Internet of Things trust analysis method and system for power market frequency regulation transactions

Abstract

The present application discloses a trust analysis method and system for the Internet of Things for power market frequency regulation transactions, which adopts a fuzzy probability calculation method for the trust of the Internet of Things for power market frequency regulation transactions. Its basic principle is: considering the data information formed by data transmission rate and data storage sharing, considering the data errors and missing formed by data collection, using the information sufficiency and missing amount to calculate the trust of the power Internet of Things, and evaluate the impact of trust on the power system frequency regulation transactions. This fuzzy probability calculation method for the trust of the Internet of Things for power market frequency regulation transactions simultaneously reflects the impact of the perception system, data transmission rate, and data storage sharing, provides theoretical guidance for the trust evaluation of the power Internet of Things for frequency regulation transactions, and provides necessary technical support for frequency regulation transactions based on the Internet of Things.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to an Internet of Things trust analysis method and system for power market frequency regulation transactions. Background Art

[0002] The three major application scenarios of 5G are a further deepening of the three major application scenarios of data transmission solutions in the power Internet of Things. Relevant experts and scholars at home and abroad have also divided the power Internet of Things business according to the three major application scenarios of 5G and carried out relevant research work.

[0003] The eMBB scenario mainly meets the needs of some high-bandwidth services, which is an enhancement of data collection application scenarios and business information transmission scenarios. At present, the application of the power Internet of Things in this regard is mainly the big video of the power grid, including substation robot inspection, transmission line drone online monitoring, distribution room video monitoring, mobile on-site construction operation control and emergency on-site self-organizing network comprehensive application. Many researchers have tried to apply 5G technology to certain scenarios and have achieved certain results.

[0004] In terms of trust calculation, there are many research results, such as: calculating the reputation value of nodes in the global scope through multiple iterations of the trust chain, introducing probability theory to solve the trust problem, and giving a trust model algorithm performance based on Bayesian network. The algorithm's time decay and penalty factor are considered when calculating trust, which improves the accuracy of the algorithm and its ability to resist strategic attacks. Trust is dynamically established and developed, and its development trend has certain rules. At the same time, there is a certain correlation between two adjacent transactions. The existing algorithms do not well reflect the time correlation, context correlation and development trend of trust. In order to better describe these characteristics of trust, on the basis of the existing Markov chain model, the steady-state probability is introduced to describe the development trend of trust. After each transaction is completed, the transfer matrix is ​​dynamically updated to more accurately describe the context correlation of trust. At the same time, the time decay factor and penalty incentive factor are added to improve the algorithm's ability to resist attacks. However, there is still little research on the existing technology of fuzzy probability calculation of IoT trust for power market frequency regulation transactions. Therefore, there is an urgent need for a fuzzy probability calculation method for IoT trust for power market frequency regulation transactions to calculate the trust of the power IoT and evaluate the impact of trust on power system frequency regulation transactions. Summary of the invention

[0005] The present application provides an Internet of Things trust analysis method and system for power market frequency regulation transactions, which are used to calculate the trust of the power Internet of Things and evaluate the impact of trust on power system frequency regulation transactions.

[0006] In view of this, the first aspect of the present application provides an Internet of Things trust analysis method for power market frequency regulation transactions, the method comprising:

[0007] The statistical analysis method is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system during the determined period; and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the hydropower units, thermal power units, nuclear power units, energy storage units and west-to-east power transmission units participating in the frequency regulation during the determined period;

[0008] According to the data transmission rate and data storage sharing scale data of the IoT monitoring data center, the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale.

[0009] According to the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the units participating in the frequency regulation, the frequency regulation power acquisition error of the units participating in the frequency regulation formed in the power Internet of Things is calculated; according to the frequency regulation price of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotations of the units participating in the frequency regulation, the frequency regulation quotation acquisition error of the units participating in the frequency regulation formed in the power Internet of Things is calculated;

[0010] According to the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data transmission rate and data storage sharing scale, the information benefit value of power Internet of Things in frequency regulation trading is calculated;

[0011] According to the frequency regulation power collection error of the frequency regulation units involved and the frequency regulation quotation collection error of the frequency regulation units involved, the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation units involved in the power Internet of Things is calculated;

[0012] According to the information gain value and the information loss value, the average value of the trust of all users in the power Internet of Things for power system frequency regulation transactions is calculated.

[0013] Optionally, the method further includes calculating an average value of trust of all users of the power Internet of Things for power system frequency regulation transactions in the power Internet of Things according to the information gain value and the information loss value, and then further including:

[0014] The trust level of the power Internet of Things is evaluated based on the average value of the trust of all users of the power Internet of Things.

[0015] Optionally, a statistical analysis method is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price of the power system in the determined time period, specifically including:

[0016] Using the IoT sensing system, the power market monitoring data center is used to obtain data information on the power system frequency regulation price;

[0017] According to the data information of the power system frequency regulation price, the statistical analysis method is used to calculate and determine the time period t, t=1,2,...,N e , N e is the number of time periods, and the three-dimensional trapezoidal fuzzy set of the power system frequency regulation price collection error

[0018]

[0019]

[0020]

[0021] In the formula, and are the three-dimensional trapezoidal fuzzy set of the frequency regulation price acquisition error of the power system in period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. j = 1, 2, 3, 4, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error of the power system in time period t.

[0022] Optionally, according to the data transmission rate of the IoT monitoring data center, a statistical analysis method is used to respectively calculate and determine the data transmission rate, and a plurality of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty, specifically including:

[0023] At the network layer of the power Internet of Things, the data transmission rate data is obtained through the Internet of Things monitoring data center;

[0024] The statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set v of nine fuzzy uncertainties of data transmission rate: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high Di , i=1,2,...,9:

[0025] v Di =(v DiL ,v DiM ,v DiU )=[(v DiL1 ,v DiL2 ,v DiL3 ,v DiL4 ;k DviL ),

[0026] (v DiM1 ,v DiM2 ,v DiM3 ,v DiM4;k DviM ),

[0027] (v DiU1 ,v DiU2 ,v DiU3 ,v DiU4 ;k DviU )];

[0028] In the formula, v Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, v DiL 、v DiM 、v DiU and k DviL , k DviM , k DviU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate, respectively. DiLj 、v DiMj 、v DiUj , j = 1, 2, 3, 4, which are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate respectively.

[0029] Optionally, the three-dimensional trapezoidal fuzzy set based on the frequency regulation power demand of the power system and the frequency regulation power collection error of the frequency regulation units involved in the frequency regulation is used to calculate the frequency regulation power collection error of the frequency regulation units involved in the power Internet of Things, specifically including:

[0030] The frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the frequency regulation units involved in the frequency regulation power acquisition error calculation formula are substituted to calculate the frequency regulation power acquisition error of the frequency regulation units involved in the power Internet of Things.

[0031]

[0032] In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power demand of the power system, They are the three-dimensional trapezoidal fuzzy sets of the acquisition errors of the frequency regulation power of hydropower units, thermal power units, nuclear power units, gas power units, energy storage units, and west-to-east power transmission units involved in frequency regulation. represents the union of fuzzy sets, and E[] is the expected value of [].

[0033] Optionally, the information benefit value of the power Internet of Things in frequency modulation trading is calculated based on the data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data storage sharing scale, specifically including:

[0034] Based on the information benefit value calculation formula, the information benefit value of the power Internet of Things in frequency regulation trading is calculated according to the data transmission rate and the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data storage and sharing scale.

[0035] The interest yield value calculation formula is:

[0036]

[0037] In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the Internet of Things providing data collection for hydropower units at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for thermal power units at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage unit at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, k Mi is the unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer; E[] is the expected value of []. Represents the union of 9 fuzzy sets.

[0038] Optionally, the calculating of the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units involved in the power Internet of Things according to the frequency regulation power collection errors of the frequency regulation units involved in the frequency regulation, specifically includes:

[0039] Substitute the frequency regulation power collection error and the frequency regulation quotation collection error of the frequency regulation units involved in the frequency regulation into the information loss value calculation formula, and calculate the information loss value L of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units involved in the power Internet of Things. RP ;

[0040] Wherein, the information loss value calculation formula is:

[0041]

[0042] Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things will cause the information loss value of the frequency regulation transaction; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation units, Collect frequency quotation errors for frequency regulation units.

[0043] Optionally, the calculating, according to the information gain value and the information loss value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for power system frequency regulation transactions specifically includes:

[0044] Substituting the information gain value and the information loss value into the average value calculation formula, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for power system frequency regulation transactions is calculated;

[0045] Wherein, the average value calculation formula is:

[0046]

[0047] In the formula, B I is the average trust of all users in the power Internet of Things, R RP is the information benefit value, L RPis the information loss value.

[0048] The second aspect of the present application provides an Internet of Things trust analysis system for power market frequency regulation transactions, the system comprising:

[0049] The first calculation unit is used to use a statistical analysis method to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system during the determined period; and calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the hydropower units, thermal power units, nuclear power units, energy storage units and west-to-east power transmission units participating in the frequency regulation during the determined period;

[0050] The second calculation unit is used to calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale by using a statistical analysis method according to the data transmission rate and data storage sharing scale data of the Internet of Things monitoring data center;

[0051] The third calculation unit is used to calculate the frequency regulation power acquisition error of the participating frequency regulation units formed in the power Internet of Things according to the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the participating frequency regulation units; calculate the frequency regulation quotation acquisition error of the participating frequency regulation units formed in the power Internet of Things according to the frequency regulation price of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotation of the participating frequency regulation units;

[0052] A fourth calculation unit is used to calculate the information benefit value of the power Internet of Things in frequency regulation trading according to a data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale;

[0053] A fifth calculation unit is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things according to the frequency regulation power collection errors of the frequency regulation units and the frequency regulation quotation collection errors of the frequency regulation units;

[0054] The sixth calculation unit is used to calculate the average value of the trust of all users in the power Internet of Things for power system frequency regulation transactions based on the information gain value and the information loss value.

[0055] Optionally, it further comprises: an evaluation unit;

[0056] The evaluation unit is used to evaluate the trust level of the power Internet of Things based on the average value of the trust of all users of the power Internet of Things.

[0057] It can be seen from the above technical solutions that this application has the following advantages:

[0058] Through the Internet of Things trust analysis method for power market frequency regulation transactions proposed in this application, the trust of the power Internet of Things for frequency regulation transactions can be calculated. This fuzzy probability calculation method for the trust of the Internet of Things for power market frequency regulation transactions simultaneously reflects the influence of the perception system, data transmission rate, and data storage sharing, providing theoretical guidance for the trust evaluation of the power Internet of Things for frequency regulation transactions and providing necessary technical support for frequency regulation transactions based on the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of an Internet of Things trust analysis method for power market frequency regulation transactions provided in an embodiment of the present application;

[0060] Figure 2 This is a structural diagram of an Internet of Things trust analysis system for power market frequency regulation transactions provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] See also Figure 1 , an Internet of Things trust analysis method for power market frequency regulation transactions provided in an embodiment of the present application includes:

[0063] Step 101, using statistical analysis method to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and frequency regulation power demand of the power system during the determined period; and calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the hydropower units, thermal power units, nuclear power units, energy storage units and west-to-east power transmission units involved in the frequency regulation during the determined period;

[0064] It should be noted that the various three-dimensional trapezoidal fuzzy set calculation methods in step 101 specifically include:

[0065] 1. Use the IoT sensing system to obtain relevant data information on the power system frequency regulation price from the power market monitoring data center. According to the data on the power system frequency regulation price obtained from the power market, use statistical analysis methods to calculate and determine the time period t (t = 1, 2, ..., N e , N e The three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error of the power system (time period number)

[0066]

[0067]

[0068]

[0069] In the formula, and are the three-dimensional trapezoidal fuzzy set of the frequency regulation price acquisition error of the power system in period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error of the power system in time period t.

[0070] 2. Use the IoT sensing system to obtain relevant data information on the power system frequency modulation power demand from the power market monitoring data center. According to the data on the power system frequency modulation power demand obtained from the power market, use statistical analysis methods to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set for power system frequency regulation power demand acquisition error

[0071]

[0072]

[0073]

[0074] In the formula, and are the three-dimensional trapezoidal fuzzy set of the power demand for frequency regulation of the power system during period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand acquisition error of the power system in time period t.

[0075] 3. Use the IoT sensing system to obtain the frequency modulation power data of the hydropower units participating in the frequency modulation from the power market monitoring data center. According to the frequency modulation power data of the hydropower units participating in the frequency modulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set of frequency regulation power acquisition error of hydropower units involved in frequency regulation

[0076]

[0077]

[0078]

[0079] In the formula, and are the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set of the hydropower units participating in frequency regulation in period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the hydropower units participating in frequency regulation in time period t.

[0080] 4. Use the IoT sensing system to obtain relevant data information on the quotations of hydropower units participating in frequency regulation from the power market monitoring data center. According to the quotations of hydropower units participating in frequency regulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set of bid collection error of hydropower units involved in frequency regulation

[0081]

[0082]

[0083]

[0084] In the formula, and are the three-dimensional trapezoidal fuzzy set of the bid collection error of the hydropower units participating in frequency regulation in time period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the quotation collection error of the hydropower units participating in frequency regulation during time period t.

[0085] 5. Use the IoT sensing system to obtain the frequency modulation power data of the thermal power units participating in the frequency modulation from the power market monitoring data center. According to the frequency modulation power data of the thermal power units participating in the frequency modulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of frequency regulation power quantity acquisition error of thermal power units involved in frequency regulation

[0086]

[0087]

[0088]

[0089] In the formula, and are the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set or the fuzzy number and membership coefficient corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set for the thermal power units participating in frequency regulation in time period t, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the thermal power units participating in frequency regulation in time period t.

[0090] 6. Use the IoT sensing system to obtain the relevant data information of the quotations of thermal power units participating in frequency regulation from the power market monitoring data center. According to the quotations of thermal power units participating in frequency regulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of the bid collection error of thermal power units participating in frequency regulation

[0091]

[0092]

[0093]

[0094] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the thermal power units participating in the frequency regulation in time period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the quotation collection error of the thermal power units participating in frequency regulation during time period t.

[0095] 7. Use the IoT sensing system to obtain the frequency modulation power data of the nuclear power units participating in the frequency modulation from the power market monitoring data center. According to the frequency modulation power data of the nuclear power units participating in the frequency modulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of nuclear power units involved in frequency regulation

[0096]

[0097]

[0098]

[0099] In the formula, and are the frequency modulation power acquisition error three-dimensional trapezoidal fuzzy set or the fuzzy number and membership coefficient corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set for the nuclear power units participating in frequency modulation in time period t, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the nuclear power units participating in frequency regulation in time period t.

[0100] 8. Use the IoT sensing system to obtain the relevant data information of the quotations of nuclear power units participating in frequency regulation from the power market monitoring data center. According to the quotations of nuclear power units participating in frequency regulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of quotation collection errors of nuclear power units participating in frequency regulation

[0101]

[0102]

[0103]

[0104] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the nuclear power units participating in the frequency regulation in time period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the quotation collection error of the nuclear power units participating in frequency regulation in time period t.

[0105] 9. Use the IoT sensing system to obtain the frequency modulation power data of the gas-fired units participating in the frequency modulation from the power market monitoring data center. According to the frequency modulation power data of the gas-fired units participating in the frequency modulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set of frequency modulation power acquisition error of gas-fired generator units involved in frequency modulation

[0106]

[0107]

[0108]

[0109] In the formula, and are the frequency modulation power acquisition error three-dimensional trapezoidal fuzzy set of the gas-fired generator units participating in frequency modulation in time period t, or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency modulation power acquisition error of the gas-fired generator units participating in frequency modulation in time period t.

[0110] 10. Use the IoT sensing system to obtain relevant data information on the quotations of gas-fired units participating in frequency regulation from the power market monitoring data center. According to the quotations of gas-fired units participating in frequency regulation in the power market, a statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set of price quotation collection error of gas-fired generator sets involved in frequency regulation

[0111]

[0112]

[0113]

[0114] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the gas-fired generator units participating in frequency modulation in time period t, or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the quotation collection error of the gas-fired power units participating in frequency modulation during time period t.

[0115] 11. Use the IoT sensing system to obtain the frequency regulation power data of the energy storage units participating in the frequency regulation from the power market monitoring data center. According to the frequency regulation power data of the energy storage units participating in the frequency regulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of energy storage units involved in frequency regulation

[0116]

[0117]

[0118]

[0119] In the formula, and are the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set or the fuzzy number and membership coefficient corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set for the energy storage unit participating in frequency regulation in time period t, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the energy storage unit participating in frequency regulation in time period t.

[0120] 12. Use the IoT sensing system to obtain the relevant data information of the quotations of the energy storage units participating in the frequency regulation from the power market monitoring data center. According to the quotations of the energy storage units participating in the frequency regulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of quotation collection error of energy storage units participating in frequency regulation

[0121]

[0122]

[0123]

[0124] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the energy storage unit participating in the frequency regulation in time period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the quotation collection error of the energy storage units participating in frequency regulation during time period t.

[0125] 13. Use the IoT sensing system to obtain the frequency modulation power data of the West-East power transmission units participating in the frequency modulation from the power market monitoring data center. According to the frequency modulation power data of the West-East power transmission units participating in the frequency modulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set of frequency regulation power acquisition error of West-East power transmission units involved in frequency regulation

[0126]

[0127]

[0128]

[0129] In the formula, and are the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set or the fuzzy number and membership coefficient corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set for the west-to-east power transmission units participating in frequency regulation in period t, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the west-to-east power transmission units participating in frequency regulation in time period t.

[0130] 14. Use the IoT sensing system to obtain the relevant data information of the price quotes of the West-East power transmission units participating in the frequency regulation from the power market monitoring data center. According to the price quotes of the West-East power transmission units participating in the frequency regulation in the power market, the statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of bid collection errors of West-East power transmission units participating in frequency regulation

[0131]

[0132]

[0133]

[0134] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the west-to-east power transmission units participating in frequency regulation in time period t, or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal set, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the quotation collection error of the west-to-east power transmission units participating in frequency regulation during time period t.

[0135] Step 102: According to the data transmission rate and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale;

[0136] It should be noted that the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data transmission rate are calculated and determined, specifically including:

[0137] In the network layer of the power Internet of Things, the relevant data information of the data transmission rate is obtained from the Internet of Things monitoring data center, and the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set v of nine fuzzy uncertainties of data transmission rate: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di (i=1,2,...,9):

[0138] v Di =(v DiL ,v DiM ,v DiU )=[(v DiL1 ,v DiL2 ,v DiL3 ,v DiL4 ;k DviL ),

[0139] (v DiM1 ,v DiM2 ,v DiM3 ,v DiM4 ;k DviM ),

[0140] (v DiU1 ,v DiU2 ,v DiU3 ,v DiU4 ;k DviU )];

[0141] In the formula, v Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, v DiL 、v DiM 、v DiU and k DviL , k DviM , k DviU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate, respectively. DiLj 、v DiMj 、v DiUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate.

[0142] It should be noted that the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data storage sharing scale are calculated and determined, including:

[0143] At the platform layer of the power Internet of Things, relevant data information on the scale of data storage sharing is obtained from the Internet of Things monitoring data center, and a statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set S of nine fuzzy uncertainties: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di (i=1,2,...,9):

[0144] S Di =(S DiL ,S DiM ,S DiU )=[(S DiL1 ,S DiL2 ,S DiL3 ,S DiL4 ;k DSiL ),

[0145] (S DiM1 ,S DiM2 ,S DiM3 ,S DiM4 ;k DSiM ),

[0146] (S DiU1 ,S DiU2 ,S DiU3 ,S DiU4 ;k DSiU )]

[0147] In the formula, S Di is the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiL , S DiM , S DiU and k DSiL , k DSiM , k DSiU They are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiLj , S DiMj , S DiUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data storage sharing scale respectively.

[0148] Step 103: Calculate the frequency regulation power acquisition error of the frequency regulation units involved in the power Internet of Things based on the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the units involved in the frequency regulation; Calculate the frequency regulation quotation acquisition error of the units involved in the power Internet of Things based on the frequency regulation price of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotation of the units involved in the frequency regulation;

[0149] It should be noted that, according to the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the frequency regulation power collection error of the frequency regulation units involved in the frequency regulation, the frequency regulation power collection error of the frequency regulation units involved in the power Internet of Things is calculated, specifically including:

[0150] The frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things is jointly determined by the frequency regulation power demand of the power system and the frequency regulation power collection error of various generator sets. The frequency regulation power collection error of various generator sets includes the frequency regulation power of hydropower units participating in frequency regulation, the frequency regulation power of thermal power units participating in frequency regulation, the frequency regulation power of nuclear power units participating in frequency regulation, the frequency regulation power of gas power units participating in frequency regulation, the frequency regulation power of energy storage units participating in frequency regulation, and the frequency regulation power collection error of west-to-east power transmission units participating in frequency regulation. Therefore, the frequency regulation power collection error of the frequency regulation units participating in the power Internet of Things is is calculated according to the following formula:

[0151]

[0152] In the formula Represents the union of fuzzy sets.

[0153] It should be noted that, according to the frequency regulation price of the power system and the three-dimensional trapezoidal fuzzy set of the collection error of the quotations of the units participating in the frequency regulation, the frequency regulation quotation collection error of the units participating in the frequency regulation formed in the power Internet of Things is calculated, specifically including:

[0154] The frequency regulation quotation collection error of the units participating in frequency regulation formed in the power Internet of Things is jointly determined by the frequency regulation power demand of the power system and the frequency regulation quotation collection error of various generating units. The frequency regulation quotation collection error of various generating units includes the frequency regulation quotation of hydropower units participating in frequency regulation, the frequency regulation quotation of thermal power units participating in frequency regulation, the frequency regulation quotation of nuclear power units participating in frequency regulation, the frequency regulation quotation of gas power units participating in frequency regulation, the frequency regulation quotation of energy storage units participating in frequency regulation, and the frequency regulation quotation collection error of the west-to-east power transmission units participating in frequency regulation. Therefore, the frequency regulation quotation collection error of the units participating in frequency regulation formed in the power Internet of Things is calculated according to the following formula:

[0155]

[0156] Step 104, calculating the information benefit value of the power Internet of Things in frequency modulation trading according to the data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale;

[0157] It should be noted that at the perception layer, the perception system is used to obtain data information such as electricity market prices, power demand for power system frequency regulation and its quotation, and frequency regulation power of various generator sets and their quotation. At the platform layer, with the help of the big data system, the sharing and interaction of power system frequency regulation data can be realized. At the network layer, with the help of the network system, data transmission can be realized, so that frequency regulation users can use the Internet of Things to obtain sufficient information and gain benefits. The information benefit value R of the power Internet of Things in frequency regulation transactions RP Calculate according to the following formula:

[0158]

[0159] Where R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the Internet of Things providing data collection for hydropower units at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for thermal power units at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage unit at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, k Mi is the unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer; E[] is the expected value of []. Represents the union of 9 fuzzy sets.

[0160] M H 、M T 、M N 、M G 、M ES 、M X It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows:

[0161] M H =(M HL ,M HM ,M HU)=[(M HL1 ,M HL2 ,M HL3 ,M HL4 ;k HL ),(M HM1 ,M HM2 ,M HM3 ,M HM4 ;k HM ),(M HU1 ,M HU2 ,M HU3 ,M HU4 ;k HU )]

[0162] M T =(M TL ,M TM ,M TU )=[(M TL1 ,M TL2 ,M TL3 ,M TL4 ;k HL ),(M TM1 ,M TM2 ,M TM3 ,M TM4 ;k TM ),(M TU1 ,M TU2 ,M TU3 ,M TU4 ;k TU )]

[0163] M N =(M NL ,M NM ,M NU )=[(M NL1 ,M NL2 ,M NL3 ,M NL4 ;k NL ),(M NM1 ,M NM2 ,M NM3 ,M NM4 ;k NM ),(M NU1 ,M NU2 ,M NU3 ,M NU4 ;k NU )]

[0164] M G =(M GL ,M GM ,M GU )=[(M GL1 ,M GL2 ,MGL3 ,M GL4 ;k GL ),(M GM1 ,M GM2 ,M GM3 ,M GM4 ;k GM ),(M GU1 ,M GU2 ,M GU3 ,M GU4 ;k GU )]

[0165] M ES =(M ESL ,M ESM ,M ESU )=[(M ESL1 ,M ESL2 ,M ESL3 ,M ESL4 ;k ESL ),(M ESM1 ,M ESM2 ,M ESM3 ,M ESM4 ;k ESM ),(M ESU1 ,M ESU2 ,M ESU3 ,M ESU4 ;k ESU )]

[0166] M X =(M XL ,M XM ,M XU )=[(M XL1 ,M XL2 ,M XL3 ,M XL4 ;k XL ),(M XM1 ,M XM2 ,M XM3 ,M XM4 ;k XM ),(M XU1 ,M XU2 ,M XU3 ,M XU4 ;k XU )]

[0167] Where M H The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for hydropower units at the perception layer, M HL 、M HM 、M HU and k HL , k HM , k HUThe fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for the hydropower unit at the perception layer, respectively. HLj 、M HMj 、M HUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for the hydropower units at the perception layer; M T The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer, M TL 、M TM 、M TU and k TL , k TM , k TU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the thermal power unit in the perception layer. TLj 、M TMj 、M TUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer; M N The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for nuclear power units at the perception layer, M NL 、M NM 、M NU and k NL , k NM , k NU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for nuclear power units at the perception layer, respectively. NLj 、M NMj 、M NUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for nuclear power units at the perception layer; M G The three-dimensional trapezoidal fuzzy set of data collection scale is provided for gas-fired units in the perception layer of the Internet of Things. GL 、M GM 、M GU and k GL , k GM , k GU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the gas-fired unit in the perception layer, respectively. GLj 、M GMj 、M GUj(j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for the gas-fired generator set at the perception layer; M ES The three-dimensional trapezoidal fuzzy set of the data collection scale is provided for the energy storage motor group in the perception layer of the Internet of Things. ESL 、M ESM 、M ESU and k ESL , k ESM , k ESU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the energy storage motor group in the perception layer. ESLj 、M ESMj 、M ESUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for the energy storage generator group at the perception layer; M X The three-dimensional trapezoidal fuzzy set of the data collection scale is provided for the West-East Power Transmission Unit in the perception layer of the Internet of Things. XL 、M XM 、M XU and k XL , k XM , k XU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set for the data collection scale provided by the Internet of Things for the West-East Power Transmission Unit at the perception layer, respectively. XLj 、M XMj 、M XUj (j=1,2,3,4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things at the perception layer for the West-East Power Transmission Units.

[0168] Step 105: Calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things according to the frequency regulation power collection errors of the frequency regulation units and the frequency regulation quotation collection errors of the frequency regulation units;

[0169] It should be noted that the frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things will cause information loss in frequency regulation transactions, and its value is calculated according to the following formula:

[0170]

[0171] Where L RP The frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things will cause the information loss value of the frequency regulation transaction; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the error in collecting frequency quotation of frequency regulation units in the power Internet of Things.

[0172] Step 106: Calculate the average trust of all users in the power Internet of Things for power system frequency regulation transactions based on the information gain value and the information loss value.

[0173] It should be noted that in the power Internet of Things for power system frequency regulation transactions, users at the perception layer, network layer and platform layer provide information and benefit users. From this, the average trust of the power Internet of Things for all users can be calculated, and the calculation formula is:

[0174]

[0175] In one embodiment, the Internet of Things trust analysis method for power market frequency regulation transactions of the present application further includes:

[0176] The trust level of the power Internet of Things is evaluated based on the average value of the trust of all users of the power Internet of Things.

[0177] It should be noted that the trust level of all users can be evaluated based on the calculated average trust of the power Internet of Things:

[0178] When B I =1, the power Internet of Things is fully adapted to the application of power system frequency regulation transactions.

[0179] When 0.8≤B I When <1, the power Internet of Things is well adapted to the application of power system frequency regulation trading.

[0180] When 0.5≤B I When <0.8, the power Internet of Things is better applied in power system frequency regulation trading.

[0181] When 0.3≤B I When <0.5, the power Internet of Things is suitable for the application of power system frequency regulation trading.

[0182] When 0≤B I When <0.3, the power Internet of Things cannot adapt to the application of power system frequency regulation trading.

[0183] The Internet of Things trust analysis method for power market frequency regulation transactions provided in the embodiment of the present application adopts a fuzzy probability calculation method for the Internet of Things trust for power market frequency regulation transactions. The basic principle of the fuzzy probability calculation of the Internet of Things trust for power market frequency regulation transactions is: considering the data information formed by data transmission rate and data storage sharing, considering the data errors and missing formed by data collection, using the information sufficiency and missing amount to calculate the trust of the power Internet of Things, and evaluating the impact of the trust on the power system frequency regulation transactions.

[0184] The above is an Internet of Things trust analysis method for power market frequency regulation transactions provided in an embodiment of the present application, and the following is an Internet of Things trust analysis system for power market frequency regulation transactions provided in an embodiment of the present application.

[0185] See also Figure 2 , an Internet of Things trust analysis system for power market frequency regulation transactions provided in an embodiment of the present application includes:

[0186] The first calculation unit 201 is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in the determined period by using the statistical analysis method; and calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and the quotation of the hydropower units, thermal power units, nuclear power units, energy storage units and west-to-east power transmission units involved in the frequency regulation in the determined period;

[0187] The second calculation unit 202 is used to calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale by using a statistical analysis method according to the data transmission rate and data storage sharing scale data of the Internet of Things monitoring data center;

[0188] The third calculation unit 203 is used to calculate the frequency regulation power acquisition error of the participating frequency regulation units formed in the power Internet of Things according to the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the participating frequency regulation units; calculate the frequency regulation quotation acquisition error of the participating frequency regulation units formed in the power Internet of Things according to the frequency regulation price of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotation of the participating frequency regulation units;

[0189] The fourth calculation unit 204 is used to calculate the information benefit value of the power Internet of Things in the frequency modulation transaction according to the data transmission rate and the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data storage sharing scale;

[0190] The fifth calculation unit 205 is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things according to the frequency regulation power collection errors of the frequency regulation units and the frequency regulation quotation collection errors of the frequency regulation units;

[0191] The sixth calculation unit 206 is used to calculate the average value of the trust of all users in the power Internet of Things for power system frequency regulation transactions based on the information gain value and the information loss value.

[0192] In one embodiment, the Internet of Things trust analysis system for power market frequency regulation transactions of the present application further includes:

[0193] The evaluation unit is used to evaluate the trust level of the power Internet of Things according to the average value of the trust of the power Internet of Things to all users.

[0194] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0195] The terms "first", "second", "third", "fourth", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0196] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0197] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0198] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0199] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0200] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0201] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An Internet of Things trust analysis method for power market frequency regulation transactions, characterized in that: include: The statistical analysis method is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in the determined period. And calculate the three-dimensional trapezoidal fuzzy set of the frequency regulation power and quotation collection error of the hydropower units, thermal power units, nuclear power units, energy storage units and west-to-east power transmission units involved in frequency regulation during the determined period; According to the data transmission rate and data storage sharing scale data of the IoT monitoring data center, the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale. According to the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the units participating in the frequency regulation, the frequency regulation power acquisition error of the units participating in the frequency regulation formed in the power Internet of Things is calculated; according to the frequency regulation price of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotations of the units participating in the frequency regulation, the frequency regulation quotation acquisition error of the units participating in the frequency regulation formed in the power Internet of Things is calculated; According to the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data transmission rate and data storage sharing scale, the information benefit value of power Internet of Things in frequency regulation trading is calculated; According to the frequency regulation power collection error of the frequency regulation units involved and the frequency regulation quotation collection error of the frequency regulation units involved, the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation units involved in the power Internet of Things is calculated; According to the information gain value and the information loss value, calculating the average trust of the power Internet of Things for all users in the power Internet of Things for power system frequency regulation transactions; in: The information benefit value of the power Internet of Things in frequency modulation trading is calculated based on the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale, specifically including: Based on the information benefit value calculation formula, the information benefit value of the power Internet of Things in frequency regulation trading is calculated according to the data transmission rate and the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data storage and sharing scale. The information benefit value calculation formula is: In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the Internet of Things providing data collection for hydropower units at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for thermal power units at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage unit at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, k Mi is the unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer; E[] is the expected value of []. represents the union of 9 fuzzy sets; The frequency regulation power amount and quotation collection error of the frequency regulation units involved in the frequency regulation are calculated in the power Internet of Things, and the information loss value of the frequency regulation transaction caused by the frequency regulation power amount and quotation collection error of the frequency regulation units involved in the frequency regulation is specifically calculated as follows: Substitute the frequency regulation power collection error and the frequency regulation quotation collection error of the frequency regulation units involved in the frequency regulation into the information loss value calculation formula, and calculate the information loss value L of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units involved in the power Internet of Things. RP ; Wherein, the information loss value calculation formula is: Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things will cause the information loss value of the frequency regulation transaction; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation units, Collect frequency quotation errors for frequency regulation units.

2. The Internet of Things trust analysis method for power market frequency regulation transactions according to claim 1 is characterized in that: The method further comprises calculating the average trust of all users of the power Internet of Things for power system frequency regulation transactions according to the information gain value and the information loss value, and then further comprising: The trust level of the power Internet of Things is evaluated based on the average value of the trust of all users of the power Internet of Things.

3. The Internet of Things trust analysis method for power market frequency regulation transactions according to claim 1 is characterized in that: The statistical analysis method is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price of the power system in the determined time period, including: Using the IoT sensing system, the power market monitoring data center is used to obtain data information on the power system frequency regulation price; According to the data information of the power system frequency regulation price, the statistical analysis method is used to calculate and determine the time period t, t=1,2,...,N e , N e is the number of time periods, and the three-dimensional trapezoidal fuzzy set of the power system frequency regulation price collection error In the formula, and are the three-dimensional trapezoidal fuzzy set of the frequency regulation price acquisition error of the power system in period t and the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. j = 1, 2, 3, 4, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error of the power system in time period t.

4. The Internet of Things trust analysis method for power market frequency regulation transactions according to claim 1 is characterized in that: According to the data transmission rate of the IoT monitoring data center, the statistical analysis method is used to calculate and determine the data transmission rate and several three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty, including: At the network layer of the power Internet of Things, the data transmission rate data is obtained through the Internet of Things monitoring data center; The statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set v of nine fuzzy uncertainties of data transmission rate: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high Di , i=1,2,...,9: v Di =(v DiL ,v DiM ,v DiU )=[(v DiL1 ,v DiL2 ,v DiL3 ,v DiL4 ;k DviL ), (v DiM1 ,v DiM2 ,v DiM3 ,v DiM4 ;k DviM ), (v DiU1 ,v DiU2 ,v DiU3 ,v DiU4 ;k DviU )]; In the formula, v Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, v DiL 、v DiM 、v DiU and k DviL , k DviM , k DviU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate, respectively, DiLj 、v DiMj 、v DiUj , j = 1, 2, 3, 4, which are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate respectively.

5. The Internet of Things trust analysis method for power market frequency regulation transactions according to claim 1 is characterized in that: The method of calculating the frequency regulation power acquisition error of the frequency regulation units involved in the power Internet of Things based on the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the units involved in the frequency regulation, specifically includes: The frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the frequency regulation units involved in the frequency regulation power acquisition error calculation formula are substituted to calculate the frequency regulation power acquisition error of the frequency regulation units involved in the power Internet of Things. In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power demand of the power system, They are the three-dimensional trapezoidal fuzzy sets of the acquisition errors of the frequency regulation power of hydropower units, thermal power units, nuclear power units, gas power units, energy storage units, and west-to-east power transmission units involved in frequency regulation. represents the union of fuzzy sets, and E[] is the expected value of [].

6. The Internet of Things trust analysis method for power market frequency regulation transactions according to claim 1 is characterized in that: The calculating, according to the information gain value and the information loss value, the average value of the trust of the power Internet of Things for all users in the power Internet of Things for power system frequency regulation transactions specifically includes: Substituting the information gain value and the information loss value into the average value calculation formula, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for power system frequency regulation transactions is calculated; Wherein, the average value calculation formula is: In the formula, B I is the average trust of all users in the power Internet of Things, R RP is the information benefit value, L RP is the information loss value.

7. An Internet of Things trust analysis system for power market frequency regulation transactions, characterized in that: include: The first calculation unit is used to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in a certain period of time by using a statistical analysis method; And calculate the three-dimensional trapezoidal fuzzy set of the frequency regulation power and quotation collection error of the hydropower units, thermal power units, nuclear power units, energy storage units and west-to-east power transmission units involved in frequency regulation during the determined period; The second calculation unit is used to calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale by using a statistical analysis method according to the data transmission rate and data storage sharing scale data of the Internet of Things monitoring data center; The third calculation unit is used to calculate the frequency regulation power acquisition error of the participating frequency regulation units formed in the power Internet of Things according to the frequency regulation power demand of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the participating frequency regulation units; calculate the frequency regulation quotation acquisition error of the participating frequency regulation units formed in the power Internet of Things according to the frequency regulation price of the power system and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotation of the participating frequency regulation units; A fourth calculation unit is used to calculate the information benefit value of the power Internet of Things in frequency regulation trading according to a data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale; A fifth calculation unit is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things according to the frequency regulation power collection errors of the frequency regulation units and the frequency regulation quotation collection errors of the frequency regulation units; a sixth calculation unit, configured to calculate, according to the information gain value and the information loss value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for power system frequency regulation transactions; in: The fourth computing unit is specifically configured to: Based on the information benefit value calculation formula, the information benefit value of the power Internet of Things in frequency regulation trading is calculated according to the data transmission rate and the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data storage and sharing scale. The information benefit value calculation formula is: In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the Internet of Things providing data collection for hydropower units at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for thermal power units at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage unit at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, k Mi The unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer: E[] is the expected value of []. represents the union of 9 fuzzy sets; The fifth computing unit is specifically configured to: Substitute the frequency regulation power collection error and the frequency regulation quotation collection error of the frequency regulation units involved in the frequency regulation into the information loss value calculation formula, and calculate the information loss value L of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units involved in the power Internet of Things. RP ; Wherein, the information loss value calculation formula is: Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things will cause the information loss value of the frequency regulation transaction; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation units, Collect frequency quotation errors for frequency regulation units.

8. The Internet of Things trust analysis system for power market frequency regulation transactions according to claim 7 is characterized in that: Also includes: Assessment Unit; The evaluation unit is used to evaluate the trust level of the power Internet of Things based on the average value of the trust of all users of the power Internet of Things.

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