Method and device for coordinating multiple types of adjustable resources in a power system

Abstract

The application provides a kind of coordination method and device of power system multi-type adjustable resource, which can be used in electric power technical field.The method comprises the following steps: in day-ahead stage, obtaining the technical parameters and economic parameters of each type of adjustable resource of power system, and the system power prediction error probability density function of power system at each time of second day;According to the system power prediction error probability density function of power system at each time of second day, determine the up-regulation reserve demand constraint and down-regulation reserve demand constraint of power system under given confidence level;According to the technical parameters and operation requirements of each type of adjustable resource of power system, establish the reserve feasible region model of the type of adjustable resource considering timing coupling constraint;With the minimum total operation cost of power system as the target, according to the reserve feasible region model of each type of adjustable resource and the economic parameters, determine the reference operation power of each type of adjustable resource on the second day and the up-regulation reserve capacity and down-regulation reserve capacity needed to be reserved.

Description

Technical Field

[0001] This application relates to the field of power technology, specifically to a method and apparatus for coordinating multiple types of adjustable resources in a power system. Background Technology

[0002] In future high-proportion renewable energy power systems, backup supply will come from adjustable resources at various stages, including energy sources, loads, and storage. Especially considering the constraints on total energy output for energy storage and adjustable loads during operation, the available backup capacity at different times is coupled. For example, the backup capacity provided by energy storage depends on the energy stored in the equipment and the capacity of the storage device at the current moment. The energy stored at the current moment is accumulated over all previous times and is closely related to the energy storage status at previous times, not just the current moment; this is where the coupling exists. The principle of adjustable loads is similar to that of energy storage.

[0003] Different types of adjustable resources have different technical and economic parameters. How to coordinate different types of adjustable resources and rationally arrange the reference power operating points and reserve capacity of various adjustable devices at different time sections in the most cost-effective way is a technical problem to be solved. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of this application provide a method and apparatus for coordinating multiple types of adjustable resources in a power system, which can at least partially solve the problems existing in the prior art.

[0005] On the one hand, this application proposes a method for coordinating multiple types of adjustable resources in a power system, including:

[0006] During the day-ahead phase, obtain the technical and economic parameters of various types of adjustable resources of the power system, and the probability density function of the system power prediction error of the power system at each time on the second day;

[0007] Based on the probability density function of the system power prediction error at each time point on the second day, the constraints on the upward adjustment of reserve demand and the downward adjustment of reserve demand of the power system at a given confidence level are determined.

[0008] Based on the technical parameters and operational requirements of each type of adjustable resource in the power system, a backup feasible domain model with timing and sequence coupling constraints for that type of adjustable resource is established.

[0009] With the goal of minimizing the total operating cost of the power system, based on the feasible reserve domain model of each type of adjustable resource, the economic parameters of each type of adjustable resource in the power system, the system power balance constraints, the system upward reserve demand constraints, and the system downward reserve demand constraints, the baseline operating power of each type of adjustable resource on the second day and the upward and downward reserve capacity to be reserved are determined.

[0010] In some embodiments, determining the upward and downward reserve demand constraints of the power system at a given confidence level at each time point on the second day, based on the probability density function of the system power prediction error at each time point on the second day, includes:

[0011] Based on the power prediction error probability density function of each type of adjustable resource of the power system at each time on the second day, the system power prediction error probability density function of the power system at each time on the second day is determined.

[0012] Based on the probability density function of the system power prediction error at each time point on the second day, determine the cumulative probability distribution function of the random variable of system upward reserve demand and the cumulative probability distribution function of the random variable of system downward reserve demand.

[0013] Based on the cumulative probability distribution function of the random variable of the system's increased reserve demand and the given first confidence level, the constraints on the increased reserve demand of the power system at each time point on the second day are determined.

[0014] Based on the cumulative probability distribution function of the random variable of the system's reduced reserve demand and the given second confidence level, the constraints on the amount of reduced reserve demand of the power system at each time point on the second day are determined.

[0015] In some embodiments, establishing a backup feasible domain model for each type of adjustable resource based on its technical parameters and operational requirements includes:

[0016] Based on the net output active power vector of each type of adjustable resource in the second-day prediction time series scenario and the linear operation constraints of that type of adjustable resource, the power feasible region of that type of adjustable resource in the second day is determined.

[0017] Based on the power feasible region of this type of adjustable resource on the second day, determine the backup feasible region model of the timing coupling constraint for this type of adjustable resource.

[0018] In some embodiments, the adjustable resources include thermal power clusters, energy storage, and / or resilient loads; wherein,

[0019] The linear operation constraints of the thermal power cluster include variable logic constraints, power upper and lower limit constraints, start-up and shutdown time constraints, and ramping constraints.

[0020] The linear operation constraints of the energy storage include upper and lower limits of charging and discharging power, storage capacity constraints, and variable logic constraints.

[0021] The linear operating constraints of the elastic load include upper and lower limits of power consumption constraints and state quantity constraints.

[0022] In some embodiments, the probability density function of the system power prediction error of the power system at each time point on the second day is obtained by statistical analysis based on the historical operating data of the power system.

[0023] On the other hand, this application provides a coordination device for multiple types of adjustable resources in a power system, comprising:

[0024] The acquisition module is used to acquire, during the day-ahead phase, the technical and economic parameters of various types of adjustable resources of the power system, and the probability density function of the system power prediction error of the power system at each time on the second day.

[0025] The first determining module is used to determine the upward adjustment constraint and the downward adjustment constraint of the reserve demand of the power system at a given confidence level, based on the probability density function of the system power prediction error of the power system at each time on the second day.

[0026] A module is established to build a backup feasible domain model of each type of adjustable resource in the power system based on the technical parameters and operating requirements of that type of adjustable resource.

[0027] The second determination module is used to determine the baseline operating power of each type of adjustable resource on the second day and the required reserve capacity for both upward and downward adjustments, with the goal of minimizing the total operating cost of the power system, based on the feasible reserve domain model of each type of adjustable resource, the economic parameters of each type of adjustable resource in the power system, the system power balance constraints, the system upward reserve demand constraints, and the system downward reserve demand constraints.

[0028] In some embodiments, the first determining module is specifically used for:

[0029] Based on the power prediction error probability density function of each type of adjustable resource of the power system at each time on the second day, the system power prediction error probability density function of the power system at each time on the second day is determined.

[0030] Based on the probability density function of the system power prediction error at each time point on the second day, determine the cumulative probability distribution function of the random variable of system upward reserve demand and the cumulative probability distribution function of the random variable of system downward reserve demand.

[0031] Based on the cumulative probability distribution function of the random variable of the system's increased reserve demand and the given first confidence level, the constraints on the increased reserve demand of the power system at each time point on the second day are determined.

[0032] Based on the cumulative probability distribution function of the random variable of the system's reduced reserve demand and the given second confidence level, the constraints on the amount of reduced reserve demand of the power system at each time point on the second day are determined.

[0033] In some embodiments, the establishment module is specifically used for:

[0034] Based on the net output active power vector of each type of adjustable resource in the second-day prediction time series scenario and the linear operation constraints of that type of adjustable resource, the power feasible region of that type of adjustable resource in the second day is determined.

[0035] Based on the power feasible region of this type of adjustable resource on the second day, determine the backup feasible region model of the timing coupling constraint for this type of adjustable resource.

[0036] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the coordination method for multiple types of adjustable resources in a power system as described in any of the above embodiments.

[0037] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the coordination method for multiple types of adjustable resources in a power system as described in any of the above embodiments.

[0038] The method and apparatus for coordinating multiple types of adjustable resources in a power system provided in this application, during the day-ahead phase, uses the prediction error probability density function of each time point on the second day and the technical and economic parameters of each type of adjustable resource as inputs to optimize the decision on the reference power operating point of various types of equipment and the reserve capacity to be reserved for upward and downward adjustments at each time point, and can minimize the total operating cost of system power generation and reserve. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0040] Figure 1 This is a flowchart illustrating a method for coordinating multiple types of adjustable resources in a power system according to an embodiment of this application.

[0041] Figure 2 This is a partial flowchart illustrating a method for coordinating multiple types of adjustable resources in a power system according to an embodiment of this application.

[0042] Figure 3 This is a partial flowchart illustrating a method for coordinating multiple types of adjustable resources in a power system according to an embodiment of this application.

[0043] Figure 4 This is a schematic diagram of the structure of a coordination device for multiple types of adjustable resources in a power system provided in an embodiment of this application.

[0044] Figure 5 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and their descriptions are used to explain this application, but are not intended to limit this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily arranged.

[0046] The terms “first,” “second,” etc., used in this document are not intended to specifically refer to order or sequence, nor are they used to limit this application; they are merely used to distinguish elements or operations described using the same technical terms.

[0047] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0048] The term "and / or" as used in this document includes any or all of the items mentioned.

[0049] To better understand this application, the research background of this application will be introduced in detail below.

[0050] Currently widely adopted unit combination models only consider the reserve capacity provided by thermal power, and assume that the reserve capacity that thermal power can provide at different times is independent of each other. This approach is difficult to apply to future high-proportion renewable energy power systems where multiple types of adjustable resources jointly provide reserves, and where the reserve capacity has a time-series coupling effect, such as scenarios that require frequent charging and discharging of energy storage.

[0051] To address the coordination and optimization problem of various adjustable resources with temporal coupling constraints, including source, load, and storage, jointly providing system reserves, this application proposes a coordination and optimization method considering multiple types of reserve resources. This application can be used for the operation plan optimization of high-proportion renewable energy power systems.

[0052] The execution subject of the coordination method for multiple types of adjustable resources in a power system provided in this application embodiment includes, but is not limited to, a computer.

[0053] Figure 1 This is a flowchart illustrating a method for coordinating multiple types of adjustable resources in a power system according to an embodiment of this application. Figure 1As shown in the embodiments of this application, the method for coordinating multiple types of adjustable resources in a power system includes:

[0054] S101. During the day-ahead phase, obtain the technical and economic parameters of various types of adjustable resources of the power system, and the probability density function of the system power prediction error of the power system at each time on the second day.

[0055] S102. Based on the probability density function of the system power prediction error of the power system at each time on the second day, determine the upward adjustment constraint and downward adjustment constraint of the reserve demand of the power system at a given confidence level.

[0056] S103. Based on the technical parameters and operational requirements of each type of adjustable resource in the power system, establish a backup feasible domain model for the timing and timing coupling constraints of that type of adjustable resource.

[0057] S104. With the goal of minimizing the total operating cost of the power system, based on the feasible reserve domain model of each type of adjustable resource, the economic parameters of each type of adjustable resource in the power system, the system power balance constraints, the system upward reserve demand constraints, and the system downward reserve demand constraints, determine the base operating power of each type of adjustable resource on the second day and the upward and downward reserve capacity to be reserved.

[0058] The coordination method for multiple types of adjustable resources in a power system provided in this application, in the day-ahead phase, uses the probability density function of the prediction error at each time of the second day and the technical and economic parameters of each type of adjustable resource as inputs to optimize the decision on the reference power operating point of various equipment and the reserve capacity to be reserved at each time, and can minimize the total operating cost of power generation and reserve of the system.

[0059] like Figure 2 As shown, in some embodiments, determining the upward and downward reserve demand constraints of the power system at a given confidence level at each time point on the second day, based on the system power prediction error probability density function of the power system at each time point on the second day, includes:

[0060] S1021. Based on the power prediction error probability density function of each type of adjustable resource of the power system at each time on the second day, determine the system power prediction error probability density function of the power system at each time on the second day.

[0061] S1022. Based on the probability density function of the system power prediction error at each time on the second day, determine the cumulative probability distribution function of the random variable of system upward reserve demand and the cumulative probability distribution function of the random variable of system downward reserve demand.

[0062] S1023. Based on the cumulative probability distribution function of the random variable of the system's upward reserve demand and the given first confidence level, determine the upward reserve demand constraints of the power system at each time on the second day.

[0063] S1024. Based on the cumulative probability distribution function of the random variable of the system's reduced reserve demand and the given second confidence level, determine the constraints on the amount of reduced reserve demand of the power system at each time point on the second day.

[0064] Specifically, the power prediction error is the difference between the actual value and the predicted value. Let the random variables of the prediction errors for wind power, photovoltaic power, and unadjustable load in the entire system at the corresponding time t be respectively... and The corresponding probability density functions are respectively and Under the independence assumption, the random variable of system prediction error It can be represented as:

[0065]

[0066] Corresponding probability density function It can be obtained from the following formula:

[0067]

[0068] in, This represents the convolution operator.

[0069] System adjusts standby requirement random variable and the random variable of the system reducing reserve requirements With the random variable of system prediction error The relationship satisfies the following equation:

[0070]

[0071]

[0072] Among them, (a) + =max{a,0}, (a) - =min{a,0}.

[0073] set up and The cumulative probability distribution functions are respectively and Based on The solution is as follows:

[0074]

[0075]

[0076] Let the confidence level for raising the reserve requirement be 1-β. + The confidence level for lowering the reserve requirement is 1-β. - The corresponding increase in reserve requirements and reduce reserve requirements The calculation formula is as follows:

[0077]

[0078]

[0079] like Figure 3 As shown, in some embodiments, establishing a standby feasible domain model for each type of adjustable resource based on its technical parameters and operational requirements includes:

[0080] S1031. Based on the net output active power vector of each type of adjustable resource in the second-day prediction time series scenario and the linear operation constraints of the adjustable resource of that type, determine the power feasible region of the adjustable resource of that type on the second day.

[0081] S1032. Based on the power feasible region of this type of adjustable resource on the second day, determine the backup feasible region model of the timing and timing coupling constraints of this type of adjustable resource.

[0082] Specifically, let the net output active power vector of adjustable resource i during the time period t = 1, ..., D be . Assuming all operational constraints are linear, define the power feasible region of adjustable resource i. for:

[0083]

[0084] Where A and b are the feasible region-related constraint coefficient matrix and coefficient vector, and A and b have the same meaning in the following text.

[0085] Let the baseline power vector of adjustable resource i in the predicted time series scenario of time period t = 1, ..., D be: Increase the reserve capacity vector to and reduce the reserve capacity vector as Assuming all constraints are linear, the alternative feasible region of adjustable resource i can be defined. for:

[0086]

[0087] The following derivation is from get The formula.

[0088] consider The constraint on the j-th row in the code:

[0089]

[0090] in, Let i be the lower power bound vector for adjustable resource i. Let i be the power upper limit vector of adjustable resource i. Let j be the j-th row / j-th element of the aforementioned coefficient matrix and coefficient vector. This expression can be equivalently expressed as:

[0091]

[0092] because and r (i)- r (i)+ The following relationships are satisfied respectively:

[0093]

[0094]

[0095] but The j-th row constraint in the equation is equivalent to the following:

[0096]

[0097] After sorting, we can obtain:

[0098]

[0099] This indicates that if the matrix Divided into blocks Then the following relationship holds:

[0100] 1)

[0101] 2) If

[0102] 3) If

[0103] The above derivation demonstrates that once the power feasible region of a certain adjustable resource is constructed, its backup feasible region can be quickly obtained. The following sections explain the modeling methods for the power feasible region and backup feasible region of several common adjustable resources. Models for other adjustable resources, as long as all operational constraints are linear, can be similarly established.

[0104] For example, in some embodiments, the adjustable resources include thermal power clusters, energy storage, and / or flexible loads; wherein the linear operation constraints of the thermal power clusters include variable logic constraints, power upper and lower limit constraints, start-stop time constraints, and ramping constraints; the linear operation constraints of the energy storage include charging and discharging power upper and lower limit constraints, stored energy constraints, and variable logic constraints; and the linear operation constraints of the flexible loads include power consumption upper and lower limit constraints and state quantity constraints.

[0105] Specifically:

[0106] (1) Thermal power clusters

[0107] The operational constraints that thermal power clusters need to meet include variable logic constraints, power upper and lower limit constraints, start-up and shutdown time constraints, and ramp-up constraints.

[0108] 1) Variable logical constraints

[0109]

[0110] in, This represents the available capacity of thermal power cluster c during time period t. and The capacity of thermal power cluster c during time period t when it transitions from shutdown to startup and from startup to shutdown.

[0111] 2) Power upper and lower limit constraints

[0112]

[0113]

[0114] in, This indicates the minimum technical output percentage requirement for thermal power cluster c. This represents the power generation capacity of thermal power cluster c during time period t. This represents the maximum power generation of thermal power cluster c during time period t. This indicates the installed capacity of thermal power cluster c.

[0115] 3) Start-stop time constraints

[0116]

[0117]

[0118] Among them, UT c This represents the minimum time interval required for thermal power cluster C to transition from a shutdown to an on-state; DT c This represents the minimum time interval required for thermal power cluster c to transition from an on-state to an off-state.

[0119] 4) Climbing constraints

[0120]

[0121]

[0122] in, This indicates the limitation of the uphill climbing capability between two time sections of the thermal power cluster c; This indicates the downhill ramping capability limitation between two time sections of the thermal power cluster c.

[0123] To obtain the feasible domain of thermal power cluster c Fourier-Motzkin elimination method was used to eliminate Other than variables. The coefficient matrix and vector results after elimination are as follows, where... and It is divided into two parts based on whether it is a temporal coupling constraint, namely

[0124]

[0125]

[0126]

[0127] Furthermore, the set of inequalities satisfied by the backup feasible region of thermal power cluster c is as follows:

[0128]

[0129]

[0130]

[0131] (2) Energy storage

[0132] The operational constraints that energy storage i needs to meet include upper and lower limits of charging and discharging power, storage capacity constraints, and variable logic constraints, as detailed below.

[0133] 1) Upper and lower limits of charging and discharging power constraints

[0134]

[0135]

[0136] The superscript "dis" indicates discharging, and "ch" indicates charging. The energy storage device here can correspond to different types such as battery energy storage and pumped hydro storage. This indicates the discharge power of the stored energy. This indicates the maximum discharge power of the stored energy. This indicates the charging power of the energy storage. This indicates the maximum charging power of the energy storage.

[0137] 2) Storage power constraints

[0138]

[0139] The superscript "S" is used to identify energy storage devices. This indicates the initial stored energy capacity. For the discharge efficiency of energy storage, For energy storage charging efficiency, This indicates the minimum required storage capacity for energy storage. This indicates the maximum energy storage capacity required for energy storage.

[0140] 3) Variable logical constraints

[0141]

[0142]

[0143] in, This represents the discharge power of the stored energy from the system side.

[0144] Based on the Fourier-Motzkin elimination method, the feasible region of energy storage i is obtained. The coefficient matrix and vectors are shown below:

[0145]

[0146]

[0147]

[0148] n D =[1,2,…,D] T ;

[0149] Among them, L D Let η be a D-dimensional lower triangular matrix with all non-zero elements being 1. i These are parameters defined using charge / discharge efficiency to simplify the expression during the derivation process.

[0150] The set of inequalities satisfied by the standby feasible region constituting energy storage i is as follows: in, Combining the relationship between the power feasible region and the backup feasible region, we obtain:

[0151]

[0152]

[0153] (3) Elastic load

[0154] The linear operating constraints that the adjustable load i must satisfy include upper and lower limits of power consumption constraints and state constraints, as shown below.

[0155] 1) Upper and lower limits of power consumption constraints

[0156]

[0157]

[0158] The superscript fL is used to identify adjustable loads. Indicates adjustable load power. This indicates the minimum electrolysis hydrogen production power. This indicates the maximum hydrogen production capacity via electrolysis.

[0159] 2) State constraints

[0160]

[0161]

[0162] in, This represents the conversion factor between adjustable load state quantities (such as hydrogen production, charging, temperature, etc.) and electrical power consumption. For state quantity requirements, This is the allowable fluctuation coefficient for the state variable.

[0163] Based on the above constraints, the power feasible region of the adjustable load i can be obtained. medium matrix sum vector The specific form is as follows:

[0164]

[0165]

[0166] Let the set of inequalities satisfied by the standby feasible region constituting the adjustable load i for electric hydrogen production be: in, Then we have:

[0167]

[0168]

[0169] By modeling the alternative feasible region, all temporal coupling constraints within the time period t = 1, ..., D can be fully characterized.

[0170] In some embodiments, the system power prediction error probability density function of the power system at each time point on the second day is obtained by statistical analysis based on the historical operating data of the power system. Solving the prediction error probability density function is a conventional technique in the art and will not be elaborated upon here.

[0171] In some embodiments, with the goal of minimizing the total operating cost of the power system, based on the feasible reserve domain model of each type of adjustable resource, the economic parameters of each type of adjustable resource in the power system, system power balance constraints, system upward reserve demand constraints, and system downward reserve demand constraints, the baseline operating power of each type of adjustable resource on the second day and the required upward and downward reserve capacity are determined as follows:

[0172] Coordinating and optimizing the generation and reserve of multiple types of adjustable resources can be achieved by solving the following optimization model, which can be solved directly by commercial software.

[0173] 1) Optimization Objective

[0174] The optimization objective of the model is to minimize the total operating cost of the system, including the cost of thermal power generation and the cost of backup power, as shown below:

[0175] minC=C o +C r ;

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182] Among them, C o This indicates the cost of thermal power generation, including fuel costs and start-up and shutdown costs. These are the coefficients of the quadratic, linear, and constant terms in the quadratic function of thermal power generation costs; C r This refers to reserve costs, including reserve costs for thermal power plants. Energy storage backup cost Adjustable load standby cost And additional backup costs obtained from other regions Each component's reserve cost is divided into an upward adjustment portion and a downward adjustment portion, where c + Used to indicate an upward adjustment of the reserve cost factor, c -Used to indicate a reduction in the reserve cost coefficient. and These represent the upward and downward adjustments to standby capacity purchased from adjustable resources outside this system, respectively.

[0183] 2) Constraints

[0184] Thermal power plant operation constraints:

[0185]

[0186] Energy storage operation constraints:

[0187]

[0188] Adjustable load operation constraints:

[0189]

[0190] Power balance based on predicted scenarios:

[0191]

[0192] in, This represents the time-series forecast value of net load.

[0193] Increase standby constraints:

[0194]

[0195] Lower the standby constraint:

[0196]

[0197] Figure 4 This is a schematic diagram of the structure of a coordination device for multiple types of adjustable resources in a power system provided in an embodiment of this application, as shown below. Figure 4 As shown in the embodiment of this application, the coordination device for multiple types of adjustable resources in a power system includes:

[0198] The acquisition module 21 is used to acquire, during the day-ahead phase, the technical and economic parameters of various types of adjustable resources of the power system, and the probability density function of the system power prediction error of the power system at each time on the second day;

[0199] The first determining module 22 is used to determine the upward adjustment of reserve demand and the downward adjustment of reserve demand of the power system at a given confidence level based on the probability density function of the system power prediction error at each time of the second day.

[0200] Module 23 is established to build a backup feasible domain model of each type of adjustable resource in the power system based on the technical parameters and operating requirements of that type of adjustable resource.

[0201] The second determining module 24 is used to determine the baseline operating power of various types of adjustable resources on the second day and the required reserve capacity for upward and downward adjustments, based on the feasible reserve domain model of each type of adjustable resource, the economic parameters of each type of adjustable resource in the power system, the system power balance constraints, the system upward reserve demand constraints, and the system downward reserve demand constraints, with the goal of minimizing the total operating cost of the power system.

[0202] The coordination method for multiple types of adjustable resources in a power system provided in this application, in the day-ahead phase, uses the probability density function of the prediction error at each time of the second day and the technical and economic parameters of each type of adjustable resource as inputs to optimize the decision on the reference power operating point of various equipment and the reserve capacity to be reserved at each time, thereby minimizing the total operating cost of power generation and reserve in the system.

[0203] In some embodiments, the first determining module is specifically used for:

[0204] Based on the power prediction error probability density function of each type of adjustable resource of the power system at each time on the second day, the system power prediction error probability density function of the power system at each time on the second day is determined.

[0205] Based on the probability density function of the system power prediction error at each time point on the second day, determine the cumulative probability distribution function of the random variable of system upward reserve demand and the cumulative probability distribution function of the random variable of system downward reserve demand.

[0206] Based on the cumulative probability distribution function of the random variable of the system's increased reserve demand and the given first confidence level, the constraints on the increased reserve demand of the power system at each time point on the second day are determined.

[0207] Based on the cumulative probability distribution function of the random variable of the system's reduced reserve demand and the given second confidence level, the constraints on the amount of reduced reserve demand of the power system at each time point on the second day are determined.

[0208] In some embodiments, the establishment module is specifically used for:

[0209] Based on the net output active power vector of each type of adjustable resource in the second-day prediction time series scenario and the linear operation constraints of that type of adjustable resource, the power feasible region of that type of adjustable resource in the second day is determined.

[0210] Based on the power feasible region of this type of adjustable resource on the second day, determine the backup feasible region model of the timing coupling constraint for this type of adjustable resource.

[0211] The embodiments of the apparatus provided in this application can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0212] Figure 5 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of this application, as shown below. Figure 5 As shown, the electronic device may include a processor 301, a communications interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communications interface 302, and the memory 303 communicate with each other via the communication bus 304. The processor 301 may call logical instructions in the memory 303 to execute the methods described in any of the above embodiments.

[0213] Furthermore, the logical instructions in the aforementioned memory 303 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0214] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can perform the methods provided in the above-described method embodiments.

[0215] This embodiment provides a computer-readable storage medium storing a computer program that causes the computer to perform the methods provided in the above-described method embodiments.

[0216] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0218] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0219] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0220] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0221] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for coordinating multiple types of adjustable resources in a power system, characterized in that, include: In the day-ahead phase, the technical and economic parameters of various types of adjustable resources in the power system, and the probability density function of the system power prediction error at each time point on the second day are obtained. Based on the probability density function of the system power prediction error at each time point on the second day, the constraints on increasing and decreasing reserve demand of the power system at a given confidence level are determined. This includes: determining the probability density function of the system power prediction error at each time point on the second day based on the probability density function of the power prediction error of various types of adjustable resources in the power system; determining the cumulative probability distribution function of the random variable of increasing and decreasing reserve demand based on the probability density function of the system power prediction error at each time point on the second day; determining the constraints on increasing reserve demand of the power system at each time point on the second day based on the cumulative probability distribution function of the random variable of increasing reserve demand and a given first confidence level; and determining the constraints on decreasing reserve demand of the power system at each time point on the second day based on the cumulative probability distribution function of the random variable of decreasing reserve demand and a given second confidence level. Based on the technical parameters and operational requirements of each type of adjustable resource in the power system, a backup feasible region model for the timing and time-series coupling constraints of that type of adjustable resource is established. This includes: determining the power feasible region of each type of adjustable resource on the second day based on the net output active power vector of each type of adjustable resource in the predicted time-series scenario on the second day and the linear operational constraints of that type of adjustable resource; and determining the backup feasible region model for the timing and time-series coupling constraints of that type of adjustable resource based on the power feasible region of that type of adjustable resource on the second day. The adjustable resources include thermal power clusters, energy storage, and / or flexible loads. The linear operational constraints of the thermal power clusters include variable logic constraints, power upper and lower limit constraints, start-up and shutdown time constraints, and ramping constraints. The linear operational constraints of the energy storage include charging and discharging power upper and lower limit constraints, stored energy constraints, and variable logic constraints. The linear operational constraints of the flexible loads include power consumption upper and lower limit constraints and state quantity constraints. With the goal of minimizing the total operating cost of the power system, based on the feasible reserve domain model of each type of adjustable resource, the economic parameters of each type of adjustable resource in the power system, the system power balance constraints, the system upward reserve demand constraints, and the system downward reserve demand constraints, the baseline operating power of each type of adjustable resource on the second day and the upward and downward reserve capacity to be reserved are determined.

2. The method according to claim 1, characterized in that, The probability density function of the system power prediction error of the power system at each time on the second day is obtained by statistical analysis based on the historical operating data of the power system.

3. A coordination device for multiple types of adjustable resources in a power system, characterized in that, The apparatus for implementing the method according to claim 1 or 2 above comprises: The acquisition module is used to acquire, during the day-ahead phase, the technical and economic parameters of various types of adjustable resources of the power system, and the probability density function of the system power prediction error of the power system at each time on the second day. The first determining module is used to determine the upward adjustment of reserve demand and the downward adjustment of reserve demand of the power system at a given confidence level based on the probability density function of the system power prediction error at each time of the second day. A module is established to build a backup feasible domain model of each type of adjustable resource in the power system based on the technical parameters and operating requirements of that type of adjustable resource. The second determination module is used to determine the baseline operating power of each type of adjustable resource on the second day and the required reserve capacity for both upward and downward adjustments, with the goal of minimizing the total operating cost of the power system, based on the feasible reserve domain model of each type of adjustable resource, the economic parameters of each type of adjustable resource in the power system, the system power balance constraints, the system upward reserve demand constraints, and the system downward reserve demand constraints.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 2.

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