Optimization model and method for electric power marketization transaction

Abstract

The invention discloses an optimization model and method for electric power marketization transaction, and the method comprises the steps: carrying out the collection of a plurality of real-time electric power parameters through a set electric power parameter collection module, thereby forming a corresponding electric power parameter group, carrying out the selection of the electric power parameter group, and locking a problem parameter group and a reasonable parameter group, the problem parameter group and the reasonable parameter group which are subjected to hierarchical numerical arrangement are separately processed, so that the diversity of numerical values is greatly enhanced, the accuracy of the numerical values is better, the real-time electric power parameters collected by a central system are accurate, and finally the conditions for stipulating electric power transaction and resource allocation are accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power market-based transactions, and in particular relates to an optimization model and method for electric power market-based transactions. Background Art

[0002] In the power generation link, competition is introduced mainly through opening up power generation investment access, establishing a wholesale power market, and allowing power generation companies to conduct bilateral transactions with users or power distribution companies. At present, countries and regions that have implemented market-oriented reforms have basically achieved competition in the power generation field. From the perspective of wholesale power market models and trading mechanisms, countries have chosen different models based on their own energy supply, grid structure and other characteristics.

[0003] From the actual situation in my country, as pointed out in the prior art solution with patent publication number "CN102361324B", real-time power parameters are sampled by intelligent sensors and transmitted to the central system. The central system uses the collected real-time power parameters and other numerical values ​​to predict the trend and fluctuation of future power demand, and finally stipulates the conditions for power trading and resource allocation. Real-time power parameters include real-time current, voltage, frequency and other power parameters in the power system.

[0004] However, due to factors such as failure of the smart sensors themselves or external interference, the real-time power parameters collected by the central system are often wrong, which leads to inaccurate real-time power parameters collected by the central system, and ultimately leads to inaccurate conditions for regulating power trading and resource allocation. Summary of the invention

[0005] In order to solve the defects existing in the prior art, the present invention proposes an optimization model and method for power market-oriented transactions, which collects a number of real-time power parameters through a set power parameter collection module to form a corresponding power parameter group, and locks in the problem parameter group and the reasonable parameter group after selecting the power parameter group, and separately handles the problem parameter group and the reasonable parameter group after hierarchical numerical sorting, which greatly enhances the diversity of the numerical values ​​and leads to better accuracy of the numerical values. In this way, the real-time power parameters collected by the central system are accurate, and ultimately the conditions for regulating power transactions and resource allocation are also accurate.

[0006] The present invention uses the following technical solutions.

[0007] An optimization method for power market transactions, comprising:

[0008] Smart sensors sample real-time power parameters and transmit them to the central system, which then compiles the collected real-time power parameters to predict future power demand trends and fluctuations, and ultimately sets the conditions for power trading and resource allocation;

[0009] The method for the central system to collect and organize the real-time power parameters includes:

[0010] Step 1: Collect a number of real-time power parameters and perform reliable control on the collection process to form a power parameter group and transmit it to the power parameter processing module;

[0011] Step 2: Select whether the power parameter group meets the pre-defined selection items, so as to lock in the reasonable parameter group and the problematic parameter group;

[0012] Step 3: Perform hierarchical numerical sorting on the problem parameter groups.

[0013] An optimization model for electricity market transactions, including:

[0014] Smart sensors are connected to the central system for communication. Smart sensors are used to sample real-time power parameters and transmit them to the central system. The central system is used to organize the collected real-time power parameters and then predict the trend and fluctuation of future power demand, and finally determine the conditions for power trading and resource allocation;

[0015] The modules running on the central system include:

[0016] Power parameter collection module and power parameter processing module;

[0017] The power parameter collection module is used to collect a number of real-time power parameters and perform reliable control on the collection process, thereby forming a power parameter group to be transmitted to the power parameter processing module;

[0018] The power parameter processing module includes a value selection submodule and a value arrangement submodule.

[0019] Preferably, the real-time power parameters include real-time current, voltage and frequency power parameters in the power system.

[0020] Preferably, the value selection submodule is used to select whether the power parameter group meets the predefined selection items, so as to lock out the reasonable parameter group and the problematic parameter group;

[0021] The numerical arrangement submodule is used to perform hierarchical numerical arrangement on the problem parameter group.

[0022] Preferably, a method for collecting a plurality of real-time power parameters and performing reliable control on the collection process to form a power parameter group includes:

[0023] Set the collection interval and define the collection interval as U 收取 , while U 收取= [U1, U2], where U1 is the start time of the collection interval, and U2 is the stop time of the collection interval. At the start time of the collection interval, the collection of several real-time power parameters is performed, and a NULL group is concurrently formed at the start time;

[0024] The process of collecting a number of real-time power parameters is reliably controlled, the relevant URLs of the real-time power parameters are obtained, and it is determined whether the relevant URLs are pre-defined valid URLs. If so, the collecting process is numerically encoded, and the real-time power parameters are packaged into a NULL group. After the corresponding packaging is completed at the stop time, a corresponding power parameter group is formed, and the power parameter group is transmitted to the power parameter processing module. If not, the relevant URLs corresponding to the corresponding real-time power parameters are registered as abnormal value source URLs, and the real-time power parameters corresponding to the abnormal value source URLs are discarded.

[0025] Preferably, the relevant website is the website of the smart sensor which samples the corresponding real-time power parameter.

[0026] Preferably, the method of selecting whether the power parameter group matches a predefined selection item to lock in a reasonable parameter group and a problematic parameter group includes:

[0027] The predefined selection items include value type, interval constraint, abnormal value detection and error detection, and the power parameter group is obtained and unpacked to obtain the value type, value interval and power value index corresponding to the power parameter group;

[0028] If the value type does not match the value type in the selected item, a code word '0' is generated, otherwise a code word '1' is generated, and the interval constraint includes the highest and lowest values ​​of the corresponding values ​​of the power parameter group. When the value interval does not match the interval constraint, a code word '0' is generated, otherwise a code word '1' is generated;

[0029] Gather code words '0' and '1' to form a selection code string, define the selection code string as Tu, and the values ​​of Tu are '00', '01', '10' and '11'. When the value of the selection code string is '00', '01' and '10', the corresponding power parameter group is locked as the problem parameter group;

[0030] If the value of the optional code string is '11', the abnormal value detection and the correctness detection are performed again.

[0031] Preferably, the method for abnormal value detection is: obtaining the completeness of the power value index and comparing it with the set ideal completeness. If the completeness matches the ideal completeness, then the abnormal value detection is successful, otherwise, it is unsuccessful.

[0032] The error detection method is: determine whether the power parameter group has a previous error value, if yes, then the error detection is unsuccessful, if not, then the error detection is successful;

[0033] When both the abnormal value detection and the error detection are successful, the power parameter group is locked as a reasonable parameter group. When either the abnormal value detection or the error detection is unsuccessful, the power parameter group is locked as a problematic parameter group.

[0034] Preferably, the numerical arrangement submodule performs a method of hierarchical numerical arrangement on the problem parameter group, comprising:

[0035] The numerical value sorting submodule sets a grouping queue to obtain all problem parameter groups, thereby obtaining the problem categories corresponding to the problem parameter groups, wherein the problem categories include numerical value category errors, missing values, abnormal values, and redundant values;

[0036] The problem parameter groups of different problem categories are associated with corresponding category symbols, and the category symbols for defining value category errors, value omissions, value anomalies, and value redundancy are K1, K2, K3, and K4 respectively;

[0037] The clustering queue performs clustering processing on the problem parameter group according to the same category mark, thereby forming different problem value clusters, and the problem value clusters include cluster one, cluster two, cluster three and cluster four;

[0038] Here, cluster one is formed by clustering the problem parameter components whose value categories are wrong according to the category mark K1, cluster two is formed by clustering the problem parameter components whose values ​​are missing according to the category mark K2, cluster three is formed by clustering the problem parameter components whose values ​​are abnormal according to the category mark K3, and cluster four is formed by clustering the problem parameter components whose values ​​are redundant according to the category mark K4;

[0039] The hierarchical numerical arrangement is set to have different arrangement levels, including level 1, level 2 and level 3. The arrangement level is initially set to level 1 for all problem numerical clusters, so as to perform corresponding arrangement and obtain the corresponding achievement degree of arrangement, and define the value of the achievement degree as β, and set the ideal achievement degree, and record its value as χ;

[0040] Here, level one < level two < level three.

[0041] Preferably, the method for hierarchical numerical sorting includes: identifying the cluster category of the problem numerical cluster, that is, the cluster category includes cluster one, cluster two, cluster three and cluster four, performing the first sorting when the sorting level is level one, if the problem numerical cluster is cluster one, then converting all the problem parameter groups with wrong numerical categories in cluster one into pre-defined benchmark numerical categories, if the problem numerical cluster is cluster two, locking out the missing numerical values ​​corresponding to all the problem parameter groups with missing numerical values ​​in cluster two, converting the missing numerical values ​​into codeword numerical values, and taking the codeword averages of the horizontal and vertical rows where the codeword numerical values ​​are located as supplementary values, and supplementing the supplementary values ​​to the missing positions corresponding to the missing numerical values; if the problem numerical cluster is cluster three, registering the abnormal values ​​corresponding to all the problem parameter groups with abnormal numerical values ​​in cluster three, and executing discarding or refreshing to rational values; if the problem numerical cluster is cluster four, registering the numerical redundant fields at specific positions in all the problem parameter groups with redundant numerical values ​​in cluster four, and executing discarding on the numerical redundant fields;

[0042] After the sorting of several problem value clusters is achieved, the achievement degree corresponding to the first sorting is obtained. The value of the achievement degree β is the ratio of the total number of values ​​of the problem parameter group corresponding to the problem value cluster after sorting that are not value category errors, value omissions, value anomalies and value redundancy to the total number of parameters. The value of the ideal achievement degree χ represents the ideal value of the ratio of the total number of values ​​that are not value category errors, value omissions, value anomalies and value redundancy to the total number of parameters after sorting, that is, the minimum limiting value for the problem parameter group to become a reasonable parameter group;

[0043] If β<χ, the sorting level of the current problem value cluster is adjusted from level 1 to level 2 and then sorting is performed, and the values ​​of β and χ after sorting at level 2 are determined. At level 2, if β<χ, level 2 is adjusted to level 3 and then sorting is performed. After β≥χ is achieved at level 3, sorting is stopped and all problem parameter groups are turned into reasonable parameter groups. At level 2, if β≥χ, sorting of the problem value cluster is achieved and all problem parameter groups are turned into reasonable parameter groups.

[0044] If β≥χ, the sorting level is not revised. After performing the sorting at the level 1 sorting level, all the problematic parameter groups are transformed into reasonable parameter groups.

[0045] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0046] The collection of several real-time power parameters is executed through the set power parameter collection module to form a corresponding power parameter group, and the problematic parameter group and the reasonable parameter group are locked after the power parameter group is selected, and the problematic parameter group and the reasonable parameter group after the hierarchical numerical sorting are handled separately, which greatly enhances the diversity of the numerical values ​​and leads to better accuracy of the numerical values. In this way, the real-time power parameters collected by the central system are accurate, and ultimately the conditions for regulating power transactions and resource allocation are also accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the optimization method of the electricity market transaction described in the present invention;

[0048] Figure 2 It is a schematic diagram of the architecture of the optimization model for electricity market-based transactions described in the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in combination with the drawings in the embodiments of the present invention. The embodiments expressed in this application are only part of the embodiments of the present invention, not all of them. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0050] like Figure 1 As shown, the optimization method of the electricity market transaction described in the present invention includes:

[0051] Smart sensors sample real-time power parameters and transmit them to the central system. The central system organizes the collected real-time power parameters and then predicts the trend and fluctuation of future power demand, and finally stipulates the conditions for power trading and resource allocation. Real-time power parameters include real-time current, voltage, and frequency power parameters in the power system.

[0052] The method for the central system to collect and organize the real-time power parameters includes:

[0053] Step 1: Collect a number of real-time power parameters and perform reliable control on the collection process to form a power parameter group and transmit it to the power parameter processing module;

[0054] Step 2: Select whether the power parameter group meets the pre-defined selection items, so as to lock in the reasonable parameter group and the problematic parameter group;

[0055] Step 3: Perform hierarchical numerical sorting on the problem parameter groups.

[0056] like Figure 2 As shown, the optimization model of the power market transaction described in the present invention includes:

[0057] Smart sensors are connected to the central system for communication. Smart sensors are used to sample real-time power parameters and transmit them to the central system. The central system is used to organize the collected real-time power parameters and then predict the trend and fluctuation of future power demand, and finally determine the conditions for power trading and resource allocation. Real-time power parameters include real-time current, voltage, frequency and other power parameters in the power system.

[0058] In a preferred but non-limiting embodiment of the present invention, the real-time power parameters include real-time current, voltage, and frequency power parameters in the power system.

[0059] The modules running on the central system include:

[0060] Power parameter collection module and power parameter processing module;

[0061] The power parameter collection module is used to collect a number of real-time power parameters and perform reliable control on the collection process, thereby forming a power parameter group to be transmitted to the power parameter processing module;

[0062] The power parameter processing module includes a value selection submodule and a value arrangement submodule.

[0063] In a preferred but non-limiting embodiment of the present invention, the value selection submodule is used to select whether the power parameter group meets the pre-defined selection items, thereby locking out the reasonable parameter group and the problematic parameter group;

[0064] The numerical arrangement submodule is used to perform hierarchical numerical arrangement on the problem parameter group.

[0065] In a preferred but non-limiting embodiment of the present invention, a method for collecting a plurality of real-time power parameters and performing reliable control on the collection process to form a power parameter group includes:

[0066] Set the collection interval and define the collection interval as U 收取 , while U 收取 = [U1, U2], where U1 is the start time of the collection interval, and U2 is the stop time of the collection interval. At the start time of the collection interval, the collection of several real-time power parameters is performed, and a NULL group is concurrently formed at the start time;

[0067] The process of collecting a number of real-time power parameters is reliably controlled, the relevant URLs of the real-time power parameters are obtained, and it is determined whether the relevant URLs are pre-defined valid URLs. If so, the collecting process is numerically encoded, and the real-time power parameters are packaged into a NULL group. After the corresponding packaging is completed at the stop time, a corresponding power parameter group is formed, and the power parameter group is transmitted to the power parameter processing module. If not, the relevant URLs corresponding to the corresponding real-time power parameters are registered as abnormal value source URLs, and the real-time power parameters corresponding to the abnormal value source URLs are discarded.

[0068] In a preferred but non-limiting embodiment of the present invention, the relevant website is the website of the smart sensor which samples the corresponding real-time power parameter.

[0069] In a preferred but non-limiting embodiment of the present invention, the method of selecting whether the power parameter group meets the predefined selection items to lock in the reasonable parameter group and the problematic parameter group includes:

[0070] The pre-defined selection items include value type, interval constraints, abnormal value detection and error detection. The power parameter group is obtained and unpacked to obtain the corresponding value type, value interval and power value index of the power parameter group; the value type can be three types such as real-time current, voltage and frequency in the power system.

[0071] If the value type does not match the value type in the selected item, a code word '0' is generated, otherwise a code word '1' is generated. The interval constraint includes the maximum and minimum values ​​of the corresponding values ​​of the power parameter group. When the value interval does not match the interval constraint, a code word '0' is generated, otherwise a code word '1' is generated; matching means matching.

[0072] Gather code words '0' and '1' to form a selection code string, define the selection code string as Tu, and the values ​​of Tu are '00', '01', '10' and '11'. When the value of the selection code string is '00', '01' and '10', the corresponding power parameter group is locked as the problem parameter group;

[0073] If the value of the optional code string is '11', the abnormal value detection and the correctness detection are performed again.

[0074] In a preferred but non-limiting embodiment of the present invention, the method for detecting abnormal values ​​is: obtaining the completeness of the power value index (the completeness of the power value index is the ratio of the number of the obtained power value index to the total number of the power value index set in advance), and comparing it with the set ideal completeness. If the completeness matches the ideal completeness, then the abnormal value detection is successful, otherwise, it is unsuccessful;

[0075] The error detection method is: determine whether the power parameter group has a previous error value, if yes, then the error detection is unsuccessful, if not, then the error detection is successful;

[0076] When both the abnormal value detection and the error detection are successful, the power parameter group is locked as a reasonable parameter group. When either the abnormal value detection or the error detection is unsuccessful, the power parameter group is locked as a problematic parameter group.

[0077] In a preferred but non-limiting embodiment of the present invention, the numerical arrangement submodule performs a hierarchical numerical arrangement method on the problem parameter group, comprising:

[0078] The numerical value sorting submodule sets a grouping queue to obtain all problem parameter groups, thereby obtaining the problem categories corresponding to the problem parameter groups, wherein the problem categories include numerical value category errors, missing values, abnormal values, and redundant values;

[0079] The problem parameter groups of different problem categories are associated with corresponding category symbols, and the category symbols for defining value category errors, value omissions, value anomalies, and value redundancy are K1, K2, K3, and K4 respectively;

[0080] The clustering queue performs clustering processing on the problem parameter group according to the same category mark, thereby forming different problem value clusters, and the problem value clusters include cluster one, cluster two, cluster three and cluster four;

[0081] Here, cluster one is formed by clustering the problem parameter components whose value categories are wrong according to the category mark K1, cluster two is formed by clustering the problem parameter components whose values ​​are missing according to the category mark K2, cluster three is formed by clustering the problem parameter components whose values ​​are abnormal according to the category mark K3, and cluster four is formed by clustering the problem parameter components whose values ​​are redundant according to the category mark K4;

[0082] The hierarchical numerical arrangement is set to have different arrangement levels, including level 1, level 2 and level 3. The arrangement level is initially set to level 1 for all problem numerical clusters, so as to perform corresponding arrangement and obtain the corresponding achievement degree of arrangement, and define the value of the achievement degree as β, and set the ideal achievement degree, and record its value as χ;

[0083] Here, level one < level two < level three.

[0084] In a preferred but non-limiting embodiment of the present invention, the method of hierarchical numerical sorting includes: identifying the cluster category of the problem numerical cluster, that is, the cluster category includes cluster one, cluster two, cluster three and cluster four, performing the first sorting when the sorting level is level one, if the problem numerical cluster is cluster one, then converting all the problem parameter groups with wrong numerical categories in cluster one into pre-defined benchmark numerical categories, if the problem numerical cluster is cluster two, then locking out the missing numerical values ​​corresponding to all the problem parameter groups with missing numerical values ​​in cluster two, converting the missing numerical values ​​into codeword numerical values, and taking the codeword averages of the horizontal and vertical rows where the codeword numerical values ​​are located as supplementary values, and supplementing the supplementary values ​​to the missing positions corresponding to the missing numerical values; if the problem numerical cluster is cluster three, then registering the abnormal values ​​corresponding to all the problem parameter groups with abnormal numerical values ​​in cluster three and executing the discarding or refreshing to the rational values; if the problem numerical cluster is cluster four, then registering the numerical redundant fields at the specific positions in all the numerical redundant problem parameter groups in cluster four, and executing the discarding of the numerical redundant fields;

[0085] After the sorting of several problem value clusters is achieved, the achievement degree corresponding to the first sorting is obtained. The value of the achievement degree β is the ratio of the total number of values ​​of the problem parameter group corresponding to the problem value cluster after sorting that are not value category errors, value omissions, value anomalies and value redundancy to the total number of parameters. The value of the ideal achievement degree χ represents the ideal value of the ratio of the total number of values ​​that are not value category errors, value omissions, value anomalies and value redundancy to the total number of parameters after sorting, that is, the minimum limiting value for the problem parameter group to become a reasonable parameter group;

[0086] If β<χ, the sorting level of the current problem value cluster is adjusted from level 1 to level 2 and then sorting is performed, and the values ​​of β and χ after sorting at level 2 are determined. At level 2, if β<χ, level 2 is adjusted to level 3 and then sorting is performed. After β≥χ is achieved at level 3, sorting is stopped and all problem parameter groups are turned into reasonable parameter groups. At level 2, if β≥χ, sorting of the problem value cluster is achieved and all problem parameter groups are turned into reasonable parameter groups.

[0087] If β≥χ, the sorting level is not revised, and after sorting is performed at the sorting level of level 1, all the problem parameter groups are turned into reasonable parameter groups. The collected real-time power parameters are sorted and then used to predict the trend and fluctuation of future power demand. The power parameters for predicting the trend and fluctuation of future power demand are the sorted real-time power parameters.

[0088] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0089] The collection of several real-time power parameters is executed through the set power parameter collection module to form a corresponding power parameter group, and the problematic parameter group and the reasonable parameter group are locked after the power parameter group is selected, and the problematic parameter group and the reasonable parameter group after the hierarchical numerical sorting are handled separately, which greatly enhances the diversity of the numerical values ​​and leads to better accuracy of the numerical values. In this way, the real-time power parameters collected by the central system are accurate, and ultimately the conditions for regulating power transactions and resource allocation are also accurate.

[0090] The present disclosure can be a system, a method and / or a computer program product. The computer program product can include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement each aspect of the present disclosure.

[0091] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (such as a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0092] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0093] The computer program instructions for performing the operations of the present disclosure can be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer-readable program instructions can be executed entirely on the controller computer, partially on the controller computer, as a separate software package, partially on the controller computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the controller computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as connecting through the Internet using an Internet service provider). In some embodiments, each aspect of the present disclosure is implemented by utilizing state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), which can execute computer-readable program instructions.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An optimization method for power market transactions, characterized in that: include: Smart sensors sample real-time power parameters and transmit them to the central system, which then compiles the collected real-time power parameters to predict future power demand trends and fluctuations, and ultimately sets the conditions for power trading and resource allocation; The method for the central system to collect and organize the real-time power parameters includes: Step 1: Collect a number of real-time power parameters and perform reliable control on the collection process to form a power parameter group and transmit it to the power parameter processing module; Step 2: Select whether the power parameter group meets the pre-defined selection items, so as to lock in the reasonable parameter group and the problematic parameter group; Step 3: Perform hierarchical numerical sorting on the problem parameter groups.

2. An optimization model for electricity market transactions, characterized in that: include: Smart sensors are connected to the central system for communication. Smart sensors are used to sample real-time power parameters and transmit them to the central system. The central system is used to organize the collected real-time power parameters and then predict the trend and fluctuation of future power demand, and finally determine the conditions for power trading and resource allocation; The modules running on the central system include: Power parameter collection module and power parameter processing module; The power parameter collection module is used to collect a number of real-time power parameters and perform reliable control on the collection process, thereby forming a power parameter group to be transmitted to the power parameter processing module; The power parameter processing module includes a value selection submodule and a value arrangement submodule.

3. The optimization model for power market transactions according to claim 2 is characterized in that: The real-time power parameters include the real-time current, voltage and frequency power parameters in the power system.

4. The optimization model for power market transactions according to claim 3 is characterized in that: The value selection submodule is used to select whether the power parameter group meets the pre-defined selection items, thereby locking out the reasonable parameter group and the problematic parameter group; The numerical arrangement submodule is used to perform hierarchical numerical arrangement on the problem parameter group.

5. The optimization model for power market transactions according to claim 4 is characterized in that: A method for collecting a plurality of real-time power parameters and reliably controlling the collection process to form a power parameter group includes: Set the collection interval and define the collection interval as U 收取 , while U 收取 = [U1, U2], where U1 is the start time of the collection interval, and U2 is the stop time of the collection interval. At the start time of the collection interval, the collection of several real-time power parameters is performed, and a NULL group is concurrently formed at the start time; The process of collecting a number of real-time power parameters is reliably controlled, the relevant URLs of the real-time power parameters are obtained, and it is determined whether the relevant URLs are pre-defined valid URLs. If so, the collecting process is numerically encoded, and the real-time power parameters are packaged into a NULL group. After the corresponding packaging is completed at the stop time, a corresponding power parameter group is formed, and the power parameter group is transmitted to the power parameter processing module. If not, the relevant URLs corresponding to the corresponding real-time power parameters are registered as abnormal value source URLs, and the real-time power parameters corresponding to the abnormal value source URLs are discarded.

6. The optimization model for power market transactions according to claim 5 is characterized in that: The relevant URL is the URL of the smart sensor which samples the corresponding real-time power parameters.

7. The optimization model for power market transactions according to claim 6 is characterized in that: The method of selecting whether the power parameter group meets the predefined selection items, thereby locking out the reasonable parameter group and the problematic parameter group, includes: The predefined selection items include value type, interval constraint, abnormal value detection and error detection, and the power parameter group is obtained and unpacked to obtain the value type, value interval and power value index corresponding to the power parameter group; If the value type does not match the value type in the selected item, a code word '0' is generated, otherwise a code word '1' is generated, and the interval constraint includes the highest and lowest values ​​of the corresponding values ​​of the power parameter group. When the value interval does not match the interval constraint, a code word '0' is generated, otherwise a code word '1' is generated; Gather code words '0' and '1' to form a selection code string, define the selection code string as Tu, and the values ​​of Tu are '00', '01', '10' and '11'. When the value of the selection code string is '00', '01' and '10', the corresponding power parameter group is locked as the problem parameter group; If the value of the optional code string is '11', the abnormal value detection and the correctness detection are performed again.

8. The optimization model for power market transactions according to claim 7 is characterized in that: The method of abnormal value detection is: obtain the completeness of the power value index and compare it with the set ideal completeness. If the completeness matches the ideal completeness, then the abnormal value detection is successful, otherwise, it is unsuccessful; The error detection method is: determine whether the power parameter group has a previous error value, if yes, then the error detection is unsuccessful, if not, then the error detection is successful; When both the abnormal value detection and the error detection are successful, the power parameter group is locked as a reasonable parameter group. When either the abnormal value detection or the error detection is unsuccessful, the power parameter group is locked as a problematic parameter group.

9. The optimization model for power market transactions according to claim 8 is characterized in that: The method in which the numerical arrangement submodule performs hierarchical numerical arrangement on the problem parameter group comprises: The numerical value sorting submodule sets a grouping queue to obtain all problem parameter groups, thereby obtaining the problem categories corresponding to the problem parameter groups, wherein the problem categories include numerical value category errors, missing values, abnormal values, and redundant values; The problem parameter groups of different problem categories are associated with corresponding category symbols, and the category symbols for defining value category errors, value omissions, value anomalies, and value redundancy are K1, K2, K3, and K4 respectively; The clustering queue performs clustering processing on the problem parameter group according to the same category mark, thereby forming different problem value clusters, and the problem value clusters include cluster one, cluster two, cluster three and cluster four; Here, cluster one is formed by clustering the problem parameter components whose value categories are wrong according to the category mark K1, cluster two is formed by clustering the problem parameter components whose values ​​are missing according to the category mark K2, cluster three is formed by clustering the problem parameter components whose values ​​are abnormal according to the category mark K3, and cluster four is formed by clustering the problem parameter components whose values ​​are redundant according to the category mark K4; The hierarchical numerical arrangement is set to have different arrangement levels, including level 1, level 2 and level 3. The arrangement level is initially set to level 1 for all problem numerical clusters, so as to perform corresponding arrangement and obtain the corresponding achievement degree of arrangement, and define the value of the achievement degree as β, and set the ideal achievement degree, and record its value as χ; Here, level one < level two < level three.

10. The optimization model for power market transactions according to claim 9, characterized in that: The method of hierarchical numerical arrangement includes: identifying the cluster category of the problem numerical cluster, that is, the cluster category includes cluster one, cluster two, cluster three and cluster four, performing the first arrangement when the arrangement level is level one, if the problem numerical cluster is cluster one, then converting all the problem parameter groups with wrong numerical categories in cluster one into pre-defined benchmark numerical categories, if the problem numerical cluster is cluster two, locking out the missing numerical values ​​corresponding to all the problem parameter groups with missing numerical values ​​in cluster two, converting the missing numerical values ​​into codeword numerical values, and taking the codeword averages of the horizontal and vertical rows where the codeword numerical values ​​are located as supplementary values, and supplementing the supplementary values ​​to the missing positions corresponding to the missing numerical values; if the problem numerical cluster is cluster three, registering the abnormal values ​​corresponding to all the problem parameter groups with abnormal numerical values ​​in cluster three, and executing the discarding or refreshing to the rational values; if the problem numerical cluster is cluster four, registering the numerical redundant fields at the specific positions in all the numerical redundant problem parameter groups in cluster four, and executing the discarding of the numerical redundant fields; After the sorting of several problem value clusters is achieved, the achievement degree corresponding to the first sorting is obtained. The value of the achievement degree β is the ratio of the total number of values ​​of the problem parameter group corresponding to the problem value cluster after sorting that are not value category errors, value omissions, value anomalies and value redundancy to the total number of parameters. The value of the ideal achievement degree χ represents the ideal value of the ratio of the total number of values ​​that are not value category errors, value omissions, value anomalies and value redundancy to the total number of parameters after sorting, that is, the minimum limiting value for the problem parameter group to become a reasonable parameter group; If β<χ, the sorting level of the current problem value cluster is adjusted from level 1 to level 2 and then sorting is performed, and the values ​​of β and χ after sorting at level 2 are determined. At level 2, if β<χ, level 2 is adjusted to level 3 and then sorting is performed. After β≥χ is achieved at level 3, sorting is stopped and all problem parameter groups are turned into reasonable parameter groups. At level 2, if β≥χ, sorting of the problem value cluster is achieved and all problem parameter groups are turned into reasonable parameter groups. If β≥χ, the sorting level is not revised. After performing the sorting at the level 1 sorting level, all the problematic parameter groups are transformed into reasonable parameter groups.

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