Remote control and intelligent settlement integrated system based on energy digital base

Abstract

The present application belongs to the technical field of smart grid regulation, and specifically relates to a remote regulation and intelligent settlement integrated system based on an energy digital base, which receives current peak regulation early warning, audits the predicted peak regulation period and predicted peak regulation load reported by the current peak regulation early warning, verifies the authenticity of the target area current peak regulation early warning, performs cooperation qualification auditing on each peak regulation intention user collected in advance, screens each qualified peak regulation participating user that can respond to the target area current peak regulation early warning, reduces the default risk at the user peak regulation response level, then formulates each implementable peak regulation scheme corresponding to the target area current peak regulation early warning according to the peak regulation load collected in advance by each qualified peak regulation participating user, screens the adaptive peak regulation scheme corresponding to the target area current peak regulation early warning according to the short-term economic benefit coefficient and long-term economic benefit coefficient of each implementable peak regulation scheme relative to the power grid, and realizes high precision and effectiveness of user-side peak regulation management.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of smart grid regulation, and specifically relates to a remote regulation and intelligent settlement integrated system based on an energy digital base. BACKGROUND

[0002] In today's power system, with the rapid growth of energy demand and large-scale grid connection of renewable energy, the power grid operation environment has become increasingly complex. On the one hand, power consumers' demand for reliability, stability and personalized services of power supply continues to rise, and on the other hand, the access of intermittent energy sources such as wind and solar energy significantly increases the difficulty of power grid balancing and dispatching. In the face of this situation, power grid regulation cannot be limited to traditional peak shaving on the generation side, and the peak shaving potential on the user side is gradually attracting the attention of the industry, but how to achieve highly effective management of user-side peak shaving has become a key problem to be solved.

[0003] There are also some related solutions in the prior art that involve user-side peak shaving management, for example, a compensation and allocation calculation method for peak shaving auxiliary service cost disclosed in Chinese Patent No. CN108539731B, which measures the deep peak shaving cost of deep peak shaving units, the compensation income of peak shaving units and the allocation cost of non-peak shaving units, realizes the secondary reasonable allocation of cost and income, guarantees the economic benefits of the units, optimizes the allocation of peak shaving resources, and improves the peak shaving capacity of the power system.

[0004] Another Chinese patent No. CN112993978A discloses an interactive method for controllable resources for peak shaving auxiliary services, which includes translatable load, transferable load, interruptible load and heat and power energy. The interactive method includes a day-ahead interactive stage, an intra-day invitation stage and a settlement stage. The day-ahead interactive stage includes signing an agreement, day-ahead bidding and CHP heat and power interaction. The intra-day invitation stage supplements the load gap in the day-ahead interactive stage. The settlement stage compensates or punishes the operator according to the specific situation of the calling capacity of the user controllable resources. The method fully considers the multiple subject and multiple energy use characteristics of controllable resources, has strong incentive, high flexibility and good sustainability, and takes into account user acceptance and operability.

[0005] Although the above-mentioned scheme proposes some user-side peak shaving management solutions, the prior art still has the following limitations, specifically: 1. The prior art lacks a peak shaving early warning authenticity verification mechanism. The reception and processing of peak shaving early warning usually have high instantaneity, which is based on the high accuracy of early warning data reporting. If there is no auditing process for the peak shaving period and peak shaving load of peak shaving early warning reporting, it may be difficult to detect false early warning signals, thereby affecting the efficiency of the power grid and even threatening the safety of the power grid.

[0006] 2. Existing technologies rely primarily on pre-emptive expressions of intent when soliciting peak-shaving cooperation users, lacking a comprehensive and systematic qualification review process. This means that as long as a user expresses their willingness to participate in peak shaving, they may be included in the peak-shaving cooperation system without undergoing rigorous qualification, technical, and credit assessments. Consequently, it is difficult to ensure the user's cooperative performance during the actual peak-shaving process. Although existing technologies incorporate penalty mechanisms during the settlement phase, the calculation process for these mechanisms is complex and time-consuming, impacting the efficiency and effectiveness of peak-shaving management.

[0007] 3. Existing technologies for evaluating the economic benefits of user-side peak shaving implementation schemes tend to be short-sighted, focusing primarily on short-term benefits while failing to adequately consider the profound impact of economic compensation on the long-term economic benefits of the billing cycle. This not only harms the operating efficiency and stability of the power grid but may also hinder the optimal allocation of resources and the effective implementation of decisions. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, the present invention provides an integrated system for remote control and intelligent settlement based on an energy digital base, which can effectively solve the problems involved in the prior art.

[0009] The objective of this invention can be achieved through the following technical solution: an integrated system for remote control and intelligent settlement based on an energy digital base, comprising: a peak-shaving authenticity verification module, a peak-shaving cooperation qualification review module, an implementable peak-shaving scheme formulation module, an adaptive peak-shaving scheme feedback module, and a cloud database.

[0010] The peak shaving authenticity verification module is connected to the peak shaving cooperation qualification review module, the peak shaving cooperation qualification review module is connected to the implementable peak shaving scheme formulation module, the implementable peak shaving scheme formulation module is connected to the adaptive peak shaving scheme feedback module, and the cloud database is connected to the peak shaving authenticity verification module, the peak shaving cooperation qualification review module, and the adaptive peak shaving scheme feedback module, respectively.

[0011] The peak shaving authenticity verification module is used to receive the current peak shaving warning of the target area, review the reported expected peak shaving period and expected peak shaving load, and verify the authenticity of the current peak shaving warning of the target area.

[0012] The peak-shaving cooperation qualification review module is used to verify the authenticity of the current peak-shaving warning in the target area, retrieve the relevant information of each peak-shaving intention user collected in the target area before the date of collection, and conduct cooperation qualification review on each peak-shaving intention user collected before the date of collection in order to screen out each qualified peak-shaving participant user who can cope with the current peak-shaving warning in the target area.

[0013] The module for implementing peak shaving schemes is used to extract the peak shaving load collected by each qualified peak shaving participant in the previous day and formulate the corresponding implementable peak shaving schemes for the current peak shaving warning in the target area.

[0014] The adaptive peak regulation scheme feedback module is used for extracting the peak regulation mode of each qualified peak regulation participating user collected in the day-ahead, the peak regulation mode is translation or interruption, the economic benefit analysis of each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area is carried out, the adaptive peak regulation scheme corresponding to the current peak regulation early warning of the target area is screened, and feedback is given.

[0015] The cloud database is used for storing the preset interruption peak regulation compensation unit price and the preset translation peak regulation compensation unit price of the target area peak regulation compensation criterion planning, storing the power consumption load sequence of each user in each day in the preset historical period of the target area, storing the user peak regulation cooperation preset permission response time threshold, and storing the preset power consumption load unit price of the target area.

[0016] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects: (1) The present application accurately understands the supply and demand relationship of the predicted peak regulation period reported by the current peak regulation early warning of the target area relative to the target area, verifies the authenticity of the current peak regulation early warning of the target area, and ensures the safety of the peak regulation early warning response.

[0017] (2) The present application combines the peak regulation compliance reliability coefficient and the relative peak regulation pressure coefficient, and carries out cooperation qualification audit on each peak regulation intention user collected in the day-ahead, so as to screen each qualified peak regulation participating user that can respond to the current peak regulation early warning of the target area, reduce the default risk of the user peak regulation response level, help promote the healthy development of the peak regulation market, and thus improve the stability and reliability of the power grid.

[0018] (3) The present application formulates each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area according to the peak regulation load collected in the day-ahead of each qualified peak regulation participating user, ensures that each implementable peak regulation scheme meets the predicted peak regulation load reported by the current peak regulation early warning of the target area, helps to ensure the effectiveness and pertinence of the peak regulation scheme, avoids waste and excessive allocation of resources, and thus improves the peak regulation efficiency.

[0019] (4) The present application screens the adaptive peak regulation scheme corresponding to the current peak regulation early warning of the target area according to the peak regulation mode collected in the day-ahead of each qualified peak regulation participating user, and combines the short-term and long-term economic benefit coefficients of each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area relative to the power grid, which helps to ensure the comprehensiveness and sustainability of the peak regulation scheme. BRIEF DESCRIPTION OF DRAWINGS

[0020] The present application is further described by using the drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by those skilled in the art without creative labor on the premise of not paying creative labor.

[0021] Figure 1A module connection diagram of the present application.

[0022] Figure 2 A target area current peak regulation early warning authenticity verification logic diagram of the present application.

[0023] Figure 3 A qualified peak regulation participating user screening logic diagram of the present application that can cope with target area current peak regulation early warning. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0025] Referring to Figure 1 The present application provides an integrated system of remote regulation and control and intelligent settlement based on an energy digital base, which comprises a peak regulation authenticity verification module, a peak regulation cooperation qualification audit module, an implementable peak regulation scheme formulation module, an adaptive peak regulation scheme feedback module and a cloud database.

[0026] The peak regulation authenticity verification module is connected with the peak regulation cooperation qualification audit module, the peak regulation cooperation qualification audit module is connected with the implementable peak regulation scheme formulation module, the implementable peak regulation scheme formulation module is connected with the adaptive peak regulation scheme feedback module, and the cloud database is connected with the peak regulation authenticity verification module, the peak regulation cooperation qualification audit module and the adaptive peak regulation scheme feedback module respectively.

[0027] Referring to Figure 2 The peak regulation authenticity verification module is configured to receive a target area current peak regulation early warning, audit a predicted peak regulation period and a predicted peak regulation load reported thereby, and verify the authenticity of the target area current peak regulation early warning.

[0028] Specifically, verifying the authenticity of the target area current peak regulation early warning comprises collecting cumulative user electricity consumption loads of each unit time point up to a current time of a target area on a current day, and arranging the cumulative user electricity consumption loads in chronological order to construct an overall user electricity consumption load sequence of the target area up to the current time on the current day.

[0029] The electricity consumption load sequence of each user in each day in the target region in the preset historical period stored in the cloud database is extracted, and the electricity consumption loads of each user at the same unit time point on the same day are accumulated to obtain the overall user electricity consumption load sequence in each day in the target region in the preset historical period. The overall user electricity consumption load sequence in each day in the target region in the preset historical period up to the current time is intercepted, and dynamic time warping calculation is performed on the overall user electricity consumption load sequence in the target region up to the current time on the same day. In this way, each day in the preset historical period that has reference value for the overall user electricity consumption load change trend in the target region on the same day is screened out, which is recorded as each reference day.

[0030] It should be noted that the dynamic time warping calculation process is as follows: the electricity consumption load sequence of a day in the target region in the preset historical period up to the current time is recorded as A, and the electricity consumption load sequence of the target region on the same day up to the current time is recorded as B. The distance between each element in the A sequence and each element in the B sequence is obtained by using the Euclidean distance calculation formula, which is recorded as D[ε][υ]. In this way, an n1xn2 distance matrix D is constructed, where n1 and n2 are the lengths of the A and B sequences, respectively, and ε and υ are the numbers of each element in the A and B sequences, respectively. ε=1, 2,..., n1, υ=1, 2,..., n2. The minimum cumulative distance of each element in the distance matrix is analyzed using the formula D cum [ε][υ]=D[ε][υ]+min(D cum [ε-1][υ],D cum [ε][υ-1],D cum [ε-1][υ-1]) Further, a cumulative distance matrix D cum is constructed. The first column and the first row of the cumulative distance matrix are initialized, and the minimum cumulative distance of each element in the distance matrix is calculated again to fill the cumulative distance matrix row by row. The last element value of the filled cumulative distance matrix is taken as the DTW distance between the A and B sequences, i.e., the dynamic time warping value between the electricity consumption load sequence of the target region on the same day up to the current time and the electricity consumption load sequence of the target region in the preset historical period up to the current time on the same day.

[0031] The basis for screening each reference day by using dynamic time warping calculation is that dynamic time warping calculation is an algorithm for measuring the similarity between two time sequences, mainly used for processing the stretching and bending of time sequences on the time axis. The basic idea is to find an optimal alignment between two time sequences to minimize the distance between them. Dynamic time warping can effectively handle the stretching or length difference of different day electricity consumption load changes in time. The cumulative distance reflects the overall difference between the two time sequences under the best alignment path. Therefore, the smaller the DTW distance, the more similar the shape and trend of the two time sequences.

[0032] Exemplarily, if A = [1, 2, 3, 4] and B = [1, 2, 2, 3], the distance matrix is calculated as The initialized accumulated distance matrix after the first column and the first row is initialized is The filled accumulated distance matrix is Thus, the DTW distance between the A and B sequences is 3.

[0033] It should be further noted that each reference day above specifically refers to each day for which the dynamic time warping calculation result of the electricity consumption load sequence of the target region in the preset historical period of the target region up to the current time and the electricity consumption load sequence of the target region up to the current time of the target region is less than or equal to the preset dynamic time warping effective threshold.

[0034] The target peak regulation period is recorded as the predicted peak regulation period of the current peak regulation warning report, and the cumulative user electricity consumption load of the target region for each unit time point in the target peak regulation period of each reference day is extracted, and the cumulative reference user electricity consumption load of each unit time point in the target peak regulation period of the target region on the current day is obtained through mean calculation.

[0035] Specifically, the method further comprises: collecting the power supply load and the monitoring value of each power supply operation correlation parameter of each type of energy power plant of the target region at each unit time point in each day in the preset historical period, analyzing the correlation coefficient of each power supply operation correlation parameter of each type of energy power plant of the target region with respect to the power supply load thereof, combining the predicted monitoring extreme value of each power supply operation correlation parameter at each unit time point in the target peak regulation period of each type of energy power plant of the target region on the current day, analyzing the predicted power supply load extreme value at each unit time point in the target peak regulation period of each type of energy power plant of the target region on the current day, and accumulating to obtain the cumulative reference power supply load extreme value at each unit time point in the target peak regulation period of the target region on the current day.

[0036] Exemplarily, each power supply operation correlation parameter of each type of energy power plant can be specifically: for a thermal power plant, the power supply operation correlation parameters include boiler steam pressure, boiler temperature, boiler flow, turbine speed, turbine power, generator output voltage, generator current, etc.

[0037] For a hydropower plant, the power supply operation correlation parameters include water head of water turbine, flow of water turbine, speed of water turbine, water level change, etc.

[0038] For a wind power plant, the power supply operation correlation parameters include wind speed, wind direction, speed and angle of wind turbine blades, etc.

[0039] For a solar power plant, the power supply operation correlation parameters include light intensity, photovoltaic panel temperature, etc.

[0040] It should be noted that the specific analysis process for the correlation coefficients of various power supply operation parameters of different types of energy power plants in the target area with respect to their power supply load is as follows: The power supply load and the monitored value of a certain power supply operation correlation parameter at each unit time point within a preset historical period of a certain type of energy power plant in the target area are substituted into a coordinate system with the monitored value on the horizontal axis and the power supply load on the vertical axis. A curve showing the relationship between the power supply operation correlation parameter and the power supply load within the preset historical period of that type of energy power plant in the target area is constructed. The linear characteristic index of the power supply operation correlation parameter and the power supply load of that type of energy power plant is determined through curve fitting. The linear characteristic index is 1 or -1, where 1 indicates a linear relationship and -1 indicates a non-linear relationship. The method involves substituting the power load and related parameters of the power generation plants of this type in the target area at each time point within a preset historical period into statistical software or spreadsheet software. If the linear characteristic index is 1, the software calculates the correlation coefficient of the power generation operation parameters of the power generation plants of this type in the target area with respect to their power load based on the built-in Pearson correlation coefficient. If the linear characteristic index is -1, the software calculates the correlation coefficient of the power generation operation parameters of the power generation plants of this type in the target area with respect to their power load based on the built-in Spearman rank correlation coefficient. This allows for the analysis of the correlation coefficients of various power generation operation parameters of different types of power generation plants in the target area with respect to their power load.

[0041] Analyze the probability η of power shortage at each unit time point within the target peak-shaving period of the target area on that day. i , i is the number of each unit time point within the target peak-shaving period of the day, i = 1, 2, ..., a. Organize the proportion of unit time points in the target area whose probability of insufficient power is greater than or equal to the preset power shortage warning probability threshold within the target peak-shaving period of the day. If it is greater than or equal to the preset proportion, the current peak-shaving warning in the target area is verified to be genuine; otherwise, it is verified to be invalid.

[0042] Specifically, the n i The specific analysis process includes: obtaining the standard deviation σ1 of the power supply load and the average power supply load of the target area during the target peak-shaving period based on the cumulative reference user electricity load and the cumulative reference power supply load extreme values ​​at each unit time point within the target area on that day. Standard deviation of electricity load σ2, average electricity load From the formula The probability of power shortage at each unit time point within the target peak-shaving period of the target area on that day is obtained, where Φ() is the cumulative distribution function of the standard normal distribution, and R is the reserve capacity coefficient of the target area during the target peak-shaving period on that day.

[0043] It should be noted that the analysis formula of the power deficiency probability of each unit time point in the target area target peak regulation period adopts the cumulative distribution function of the standard normal distribution, and the basis is that the central limit theorem indicates that when the sample size is large enough, the distribution of the sum of multiple independent random variables will tend to be normal distribution, regardless of the distribution of these random variables themselves. In the power system, load demand and power generation capacity can be regarded as the sum of multiple random variables, for example, load demand is affected by weather, economic activity, social behavior and other factors, power generation capacity is affected by equipment availability, fuel supply, environmental conditions and other factors, the influencing factors are independent random variables, due to the independence and large number of variables, according to the central limit theorem, the distribution of load demand and power generation capacity can be approximated as normal distribution, in addition, through statistical analysis of historical data, it is found that the distribution of load demand and power generation capacity is often close to normal distribution, and this empirical observation further supports the rationality of using normal distribution model.

[0044] The embodiment of the present application can accurately understand the supply-demand relationship of the target area current peak regulation warning reported predicted peak regulation period by auditing the predicted peak regulation period and the predicted peak regulation load reported by the current peak regulation warning, to verify the authenticity of the target area current peak regulation warning, and ensure the safety of peak regulation warning response.

[0045] Referring to Figure 3 The peak regulation cooperation qualification auditing module is configured to, after verifying the authenticity of the target area current peak regulation warning, retrieve the relevant information of each peak regulation intention user collected in the day-ahead, and perform cooperation qualification auditing on each peak regulation intention user collected in the day-ahead, to screen each qualified peak regulation participating user that can respond to the target area current peak regulation warning.

[0046] Specifically, the screening of each qualified peak regulation participating user that can respond to the target area current peak regulation warning includes: extracting the electricity load sequence of each peak regulation intention user in each day in a preset historical period of the target area, calculating the historical load rate θ j , the historical peak-valley difference rate ω j and the historical load level index μ j of each peak regulation intention user for the target peak regulation period, and evaluating the relative peak regulation pressure coefficient of each peak regulation intention user for the target peak regulation period of the day.

[0047] It should be noted that the above θ j , ω j and μ jThe specific calculation process is as follows: the average power load, the maximum power load and the minimum power load in the target peak shaving period in the power load sequence of each peak shaving intention user in each day in the target region in the preset historical period are obtained, the ratio of the average power load to the maximum power load is taken as the load rate, the difference between the maximum power load and the minimum power load is taken, and the ratio of the difference to the maximum power load is taken as the peak-valley difference rate, so as to obtain the load rate and the peak-valley difference rate of each peak shaving intention user in the target peak shaving period in the target region in the preset historical period, and the historical load rate and the historical peak-valley difference rate of each peak shaving intention user in the target peak shaving period are obtained through mean value calculation.

[0048] According to the preset power load interval and the preset load level index of each unit period corresponding to each power load grade stored in the cloud database, the average power load in the target peak shaving period in the power load sequence of each peak shaving intention user in each day in the target region in the preset historical period is applied to the power load grade, the historical days of each power load grade corresponding to the target peak shaving period of each peak shaving intention user in the target region in the preset historical period are counted, the power load grade corresponding to the maximum historical day is selected as the regular power load grade of the peak shaving intention user in the target peak shaving period, the preset load level index corresponding to the regular power load grade is taken as the historical load level index of the peak shaving intention user in the target peak shaving period, and thus the historical load level index of each peak shaving intention user in the target peak shaving period is obtained.

[0049] The peak shaving response time t of each peak shaving intention user in each peak shaving cooperation in the preset historical period is called jc and the peak shaving load requirement completion rate λ jc , c is the number of each peak shaving cooperation in the preset historical period, c=1, 2,..., l, and the peak shaving performance reliability coefficient of each peak shaving intention user for the target peak shaving period of the day is evaluated.

[0050] The ratio of the peak shaving performance reliability coefficient to the relative peak shaving pressure coefficient is taken as the cooperation qualification evaluation index, so as to obtain the cooperation qualification evaluation index of each peak shaving intention user for the target peak shaving period of the day, and each peak shaving intention user whose cooperation qualification evaluation index is greater than the preset cooperation qualification evaluation index threshold is selected as each qualified peak shaving participation user who can cope with the current peak shaving warning of the target region.

[0051] Specifically, the calculation formula of the relative peak shaving pressure coefficient of each peak shaving intention user for the target peak shaving period of the day is as follows:

[0052] Specifically, the calculation formula of the peak shaving performance reliability coefficient of each peak shaving intention user for the target peak shaving period of the day is as follows: Wherein, l is the number of peak shaving cooperation in the preset historical period, t0 is the user peak shaving cooperation preset permission response time threshold stored in the cloud database.

[0053] The embodiment of the present application combines the peak regulation compliance reliability coefficient and the relative peak regulation pressure coefficient, performs cooperation qualification auditing on each peak regulation intention user collected in the day-ahead, to screen each qualified peak regulation participating user that can respond to the current peak regulation early warning of the target area, reduce the default risk of the user peak regulation response level, help to promote the healthy development of the peak regulation market, and thus improve the stability and reliability of the power grid.

[0054] The implementable peak regulation scheme formulation module is used to extract the peak regulation load collected in the day-ahead by each qualified peak regulation participating user, and formulate each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area.

[0055] Specifically, the formulation of each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area includes: according to the peak regulation load collected in the day-ahead by each qualified peak regulation participating user, integrating and arranging each cooperation peak regulation participating user in a random combination manner, requiring that the peak regulation load cumulative value of each cooperation peak regulation participating user after the integration and arrangement is greater than or equal to the predicted peak regulation load reported by the current peak regulation early warning of the target area, and the relative excess peak regulation load is within a preset reasonable deviation load interval, thereby generating each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area.

[0056] The embodiment of the present application formulates each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area according to the peak regulation load collected in the day-ahead by each qualified peak regulation participating user, ensures that each implementable peak regulation scheme meets the predicted peak regulation load reported by the current peak regulation early warning of the target area, helps to ensure the effectiveness and pertinence of the peak regulation scheme, avoids waste and excessive allocation of resources, and thus improves the peak regulation efficiency.

[0057] The adaptive peak regulation scheme feedback module is used to extract the peak regulation mode collected in the day-ahead by each qualified peak regulation participating user, the peak regulation mode is translation or interruption, perform economic benefit analysis on each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area, screen the adaptive peak regulation scheme corresponding to the current peak regulation early warning of the target area, and feedback.

[0058] Specifically, the screening of the adaptive peak regulation scheme corresponding to the current peak regulation early warning of the target area includes: according to the preset interruption peak regulation compensation unit price and the preset translation peak regulation compensation unit price planned by the target area peak regulation compensation criterion stored in the cloud database, screening and obtaining the compensation amount corresponding to each interruption peak regulation user and each translation peak regulation user in each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area.

[0059] Statistically, the overall user compensation amount of each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area w is the number of each implementable peak regulation scheme, w=1, 2,..., m, and the formula is The short-term economic benefit coefficient of each implementable peak regulation scheme relative to the power grid is calculated.

[0060] The total electricity load q of each cooperative peak regulation participant in each implementable peak regulation scheme for the pre-design fee period after peak regulation compensation is predicted. wj′ and the total payment amount ψ wj′ j' is the number of each cooperative peak regulation participant, j' = 1, 2,..., b', and the long-term economic benefit coefficient of each implementable peak regulation scheme relative to the power grid is calculated.

[0061] It should be noted that the specific prediction process of q wj′ and ψ wj′ includes: obtaining the time sequence position of the pre-design fee period on the current day, obtaining the remaining days to the end of the pre-design fee period on the current day, obtaining the cumulative electricity load of a cooperative peak regulation participant in an implementable peak regulation scheme corresponding to the pre-design fee period at the current time on the current day, extracting the electricity load sequence of the cooperative peak regulation participant in the implementable peak regulation scheme on each day in the preset historical period of the target region, planning the reference electricity load of the cooperative peak regulation participant in the implementable peak regulation scheme corresponding to the remaining days in the pre-design fee period, and adding the cumulative electricity load of the cooperative peak regulation participant in the implementable peak regulation scheme corresponding to the pre-design fee period at the current time on the current day to obtain the total electricity load q of the cooperative peak regulation participant in the implementable peak regulation scheme for the pre-design fee period, obtaining the payment amount of the cooperative peak regulation participant in the implementable peak regulation scheme corresponding to the pre-design fee period at the current time on the current day, excluding the compensation amount of the cooperative peak regulation participant in the implementable peak regulation scheme for the current peak regulation warning, combining the reference electricity load corresponding to the remaining days in the pre-design fee period and the product of the target region preset electricity load unit price, and adding to obtain the total payment amount ψ of the cooperative peak regulation participant in the implementable peak regulation scheme for the pre-design fee period, thereby predicting the total electricity load q and the total payment amount ψ of each cooperative peak regulation participant in each implementable peak regulation scheme for the pre-design fee period after peak regulation compensation.

[0062] The short-term economic benefit coefficient and the long-term economic benefit coefficient relative to the power grid are multiplied by the preset weight, respectively, and the cumulative value of the product is taken as the reference economic benefit coefficient, so as to obtain the reference economic benefit coefficient of each implementable peak regulation scheme corresponding to the current peak regulation warning of the target region, and the implementable peak regulation scheme corresponding to the maximum reference economic benefit coefficient is selected as the adaptive peak regulation scheme corresponding to the current peak regulation warning of the target region.

[0063] The formula for calculating the long-term economic benefit coefficient of each implementable peak regulation scheme relative to the power grid corresponding to the current peak regulation warning of the target region is: Where τ0 is the preset electricity load unit price of the target area for cloud database storage, and b′ is the number of users participating in cooperative peak shaving.

[0064] Based on the peak shaving methods collected by each qualified peak shaving participant in the current period, and combined with the short-term and long-term economic benefit coefficients of each implementable peak shaving scheme relative to the power grid corresponding to the current peak shaving warning in the target area, the embodiments of the present invention screen the appropriate peak shaving scheme corresponding to the current peak shaving warning in the target area, which helps to ensure the comprehensiveness and sustainability of the peak shaving scheme.

[0065] The cloud database is used to store the preset interruption peak shaving compensation unit price and preset shift peak shaving compensation unit price of the target area peak shaving compensation criterion planning, store the electricity load sequence of each user on each day within the preset historical period of the target area, store the preset permission response time threshold of user peak shaving cooperation, store the preset electricity load unit price of the target area, and store the preset electricity load range and preset load level index of each unit time period corresponding to each electricity load level.

[0066] The data sources in the cloud database of this embodiment are shown in Table 1 below.

[0067] Table 1. Details of Data Sources in Cloud Databases

[0068]

[0069] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A remote control and intelligent settlement integrated system based on an energy digital base, characterized in that, The application relates to a peak regulation real-time verification system, which comprises the following modules: a peak regulation authenticity verification module, which is used for receiving a current peak regulation early warning of a target area, auditing a predicted peak regulation period and a predicted peak regulation load reported by the current peak regulation early warning, and verifying the authenticity of the current peak regulation early warning of the target area; a peak regulation cooperation qualification auditing module, which is used for auditing the cooperation qualifications of all peak regulation intention users in the target area after verifying that the current peak regulation early warning of the target area is authentic, screening all qualified peak regulation participation users who can cope with the current peak regulation early warning of the target area, and extracting the peak regulation modes of the qualified peak regulation participation users; a peak regulation scheme making module, which is used for extracting the peak regulation loads of the qualified peak regulation participation users, making all peak regulation schemes corresponding to the current peak regulation early warning of the target area, and screening all adaptive peak regulation schemes corresponding to the current peak regulation early warning of the target area; an adaptive peak regulation scheme feedback module, which is used for extracting the peak regulation modes of the qualified peak regulation participation users, performing economic benefit analysis on all peak regulation schemes corresponding to the current peak regulation early warning of the target area, screening all adaptive peak regulation schemes corresponding to the current peak regulation early warning of the target area, and feeding back the adaptive peak regulation schemes; a cloud database, which is used for storing preset interrupt peak regulation compensation unit prices and preset shift peak regulation compensation unit prices of a peak regulation compensation criterion planning of the target area, storing power consumption load sequences of all users in each day in a preset historical period of the target area, storing a user peak regulation cooperation preset permission response time threshold, and storing a preset power consumption load unit price of the target area; The screening can cope with each qualified peak shaving participating user of the target area current peak shaving early warning, including: extracting the electricity load sequence of each peak shaving intended user in each day of the target area preset historical period, calculating the historical load rate of each peak shaving intended user for the target peak shaving period , historical peak valley difference rate and historical load level index , , the number of each peak shaving intended user, , evaluating the relative peak shaving stress coefficient of each peak shaving intended user for the target peak shaving period of the day; calling the peak shaving response time length and peak shaving load requirement completion rate , , the number of each peak shaving cooperation in the preset historical period, , evaluating the peak shaving performance reliable coefficient of each peak shaving intended user for the target peak shaving period of the day; taking the ratio of the peak shaving performance reliable coefficient and the relative peak shaving stress coefficient as the cooperation qualification evaluation index, thereby obtaining the cooperation qualification evaluation index of each peak shaving intended user for the target peak shaving period of the day, screening each peak shaving intended user with cooperation qualification evaluation index greater than the preset cooperation qualification evaluation index threshold value as each qualified peak shaving participating user of the target area current peak shaving early warning; The calculation formula of the relative peak regulation pressure coefficient of each peak regulation intention user for the daily target peak regulation period is: ; The calculation formula of the peak regulation compliance reliability coefficient of each peak regulation intention user for the target peak regulation period of the day is: Wherein is the number of peak regulation cooperation in the preset historical period, is the user peak regulation cooperation preset permission response time threshold stored in the cloud database.

2. The integrated system for remote regulation and intelligent settlement based on the energy digital base according to claim 1, characterized in that: the verification of the authenticity of the current peak regulation early warning of the target area comprises the following steps: collecting cumulative user power consumption loads of all unit time points of the target area on the current day until the current time, arranging the cumulative user power consumption loads in chronological order to construct an overall user power consumption load sequence of the target area on the current day until the current time, extracting the power consumption load sequences of all users in each day in the preset historical period of the target area stored in the cloud database, adding the power consumption loads of all users at the same unit time point on the same day to obtain overall user power consumption load sequences of each day in the preset historical period of the target area, intercepting the overall user power consumption load sequences of each day in the preset historical period of the target area until the current time, and performing dynamic time warping calculation on the overall user power consumption load sequences of each day in the preset historical period of the target area until the current time and the overall user power consumption load sequence of the target area on the current day until the current time, so as to screen all days in the preset historical period which have reference value for the overall user power consumption load change trend of the target area on the current day, and the days are recorded as reference days; a predicted peak regulation period reported by the current peak regulation early warning is recorded as a target peak regulation period, and cumulative reference user power consumption loads of all unit time points in the target peak regulation period of the target area on each reference day are extracted, and the cumulative reference user power consumption loads of all unit time points in the target peak regulation period of the target area on each reference day are obtained through mean value calculation. ​ 3.The energy digital base integrated system for remote regulation and control and intelligent settlement according to claim 2, characterized in that: The authenticity of the current peak regulation early warning of the target area also includes: collecting the power supply load and the monitoring value of each power supply operation related parameter of each type of energy power plant in the target area in each unit time point of each day in a preset historical period, analyzing the correlation coefficient of each power supply operation related parameter of each type of energy power plant in the target area with respect to the power supply load, combining the predicted monitoring extreme value of each power supply operation related parameter of each type of energy power plant in the target area in each unit time point in the target peak regulation period of the day, analyzing the predicted power supply load extreme value of each unit time point in the target peak regulation period of the day of each type of energy power plant in the target area, and accumulating to obtain the cumulative reference power supply load extreme value of each unit time point in the target peak regulation period of the day of the target area; analyze the power deficiency probability of each unit time point in the target peak regulation period of the target region , number each unit time point in the target peak regulation period of the target region, arrange the proportion of the number of unit time points with power deficiency probability greater than or equal to the preset power deficiency warning probability threshold in the target peak regulation period of the target region, and if the proportion is greater than or equal to the preset proportion, it is verified that the current peak regulation warning of the target region is real, otherwise it is not verified.

4. The integrated system of remote regulation and intelligent settlement based on the energy digital base according to claim 3, characterized in that: The The specific analysis process comprises the following steps: according to the extreme value of the cumulative reference power supply load and the cumulative reference power consumption load of each unit time point in the target peak regulation period of the target area on the day, obtaining the power supply load standard deviation of the target peak regulation period of the target area on the day , the average power supply load , the power consumption load standard deviation , the average power consumption load , the power shortage probability of each unit time point in the target peak regulation period of the target area on the day is obtained by the formula , wherein is the cumulative distribution function of the standard normal distribution, is the standby capacity coefficient of the target peak regulation period of the target area on the day, .

5. The integrated system of remote regulation and intelligent settlement based on the energy digital base according to claim 1, characterized in that: The method for formulating the corresponding implementable peak regulation scheme of the current peak regulation early warning of the target area includes: according to the peak regulation load collected from each qualified peak regulation participant user in advance, integrating and arranging each cooperative peak regulation participant user in a random combination manner, requiring that the cumulative value of the peak regulation load of each cooperative peak regulation participant user after integration and arrangement is greater than or equal to the predicted peak regulation load reported by the current peak regulation early warning of the target area, and the relative excess peak regulation load is within a preset reasonable deviation load interval, thereby generating the corresponding implementable peak regulation scheme of the current peak regulation early warning of the target area. 6.The energy digital base integrated system for remote regulation and control and intelligent settlement according to claim 5, characterized in that: The method for screening the corresponding adaptive peak regulation scheme of the current peak regulation early warning of the target area includes: according to the preset interrupt peak regulation compensation unit price and the preset translation peak regulation compensation unit price planned by the target area peak regulation compensation criterion stored in the cloud database, screening and obtaining the compensation amount corresponding to each interrupt peak regulation user and each translation peak regulation user in each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area; Statistically, the overall user compensation amount of each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area is calculated, and the short-term economic benefit coefficient of each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area relative to the power grid is calculated. The total power load of each cooperative peak shaving participating user in the target region for the pre-designed fee period corresponding to each implementable peak shaving scheme and the total amount of payment , is the number of each implementable peak shaving scheme, , is the number of each cooperative peak shaving participating user, , calculate the long-term economic benefit coefficient of the target region current peak shaving warning corresponding to each implementable peak shaving scheme relative to the power grid; The cumulative value of the product of the short-term economic benefit coefficient and the long-term economic benefit coefficient relative to the power grid and the corresponding preset weight is taken as the reference economic benefit coefficient, so as to obtain the reference economic benefit coefficient of each implementable peak regulation scheme corresponding to the current peak regulation early warning of the target area, and the implementable peak regulation scheme corresponding to the maximum reference economic benefit coefficient is selected as the adaptive peak regulation scheme corresponding to the current peak regulation early warning of the target area.

7. The integrated system of remote regulation and intelligent settlement based on the energy digital base according to claim 6, characterized in that: The calculation formula of the long-term economic benefit coefficient of each implementable peak regulation scheme relative to the power grid corresponding to the target area current peak regulation early warning is: Wherein is the preset electricity load unit price of the target area stored in the cloud database, is the number of cooperative peak regulation participants.

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