage system constraints and real-time reserve capacity constraints. The expressions among the three are similar, and it is not difficult for those skilled in the art to know how to set the constraint conditions according to the physical limitations of the real-time level reserve resources, which will not be repeated here.

[0133] In the embodiments of the present application, the source-load reserve coordinated rolling model connects the day-ahead reserve optimization model, the intra-day reserve optimization model and the real-time reserve optimization model of different stages into a dynamic iteration as a whole based on the progression of time scales and the real-time update of data. In the rolling process, the optimization results of the previous stage are used as the basis for the next stage, and at the same time, the optimization objectives and constraints of the subsequent stages are constantly corrected with the input of new data, ensuring that the decision always fits the latest system state and avoiding the accumulation of errors caused by static decision-making, thereby providing better decision support for the safe and efficient operation of the power system.

[0134] The source-load reserve coordinated rolling scheduling method provided by the embodiments of the present application can integrate reserve resources of different time scales into a reserve optimization framework, and the reserve resources determined in the previous stage can be called in the subsequent reserve optimization stage like a conventional unit, and the maximum calling limit value is the reserve capacity optimized in the previous stage. Compared with only considering the day-ahead scheduling model, the rolling scheduling model requires less spinning reserve capacity, which helps to reduce the total operating cost of the system.

[0135] The embodiments of the present application provide a source-load reserve coordinated rolling scheduling system. Please refer to Figure 2 The source-load reserve coordinated rolling scheduling system comprises a resource division module 11, a day-ahead model construction module 12, an intra-day model construction module 13, a real-time model construction module 14 and a rolling scheduling module 15, wherein:

[0136] The resource division module 11 is used for dividing the reserve resources into day-ahead level reserve resources, intra-day level reserve resources and real-time level reserve resources according to the time response characteristics of the reserve resources.

[0137] The day-ahead model construction module 12 is used for obtaining a day-ahead out-clearing result, and constructing a day-ahead reserve optimization model with a first cost minimum as an objective function according to the day-ahead out-clearing result and the day-ahead level reserve resources; the first cost includes the comprehensive operating cost of the day-ahead level reserve resources in the day-ahead stage.

[0138] The day-ahead model construction module 13 is configured to construct a day-ahead reserve optimization model with a second cost minimization as an objective function according to the day-ahead level reserve resource; the second cost includes a comprehensive operation cost of the day-ahead level reserve resource and a calling cost of the day-ahead level reserve resource;

[0139] The real-time model construction module 14 is configured to construct a real-time reserve optimization model with a third cost minimization as an objective function according to the real-time level reserve resource; the third cost includes a comprehensive operation cost of the real-time level reserve resource, a calling cost of the day-ahead level reserve resource and an uncertainty risk cost;

[0140] The rolling scheduling module 15 is configured to construct a source-load reserve coordination rolling model according to the day-ahead reserve optimization model, the day-ahead reserve optimization model and the real-time reserve optimization model, solve the source-load reserve coordination rolling model and obtain a reserve configuration result.

[0141] As a preferred implementation, the resource division module 11 is specifically configured to:

[0142] divide the reserve resource into a generation-side reserve resource and a demand-side reserve resource;

[0143] divide the generation-side reserve resource into a slow machine unit, a fast machine unit and an automatic generation control unit according to a start-stop machine response capability of the generation-side reserve resource;

[0144] divide the demand-side reserve resource into an A-type IDR, a B-type IDR and a C-type IDR according to an advance notice time of the demand-side reserve resource; the A-type IDR has the longest advance notice time and the C-type IDR has the shortest advance notice time;

[0145] regard the slow machine unit and the A-type IDR as the day-ahead level reserve resource, regard the fast machine unit and the B-type IDR as the day-ahead level reserve resource, and regard the automatic generation control unit and the C-type IDR as the real-time level reserve resource.

[0146] As a preferred implementation, the day-ahead model construction module 12 includes:

[0147] a day-ahead clearing result acquisition unit configured to acquire a day-ahead clearing result, the day-ahead clearing result including a start-stop machine plan of the day-ahead level reserve resource for the next day, a dispatching output plan, a spinning reserve and a purchase amount;

[0148] a first cost calculation unit configured to calculate a comprehensive operation cost of the day-ahead level reserve resource according to the day-ahead clearing result and obtain a first cost;

[0149] a first constraint condition construction unit configured to construct a first constraint condition according to the day-ahead level reserve resource;

[0150] a first model construction unit, configured to construct a day-ahead reserve optimization model with the first cost minimum as an objective function, and a constraint of the day-ahead reserve optimization model being the first constraint condition.

[0151] Further, preferably, the first cost calculation unit is specifically configured to:

[0152] by f DA = f fuel,DA + f SUSD,DA + f curt,DA + f ESS,DA + f Recap_s,DA + f A_IDRcap,DA calculate the first cost f DA .

[0153] wherein f fuel,DA is a fuel cost of a thermal power unit in a day-ahead stage; f SUSD,DA is a start-stop cost of the thermal power unit in the day-ahead stage; f curt,DA is a wind curtailment penalty cost in the day-ahead stage; f ESS,DA is an ESS charging-discharging cost in the day-ahead stage; f Recap_s,DA is a slow machine unit reserve capacity cost in the day-ahead stage; and f A_IDRcap,DA is a type-A IDR reserve capacity cost in the day-ahead stage.

[0154] Preferably, the first constraint condition construction unit is specifically configured to:

[0155] by establish a day-ahead power balance constraint; wherein, is a unit output of a thermal power unit i at time t, N Th is a total number of thermal power units, is an actual wind power in the day-ahead stage, N w is a total number of wind power units, is a discharging power of an ESS at time t in the day-ahead stage, is an actual load power in the day-ahead stage, is a charging power of the ESS at time t in the day-ahead stage;

[0156] by establish a day-ahead unit output constraint; wherein, and are a maximum output and a minimum output of a thermal power unit i respectively, is an operating state variable of the thermal power unit i at time t in the day-ahead stage, is a unit output of the thermal power unit i at time t;

[0157] by establish a day-ahead ramping constraint; wherein, Pi Th ,RDmin P is the lower limit of the gradeability of thermal power unit i. i Th,RUmax This represents the upper limit of the ramp rate for thermal power unit i;

[0158] pass Establish daily minimum start-up and shutdown time constraints; among which, This represents the number of hours that thermal power unit i has been continuously running at time t during the current phase. This represents the number of hours that thermal power unit i is continuously shut down at time t during the current period.

[0159] pass Establish constraints for day-ahead energy storage systems; among which, This represents the charging state of the ESS at time t during the current phase. This represents the discharge state of the ESS at time t during the current phase. P represents the charging power of the energy storage system at time t during the day-ahead phase. char,max This represents the maximum charging power of the energy storage system. P represents the discharge power of the energy storage system at time t during the day-ahead phase. dis,max The maximum discharge power of the energy storage system η represents the energy storage capacity of the ESS at time t during the current phase. char The efficiency coefficient, η, of the charging process of an energy storage system. dis E represents the efficiency coefficient of the energy storage system during the discharge process. cap ε represents the maximum capacity of the energy storage system, and ε is a preset proportional parameter.

[0160] pass Establish day-ahead reserve capacity constraints; among which, This represents the reserve capacity of the slow-running unit s at time t during the current phase. This represents the power generation capacity of the slow-speed unit s at time t during the current phase. This represents the maximum output of the slow-speed unit s. The reserve capacity of the j-th type A IDR at time t, P j A_IDR,max For the maximum reserve capacity of the j-th Class A IDR, P Recap,DA This represents the total reserve capacity requirement for the current phase.

[0161] Based on the day-ahead power balance constraint, the day-ahead unit output constraint, the day-ahead ramp-up constraint, the day-ahead minimum start-up and shutdown time constraint, the day-ahead energy storage system constraint, and the day-ahead reserve capacity constraint, a first constraint condition is established.

[0162] As a preferred implementation, the intraday model construction module 13 comprises:

[0163] An intraday configuration obtaining unit is configured to obtain capacity configuration of intraday backup resources based on the day-ahead clearing result.

[0164] A second cost calculation unit is configured to calculate comprehensive operation cost of the intraday backup resources and calling cost of the day-ahead backup resources according to the capacity configuration of the intraday backup resources, and obtain second cost.

[0165] A second constraint condition establishing unit is configured to establish a second constraint condition according to the intraday backup resources.

[0166] A second model construction unit is configured to construct an intraday backup optimization model with the second cost minimization as an objective function, and the constraint of the intraday backup optimization model is the second constraint condition.

[0167] Further, preferably, the second cost calculation unit is specifically configured to:

[0168] The second cost is calculated by f ID = f fuel,ID + f SUSD,ID + f curt,ID + f ESS,ID + f Recap_f,ID + f B_IDRcap,ID + f Reinv_s,ID + f A_IDRinv,ID

[0169] Wherein, f ID is total intraday stage cost, f fuel,ID is intraday stage thermal power unit fuel cost, f SUSD,ID is intraday stage thermal power unit start-stop cost, f curt,ID is intraday stage wind power penalty cost, f ESS,ID is intraday stage ESS charging / discharging cost, f Recap_f,ID is intraday stage fast machine backup capacity cost, f B_IDRcap,ID is intraday stage B-type IDR backup capacity cost, f Reinv_s,ID is intraday stage slow machine backup calling cost, and f A_IDRinv,ID is intraday stage A-type IDR backup calling cost.

[0170] As a preferred implementation, the real-time model construction module 14 comprises:

[0171] A real-time configuration obtaining unit is configured to obtain real-time wind power, load prediction result and generator availability state, and obtain capacity configuration of real-time backup resources in combination with the day-ahead clearing result.

[0172] ​a third cost calculation unit, configured to calculate a comprehensive operation cost of the real-time level backup resource and a calling cost and an uncertainty risk cost of the intra-day level backup resource according to the capacity configuration of the real-time level backup resource, to obtain a third cost;

[0173] a third constraint condition establishment unit, configured to establish a third constraint condition according to the real-time level backup resource;

[0174] a third model construction unit, configured to construct a real-time backup optimization model with the third cost minimization as an objective function, and the constraint of the real-time backup optimization model being the third constraint condition.

[0175] Further, preferably, the third cost calculation unit is specifically configured to:

[0176] by f RT = f fuel,RT + f SUSD,RT + f curt,RT + f ESS,RT + f Recap_AGC,RT + f C_IDRcap,RT + f Reinv_s,RT + f A_IDRinv,RT + f B_IDRinv,RT + f risk,RT calculate the third cost;

[0177] wherein f RT is a total cost in a real-time stage, f fuel,RT is a fuel cost of a thermal power unit in the real-time stage, f SUSD,RT is a start-stop cost of the thermal power unit in the real-time stage, f curt,RT is a wind curtailment penalty cost in the real-time stage, f ESS,RT is an ESS charging and discharging cost in the real-time stage, f Recap_AGC,RT is an AGC unit backup capacity cost in the real-time stage, f C_IDRcap,RT is a C-type IDR backup capacity cost in the real-time stage, f Reinv_s,RT is a slow unit backup calling cost in the real-time stage, f Reinv_f,RT is a fast unit backup calling cost in the real-time stage, f A_IDRinv,ID is an A-type IDR backup calling cost in the real-time stage, f B_IDRinv,RT is a B-type IDR backup calling cost in the real-time stage, and f risk,RT is an uncertainty risk cost in the real-time stage.

[0178] The rolling scheduling system with source-load backup coordination provided by the embodiment of the application can integrate backup resources of different time scales into a backup optimization framework, and the backup resources determined in the previous stage can be called in the subsequent backup optimization stage like a conventional unit, and the maximum calling limit value is the backup capacity optimized in the previous stage. Compared with only considering the day-ahead scheduling model, the rolling scheduling model requires less rotating backup capacity, which helps to reduce the total operation cost of the system.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of each method. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.

[0180] The above is the preferred embodiment of the application. It should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the application. These improvements and refinements are also considered within the protection scope of the application.

Claims

1. A rolling scheduling method for source-load standby coordination, characterized in that, include: Based on the time response characteristics of backup resources, backup resources are divided into day-ahead backup resources, intraday backup resources, and real-time backup resources. Obtain the day-ahead clearing results, and based on the day-ahead reserve resources according to the day-ahead clearing results, construct a day-ahead reserve optimization model with the objective function of minimizing the first cost; the first cost includes the comprehensive operating cost of the day-ahead reserve resources in the day-ahead phase; Based on the intraday-level reserve resources, an intraday reserve optimization model is established with the objective function of minimizing the second cost; the second cost includes the comprehensive operating cost of intraday-level reserve resources and the cost of calling up day-level reserve resources during the intraday phase; Based on the real-time backup resources, a real-time backup optimization model is established with the objective function of minimizing the third cost; the third cost includes the comprehensive operating cost of real-time backup resources, the uncertainty risk cost, and the call cost of day-ahead and intraday backup resources in the real-time stage. Based on the day-ahead reserve optimization model, the intraday reserve optimization model, and the real-time reserve optimization model, a source-load reserve coordination rolling model is established, and the source-load reserve coordination rolling model is solved to obtain the reserve configuration result.

2. The rolling scheduling method for source-load standby coordination as described in claim 1, characterized in that, Based on the time response characteristics of the backup resources, the backup resources are divided into day-ahead backup resources, intraday backup resources, and real-time backup resources, including: The backup resources are divided into power generation-side backup resources and demand-side backup resources; Based on the start-up and shutdown response capabilities of the backup resources on the power generation side, the backup resources on the power generation side are divided into slow-speed generator units, fast-speed generator units, and automatic generation control generator units. Based on the advance notice time of the demand-side reserve resources, the demand-side reserve resources are divided into Class A IDR, Class B IDR and Class C IDR; among them, Class A IDR has the longest advance notice time and Class C IDR has the shortest advance notice time. The slow generator units and the Class A IDR are considered as day-ahead reserve resources, the fast generator units and the Class B IDR are considered as intraday reserve resources, and the automatic generation control units and the Class C IDR are considered as real-time reserve resources.

3. The rolling scheduling method for source-load standby coordination as described in claim 1, characterized in that, The step of obtaining the day-ahead clearing result and constructing a day-ahead reserve optimization model with the objective function of minimizing the first cost based on the day-ahead reserve resources obtained from the day-ahead clearing result includes: Obtain the day-ahead clearing results, which include the day-ahead reserve resource start-up and shutdown plan, dispatch output plan, spinning reserve and purchase quantity for the next 24 hours; Based on the aforementioned day-ahead clearing results, the comprehensive operating cost of day-ahead reserve resources is calculated to obtain the first cost; Based on the aforementioned day-ahead reserve resources, establish the first constraint condition; A day-ahead reserve optimization model is constructed with the first cost minimization as the objective function, and the constraints of the day-ahead reserve optimization model are the first constraint conditions.

4. The rolling scheduling method for source-load standby coordination as described in claim 3, characterized in that, The step of calculating the comprehensive operating cost of day-ahead reserve resources based on the day-ahead clearing results to obtain the first cost includes: via f DA =f fuel,DA +f SUSD,DA +f curt,DA +f ESS,DA +f Recap_s,DA +f A_IDRcap,DA Calculate the first cost f DA ; Among them, f fuel,DA This represents the fuel cost of thermal power units in the current phase; f SUSD,DA The current stage of thermal power unit start-up and shutdown costs; f curt,DA The cost of wind curtailment penalties during the current phase; f ESS,DA The current stage ESS charge / discharge cost; f Recap_s,DA The cost of standby capacity for slow-moving units during the current phase; f A_IDRcap,DA This represents the cost of Class A IDR spare capacity for the current phase.

5. The rolling scheduling method for source-load standby coordination as described in claim 3, characterized in that, The establishment of the first constraint condition based on the day-ahead reserve resources includes: pass Establish day-ahead power balance constraints; among which, For the unit output of thermal power unit i at time t, N Th This represents the total number of thermal power units. For the actual wind power generation in the current phase, N w This represents the total number of wind turbine units. The discharge power of the ESS at time t during the current phase. This represents the actual load power at the current date. The charging power of ESS at time t during the current phase; pass Establish day-ahead unit output constraints; among which, and These represent the maximum and minimum output of thermal power unit i, respectively. Let be the operating state variable of thermal power unit i at time t during the current phase. The unit output of thermal power unit i at time t; pass Establish day-ahead ramp constraints; where P i Th,RDmin P is the lower limit of the gradeability of thermal power unit i. i Th,RUmax This represents the upper limit of the ramp rate for thermal power unit i; pass Establish daily minimum start-up and shutdown time constraints; among which, This represents the number of hours that thermal power unit i has been continuously running at time t during the current phase. This represents the number of hours that thermal power unit i is continuously shut down at time t during the current period. pass Establish constraints for day-ahead energy storage systems; among which, This represents the charging state of the ESS at time t during the current phase. This represents the discharge state of the ESS at time t during the current phase. P represents the charging power of the energy storage system at time t during the day-ahead phase. char,max This represents the maximum charging power of the energy storage system. P represents the discharge power of the energy storage system at time t during the day-ahead phase. dis,max The maximum discharge power of the energy storage system η represents the energy storage capacity of the ESS at time t during the current phase. char The efficiency coefficient, η, of the charging process of an energy storage system. dis E represents the efficiency coefficient of the energy storage system during the discharge process. cap ε represents the maximum capacity of the energy storage system, and ε is a preset proportional parameter. pass Establish day-ahead reserve capacity constraints; among which, This represents the reserve capacity of the slow-running unit s at time t during the current phase. The power generation of the slow-speed unit s at time t during the current phase. This represents the maximum output of the slow-speed unit s. The reserve capacity of the j-th type A IDR at time t. For the maximum reserve capacity of the j-th Class A IDR, P Recap,DA This represents the total reserve capacity requirement for the current phase. Based on the day-ahead power balance constraint, the day-ahead unit output constraint, the day-ahead ramp-up constraint, the day-ahead minimum start-up and shutdown time constraint, the day-ahead energy storage system constraint, and the day-ahead reserve capacity constraint, a first constraint condition is established.

6. The rolling scheduling method for source-load standby coordination as described in claim 1, characterized in that, The step of establishing an intraday reserve optimization model based on the intraday reserve resources, with the objective function of minimizing the second cost, includes: Based on the day-ahead clearing results, the capacity configuration of intraday-level reserve resources is obtained; Based on the capacity configuration of the intraday-level backup resources, the comprehensive operating cost of the intraday-level backup resources and the call cost of the day-ahead-level backup resources are calculated to obtain the second cost; Based on the intraday-level backup resources, establish a second constraint condition; Using the second minimum cost as the objective function, an intraday reserve optimization model is constructed, and the constraints of the intraday reserve optimization model are the second constraint conditions.

7. The rolling scheduling method for source-load standby coordination as described in claim 6, characterized in that, The second cost is obtained by calculating the comprehensive operating cost of the intraday-level backup resources and the call cost of the day-ahead-level backup resources based on the capacity configuration of the intraday-level backup resources, including: via f ID =f fuel,ID +f SUSD,ID +f curt,ID +f ESS,ID +f Recap_f,ID +f B_lDRcap,ID +f Reinv_s,ID +f A_IDRinv,ID Calculate the second cost; Among them, f ID f represents the total cost for the intraday phase. fuel,ID For the intraday fuel cost of thermal power units, f SUSD,ID For the intraday start-up and shutdown costs of thermal power units, f curt,ID For the intraday stage of wind curtailment penalty cost, f ESS,ID For intraday ESS charging and discharging costs, f Recap_f,ID For intraday phase fast-flying unit standby capacity costs, f B_IDRcap,ID For intraday phase B-type IDR standby capacity cost, f Reinv_s,ID For intraday phase standby call costs of slow-moving units, f A_IDRinv,ID Cost of backup call for Class A IDR during the intraday phase.

8. The rolling scheduling method for source-load standby coordination as described in claim 1, characterized in that, The step of establishing a real-time backup optimization model with the objective function of minimizing the third cost based on the real-time backup resources includes: Obtain real-time wind power and load forecast results and generator unit availability status, and combine them with the day-ahead clearing results to obtain the real-time standby resource capacity configuration; Based on the capacity configuration of the real-time backup resources, the comprehensive operating cost of the real-time backup resources and the call cost and uncertain risk cost of the intraday backup resources are calculated to obtain the third cost; Based on the aforementioned real-time backup resources, a third constraint condition is established; A real-time standby optimization model is constructed with the third cost minimization as the objective function, and the constraints of the real-time standby optimization model are the third constraint conditions.

9. The rolling scheduling method for source-load standby coordination as described in claim 8, characterized in that, The third cost is calculated based on the capacity configuration of the real-time backup resources, the comprehensive operating cost of the real-time backup resources, the call cost of the intraday backup resources, and the uncertain risk cost, and includes: via f RT =f fuel,RT +f SUSD,RT +f curt,RT +f ESS,RT +f Recap_AGC,RT +f C_IDRcap,RT +f Reinv_s,RT +f A_IDRinv,RT +f B_IDRinv,RT +f risk,RT Calculate the third cost; Among them, f RT f represents the total cost of the real-time stage. fuel,RT For real-time stage fuel costs of thermal power units, f SUSD,RT For the real-time start-up and shutdown costs of thermal power units, f curt,RT To account for the real-time stage of wind curtailment penalty costs, f ESS,RT For the real-time stage ESS charge and discharge cost, f Recap_AGC,RT For the standby capacity cost of AGC units in the real-time phase, f C_IDRcap,RT For the real-time phase Class C IDR spare capacity cost, f Reinv_s,RT For the cost of real-time slow unit standby call, f Reinv_f,RT For the cost of real-time standby call of fast-deployment units, f A_IDRinv,ID For the real-time phase, the cost of backup calls for Class A IDR is f. B_IDRinv,RT For the real-time phase B-type IDR backup call cost, f risk,RT Costs associated with uncertainty risks in the real-time phase.

10. A rolling scheduling system for source-load backup coordination, characterized in that, include: The resource allocation module is used to classify backup resources into day-ahead backup resources, intraday backup resources, and real-time backup resources based on their time response characteristics. The day-ahead model construction module is used to obtain the day-ahead clearing results and, based on the day-ahead clearing results and the day-ahead reserve resources, construct a day-ahead reserve optimization model with the objective function of minimizing the first cost; the first cost includes the comprehensive operating cost of the day-ahead reserve resources in the day-ahead stage. The intraday model building module is used to establish an intraday backup optimization model with the objective function of minimizing the second cost based on the intraday-level backup resources; the second cost includes the comprehensive operating cost of intraday-level backup resources and the call cost of day-ahead-level backup resources during the intraday phase; The real-time model building module is used to establish a real-time backup optimization model with the objective function of minimizing the third cost based on the real-time backup resources; the third cost includes the comprehensive operating cost of real-time backup resources in the real-time stage, the call cost of intraday backup resources, and the uncertainty risk cost; The rolling scheduling module is used to establish a source-load backup coordination rolling model based on the day-ahead backup optimization model, the intraday backup optimization model, and the real-time backup optimization model, solve the source-load backup coordination rolling model, and obtain the backup configuration result.

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