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CN-122022366-A - User reliability responsibility measuring and calculating method and system considering flexibility constraint and fairness optimization

CN122022366ACN 122022366 ACN122022366 ACN 122022366ACN-122022366-A

Abstract

The invention provides a user reliability responsibility measuring and calculating method and a system for flexibility constraint and fairness optimization, wherein the method firstly obtains daily data of each user of a power system to form a net load curve so as to obtain a thermal power unit scheduling plan and the maximum callable capacity of each thermal power unit in each period; the method comprises the steps of calculating expected values of insufficient electric quantity of each period of a system, calculating expected values of insufficient electric quantity of each period of the power system in an absence scene of each user, constructing a linear programming model based on a minimum kernel, and finally obtaining final reliability responsibility of time sequences of each user in each period. The invention constructs a complete method system of 'accurate assessment of time sequence risk-public responsibility total amount and responsibility definition-time sequence characteristic weight mapping', effectively improves fairness and stability of user reliability responsibility allocation, and simultaneously realizes excitation of responsibility signal guiding user to optimize electricity consumption behavior through time sequence allocation based on marginal responsibility.

Inventors

  • JIANG YUEWEN
  • HUANG SHIXIN

Assignees

  • 福州大学

Dates

Publication Date
20260512
Application Date
20260212

Claims (10)

  1. 1. A user reliability responsibility measuring and calculating method considering flexibility constraint and fairness optimization is characterized by comprising the following steps: Step S1, acquiring power consumption data of each user within a day at a specified time granularity in a power system, forming a system total load curve, acquiring the number of thermal power units, technical parameters and economic parameters of each thermal power unit, and acquiring predicted value data of the output of each wind power plant and each photovoltaic power station within the day at the specified time granularity; S2, subtracting predicted values of output of each wind power station and each photovoltaic power station in each period from a total load curve of the power system under the granularity of the time in the day to obtain a net load curve of the granularity of the time in the power system, thereby executing the daily safety constraint economic dispatch to obtain a thermal power unit dispatch plan and the maximum callable capacity of each thermal power unit in each period; S3, constructing available capacity probability distribution of available-outage states of each thermal power generating unit in each period, constructing total available capacity probability distribution of thermal power in each period of a power system by adopting discrete recursive convolution, carrying out probability weighted summation on power shortage of all the thermal power systems in each period when the total available capacity of the thermal power systems in each period is smaller than a net load state based on the distribution, calculating an expected value EENS of electric quantity deficiency in each period of the system, and accumulating the expected value of total electric quantity deficiency in each period of the whole system; step S4, removing each user load curve from the system total load curve one by one based on a VCG principle, repeating the steps S2 and S3, calculating the expected value of the electric power system in each period of time under the user absence scene to obtain the expected value of the system time sequence electric power under the user absence scene, differentiating the expected value of the whole system time sequence electric power obtained in the step S3 with the expected value of the system time sequence electric power when the user is absent from time to time, quantifying the marginal influence of the existence of the user on the expected value of the system electric power in each period of time, thereby obtaining the time sequence marginal reliability responsibility of the user in each period of time, and summing to obtain the daily total marginal reliability responsibility of the user; S5, constructing a linear programming model based on a minimum kernel, and solving an optimal solution of the final reliability responsibility of each user in a day by taking the minimum of the stability violations of the coalitions of the maximum proper subset as a target; And S6, taking the duty ratio of the time sequence marginal reliability responsibility of each user obtained in the step S4 to the total time sequence marginal reliability responsibility of the user as a time sequence weight factor of the user, and distributing the final reliability responsibility of each user in the day to each time period in the day according to the time sequence weight factor of the user to obtain the final reliability responsibility of each user in each time period.
  2. 2. The method for measuring and calculating the user reliability responsibility for optimizing the flexibility constraint and the fairness according to claim 1, wherein the step S2 comprises the following specific contents: Considering the power consumption of each user to be constant under the granularity of the specified time, considering the total load of the power system in the period t as a determined value : Wherein, the The power consumption of the user d in the period t is shown as N, which is the total number of the users in the power system; in the period t, the output predicted value of each wind power plant and each photovoltaic power station is 、 Total load of the power system in period t Subtracting the total predicted value of the output of the S wind power plants and the G photovoltaic power stations in the period t to obtain the net load of the power system in the period t before the thermal power unit is scheduled A net load curve of the granularity of a specified time in a day of a power system is formed: And executing daily safety constraint economic dispatch on the common M thermal power units in the power system, wherein the daily thermal power generation cost of the system is minimized as an objective function: Wherein T is the total period number of the research period; ; Generating cost for each thermal power unit acquired in the step S1; the output of the thermal power generating unit j in the period t is obtained.
  3. 3. The method for measuring and calculating the user reliability responsibility for the flexibility constraint and fairness optimization according to claim 2, wherein considering the system operation constraint comprises: System power balance constraint Upper and lower limit constraint of unit output Wherein, the Is the minimum technical output of the thermal power generating unit j, The maximum technical output of the thermal power unit j is obtained; and (3) unit climbing constraint: Wherein, the 、 The upward and downward climbing rates of the thermal power unit j within 15 minutes are respectively, and P j,t-1 is the output of the thermal power unit j in the period t-1; Rotation reserve constraint Wherein, the 、 The minimum and maximum technical output of the thermal power unit j are respectively; 、 Respectively providing positive and negative rotation standby requirements of the system in a period t, wherein M represents the total number of thermal power units; the scheduling plan of each time period in the thermal power unit day is obtained, and the maximum callable capacity of each thermal power unit in each time period is calculated: Wherein, the Is the installed capacity of the thermal power generating unit j.
  4. 4. The method for measuring and calculating the user reliability responsibility for optimizing the flexibility constraint and the fairness according to claim 1, wherein the step S3 comprises the following specific contents: Step S1, the forced outage rate of the thermal power generating unit j is Its availability is Definition of random variables For the available capacity of the thermal power unit j in the period t, the maximum callable capacity of the thermal power unit in the period t is based Building a two-state available capacity probability distribution model of 'available-outage': Definition of the definition Representing the probability that the total available capacity of j thermal power units aggregated before the time period t is equal to X, and according to the probability distribution property of the sum of independent random variables, the probability distribution of the total available capacity of the thermal power of the current system is represented by the following formula The total available capacity probability distribution of the thermal power generating unit and the available capacity probability distribution of the j thermal power generating unit are subjected to discrete convolution to obtain: probability distribution model for j available capacity of single thermal power generating unit Non-zero values of (i.e. only when And The time probability is not 0, and the convolution formula is substituted, and the summation formula is simplified into the following recurrence relation: Wherein, the In order to obtain the initial state when the system does not contain any thermal power generating unit, the probability of the total available capacity of the system being 0 is 1, wherein, Representing the j-th thermal power unit and the front when the j-th thermal power unit is in an operating state If the total available capacity target value X of the current system is smaller than the maximum callable capacity of the thermal power unit j Front part is provided with Total available capacity of the power generating unit The total available capacity target value of the current system is X, and the non-negative physical constraint of the available capacity of the thermal power unit is violated, so that the probability of an impossible event corresponding to the state is 0, and therefore, if Then take 。
  5. 5. The method for measuring and calculating user reliability responsibility for optimizing flexibility constraint and fairness according to claim 4, wherein the total available capacity probability distribution of thermal power of the time period t power system is obtained after recursive convolution of M thermal power units Representing the probability of X of the total available power generation capacity of the system in the period t, and defining the domain The method comprises the steps of (1) collecting the maximum callable capacity combination of all thermal power units; Probability distribution of total available capacity of thermal power system based on time period t In combination with the net load of the power system during period t Performing system supply and demand balance analysis when the total available capacity X of the system thermal power is smaller than the system payload When the system is not enough, the power shortage in the state is And under the granularity of the specified time, the expected value EENS t of the shortage of the electric quantity in the period t is the weighted sum of the power shortage and the occurrence probability of the power shortage in all possible power shortage states in the period, and the weighted sum is multiplied by the duration of the period for the specified time: Wherein, the For a period of time to last for a prescribed time; Summing the expected value EENS t of the electric quantity deficiency of the electric power system in each period of the day to obtain the expected value EENS total of the total electric quantity deficiency of the day: wherein T is the total number of study periods.
  6. 6. The method for measuring and calculating the user reliability responsibility for optimizing the flexibility constraint and the fairness according to claim 1, wherein the step S4 comprises the following specific contents: Make all users in the power system be integrated into For any user Constructing a total load curve of the granularity of the specified time in the system day after the user load is removed : Wherein T is the total period number of the research period, ; The power consumption of the user d in the period t is carried out; after removing user d, the total load of the power system during period t Subtracting the total predicted value of the output of the S wind power plants and the G photovoltaic power stations in the period t to obtain the net load of the power system in the period t Constructing a net load curve of the granularity of the specified time in the day of the power system based on the net load curve of the system after removing the user d Repeatedly executing the thermal power unit safety constraint economic dispatch described in the step S2 to obtain a thermal power unit dispatch plan under the scene and the maximum callable capacity of each thermal power unit in each period Repeatedly executing the recursive convolution of the thermal power generating unit in the step S3, and calculating expected values of insufficient electric quantity of the system in each period of time under the absence scene of the user d ; Defining the timing margin reliability responsibility of user d during period t For the expected value EENS t of the power shortage of the whole system in the period t and the expected value of the power shortage in the period t under the absent scene of the user d obtained in the step S3 Is the difference of (a): to characterize the marginal impact of the user on the system reliability risk for the period of time; Summing the time sequence marginal reliability responsibilities of the user d in each period to obtain the daily total marginal reliability responsibilities of the user d : 。
  7. 7. The method for measuring and calculating the user reliability responsibility for optimizing the flexibility constraint and the fairness according to claim 1, wherein the step S5 comprises the following specific contents: Defining a collaboration federation under non-subjective willingness of all users of a power system as a large federation N is the total number of users in the power system, and the non-vacuum proper subset alliance of any user Satisfy the following requirements Let the characteristic function Non-vacuum proper subset alliance for users Independent operation is considered only The total daily electric quantity of the system generated when the user is loaded in the system is less than the expected value Definition of The vectors are allocated for reliability responsibility within a day for the whole system i.e. large league D users, Wherein Daily reliability liability for user d; Federation rationality requirements non-hollow proper subset federation for arbitrary users The total amount of reliability responsibilities borne by the large alliance allocation mechanism Should not exceed System daily total electric quantity deficiency expected value caused by independent operation I.e. should satisfy If (1) Then To assume additional responsibility beyond its independent operating cost, there is theoretically an inherent incentive to depart from the large consortium.
  8. 8. The method for measuring and calculating user reliability responsibility according to claim 1, wherein the proper subset alliance stability violation vector is introduced , To characterize the exceeding of its rational boundaries undertaken by non-empty proper subset alliances of individual users By constructing a linear programming model based on a minimum kernel, aiming at minimizing the maximum value of the stability violations of the proper subset alliance, the maximum unfairness born by the non-proper subset alliance of all users is reduced to the minimum, so that a group of balanced solutions with optimal shared fairness and highest stability of the large alliance are found under the constraint of ensuring the whole balance; Objective function: the objective function aims to find the daily reliability responsibility allocation vector of the large alliance D user Such that proper subset coalition stability violation vectors Is minimized; constraint conditions: 1) Proper subset coalition rational constraints Non-vacuum proper subset alliance for arbitrary users The sum of reliability responsibilities born by the large alliance allocation mechanism should not exceed Independent risk value and the degree of stability violation currently sustained by the federation And (2) sum: Wherein, the Is shown at the present time Under the allocation scheme, the federation assumes that its logical boundaries are exceeded Is an additional responsibility amount of (2); 2) Integral balance constraint The sum of the reliability responsibilities of the system-wide user must be equal to the expected value EENS total of the total power shortage of the system-wide daily: obtaining an optimal solution vector by solving the linear programming model Wherein I.e. the total final reliability liability for user d 。
  9. 9. The method for measuring and calculating the user reliability responsibility for optimizing the flexibility constraint and the fairness according to claim 1, wherein the step S6 comprises the following specific contents: In order to scientifically apportion the final reliability responsibility of each user in the day determined in the step S5 to each period to reflect the reliability risk cause degree of the user in different periods such as peaks, valleys and the like, a time sequence weight mapping method is adopted for apportionment, and the time sequence weight factor of the user d in the period t is calculated The factor is determined by the ratio of the time-series marginal reliability responsibility obtained in step S4 to the total marginal reliability responsibility of the day: Further, the final reliability responsibility of the user d obtained in step S5 is set According to the time sequence weight factors, the final reliability responsibility of the user d in the time period t is calculated : The final time sequence reliability responsibility allocation result which reflects the influence characteristic of the user electricity behavior on the system reliability risk on the time scale is obtained.
  10. 10. A user reliability responsibility measurement system for taking into account flexibility constraints and fairness optimization, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executed by the processor, and wherein the processor and the memory communicate via the bus when the system is running, and the machine-readable instructions are executed by the processor, a user reliability responsibility measurement method for taking into account flexibility constraints and fairness optimization according to any of claims 1-9.

Description

User reliability responsibility measuring and calculating method and system considering flexibility constraint and fairness optimization Technical Field The invention relates to the technical field of electric power, in particular to a user reliability responsibility measuring and calculating method and system considering flexibility constraint and fairness optimization. Background Under the drive of a double-carbon target, the construction of a novel power system taking new energy as a main body has become a core path for energy transformation. With the access of renewable energy sources with high proportion such as wind power, photovoltaic and the like, the power supply structure and the operation characteristics of a power system are undergoing profound changes, and the conventional power system reliability evaluation and planning method system faces multiple challenges when adapting to the changes. Firstly, the available capacity modeling of the traditional generator set has deviation from the functional positioning of the system, the traditional reliability assessment focuses on medium-long time scales such as the year or month, and the macro balance of static adequacy and predictive load of the system is focused, and the thermal power unit is generally simplified into a two-state probability model of 'full-power-off'. The modeling mode ignores flexibility regulation capacity constraint of the thermal power unit, is only suitable for traditional scenes of taking thermal power as a base load power supply, and is difficult to describe the actual callable capacity limited by the flexibility constraint after the thermal power is converted into the regulated power supply in the novel power system. Secondly, a scientific system reliability risk tracing and responsibility apportionment mechanism is lacking. With the increasing rise of the system reliability guarantee cost, how to fairly and reasonably distribute the cost becomes a core problem of the electric power market design. The existing allocation method is based on the electricity consumption or peak load proportion, and lacks of accurate quantification of causal relation between the electricity consumption behavior of a user and the time sequence reliability risk of the system. Under the guidance of the principle of 'payment by causator', effective responsibility signals are difficult to form to guide users to cut peaks and fill valleys, and unreasonable phenomena of cross-patch and 'riding' can be caused. In view of the foregoing, it is desirable to construct a system timing reliability evaluation method capable of implementing deep coupling system flexibility constraint and real-time. Based on the method, the problem of apportionment of reliability responsibility of users to the system is solved through accurate traceability fairness and effectively, and scientific and fair quantitative basis and theoretical support are provided for establishing a novel power system reliability guarantee mechanism compatible with excitation. Disclosure of Invention Therefore, the invention aims to provide the user reliability responsibility measurement and calculation method and the system for considering the flexibility constraint and fairness optimization, so that the fairness and the stability of user reliability responsibility allocation are effectively improved, and meanwhile, the excitation effect of guiding the user to optimize electricity utilization behaviors by responsibility signals is realized through time-series allocation based on marginal responsibility. In order to achieve the purpose, the invention adopts the following technical scheme that the user reliability responsibility measuring and calculating method considering flexibility constraint and fairness optimization comprises the following steps: Step S1, acquiring power consumption data of each user within a day at a specified time granularity in a power system, forming a system total load curve, acquiring the number of thermal power units, technical parameters and economic parameters of each thermal power unit, and acquiring predicted value data of the output of each wind power plant and each photovoltaic power station within the day at the specified time granularity; S2, subtracting predicted values of output of each wind power station and each photovoltaic power station in each period from a total load curve of the power system under the granularity of the time in the day to obtain a net load curve of the granularity of the time in the power system, thereby executing the daily safety constraint economic dispatch to obtain a thermal power unit dispatch plan and the maximum callable capacity of each thermal power unit in each period; S3, constructing available capacity probability distribution of available-outage states of each thermal power generating unit in each period, constructing total available capacity probability distribution of thermal power in each period of a power system by adopting