Power auxiliary service providing method and system considering new energy prediction error

Abstract

The invention discloses a new energy prediction error-considered power auxiliary service providing method and system. According to the method, frequency modulation auxiliary service total cost is calculated based on clearing price and bid-winning capacity of bid-winning frequency modulation of a thermal power generating unit, and frequency modulation auxiliary service cost shared by a power generation side and a user side is calculated based on the frequency modulation auxiliary service total cost and on-grid electric quantity of the power generation side and the user side; based on the electric quantity correction coefficient, respectively calculating the apportioned peak regulation auxiliary service cost and the apportioned standby auxiliary service cost of the power generation side and the user side; and constructing a feature function based on a prediction error, quantifying an excess function according to the feature function, and solving the excess function by adopting a kernel method to form a new energy auxiliary service cost allocation scheme. According to the scheme of the invention, the fairness, accuracy and stability of an electric power auxiliary service cost allocation mechanism are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of new energy auxiliary services, and particularly relates to a power auxiliary service providing method and system considering new energy prediction error. BACKGROUND

[0002] With the continuous rise of new energy installed capacity, high proportion of new energy grid connection has led to a surge in the demand for auxiliary services such as frequency modulation, peak regulation and standby. The Basic Rules of Power Auxiliary Service Market clearly requires to establish an auxiliary service cost transmission mechanism according to the principle of "who benefits, who bears", and to promote the reasonable allocation of costs among market participants. However, the existing allocation mechanism still takes the on-grid power of the power generation side or the electricity consumption of the user side as the core basis, without considering the key influencing factor of new energy generation prediction error: the randomness of new energy output leads to frequent prediction errors, and the error is one of the main reasons for increasing the demand for system auxiliary services. The rapid development of new energy generation has brought profound changes in power grid operation mode, and its intermittency and volatility have increased the demand for auxiliary services, including frequency modulation, peak regulation and standby auxiliary services. Due to the additional demand for peak regulation services caused by the characteristics of new energy generation, new energy generation prediction error will exacerbate the demand for grid peak regulation, so new energy should allocate system peak regulation cost according to its generation prediction error. The prediction error of new energy generation requires the system to provide additional standby, so it also needs to allocate system standby cost. The explicitness and reasonable allocation of these costs are of great significance to the formation of new energy bidding and the healthy development of the electricity market.

[0003] The power generation side is the main provider of auxiliary services, for example, thermal power plants provide frequency modulation and peak regulation services by adjusting power generation. In order to compensate for the additional costs incurred by power generation enterprises in the process of providing auxiliary services, such as equipment wear and tear, increased fuel consumption, etc., it is necessary to allocate these costs to other beneficiaries through an allocation mechanism. The allocation part is allocated to auxiliary service costs by thermal power units, renewable energy generators (wind power, photovoltaic) that do not provide auxiliary services according to the proportion of power generation in the total power generation in the spot trading period. The more power generation, the more costs may be allocated. This way is simple and easy to implement, but may not fully reflect the differences in the ability of different power generation types to provide auxiliary services.

[0004] The user side refers to the power user participating in the power market transaction, directly signing a power purchase contract with a power generation enterprise or a power selling company. The market-oriented power user enjoys stable and reliable power supply brought by auxiliary services. If there is no auxiliary service, the voltage and frequency parameters of the power system may not be maintained within the normal range, and the normal production and operation activities of the user will be affected. According to the market-oriented principle of 'who benefits, who bears', the market-oriented power user should share the auxiliary service cost, which helps to cultivate the market consciousness and responsibility consciousness of the user, and promotes the fair competition of the power market. The auxiliary service cost is shared according to the proportion of the power consumption of the market-oriented power user in the total power consumption in a certain period. The more the power consumption is, the more the shared cost is. This way of operation is relatively simple, but it may not fully consider the actual demand and benefit difference of the user for the auxiliary service.

[0005] The traditional sharing mechanism takes the on-grid power of the power generation side or the power consumption of the user side as the only basis, does not consider the actual influence of the new energy power generation prediction error on the auxiliary service demand such as system frequency regulation, peak regulation and standby, and leads to the fact that the cost borne by the new energy station with large prediction error (i.e. large contribution to auxiliary service demand) does not match its responsibility, while the station with high prediction accuracy may bear more responsibility, which violates the market-oriented principle of 'who causes, who bears'. At the same time, the existing method lacks a quantitative means for the prediction error between different new energy stations, and cannot accurately divide the sharing proportion of each station, causing the problem of 'one size fits all' on the new energy side. In addition, the traditional cooperative game distribution method (such as Shapley value method) is easy to cause some alliances to have high dissatisfaction with the distribution scheme in the multi-agent (multi-new energy station + conventional power source) sharing scene, which may cause market agent exit risk and affect the long-term stability of the sharing mechanism. The traditional mode not only violates the responsibility matching principle emphasized by the new regulation, but also causes the imbalance of the cost borne by the station with large prediction error and the station with high accuracy, and lacks a precise quantitative error means. Moreover, the traditional distribution method is easy to cause the dissatisfaction of some market agents, which has obvious gap with the requirements of fairness and stability in the construction of the national unified power market. SUMMARY

[0006] In order to solve the problems in the prior art, the application provides a power auxiliary service providing method and system considering new energy prediction error, to solve the technical problems of fairness, accuracy and stability in the existing power auxiliary service cost sharing mechanism.

[0007] To solve the above technical problems, the application adopts the following technical solutions.

[0008] The application first discloses a power auxiliary service providing method considering new energy prediction error, which comprises the following steps: Step 1: Calculate the total cost of frequency regulation ancillary services based on the clearing price and winning capacity of the winning bid for frequency regulation of thermal power units. Based on the total cost of frequency regulation ancillary services and the on-grid electricity on the generation side and the user side, construct a cost-sharing model for frequency regulation ancillary services on the generation side and the user side. Step 2: Determine the power correction coefficient based on the waveform overlap between the actual power output and the equivalent power output curve of the new energy power station, and construct a peak-shaving ancillary service cost sharing model for the power generation side and the user side based on the power correction coefficient. Step 3: Construct a cost-sharing model for backup ancillary services on the generation and user sides based on the grid-connected electricity volume on the generation and user sides; Step 4: Based on the frequency regulation ancillary service cost sharing model, peak shaving ancillary service cost sharing model, and reserve ancillary service cost sharing model, construct a feature function based on prediction error, quantify the excess function according to the feature function, and solve the excess function using the nucleolus method to form a new energy ancillary service cost sharing scheme.

[0009] The present invention further includes the following preferred embodiments: The construction of the frequency regulation ancillary service cost-sharing model between the generation side and the user side further includes: Calculate the generation-side apportionment of frequency regulation ancillary service costs: (1) In the formula, For the first The cost of frequency modulation ancillary services shared by each unit that does not provide frequency modulation ancillary services; This indicates the number of generating units that do not provide frequency regulation ancillary services; This represents the total cost of meeting the demand for frequency modulation ancillary services within the billing cycle; This indicates the proportion of frequency regulation ancillary service costs borne by the power generation side; For the first The power consumption of units that do not provide frequency modulation auxiliary services; Set supply and demand balance constraints for frequency modulation: (2) In the formula, This indicates the total number of periods for daily spot clearing; Indicates the number of thermal power units; This indicates the clearing price of the winning bid for frequency regulation in thermal power units; For the first Taiwan thermal power units The winning bid capacity for frequency modulation at any given time.

[0010] The construction of the frequency regulation ancillary service cost-sharing model between the generation side and the user side further includes: Calculate the user-side apportionment of FM ancillary service costs: (3) In the formula, is the frequency modulation auxiliary service cost shared by the i-th user; represents the total number of users participating in the frequency modulation cost sharing; is the metering index of the i-th user; represents the sum of the metering indexes of all users participating in the sharing.

[0011] The power correction coefficient is determined according to the waveform coincidence degree of the actual power generation output of the new energy station and the equivalent output curve, and the method comprises the following steps: Calculate the similarity corresponding to the waveform coincidence degree of the actual output curve and the equivalent output curve of the new energy station

[0012] (4) In the formula, represents the actual output of the new energy station in the t-th period; represents the equivalent output of the new energy station in the t-th period; The calculation of the power correction coefficient is as follows: (6). The peak regulation auxiliary service cost sharing model of the power generation side and the user side is constructed, and the method comprises the following steps: Calculate the peak regulation sharing cost of the power generation side:

[0013] (7) In the formula, is the peak regulation auxiliary service cost shared by the i-th unit that does not provide peak regulation auxiliary service; represents the number of units that do not provide peak regulation auxiliary service; represents the total cost of meeting the peak regulation auxiliary service demand in a unit statistical period; represents the peak regulation auxiliary service cost sharing proportion borne by the power generation side; is the on-grid power of the i-th unit that does not provide peak regulation auxiliary service; Calculate the peak regulation market compensation cost of the user side: (8) In the formula, represents the total number of users participating in the peak regulation cost sharing; is the metering index of the i-th user, is the metering index of the i-th user, ​​​​​​​The peak shaving auxiliary service cost shared by the user.

[0014] The construction of the standby auxiliary service cost sharing model of the power generation side and the user side further comprises: The standby auxiliary service cost sharing of the power generation side is calculated: (9) In the formula, is the standby auxiliary service cost shared by the i-th unit not providing standby auxiliary service; represents the number of units not providing standby auxiliary service; represents the total cost of meeting standby auxiliary service demand within the billing period; represents the standby auxiliary service cost sharing proportion borne by the power generation side; is the on-grid power of the i-th unit not providing standby auxiliary service; The standby auxiliary service cost sharing of the user side is calculated: (10) In the formula, is the standby auxiliary service cost shared by the i-th user.

[0015] The construction of the feature function based on the prediction error, and the quantification of the excess function according to the feature function further comprises: The normalized root mean square error of each new energy station is calculated, and the eigenvalue of each alliance is calculated; The evaluation index of the i-th new energy station is the normalized root mean square error (15) In the formula, represents a numerical range; represents the number of time periods in a day; is the actual power of the i-th new energy station in the j-th time period; is the predicted power of the i-th new energy station in the j-th time period; Determine the set of each participant, i.e. new energy station , the error evaluation index of each new energy station, the normalized root mean square error , is defined as the eigenvalue of each alliance, i.e. new energy station ; is defined as the eigenvalue of the alliance ​​​​​​​​​The maximum total payoff that all members can guarantee to obtain without cooperating with other players through internal cooperation; Two types of core constraints are set: The allocation scheme represents the allocation value obtained by each participant, which satisfies individual rationality and collective effectiveness: (16) (17) In the formula, is the allocation cost of the i th station; represents the cost required for a user to complete the service or facility alone; represents the total cost of joint construction and sharing by all users; For each alliance , the excess value is defined as the dissatisfaction degree of the alliance to the allocation scheme : (18) In the formula, represents the excess function value of the alliance ; represents the characteristic function value of the alliance ; represents the sum of the allocation costs of all stations in the alliance .

[0016] The application also discloses a power auxiliary service providing system for considering new energy prediction errors, which utilizes the aforementioned power auxiliary service providing method for considering new energy prediction errors. The frequency modulation cost allocation module is used for calculating the total cost of frequency modulation auxiliary services based on the clearing price and the winning capacity of the frequency modulation in the thermal power unit, and constructing a frequency modulation auxiliary service cost allocation model of the power generation side and the user side based on the total cost of the frequency modulation auxiliary services and the online power of the power generation side and the user side. The peak regulation cost allocation module is used for determining an electric quantity correction coefficient according to the waveform coincidence degree of the actual power generation output of the new energy station and the equivalent output curve, and constructing a peak regulation auxiliary service cost allocation model of the power generation side and the user side based on the electric quantity correction coefficient. The standby cost allocation module is used for constructing a standby auxiliary service cost allocation model of the power generation side and the user side based on the online power of the power generation side and the user side. ​​The solving module is configured to construct a feature function based on a prediction error and quantify an excess function according to the feature function, and solve the excess function by using a kernel method to form a new energy auxiliary service cost allocation scheme based on the frequency modulation auxiliary service cost allocation model, the peak regulation auxiliary service cost allocation model and the standby auxiliary service cost allocation model.

[0017] Correspondingly, the application further discloses a terminal, including a processor and a storage medium. The storage medium is configured to store instructions. The processor is configured to operate according to the instructions to perform the steps of the method for providing power auxiliary services considering new energy prediction errors.

[0018] Correspondingly, the application further discloses a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for providing power auxiliary services considering new energy prediction errors.

[0019] The application has the advantages that, compared with the prior art, the application provides a method and system for providing power auxiliary services considering new energy prediction errors, which is based on the fairness framework of the kernel method, accurately converts the prediction error of a new energy station into an allocation responsibility, solves the defects of the traditional 'allocation according to the online power' which ignores individual differences, and ensures that the scheme can be implemented through clear quantitative indicators and constraint conditions. The allocation result can not only realize the reasonable allocation of auxiliary service costs, but also guide the new energy station to optimize the prediction technology, reduce the demand for system auxiliary services, and help the economic and efficient operation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of the method for providing power auxiliary services considering new energy prediction errors in the application. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions and advantages of the application clearer, the technical solutions of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application.

[0022] The embodiments described in the application are only part of the embodiments of the application, not all the embodiments. Based on the spirit of the application, other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0023] In view of the deficiencies of the prior art, the present application provides a power auxiliary service providing method and system considering new energy prediction error, which calculates the generation prediction error of a new energy station and takes it as a characteristic value for calculating the new energy auxiliary service cost, so as to obtain different responsibility allocation ratios of the new energy stations due to the error calculation.

[0024] Referring to Figure 1 The power auxiliary service providing method considering new energy prediction error disclosed in the present application comprises the following steps: Step 1: calculating the total cost of frequency modulation auxiliary service based on the clearing price and the winning capacity of the frequency modulation in the thermal power unit, and constructing a frequency modulation auxiliary service cost allocation model of the power generation side and the user side based on the total cost of the frequency modulation auxiliary service and the on-grid power of the power generation side and the user side.

[0025] The frequency modulation auxiliary service cost allocation refers to allocating the frequency modulation service cost paid by the power grid operator for maintaining the frequency stability according to the contribution of the generator unit, energy storage system and the like to the frequency regulation capability of the power grid.

[0026] The frequency modulation auxiliary service cost compensation mechanism is according to the principle of “who benefits, who bears”, and the corresponding cost is provided according to the on-grid and power consumption of the power generation side and the user side as the allocation standard.

[0027] The specific formula for the power generation side to allocate the frequency modulation auxiliary service cost is as follows: (1) In the formula, is the frequency modulation auxiliary service cost allocated to the i-th unit not providing frequency modulation auxiliary service; is the on-grid power of the i-th unit not providing frequency modulation auxiliary service. represents the number of units not providing frequency modulation auxiliary service; represents the total cost meeting the frequency modulation auxiliary service demand in the billing period; represents the frequency modulation auxiliary service cost allocation ratio borne by the power generation side, which is pre-set by market rules; is the frequency modulation auxiliary service cost allocated to the i-th unit not providing frequency modulation auxiliary service; is the on-grid power of the i-th unit not providing frequency modulation auxiliary service.

[0028] On this basis, the present application further sets the supply-demand balance constraint for frequency modulation, and the total frequency modulation service purchase cost of the auxiliary service market region (or the total frequency modulation income of the winning frequency modulation market unit in the region) is as follows: (2) In the formula, This indicates the total number of periods for daily spot clearing; Indicates the number of thermal power units; This indicates the clearing price of the winning bid for frequency regulation in thermal power units; For the first Taiwan thermal power units The winning bid capacity for frequency modulation at any given time.

[0029] The specific formula for the user-side cost sharing of FM ancillary services is as follows: (3) In the formula, For the first The cost of frequency modulation ancillary services shared by each user; This represents the total number of users participating in the frequency modulation cost sharing. For the first Metrics for each user; This represents the sum of metrics for all users who participated in the cost-sharing.

[0030] Step 2: Determine the power correction coefficient based on the waveform overlap between the actual power output and the equivalent power output curve of the new energy power station, and construct a peak-shaving ancillary service cost sharing model between the power generation side and the user side based on the power correction coefficient.

[0031] The cost of peak shaving ancillary services on the power generation side should be shared by new energy power plants such as wind and solar power plants and thermal power units that do not provide peak shaving ancillary services. The standard for sharing the cost should be based on their respective on-grid electricity volume in the power system.

[0032] The additional peak-shaving ancillary services resulting from the grid connection of renewable energy generation are due to the inability of the grid-connected renewable energy capacity to meet the increased load demand and the curtailment of wind and solar power. Therefore, renewable energy power plants are responsible for the cost of these additional peak-shaving ancillary services and bear the corresponding expenses. Because renewable energy power plants have different capacity reliability (reflecting the contribution of renewable energy plant grid connection to system adequacy, i.e., the size of the load that the renewable energy plant can handle), their shared ancillary service costs differ. For example, renewable energy power plants with lower reliability contribute less to the power system and must bear more peak-shaving ancillary service costs, thus highlighting fairness.

[0033] Because new energy stations do not have the ability to adjust the peak, the standard of new energy stations sharing the peak regulation auxiliary service fee and the mechanism of thermal power units according to the on-grid power are different. When the output curve of the new energy station changes correspondingly with the load curve, the output of the new energy station changes accordingly, and this corresponding change mode can reduce the pressure on the system. The equal power-load method is used to transform the output curve of the new energy station into an equivalent output curve of the new energy which is the same as the fluctuation of the power grid load. By comparing the waveform coincidence degree of the actual power generation output of the new energy station and the equivalent output curve, the influence of the output of the new energy station on the peak regulation pressure of the power system can be judged, and the cost of the peak regulation auxiliary service of the new energy station can be shared through the similarity factor.

[0034] The higher the waveform coincidence degree of the actual output curve of the new energy station and the equivalent output curve, the greater the corresponding similarity , which means that the change of the new energy output is consistent with the change of the power system load, and the difficulty of the peak regulation of the power system is lower.

[0035] (4) In the formula, represents the actual output of the new energy station in the period; represents the equivalent output of the new energy station in the period.

[0036] The peak regulation auxiliary service cost of the new energy station is shared through the modified power standard of the new energy station: (5) In the formula, represents the power correction coefficient; represents the actual power generation of the new energy station in the period.

[0037] , The smaller the waveform coincidence degree, the greater the difficulty of the system peak regulation, and the corresponding power correction coefficient is: (6) The calculation formula of the peak regulation cost on the power generation side is as follows: (7) In the formula, is the peak regulation auxiliary service cost shared by the unit which does not provide the peak regulation auxiliary service; represents the number of units which do not provide the peak regulation auxiliary service; represents the total cost of meeting the peak regulation auxiliary service demand in a unit statistical period; ​represents the proportion of the peak regulation auxiliary service cost borne by the power generation side, which is pre-set by market rules; the online electricity quantity of the nth unit not providing the peak regulation auxiliary service, which is counted using the revised electricity quantity standard The user-side peak regulation market compensation cost is:

[0038] (8) The user-side peak regulation market compensation cost is: represents the total number of users participating in the peak regulation cost allocation; the metering index of the nth user, which is counted using the revised electricity quantity standard the peak regulation auxiliary service cost allocated to the nth user. Step 3: Based on the online electricity quantity of the power generation side and the user side, a standby auxiliary service cost allocation model of the power generation side and the user side is constructed.

[0039] The specific formula of the standby auxiliary service cost allocation of the power generation side is as follows:

[0040] (9) The specific formula of the standby auxiliary service cost allocation of the power generation side is as follows: the standby auxiliary service cost allocated to the nth unit not providing the standby auxiliary service; represents the number of units not providing the standby auxiliary service; represents the total cost meeting the standby auxiliary service demand within the billing period; represents the proportion of the standby auxiliary service cost borne by the power generation side, which is pre-set by market rules; the online electricity quantity of the nth unit not providing the standby auxiliary service. The specific formula of the standby auxiliary service cost allocation of the user side is as follows:

[0041] (10) The specific formula of the standby auxiliary service cost allocation of the user side is as follows: the standby auxiliary service cost allocated to the nth user. Step 4: Based on the frequency regulation auxiliary service cost allocation model, the peak regulation auxiliary service cost allocation model and the standby auxiliary service cost allocation model, a feature function based on the prediction error is constructed, the excess function is quantified according to the feature function, the excess function is solved by using the kernel method, and a new energy auxiliary service cost allocation scheme is formed.

[0042]

[0043] ​​​​​​​​Nucleolus is a method for solving the problem of benefit distribution in cooperative games, especially in multi-person cooperative games, it can provide a relatively fair and stable distribution scheme. The core idea of nucleolus is to find a reasonable distribution point by minimizing the "discontent" of all participants.

[0044] The mathematical formula of nucleolus is as follows: suppose there is a cooperative game where is the set of participants, is the characteristic function, which represents the cooperative benefit of any subset The goal of nucleolus is to find a distribution vector such that the distribution of each participant satisfies the following conditions: 1)Individual rationality: the distribution of each participant is at least equal to the benefit when acting alone, that is (11) 2)Collective rationality: the total distribution of all participants is equal to the total benefit of the entire cooperation, that is (12) 3)Minimize the maximum discontent: nucleolus seeks a reasonable distribution point by minimizing the "discontent" of all participants. Define the discontent of the th participant as: (13) The goal of nucleolus is to find a distribution vector that minimizes the "discontent" of all participants, that is (14) The solution of nucleolus is unique, that is, in a given cooperative game, nucleolus can only find a unique distribution vector. Nucleolus tries to achieve a relatively fair distribution among all participants by minimizing the "discontent" of all participants. The solution of nucleolus is usually considered stable because it can minimize the maximum value of the excess function of all subsets, thereby reducing the risk of cooperation breakdown.

[0045] The specific solving steps are as follows: Step 4.1 Define the cooperative game system (1) Define all new energy station participants in auxiliary service cost allocation as the set of game participants (where n is the total number of new energy stations). As cooperative subjects, each new energy station jointly bears the incremental cost of auxiliary services caused by its own uncertainty, and there is no individual decision to leave the alliance.

[0046] The game core target is to determine the cost allocation proportion of each station under the double requirements of "total cost of auxiliary services fully covered" and "individual allocation responsibility matching actual impact", minimize the dissatisfaction of any single station or station alliance, and achieve the unity of fairness and economy.

[0047] (2) Key assumptions: The prediction error of all new energy stations can be quantified by a unified index, and the error data is real and traceable (based on historical actual output and predicted output).

[0048] The incremental cost of auxiliary services has been clearly defined (i.e. the total additional cost of frequency modulation, peak regulation, backup, etc. due to the connection of new energy in step 1-3).

[0049] Step 4.2 Construction of characteristic function based on prediction error The characteristic function is the core input of the kernel method, which is used to quantify the responsibility weight of each participant or alliance for "auxiliary service cost generation". This invention takes the power generation prediction error of new energy stations as the core basis to construct the characteristic function: (1) Prediction error quantification index: normalized root mean square error (NRMSE) According to the historical actual data of new energy stations, the normalized root mean square error of each new energy station is calculated by formula (15), and the characteristic value of each alliance is calculated. The evaluation index of the first new energy station is the normalized root mean square error : (15) In the formula, represents the numerical range; represents the number of time periods in a day; is the actual power of the th new energy station in the th time period; is the predicted power of the th new energy station in the th time period.

[0050] (2) Define characteristic function Determine the set of each participant, i.e. new energy station , and then normalize the error evaluation index of each new energy station, i.e. the normalized root mean square error , as the characteristic value of each alliance, i.e. new energy station ( can represent a single station, a combination of multiple stations, or all stations), and the normalized root mean square error measures the error between the predicted value and the actual value of the power generation of new energy stations.

[0051] where , indicating the alliance The maximum total benefit (or minimum total cost) that all members can guarantee through internal cooperation without cooperating with other players (i.e., players in N / S). It is a special Among them, the alliance It is a collection of all players It represents the maximum total revenue that the entire system can generate, or the minimum total cost that it must bear, when all players cooperate (forming a "grand alliance").

[0052] Step 4.3 Set constraints for the allocation scheme To ensure the feasibility and rationality of the cost-sharing scheme, the individual and collective rationality requirements of the nuclear law must be met, and two types of core constraints are set: Among them, the cost-sharing scheme This represents the share of the value obtained by each participant, and must satisfy the conditions of equations (16) and (17) to satisfy individual rationality and collective effectiveness.

[0053] (16) (17) In the formula, For the first The shared costs of each station; Indicates user The cost required to complete the service or facility independently; This represents the total cost of joint construction and sharing by all users.

[0054] Step 4.4 Quantifying the excess function and dissatisfaction The nucleolus method quantifies the dissatisfaction of each alliance with the cost-sharing plan through an excess function. The higher the dissatisfaction, the more serious the alliance believes that its share of costs is mismatched with its responsibilities.

[0055] (1) Definition of excess function For each alliance Its excess value , indicating the alliance For the cost-sharing scheme Dissatisfaction level: (18) In the formula, Indicates alliance The excess function value (dimensionless; a positive value indicates that the alliance believes the shared cost is lower than its due responsibility, indicating high dissatisfaction; a negative value indicates that the shared cost is higher than its responsibility, indicating low dissatisfaction). Indicates alliance The characteristic function value (total liability weight) of Indicates the alliance The total cost of all stations in the alliance.

[0056] (2) Full Alliance Excess Calculation Traverse all possible station alliances (including single stations, two-by-two combinations, three-by-three combinations, and so on until the full alliance), calculate the excess function value of each alliance, and form a complete dissatisfaction matrix (excluding the empty set).

[0057] Step 4.5 Kernel Solution and Allocation Ratio Determination The goal of the kernel method is to find an allocation scheme that minimizes the "maximum excess function value of all alliances", which is the kernel solution, which can be solved by a linear programming model: The goal of the kernel method is to find an allocation vector that minimizes the maximum value of the excess function of all subsets, i.e. (19) (1) Linear Programming Model Construction Introduce auxiliary variables (representing the maximum excess function value of all alliances), construct the following optimization model: (20) (21) (22) In addition, it also needs to satisfy equation (17).

[0058] (2) Optimal Allocation Ratio Calculation Solve the above model by linear programming algorithm to get the optimal allocation cost of each station, and further calculate the allocation ratio (the proportion of the allocation cost of a single station to the total cost): (23) The final allocation ratio is positively related to the station prediction error : the larger the prediction error of a station, the higher the allocation ratio, fully consistent with the fair principle of "who causes auxiliary service demand, who bears the corresponding cost", and at the same time, through economic incentives, it forces new energy stations to improve the accuracy of power generation prediction.

[0059] The application has the beneficial effect that, compared with the prior art, the application provides a power auxiliary service providing method and system considering new energy prediction error, which is based on the fairness framework of the kernel method, accurately converts the prediction error of the new energy station into the apportioned responsibility, solves the defects of the traditional "apportioning according to the online power" ignoring individual differences, and ensures that the scheme can be implemented through clear quantitative indicators and constraint conditions. The apportioning result can not only realize the reasonable distribution of auxiliary service cost, but also guide the new energy station to optimize the prediction technology, reduce the system auxiliary service demand, and help the economic and efficient operation of the power system.

[0060] The application can be a system, a method and / or a computer program product. The application also discloses a power auxiliary service providing system considering new energy prediction error based on the foregoing power auxiliary service providing method considering new energy prediction error, which comprises: A frequency modulation cost apportioning module is configured to calculate the total cost of frequency modulation auxiliary service based on the clearing price and the winning capacity of the frequency modulation in the thermal power unit, and to construct a frequency modulation auxiliary service cost apportioning model of the power generation side and the user side based on the total cost of the frequency modulation auxiliary service and the online power of the power generation side and the user side. A peak regulation cost apportioning module is configured to determine an electricity correction coefficient according to the waveform coincidence degree of the actual power generation output of the new energy station and the equivalent output curve, and to construct a peak regulation auxiliary service cost apportioning model of the power generation side and the user side based on the electricity correction coefficient. A standby cost apportioning module is configured to construct a standby auxiliary service cost apportioning model of the power generation side and the user side based on the online power of the power generation side and the user side. A solving module is configured to construct a characteristic function based on the frequency modulation auxiliary service cost apportioning model, the peak regulation auxiliary service cost apportioning model and the standby auxiliary service cost apportioning model, to quantify an excess function according to the characteristic function, to solve the excess function by using the kernel method, and to form a new energy auxiliary service cost apportioning scheme.

[0061] Based on the spirit of the application, those skilled in the art can easily think of a computer program product based on the foregoing power auxiliary service providing method considering new energy prediction error. The computer program product can include a computer readable storage medium on which computer readable program instructions for causing a processor to implement various aspects of the present disclosure are loaded. That is, the present application also includes a terminal comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform the steps of the foregoing power auxiliary service providing method considering new energy prediction error.

[0062] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0063] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0064] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0065] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limiting the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for providing ancillary power services taking into account the prediction error of new energy sources, characterized in that, Includes the following steps: Step 1: Calculate the total cost of frequency regulation ancillary services based on the clearing price and winning capacity of the winning bids for frequency regulation in thermal power units. Based on the total cost of frequency regulation ancillary services and the on-grid electricity on the generation and user sides, construct a cost-sharing model for frequency regulation ancillary services on the generation and user sides. Step 2: Determine the electricity correction coefficient based on the waveform overlap between the actual power generation output and the equivalent power output curve of the renewable energy power plant. Based on the electricity correction coefficient, construct a cost-sharing model for peak shaving ancillary services on the generation and user sides. Step 3: Construct a cost-sharing model for reserve ancillary services on the generation and user sides based on the on-grid electricity on the generation and user sides. Step 4: Based on the cost-sharing models for frequency regulation ancillary services, peak shaving ancillary services, and reserve ancillary services, construct a characteristic function based on prediction error, quantify the excess function according to the characteristic function, and solve the excess function using the nucleolus method to form a cost-sharing scheme for renewable energy ancillary services.

2. The method for providing ancillary power services taking into account the prediction error of new energy sources according to claim 1, characterized in that, The construction of the frequency regulation ancillary service cost-sharing model between the generation side and the user side further includes: Calculate the generation-side apportionment of frequency regulation ancillary service costs: （1） In the formula, For the first The cost of frequency modulation ancillary services shared by each unit that does not provide frequency modulation ancillary services; This indicates the number of generating units that do not provide frequency regulation ancillary services; This represents the total cost of meeting the demand for frequency modulation ancillary services within the billing cycle; This indicates the proportion of frequency regulation ancillary service costs borne by the power generation side; For the first The power consumption of units that do not provide frequency modulation auxiliary services; Set supply and demand balance constraints for frequency modulation: （2） In the formula, This indicates the total number of periods for daily spot clearing; Indicates the number of thermal power units; This indicates the clearing price of the winning bid for frequency regulation in thermal power units; For the first Taiwan thermal power units The winning bid capacity for frequency modulation at any given time.

3. The method for providing power ancillary services taking into account the prediction error of new energy sources according to claim 2, characterized in that, The construction of the frequency regulation ancillary service cost-sharing model between the generation side and the user side further includes: Calculate the user-side apportionment of FM ancillary service costs: （3） In the formula, For the first The cost of frequency modulation ancillary services shared by each user; This represents the total number of users participating in the frequency modulation cost sharing. For the first Metrics for each user; This represents the sum of metrics for all users who participated in the cost-sharing.

4. The method for providing ancillary power services taking into account the prediction error of new energy sources according to claim 3, characterized in that, The determination of the power correction coefficient based on the waveform overlap between the actual power output and the equivalent power output curve of the new energy power station includes: Calculate the similarity between the waveform overlap of the actual power output curve and the equivalent power output curve of a renewable energy power station. （4） In the formula, Indicates new energy power stations Actual output during the time period; Indicates new energy power stations Equivalent output over a given time period; The power correction factor is calculated as follows: （6）。 5. The method for providing ancillary power services taking into account the prediction error of new energy sources according to claim 4, characterized in that, The construction of the peak-shaving ancillary service cost-sharing model between the generation side and the user side includes: Calculate the peak-shaving cost allocation on the generation side: （7） In the formula, For the first The cost of peak shaving ancillary services shared by each unit that does not provide peak shaving ancillary services; This indicates the number of generating units that did not provide peak-shaving ancillary services; This represents the total cost of meeting peak-shaving ancillary service needs within a single statistical period. This indicates the proportion of peak-shaving ancillary service costs borne by the power generation side; For the first The on-grid power consumption of units that do not provide peak-shaving auxiliary services; Calculate the user-side peak-shaving market compensation cost: （8） In the formula, This represents the total number of users participating in the peak-shaving cost sharing. For the first Metrics for individual users For the first Peak shaving ancillary service fees shared by each user.

6. The method for providing ancillary power services taking into account the prediction error of new energy sources according to claim 5, characterized in that, The construction of the backup ancillary service cost-sharing model between the generation side and the user side further includes: Calculate the cost allocation for backup ancillary services on the generation side: （9） In the formula, For the first The cost of backup ancillary services shared by each flight unit that does not provide backup ancillary services; This indicates the number of units that did not provide backup ancillary services; This represents the total cost of meeting the standby ancillary service requirements within the billing cycle. This indicates the proportion of the cost of backup ancillary services borne by the power generation side; For the first The power consumption of units that do not provide backup auxiliary services; Calculate the cost sharing of user-side backup ancillary services: （10） In the formula, For the first The cost of backup ancillary services is shared by each user.

7. The method for providing ancillary power services taking into account the prediction error of new energy sources according to claim 6, characterized in that, The construction of a feature function based on prediction error, and the quantization of the excess function based on the feature function, further includes: Calculate the normalized root mean square error of each new energy power station, and then calculate the characteristic value of each alliance; The evaluation metric for each new energy power station is the normalized root mean square error. : （15） In the formula, Indicates a range of values; Indicates the number of time periods in a day; For the first A new energy station Actual power during the time period; For the first A new energy station Predicted power for the time period; Determine each participant, i.e., the set of new energy power stations. The error assessment index for each new energy power station is normalized to the root mean square error. Defined as each alliance, i.e., new energy power station eigenvalues; definition For the alliance The maximum total benefit that all members can guarantee through internal cooperation without cooperating with other players; Two types of core constraints are defined: Among them, the cost-sharing scheme This represents the share received by each participant, satisfying both individual rationality and collective validity: （16） （17） In the formula, For the first The shared costs of each station; Indicates user The cost required to complete the service or facility independently; This represents the total cost of joint construction and sharing by all users; For each alliance Define an out-of-value Indicates alliance For the cost-sharing scheme Dissatisfaction level: （18） In the formula, Indicates alliance The excess function value; Indicates alliance The characteristic function values; Indicates alliance The total cost shared by all stations within the area.

8. A power auxiliary service provision system that takes into account the prediction error of new energy sources, characterized in that, include: The frequency regulation cost allocation module is used to calculate the total cost of frequency regulation ancillary services based on the clearing price and winning capacity of the winning bid for frequency regulation of thermal power units, and to construct a frequency regulation ancillary service cost allocation model for the generation side and the user side based on the total cost of frequency regulation ancillary services and the on-grid electricity of the generation side and the user side. The peak-shaving cost sharing module is used to determine the power correction coefficient based on the waveform overlap between the actual power generation output and the equivalent power output curve of the new energy power station, and to construct a peak-shaving ancillary service cost sharing model between the power generation side and the user side based on the power correction coefficient. The standby cost sharing module is used to construct a standby ancillary service cost sharing model for the generation side and the user side based on the on-grid electricity volume on the generation side and the user side. The solution module is used to construct a feature function based on the prediction error based on the frequency regulation ancillary service cost sharing model, peak shaving ancillary service cost sharing model and reserve ancillary service cost sharing model, quantify the excess function according to the feature function, and solve the excess function using the nucleolus method to form a new energy ancillary service cost sharing scheme.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method for providing power ancillary services taking into account the error of new energy prediction as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method for providing power auxiliary services taking into account the prediction error of new energy sources as described in any one of claims 1-7.

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