Power distribution and utilization system scheduling method and device

Abstract

The invention discloses a power distribution and utilization system scheduling method and device, and belongs to the technical field of power distribution and utilization system scheduling. The method comprises the following steps: acquiring system basic data of a power distribution and utilization system; constructing fluctuation power constraint and equipment regulation and control constraint according to the system basic data; randomly sampling an initial regulation power group based on equipment regulation constraints; randomly sampling a plurality of uncertain fluctuation power groups based on fluctuation power constraints; based on each uncertain fluctuation power group, performing power correction processing on the initial adjustment power group to obtain a plurality of fluctuated adjustment power groups; performing weighting integration on the basis of each post-fluctuation adjustment power group and the corresponding uncertain fluctuation power group, and constructing an objective function of the power distribution and utilization system scheduling model; solving the target function based on the equipment regulation and control constraint to obtain an optimal regulation power group; and performing power scheduling on the power distribution and utilization system by using the optimal regulation power group. According to the invention, the problem of low power scheduling stability of the power distribution and utilization system in the prior art can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution system scheduling technology, in particular to a power distribution system scheduling method and device. BACKGROUND

[0002] Under the dual driving of the "double carbon" goal and the power market reform, the power distribution system is transforming from the traditional "source following load" mode to the "source grid load storage" collaborative interaction mode. The gradual improvement of multi-market mechanisms such as demand response and carbon quota trading provides economic incentives for multiple subjects such as aggregators, large users, and dispersed users to participate in system regulation, and also makes the operation environment of the power distribution system increasingly complex.

[0003] Currently, the power distribution system is facing the problem of difficulty in quantifying the influence of multi-source uncertainty. Carbon price fluctuations, user behavior randomness, and distributed power output uncertainty, etc. multi-source factors, lead to significant fluctuations in the response of the power distribution system. The existing planning model is not perfect in quantifying these uncertainties, affecting the safety and economy of system scheduling. In addition, the determination method of key parameters in the model lacks combination with actual data, affecting the accuracy and practicality of the model, and thus affecting the stability of power scheduling of the power distribution system. SUMMARY

[0004] The present application provides a power distribution system scheduling method and device, which can solve the problem of low power scheduling stability of the power distribution system in the prior art.

[0005] To solve the above technical problems, the present application provides a power distribution system scheduling method, comprising:

[0006] obtaining system basic data of the power distribution system;

[0007] constructing fluctuation power constraints and device control constraints according to the system basic data;

[0008] Based on the device control constraints, a group of initial adjustment power is randomly sampled; wherein the initial adjustment power group includes initial adjustment power corresponding to several types of power distribution resources;

[0009] Based on the fluctuation power constraints, a plurality of uncertain fluctuation power groups are randomly sampled;

[0010] Based on a plurality of uncertain fluctuation power groups, power correction processing is performed on the initial adjustment power group respectively, and a plurality of fluctuation adjustment power groups are obtained;

[0011] Based on a plurality of fluctuation adjustment power groups and corresponding uncertain fluctuation power groups, a weight integration is performed to construct the objective function of the power distribution system scheduling model;

[0012] Solve the target function based on the device regulation constraint to obtain an optimal adjustment power set;

[0013] Perform power scheduling on the power utilization system by using the optimal adjustment power set.

[0014] As a preferred solution, the construction of the fluctuation power constraint and the device regulation constraint based on the system basic data comprises:

[0015] Obtain total controllable capacity and regulation power ranges of several types of power utilization resources in the system basic data, and construct a fluctuation power constraint and a deterministic response power constraint based on the total controllable capacity and the regulation power ranges of each power utilization resource; wherein the power utilization resource is an industrial high-load energy load resource, an industrial and commercial interruptible load resource, a user-side energy storage resource, an electric vehicle charging resource, a user-side distributed power generation resource, or a smart terminal intelligent power utilization resource;

[0016] Obtain an interruption proportion limit range of the industrial and commercial interruptible load resource in the system basic data, and construct an industrial and commercial interruptible load interruption proportion limit constraint;

[0017] Obtain a basic electricity price, an incentive electricity price, and a unit power carbon emission in the system basic data, and construct an electricity price and carbon price comprehensive change constraint;

[0018] Obtain a fluctuation power range and a response sensitivity of the electric vehicle charging resource in the system basic data, and construct an electric vehicle fluctuation power constraint;

[0019] Obtain a cross-period load transfer rate range and a reference load amount of the smart terminal intelligent power utilization resource in the system basic data, and construct a smart terminal intelligent power utilization transfer load constraint;

[0020] Obtain a self-use rate lower limit and a total power generation amount of the user-side distributed power generation resource in the system basic data, and construct a user-side distributed power generation self-use rate constraint;

[0021] Obtain an aggregator load agent coverage rate lower limit in the system basic data, and construct an aggregator load agent coverage rate constraint;

[0022] Obtain regulation power upper limits and aggregator agent user amounts of several types of power utilization resources in the system basic data, and construct a regulation total load constraint;

[0023] Obtain a regulation efficiency range of the user-side energy storage resource in the system basic data, and construct a user-side energy storage regulation efficiency constraint;

[0024] The deterministic response power constraint, the industrial interruptible load interruption ratio limit constraint, the electricity price-carbon price comprehensive change amount constraint, the electric vehicle fluctuation power constraint, the smart terminal smart electricity transfer load constraint, the user-side distributed generation self-use rate constraint, the aggregator load agent coverage constraint, the regulation total load constraint, and the user-side energy storage regulation efficiency constraint are integrated to form a device regulation constraint.

[0025] As a preferred solution, the fluctuation power constraint is:

[0026] -(P i,max -P i,min )≤ΔP i,j ≤P i,max -P i,min

[0027]

[0028] In the formula, ΔP i,j is the fluctuation power of the i-th type of distribution resource at time j; P i,max is the upper limit of the fluctuation power of the i-th type of distribution resource; P i,min is the lower limit of the fluctuation power of the i-th type of distribution resource; C L is the total controllable capacity of each type of resource; and n is the number of resources.

[0029] As a preferred solution, based on the fluctuation power constraint, a plurality of uncertain fluctuation power groups are randomly sampled, including:

[0030] The uncertain fluctuation power probability distribution model of each type of distribution resource is determined respectively;

[0031] For each type of distribution resource, based on the uncertain fluctuation power probability distribution model, a plurality of uncertain fluctuation powers that meet the fluctuation power constraint are randomly sampled;

[0032] The plurality of uncertain fluctuation powers corresponding to each type of distribution resource are randomly combined to generate a plurality of uncertain fluctuation power groups; wherein each uncertain fluctuation power group includes the uncertain fluctuation power corresponding to each type of distribution resource.

[0033] As a preferred solution, the uncertain fluctuation power probability distribution model of each type of distribution resource is determined respectively, including:

[0034] The uncertain fluctuation power probability distribution model of the industrial high-load energy load resource is:

[0035]

[0036] In the formula, is the uncertain fluctuation power of industrial high-load energy resource at time j; N is normal distribution; μ 1,j is the mean value of uncertain fluctuation power of industrial high-load energy resource at time j; is the basic variance of uncertain fluctuation power of industrial high-load energy resource at time j; κ1 is the load adjustment amount influence coefficient; P base,1,j is the reference power of industrial high-load energy resource at time j; is the deterministic response power at time j; κ 1,c is the carbon price fluctuation coefficient; C carbon,j is the carbon quota transaction price at time j; is the carbon price mean value;

[0037] The uncertain fluctuation power probability distribution model of industrial and commercial interruptible load resource is:

[0038]

[0039] In the formula, is the uncertain fluctuation power of industrial and commercial interruptible load resource at time j; P base,2,j is the reference power of industrial and commercial interruptible load resource at time j; X 2,j is the actual number of interruptible devices at time j; n 2,j is the number of interruptible devices at time j; β 2,j is the Sigmoid function slope parameter of industrial and commercial interruptible load resource at time j; ΔC is the emission reduction cost increment; e2 is the unit power carbon emission of industrial and commercial interruptible load resource; Δt j is the interval time at time j; δ2 is the midpoint threshold of interruption proportion of industrial and commercial interruptible load resource; wherein, the actual number of interruptible devices is subject to binomial distribution;

[0040] The uncertain fluctuation power probability distribution model of user-side energy storage resource is:

[0041]

[0042] In the formula, is the uncertain fluctuation power of intelligent terminal wisdom electricity resource at time j; U is uniform distribution; δ 3,j is the fluctuation coefficient of user-side energy storage resource at time j; SOC 3,j is the state of charge of user-side energy storage resource at time j; SOC min is the lower limit of state of charge of user-side energy storage resource; SOC max is the upper limit of state of charge of user-side energy storage resource; ζ3 is the amplification coefficient of carbon price to fluctuation;

[0043] The uncertain fluctuation power probability distribution model of electric vehicle charging resource is:

[0044]

[0045] wherein, is the uncertain fluctuation power of the electric vehicle charging resource at time j; Truncated-N is the truncated normal distribution; μ 4,j is the mean of the uncertain fluctuation power of the electric vehicle charging resource at time j; is the variance of the uncertain fluctuation power of the electric vehicle charging resource at time j; t dep,j is the off-duty time of the electric vehicle at time j; j is the current time number; τ4 is the characteristic decay constant of the time distance to the charging uncertainty; ξ4 is the disturbance coefficient of the carbon price to the user behavior; P total,4,j is the upper limit of the uncertain fluctuation power of the electric vehicle charging resource at time j;

[0046] The uncertain fluctuation power probability distribution model of the user-side distributed power generation resource is:

[0047]

[0048] wherein, is the uncertain fluctuation power of the user-side distributed power generation resource at time j; is the natural output of the user-side distributed power generation resource at time j; I j is the illumination intensity at time j; ΔI j is the illumination intensity fluctuation at time j; I ref is the reference illumination intensity; θ 5,j is the adjustment coefficient of the user-side distributed power generation resource at time j; e grid,j is the average carbon emission factor of the power grid at time j; e dg is the carbon emission factor of the distributed power generation; ΔC carbon,j is the fluctuation carbon price at time j; wherein, the fluctuation carbon price obeys the normal distribution;

[0049] The uncertain fluctuation power probability distribution model of the intelligent terminal smart power consumption resource is:

[0050]

[0051] wherein, is the uncertain fluctuation power of the intelligent terminal smart power consumption resource at time j; Laplace is the Laplace distribution; b 6,j is the uncertain fluctuation scale parameter of the intelligent terminal smart power consumption resource at time j; λ 6,jk is the cross-period elasticity coefficient of the intelligent terminal smart power consumption resource between time j and time k; C inc,k is the incentive electricity price at time k; C inc,jis the incentive electricity price at time j; e grid,k is the average grid carbon emission factor of time k.

[0052] As a preferred solution, the initial adjustment power group is subjected to power correction processing based on the plurality of uncertain fluctuation power groups, to obtain a plurality of post-fluctuation adjustment power groups, comprising:

[0053] For each uncertain fluctuation power group, each uncertain fluctuation power in the uncertain fluctuation power group is superimposed with the initial adjustment power of the same power utilization resource in the initial adjustment power group to form a plurality of post-fluctuation adjustment powers;

[0054] The post-fluctuation adjustment powers are summarized to form a post-fluctuation adjustment power group.

[0055] As a preferred solution, the initial adjustment power group is subjected to power correction processing based on the plurality of uncertain fluctuation power groups, to obtain a plurality of post-fluctuation adjustment power groups, comprising:

[0056] For each post-fluctuation adjustment power group, the power utilization system revenue value and the total carbon emission amount of the power utilization system are calculated in combination with the corresponding uncertain fluctuation power group of the post-fluctuation adjustment power group; wherein the power utilization system revenue value comprises the aggregator revenue value and the revenue value of each type of power utilization resource; and the total carbon emission amount of the power utilization system comprises the resource-based carbon emission amount and the resource uncertainty carbon emission increment;

[0057] The power utilization system revenue value and the total carbon emission amount of the power utilization system are subjected to weighted integration to obtain the uncertain scenario target value of the post-fluctuation adjustment power group;

[0058] The uncertain scenario target values of all post-fluctuation adjustment power groups are subjected to weighted integration to construct the objective function of the power utilization system scheduling model.

[0059] As a preferred solution, the aggregator revenue value is calculated using the following formula:

[0060]

[0061] In the formula, R A is the aggregator revenue value; λ i is the proxy weight of the i-th type of power utilization resource; C base,j is the basic electricity price at time j; ΔP i,j is the fluctuation power of the i-th type of power utilization resource at time j; e i is the unit power carbon emission amount of the i-th type of power utilization resource;

[0062] The revenue value of the industrial high-load resource is calculated using the following formula:

[0063]

[0064] In the formula, R1 is the income value of industrial high-load energy resource; e1 is the unit power carbon emission of industrial high-load energy resource;

[0065] The income value of commercial and industrial interruptible load resource is calculated by the following formula:

[0066] R2 = (C inc,j -C base,j )·ΔP 2,j +e2·ΔP 2,j ·Δt j ·C carbon,j

[0067] In the formula, R2 is the income value of commercial and industrial interruptible load resource; ΔP 2,j is the fluctuation power of commercial and industrial interruptible load resource at time j;

[0068] The income value of user-side energy storage resource is calculated by the following formula:

[0069]

[0070] In the formula, R 3,chg is the energy storage charging income value of user-side energy storage resource; is the charging power of user-side energy storage resource at time j; e ren,j is the renewable energy generation carbon emission factor at time j; R 3,dsg is the energy storage discharging income value of user-side energy storage resource; is the discharging power of user-side energy storage resource at time j; η3 is the regulation efficiency of user-side energy storage resource;

[0071] The income value of electric vehicle charging resource is calculated by the following formula:

[0072]

[0073] In the formula, R4 is the income value of electric vehicle charging resource; is the electric vehicle charging power at time j;

[0074] The income value of user-side distributed generation resource is calculated by the following formula:

[0075]

[0076] In the formula, R5 is the income value of user-side distributed generation resource; is the total power generation of user-side distributed generation resource at time j; is the self-use power generation of user-side distributed generation resource at time j;

[0077] The income value of the intelligent terminal smart power resource is calculated by the following formula:

[0078]

[0079] In the formula, R6 is the income value of the intelligent terminal smart power resource; P 6,k→j is the load quantity of the intelligent terminal smart power resource transferred from k time to j time.

[0080] As a preferred solution, the resource-based carbon emission is calculated by the following formula:

[0081] E i = ΔP i,j × e i

[0082] In the formula, E i is the resource-based carbon emission of the i-th type of power distribution resource;

[0083] The resource uncertainty carbon emission increment is calculated by the following formula:

[0084]

[0085] In the formula, ΔE i,unc is the resource uncertainty carbon emission increment of the i-th type of power distribution resource; is the uncertainty power of the i-th type of power distribution resource.

[0086] Correspondingly, the application provides a power distribution system scheduling device, comprising a data acquisition module, a constraint construction module, a first sampling module, a second sampling module, a power correction module, a model construction module, a model solving module and a power scheduling module.

[0087] The data acquisition module is used to acquire system basic data of the power distribution system.

[0088] The constraint construction module is used to construct fluctuation power constraints and device regulation constraints according to the system basic data.

[0089] The first sampling module is used to randomly sample an initial adjustment power group based on the device regulation constraints; wherein the initial adjustment power group comprises initial adjustment powers corresponding to several types of power distribution resources.

[0090] The second sampling module is used to randomly sample several uncertain fluctuation power groups based on the fluctuation power constraints.

[0091] The power correction module is used for performing power correction processing on the initial adjustment power group respectively based on the plurality of uncertain fluctuation power groups, so as to obtain a plurality of adjusted power groups after fluctuation.

[0092] The model construction module is used for performing weight integration on the plurality of adjusted power groups after fluctuation and the corresponding uncertain fluctuation power groups, so as to construct a target function of the power scheduling model of the power utilization system.

[0093] The model solution module is used for solving the target function based on the device regulation constraint, so as to obtain an optimal adjustment power group.

[0094] The power scheduling module is used for scheduling power of the power utilization system by using the optimal adjustment power group.

[0095] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0096] The present application provides a power utilization system scheduling method, acquires system basic data of a power utilization system, constructs fluctuation power constraints and device regulation constraints according to the system basic data, randomly samples an initial adjustment power group based on the device regulation constraint, randomly samples a plurality of uncertain fluctuation power groups based on the fluctuation power constraints, performs power correction processing on the initial adjustment power group respectively based on each uncertain fluctuation power group, so as to obtain a plurality of adjusted power groups after fluctuation, performs weight integration on each adjusted power group after fluctuation and the corresponding uncertain fluctuation power group, so as to construct a target function of a power scheduling model of the power utilization system, solves the target function based on the device regulation constraint, so as to obtain an optimal adjustment power group, and schedules power of the power utilization system by using the optimal adjustment power group. The present application randomly samples an initial adjustment power group and a plurality of uncertain fluctuation power groups embodying uncertain factors of the power utilization system based on system basic data of the power utilization system, so as to consider the power scheduling optimization of the power utilization system by each uncertain fluctuation power group, obtain an optimal adjustment power group, and schedule power of the power utilization system by using the optimal adjustment power group, which can effectively improve the adaptability of the power utilization system to uncertain factors, so as to improve the stability of power scheduling of the power utilization system. BRIEF DESCRIPTION OF DRAWINGS

[0097] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0098] Figure 1 A flowchart of an embodiment of the power utilization system scheduling method provided by the present application;

[0099] Figure 2 Fig. 1 is a structural schematic diagram of an embodiment of the power distribution system scheduling device provided by the present application. DETAILED DESCRIPTION

[0100] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some 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 of ordinary skill in the art without any creative work fall within the scope of protection of the present application.

[0101] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application; the terms "include" and "have" and any variations thereof used in the specification and the claims and the above description of drawings are intended to cover the process of inclusion without limitation.

[0102] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0103] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, or necessarily alternatives to other embodiments. It will be explicitly and implicitly appreciated by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments.

[0104] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects.

[0105] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0106] In the description of the embodiments of the present application, unless specifically defined and limited otherwise, the terms "mounting", "connection", "connecting", "fixing" and the like should be interpreted broadly, for example, can be fixed connection, can also be detachable connection, or integrated; can be mechanical connection, can also be electrical connection; can be direct connection, can also be indirect connection through an intermediate medium, can be internal communication of two elements or interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0107] Reference Figure 1 To solve the problem of low power scheduling stability of the power distribution system in the prior art, an embodiment of the present application provides a power distribution system scheduling method, which comprises steps 101 to 108, and each step is specifically as follows:

[0108] Step 101: Obtain system basic data of the power distribution system.

[0109] In the embodiment of the present application, the actual system data of the power distribution system is needed for power scheduling analysis, so it is necessary to obtain the system basic data. The system basic data of the power distribution system includes system physical characteristic data and system control resource data. The system physical characteristic data includes system topology data and device parameter data, and the system topology data includes power grid wiring diagram, voltage level, node number and topology matrix data. The device parameter data includes line parameters, transformer parameters, switch device parameters and related data of reactive power compensation devices. The system control resource data includes energy storage resource data and power consumption resource data, such as energy storage capacity, charging and discharging characteristics, operation constraints and cost parameters. The power consumption resource data includes power regulation range, response accuracy, communication delay and control reliability, multi-device cooperative scheduling protocol, etc.

[0110] Step 102: Construct fluctuation power constraints and device control constraints according to the system basic data.

[0111] In the embodiment of the present application, in order to introduce uncertain factors to participate in the power optimization analysis of the power distribution system, first, the fluctuation power constraints and the device control constraints for constraining uncertain factors are constructed. The actual data of the power distribution system, i.e. the system basic data, is used for constraint construction, so that the generated uncertain data can better adapt to the actual situation of the power distribution system, and the accuracy of the uncertain data is improved.

[0112] As a preferred scheme of the embodiment, constructing fluctuation power constraints and device control constraints according to the system basic data comprises:

[0113] The total controllable capacity and the regulation power range of the several types of power utilization resources are obtained from the system basic data, and the fluctuation power constraint and the deterministic response power constraint are constructed based on the total controllable capacity and the regulation power range of each power utilization resource; wherein the power utilization resource is an industrial high-load energy load resource, an industrial and commercial interruptible load resource, a user-side energy storage resource, an electric vehicle charging resource, a user-side distributed power generation resource or a smart terminal intelligent power utilization resource;

[0114] The interruption proportion limit range of the industrial and commercial interruptible load resource is obtained from the system basic data, and the industrial and commercial interruptible load interruption proportion limit constraint is constructed;

[0115] The basic electricity price, the incentive electricity price and the unit power carbon emission are obtained from the system basic data, and the electricity price and carbon price comprehensive change amount constraint is constructed;

[0116] The fluctuation power range and the response sensitivity of the electric vehicle charging resource are obtained from the system basic data, and the electric vehicle fluctuation power constraint is constructed;

[0117] The cross-period load transfer rate range and the reference load amount of the smart terminal intelligent power utilization resource are obtained from the system basic data, and the smart terminal intelligent power utilization transfer load constraint is constructed;

[0118] The self-use rate lower limit and the total power generation amount of the user-side distributed power generation resource are obtained from the system basic data, and the user-side distributed power generation self-use rate constraint is constructed;

[0119] The aggregator load agent coverage rate lower limit is obtained from the system basic data, and the aggregator load agent coverage rate constraint is constructed;

[0120] The regulation power upper limit of the several types of power utilization resources and the aggregator agent user amount are obtained from the system basic data, and the regulation total load constraint is constructed;

[0121] The regulation efficiency range of the user-side energy storage resource is obtained from the system basic data, and the user-side energy storage regulation efficiency constraint is constructed;

[0122] The deterministic response power constraint, the industrial and commercial interruptible load interruption proportion limit constraint, the electricity price and carbon price comprehensive change amount constraint, the electric vehicle fluctuation power constraint, the smart terminal intelligent power utilization transfer load constraint, the user-side distributed power generation self-use rate constraint, the aggregator load agent coverage rate constraint, the regulation total load constraint and the user-side energy storage regulation efficiency constraint are integrated to form the equipment regulation constraint.

[0123] As a preferred scheme of the embodiment, the fluctuation power constraint is:

[0124] -(P i,max -Pi,min )≤ΔP i,j ≤P i,max -P i,min

[0125]

[0126] wherein, ΔP i,j is the fluctuation power of the ith type of power utilization resource at j time; P i,max is the upper limit of the fluctuation power of the ith type of power utilization resource; P i,min is the lower limit of the fluctuation power of the ith type of power utilization resource; C L is the total controllable capacity of each type of resource; and n is the number of resources.

[0127] In the embodiment of the present application, the deterministic response power constraint can be expressed as:

[0128]

[0129] wherein, is the deterministic response power of the ith type of power utilization resource at j time, i.e., the adjusted actual load.

[0130] The interruption proportion limit constraint of the industrial and commercial interruptible load can be expressed as:

[0131] ΔP 2,j =P base,2,j ×R 2r ×N 2,tot

[0132] 0≤R 2r ≤R 2r,max

[0133] wherein, ΔP 2,j is the fluctuation power of the industrial and commercial interruptible load resource at j time; P base,2,j is the reference power of the industrial and commercial interruptible load resource at j time; R 2r is the interruption proportion of the industrial and commercial interruptible load resource; N 2,tot is the total number of interruptible devices; and R 2r,max is the upper limit of the interruption proportion of the industrial and commercial interruptible load resource.

[0134] The comprehensive variation amount constraint of the electricity price and carbon price can be expressed as:

[0135] ΔP=C inc,j +e4·(C base,j -C inc,j )

[0136] wherein, ΔP is the comprehensive variation amount of the electricity price and carbon price; C inc,j is the incentive electricity price at j time; e4 is the unit power carbon emission amount; and Cbase,j is the basic electricity price at time j.

[0137] The fluctuation power constraint of the electric vehicle can be expressed as:

[0138] |ΔP 4,j |≤S×|ΔP|

[0139] where ΔP 4,j is the fluctuation power range of the electric vehicle charging resource at time j; and S is the response sensitivity of the electric vehicle charging resource.

[0140] The smart terminal smart electricity transfer load constraint can be expressed as:

[0141] ΔP 6,j ≤T6×L 6,base

[0142] 0≤T6≤1

[0143] where T6 is the cross-period load transfer rate of the smart terminal smart electricity resource; ΔP 6,j is the fluctuation power of the smart terminal smart electricity resource at time j; L 6,base is the reference load of the smart terminal smart electricity resource.

[0144] The user-side distributed generation self-use rate constraint can be expressed as:

[0145]

[0146] U 5,min ≤U5≤1

[0147] where ΔP 5,j is the fluctuation power of the user-side distributed generation resource at time j; P is the total power generation of the user-side distributed generation resource at time j; U5 is the self-use rate of the user-side distributed generation resource; and U 5,min is the lower limit of the self-use rate of the user-side distributed generation resource.

[0148] The aggregator load agent coverage constraint can be expressed as:

[0149] C A,min ≤C A ≤1

[0150] where C A,min is the lower limit of the aggregator load agent coverage; and C A is the aggregator load agent coverage.

[0151] The total load regulation constraint can be expressed as:

[0152]

[0153] wherein N A,agent is the number of users of the aggregator agent; P i,max is the upper limit of the regulated power of the i-th type of distribution resource.

[0154] The user-side energy storage regulation efficiency constraint can be expressed as:

[0155]

[0156] η 3,min ≤η3≤1

[0157] wherein, is the charging power of the user-side energy storage resource at time j; is the discharging power of the user-side energy storage resource at time j; η3 is the regulation efficiency; η 3,min is the lower limit of the regulation efficiency.

[0158] Step 103: based on the device regulation constraint, randomly sampling an initial adjustment power group; wherein the initial adjustment power group includes initial adjustment power corresponding to several types of distribution resources.

[0159] In the embodiment of the present application, according to the generated device regulation constraint, first randomly sample an initial adjustment power group as the basis value of power optimization. The initial adjustment power group contains multiple initial adjustment powers, and each initial adjustment power corresponds to a type of distribution resource. The distribution resource includes industrial high-load energy load resource, commercial and industrial interruptible load resource, user-side energy storage resource, electric vehicle charging resource, user-side distributed power generation resource and intelligent terminal smart power resource.

[0160] Step 104: based on the fluctuation power constraint, randomly sampling several uncertain fluctuation power groups.

[0161] In the embodiment of the present application, multiple uncertain fluctuation power groups are randomly generated, so as to reflect the uncertainty factors of carbon price fluctuation, user behavior randomness, distributed power output and the like. In order to improve the accuracy of the uncertain fluctuation power group, the generated uncertain fluctuation power group needs to satisfy the fluctuation power constraint constructed according to the system basic data of the distribution system.

[0162] As a preferred scheme of the embodiment, based on the fluctuation power constraint, randomly sampling several uncertain fluctuation power groups, including:

[0163] determining the uncertain fluctuation power probability distribution model of each type of distribution resource respectively;

[0164] For each type of distribution resource, based on the uncertain fluctuation power probability distribution model, randomly sampling several uncertain fluctuation powers that satisfy the fluctuation power constraint;

[0165] Randomly combine the uncertain fluctuation powers corresponding to various types of power utilization resources to generate a plurality of uncertain fluctuation power groups; wherein each uncertain fluctuation power group comprises uncertain fluctuation powers corresponding to various types of power utilization resources.

[0166] In the embodiment of the present application, based on the characteristics of various types of power utilization resources, the uncertainty of various types of power utilization resources can be quantified by using a probability distribution method, so as to sample the uncertain fluctuation powers of various types of power utilization resources according to the probability distribution of various types of power utilization resources. Therefore, each uncertain fluctuation power group generated comprises uncertain fluctuation powers corresponding to various types of power utilization resources, and these uncertain fluctuation powers meet the fluctuation power constraint and conform to the probability distribution of the corresponding power utilization resources.

[0167] As a preferred scheme of the embodiment, the uncertain fluctuation power probability distribution model of each type of power utilization resource is determined respectively, comprising:

[0168] The uncertain fluctuation power probability distribution model of the industrial high-load energy resource reflects the deviation between the actual operating power and the deterministic response power, is subject to a normal distribution, and the fluctuation degree is affected by the load adjustment amount and the carbon price fluctuation. It can be expressed as:

[0169]

[0170] In the formula, is the uncertain fluctuation power of the industrial high-load energy resource at time j; N is a normal distribution, is the mean value of the normal distribution, and is the standard deviation of the normal distribution. 1,j is the mean value of the uncertain fluctuation power of the industrial high-load energy resource at time j; is the basic variance of the uncertain fluctuation power of the industrial high-load energy resource at time j; κ1 is a load adjustment amount influence coefficient (>0), reflecting the influence of the deviation between the actual power and the reference power on the fluctuation. The greater the deviation (i.e. the greater the load adjustment amount), the more violent the fluctuation; P base,1,j is the reference power of the industrial high-load energy resource at time j; is the deterministic response power at time j; κ 1,c is a carbon price fluctuation coefficient (>0), representing the influence of carbon price fluctuation on uncertainty. The farther the carbon price deviates from the mean value, the greater the coefficient makes the fluctuation variance, and the greater the uncertainty of production plan adjustment; C carbon,j is the carbon quota transaction price at time j; is the mean value of the carbon price;

[0171] The uncertain fluctuation power probability distribution model of the industrial high-load energy resource reflects the deviation between the actual operating power and the deterministic response power, is subject to a normal distribution, and the fluctuation degree is affected by the load adjustment amount and the carbon price fluctuation. It can be expressed as:

[0172]

[0173] In the formula, is the uncertain fluctuation power of the industrial and commercial interruptible load resource at time j; P base,2,j is the reference power of the industrial and commercial interruptible load resource at time j; X 2,j is the actual number of interruptible devices at time j; n 2,j is the number of interruptible devices at time j, which is the number of trials of a binomial distribution, determines the upper limit of the uncertainty fluctuation, and is limited by the number of devices; β 2,j is the Sigmoid function slope parameter of the industrial and commercial interruptible load resource at time j, reflecting the steepness of the price response curve; ΔC is the incremental abatement cost; e2 is the unit power carbon emission of the industrial and commercial interruptible load resource; Δt j is the interval time at time j; δ2 is the midpoint threshold of the interruptible proportion of the industrial and commercial interruptible load resource, which is the inflection point corresponding to the comprehensive incentive value of the S-shaped response curve, when the sum of the price difference and the carbon income is equal to the threshold, the interruptible proportion reaches 50%, and exceeds the value, the interruptible proportion rapidly rises; wherein, the actual number of interruptible devices is subject to a binomial distribution, embodying the randomness of users in interruptible decision-making, and describes the probability distribution that X 2,j out of n 2,j interruptible devices actually interrupt, that is, X 2，j ~ Binomial(n 2，j , p 2，j ), p 2,j is the interruptible probability of a single device, which is positively correlated with the carbon price, the higher the carbon price, the greater the carbon income of interruption, the higher the probability of device participation in interruption, and the distribution characteristics of the uncertainty fluctuation change accordingly;

[0174] The probability distribution model of the uncertain fluctuation power of the user-side energy storage resource is subject to a uniform distribution, reflecting the deviation of the actual charging and discharging power from the deterministic model, and is jointly affected by the SOC and the carbon price, and can be expressed as:

[0175]

[0176] In the formula, is the uncertain fluctuation power of the intelligent terminal smart electricity resource at time j; U is a uniform distribution, consistent with the power random deviation characteristics caused by prediction error in energy storage scheduling; δ 3,j is the fluctuation coefficient of the user-side energy storage resource at time j, determining the basic fluctuation amplitude, the greater the coefficient, the wider the fluctuation range under the same state of charge; max(SOC 3,j -SOC min , SOC max -SOC 3,j) SOC safety margin, i.e. the difference between the current state of charge and the recent limit, the larger the safety margin, the more sufficient the buffer space of energy storage adjustment, the larger the fluctuation range; SOC 3,j is the state of charge of the user-side energy storage resource at time j; SOC min is the lower limit of the state of charge of the user-side energy storage resource; SOC max is the upper limit of the state of charge of the user-side energy storage resource; ζ3 is the amplification coefficient of carbon price to fluctuation (>0), reflecting the enhancing effect of carbon price rise on uncertainty, the higher the carbon price, the more frequent the user will adjust the charging and discharging strategy to pursue carbon benefits, resulting in the expansion of power fluctuation range;

[0177] The uncertain fluctuation power probability distribution model of the electric vehicle charging resource reflects the deviation between the actual charging power and the deterministic response power, obeys the truncated normal distribution, and can be expressed as:

[0178]

[0179] In the formula, is the uncertain fluctuation power of the electric vehicle charging resource at time j; Truncated-N is the truncated normal distribution, indicating that the uncertain power fluctuation is limited within a certain range and will not exceed the actual possible charging power interval; μ 4,j is the mean value of the uncertain fluctuation power of the electric vehicle charging resource at time j; is the variance of the uncertain fluctuation power of the electric vehicle charging resource at time j; t dep,j is the electric vehicle off-duty time corresponding to time j; j is the current time number; τ4 is the characteristic decay constant of time distance to charging uncertainty; ξ4 is the disturbance coefficient of carbon price to user behavior (>0), the higher the carbon price, the larger the coefficient makes the variance of uncertain power fluctuation, indicating that the user adjusts the charging period more frequently and the power fluctuation is larger; P total,4,j is the upper limit of the uncertain fluctuation power of the electric vehicle charging resource at time j, i.e. the fluctuation range is limited between 0 and the total charging power potential, ensuring that the fluctuation conforms to the actual charging scenario;

[0180] The uncertain fluctuation power probability distribution model of the user-side distributed power generation resource reflects the deviation between the actual power generation and the deterministic response power, which is affected by factors such as light intensity fluctuation and carbon price fluctuation, and can be expressed as:

[0181]

[0182] In the formula, is the uncertain fluctuation power of the user-side distributed power generation resource at time j; Pj is the natural output of the user-side distributed generation resource at time j, i.e., the basic generation power without considering the incentive factors such as carbon price and electricity price, which is only affected by meteorological conditions; j Ij is the light intensity at time j; j ΔIj is the light intensity fluctuation at time j; ref Iref is the reference light intensity; 5,j θj is the adjustment coefficient of the user-side distributed generation resource at time j, which reflects the response sensitivity of the generation power to the sum of the electricity price difference and the carbon benefit, and the greater the coefficient, the greater the adjustment range of the generation power under the same comprehensive incentive; grid,j ej is the average carbon emission factor of the power grid at time j; dg ec is the carbon emission factor of the distributed generation, which is usually zero or very low; carbon,j ΔCj is the fluctuation carbon price at time j, which reflects the influence of the uncertainty of the carbon market on the adjustment amount of the distributed generation; wherein, the fluctuation carbon price is subject to normal distribution;

[0183] The uncertain fluctuation power probability distribution model of the smart terminal wisdom power resource reflects the deviation between the actual power and the deterministic response power, is subject to Laplace distribution, and its fluctuation range is related to the cross-period incentive intensity, and can be expressed as:

[0184]

[0185] In the formula, Lj is the uncertain fluctuation power of the smart terminal wisdom power resource at time j; Laplace is the Laplace distribution; 6,j b is the uncertainty fluctuation scale parameter of the smart terminal wisdom power resource at time j, which affects the range of fluctuation, and the greater the parameter, the more intense the fluctuation; 6,jk λjk is the cross-period elasticity coefficient of the smart terminal wisdom power resource between time j and time k, which reflects the difficulty and sensitivity of the load transfer from time j to time k, and the greater the coefficient, the greater the load transferred from time j to time k under the same cross-period incentive; inc,k Ck is the incentive electricity price at time k; inc,j Cj is the incentive electricity price at time j; grid,k ek is the average carbon emission factor of the power grid at time k; |ΔCk-Cj| is the sum of the absolute values of the cross-period comprehensive incentive, which reflects the total incentive intensity driving the cross-period load transfer. The greater the carbon price difference, the greater the value, and the wider the uncertainty fluctuation range, indicating that the uncertainty of the cross-period load transfer is stronger.

[0186] Step 105: based on a plurality of said uncertain fluctuation power groups, respectively, power correction processing is performed on said initial adjustment power group, to obtain a plurality of fluctuation adjustment power groups.

[0187] As a preferred scheme of the embodiment, the initial adjustment power set is subjected to power correction processing based on the plurality of uncertain fluctuation power sets respectively, to obtain a plurality of post-fluctuation adjustment power sets, comprising:

[0188] For each uncertain fluctuation power set, each uncertain fluctuation power in the uncertain fluctuation power set is superimposed with the initial adjustment power of the same power utilization resource in the initial adjustment power set to form a plurality of post-fluctuation adjustment powers;

[0189] The post-fluctuation adjustment powers are summarized to form a post-fluctuation adjustment power set.

[0190] In the embodiment, after the plurality of uncertain fluctuation power sets are sampled out, each fluctuation power set is superimposed with the initial adjustment power set respectively, to form a plurality of corresponding post-fluctuation adjustment power sets. The fluctuation power in the fluctuation power set can be positive or negative. When the fluctuation power in the fluctuation power set is positive, it is added to the corresponding initial adjustment power, and the obtained post-fluctuation adjustment power is greater than the initial adjustment power. When the fluctuation power in the fluctuation power set is negative, it is added to the corresponding initial adjustment power, and the obtained post-fluctuation adjustment power is less than the initial adjustment power.

[0191] Step 106: Based on the plurality of post-fluctuation adjustment power sets and the corresponding uncertain fluctuation power sets, weight integration is performed to construct a target function of the power utilization system scheduling model.

[0192] As a preferred scheme of the embodiment, based on the plurality of post-fluctuation adjustment power sets and the corresponding uncertain fluctuation power sets, weight integration is performed to construct a target function of the power utilization system scheduling model, comprising:

[0193] For each post-fluctuation adjustment power set, the power utilization system revenue value and the total carbon emission amount of the power utilization system are calculated in combination with the corresponding uncertain fluctuation power set of the post-fluctuation adjustment power set. The power utilization system revenue value includes the aggregator revenue value and the revenue value of various types of power utilization resources. The total carbon emission amount of the power utilization system includes the resource-based carbon emission amount and the resource uncertainty carbon emission increment.

[0194] The power utilization system revenue value and the total carbon emission amount of the power utilization system are subjected to weighted integration to obtain the uncertain scenario target value of the post-fluctuation adjustment power set.

[0195] The uncertain scenario target values of all post-fluctuation adjustment power sets are subjected to weighted integration to construct a target function of the power utilization system scheduling model.

[0196] In this embodiment of the invention, to ensure that the optimal adjustment power group obtained through optimization can adapt to the uncertainties of the power distribution system, it is necessary to calculate the revenue value and carbon emissions of the power distribution system for the fluctuating adjustment power groups obtained through random sampling, thereby constructing the objective function of the power distribution system scheduling model. Specifically, firstly, the revenue value and total carbon emissions of the power distribution system are calculated for each fluctuating adjustment power group, and then the target value of the uncertain scenario for each fluctuating adjustment power group is calculated. Finally, the target values ​​of the uncertain scenario for each fluctuating adjustment power group are weighted and superimposed to form the objective function of the power distribution system scheduling model.

[0197] As a preferred embodiment, the aggregator's revenue is calculated using the following formula:

[0198]

[0199] In the formula, R A λ represents the aggregator's revenue. i C represents the agency weight of the i-th type of power distribution resource; base,j Let ΔP be the base electricity price at time j; i,j Let e ​​be the fluctuating power of the i-th type of power distribution resource at time j; i For the i-th type of power distribution resources, the carbon emissions per unit power are denoted as ;

[0200] The revenue value of high-energy-consuming industrial resources is calculated using the following formula:

[0201]

[0202] In the formula, R1 is the revenue value of industrial high energy load resources; e1 is the carbon emission per unit power of industrial high energy load resources.

[0203] The revenue value of interruptible load resources for industrial and commercial use is calculated using the following formula:

[0204] R2=(C inc,j -C base,j )·ΔP 2,j +e2·ΔP 2,j ·Δt j ·C carbon,j

[0205] In the formula, R2 represents the revenue value of interruptible load resources for industrial and commercial use; ΔP 2,j The fluctuating power of interruptible load resources in industry and commerce at time j;

[0206] The revenue from user-side energy storage resources is calculated using the following formula:

[0207]

[0208] In the formula, R 3,chg is the energy storage charging revenue value of the user-side energy storage resource; is the charging power of the user-side energy storage resource at the jth moment; e ren,j is the renewable energy generation carbon emission factor at the jth moment; R 3,dsg is the energy storage discharging revenue value of the user-side energy storage resource; is the discharging power of the user-side energy storage resource at the jth moment; η3 is the regulation efficiency of the user-side energy storage resource;

[0209] The revenue value of the electric vehicle charging resource is calculated by the following formula:

[0210]

[0211] In the formula, R4 is the revenue value of the electric vehicle charging resource; is the electric vehicle charging power at the jth moment;

[0212] The revenue value of the user-side distributed power generation resource is calculated by the following formula:

[0213]

[0214] In the formula, R5 is the revenue value of the user-side distributed power generation resource; is the total power generation of the user-side distributed power generation resource at the jth moment; is the self-use power generation of the user-side distributed power generation resource at the jth moment;

[0215] The revenue value of the intelligent terminal smart power utilization resource is calculated by the following formula:

[0216]

[0217] In the formula, R6 is the revenue value of the intelligent terminal smart power utilization resource; P 6,k→j is the load quantity of the intelligent terminal smart power utilization resource transferred from the kth moment to the jth moment.

[0218] In the embodiment of the present application, according to the calculation formula of the above-mentioned aggregator revenue value and the revenue value of each type of power utilization resource, the power utilization system revenue value can be represented as:

[0219]

[0220] In the formula, F1 is the power utilization system revenue value; R A is the aggregator revenue value; R i is the power utilization resource revenue value of the ith type of power utilization resource; C i is the cost value of the ith type of power utilization resource; is the expectation operator.

[0221] As a preferred scheme of the embodiment, the resource-based carbon emission is calculated by the following formula:

[0222] E i = ΔP i,j × e i

[0223] In the formula, E i is the resource-based carbon emission of the i-th type of power utilization resource;

[0224] The resource uncertainty carbon emission increment is calculated by the following formula:

[0225]

[0226] In the formula, ΔE i,unc is the resource uncertainty carbon emission increment of the i-th type of power utilization resource; is the uncertainty power of the i-th type of power utilization resource.

[0227] In the embodiment, according to the calculation formula of the resource-based carbon emission and the resource uncertainty carbon emission increment, the total carbon emission of the power utilization system can be expressed as:

[0228]

[0229] In the formula, F2 is the total carbon emission of the power utilization system; E A is the carbon emission generated by other non-adjustable / fixed power utilization activities except for the six types of controllable resources (industrial high-load energy load resource, industrial and commercial interruptible load resource, user-side energy storage resource, electric vehicle charging resource, user-side distributed power generation resource, and intelligent terminal smart power utilization resource); E i is the resource-based carbon emission of the i-th type of power utilization resource; ΔE i,unc is the resource uncertainty carbon emission increment of the i-th type of power utilization resource.

[0230] In the embodiment, the uncertainty scenario target value corresponding to each fluctuation post-adjustment power group can be expressed as:

[0231] F s = w 1,s · F 1,s - w 2,s · F 2,s

[0232] In the formula, F s is the uncertainty scenario target value of the s-th fluctuation post-adjustment power group; w 1,s is the benefit weight coefficient of the s-th fluctuation post-adjustment power group; w 2,s is the carbon emission weight coefficient of the s-th fluctuation post-adjustment power group; F 1,sThe power utilization system benefit value of the s-th fluctuation-adjusted power group; F 2,s The total carbon emission of the power utilization system of the s-th fluctuation-adjusted power group.

[0233] In the embodiment of the application, the uncertain scenario target values of all the fluctuation-adjusted power groups are weighted and integrated to construct a target function of the power utilization system scheduling model.

[0234]

[0235] In the formula, F is the target function of the power utilization system scheduling model; ω s The scenario coefficient of the s-th fluctuation-adjusted power group; F s The uncertain scenario target value of the s-th fluctuation-adjusted power group; S is the number of the fluctuation-adjusted power groups.

[0236] The power utilization system scheduling model aims to maximize the total benefit of the aggregator and the six types of resources and minimize the total carbon emission, and therefore, the uncertain scenario target values of the respective fluctuation-adjusted power groups corresponding to the target function are obtained by subtracting the carbon emission from the benefit value, so that the maximization of the target function of the power utilization system scheduling model can reflect the model optimization objective of maximizing the total benefit of the aggregator and the six types of resources and minimizing the total carbon emission.

[0237] Step 107: Based on the equipment regulation constraint, the target function is solved to obtain an optimal fluctuation-adjusted power group.

[0238] In the embodiment of the application, the target function of the power utilization system scheduling model is solved based on the regulation constraint constructed according to the system basic data of the power utilization system, and a set of optimal fluctuation-adjusted power groups can be obtained. The solution of the power utilization system scheduling model can adopt an improved robust NSGA-III algorithm, the number of iterations is set, a plurality of individuals meeting the equipment regulation constraint are first determined, genetic operations such as selection, crossover and mutation are performed on these individuals to generate a plurality of offspring individuals meeting the equipment regulation constraint, non-dominated sorting is performed on each generation population, solutions meeting all the equipment regulation constraints are reserved, and the solution with the highest comprehensive membership degree is selected as the final optimal fluctuation-adjusted power group through fuzzy membership function evaluation.

[0239] Step 108: The optimal fluctuation-adjusted power group is used to perform power scheduling on the power utilization system.

[0240] In the embodiment of the present application, the optimal adjustment power group obtained by solving the power scheduling model of the distribution and utilization system includes optimal adjustment power corresponding to industrial high-load energy load resources, industrial and commercial interruptible load resources, user-side energy storage resources, electric vehicle charging resources, user-side distributed power generation resources and intelligent terminal smart power utilization resources, so that power scheduling is performed on the corresponding distribution and utilization resources based on the optimal adjustment power, and more optimal distribution and utilization system scheduling and improved stability of power scheduling can be achieved.

[0241] The above embodiment has the following effects:

[0242] The present application provides a distribution and utilization system scheduling method, acquires system basic data of a distribution and utilization system, constructs fluctuation power constraints and device control constraints according to the system basic data, randomly samples an initial adjustment power group based on the device control constraints, randomly samples multiple uncertain fluctuation power groups based on the fluctuation power constraints, respectively performs power correction processing on the initial adjustment power group based on each uncertain fluctuation power group, and obtains multiple fluctuation adjustment power groups, integrates the weight of each fluctuation adjustment power group and the corresponding uncertain fluctuation power group, constructs an objective function of a distribution and utilization system scheduling model, solves the objective function based on the device control constraints, and obtains an optimal adjustment power group, and utilizes the optimal adjustment power group to perform power scheduling on the distribution and utilization system. The present application randomly samples an initial adjustment power group and multiple uncertain fluctuation power groups embodying uncertainty factors of the distribution and utilization system based on the system basic data of the distribution and utilization system, thereby considering power scheduling optimization of the distribution and utilization system by each uncertain fluctuation power group, obtaining an optimal adjustment power group, utilizing the optimal adjustment power group to perform power scheduling on the distribution and utilization system, effectively improving the adaptability of the distribution and utilization system to uncertain factors, and thereby improving the stability of power scheduling of the distribution and utilization system.

[0243] As shown in the above method embodiment, corresponding device embodiments are provided; Figure 2

[0244] An embodiment of the present application provides a distribution and utilization system scheduling device, which comprises a data acquisition module, a constraint construction module, a first sampling module, a second sampling module, a power correction module, a model construction module, a model solving module and a power scheduling module.

[0245] The data acquisition module is used to acquire system basic data of a distribution and utilization system.

[0246] The constraint construction module is used to construct fluctuation power constraints and device control constraints according to the system basic data.

[0247] The first sampling module is used to randomly sample an initial adjustment power group based on the device control constraints; wherein the initial adjustment power group includes initial adjustment power corresponding to several types of distribution and utilization resources.​

[0248] The second sampling module is configured to randomly sample a plurality of uncertain fluctuation power groups based on the fluctuation power constraint;

[0249] The power correction module is configured to perform power correction processing on the initial adjustment power group based on the plurality of uncertain fluctuation power groups, respectively, to obtain a plurality of fluctuation adjustment power groups;

[0250] The model construction module is configured to perform weight integration based on the plurality of fluctuation adjustment power groups and corresponding uncertain fluctuation power groups, to construct a target function of a power utilization system scheduling model;

[0251] The model solving module is configured to solve the target function based on the device regulation constraint, to obtain an optimal adjustment power group;

[0252] The power scheduling module is configured to perform power scheduling on the power utilization system by using the optimal adjustment power group.

[0253] It can be understood that the above-mentioned device item embodiments are corresponding to the method item embodiments of the present application, and can realize the power utilization system scheduling method provided by any one of the above-mentioned method item embodiments of the present application.

[0254] It should be noted that the device embodiments described above are only schematic, and part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. In addition, in the device embodiment provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0255] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only specific embodiments of the present application and are not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dispatching a power distribution system, characterized in that, include: Obtain basic system data for the power distribution system; Based on the system's basic data, fluctuation power constraints and equipment control constraints are constructed. Based on the equipment control constraints, a set of initial adjustment power groups is randomly sampled; wherein, the initial adjustment power group includes the initial adjustment power corresponding to several types of power distribution resources; Based on the aforementioned fluctuation power constraint, several uncertain fluctuation power groups are randomly sampled; Based on several uncertain fluctuation power groups, the initial adjustment power group is subjected to power correction processing to obtain several post-fluctuation adjustment power groups; Based on the weighted integration of several fluctuating power groups and corresponding uncertain fluctuating power groups, the objective function of the power distribution system scheduling model is constructed. Based on the aforementioned equipment control constraints, the objective function is solved to obtain the optimal control power set; The optimal regulating power group is used to perform power dispatching on the power distribution system.

2. The power distribution system dispatching method according to claim 1, characterized in that, The construction of fluctuation power constraints and equipment control constraints based on the system's basic data includes: The total controllable capacity and the regulation power range of several types of power distribution resources are obtained from the system's basic data. Based on the total controllable capacity and the regulation power range of each of the power distribution resources, fluctuation power constraints and deterministic response power constraints are constructed. The power distribution resources are industrial high-energy-consuming load resources, industrial and commercial interruptible load resources, user-side energy storage resources, electric vehicle charging resources, user-side distributed generation resources, or smart terminal smart power resources. Obtain the interruption ratio limit range of industrial and commercial interruptible load resources from the system basic data, and construct the interruption ratio limit constraint of industrial and commercial interruptible loads; The basic electricity price, incentive electricity price, and carbon emissions per unit power are obtained from the system's basic data to construct a constraint on the comprehensive change in electricity price and carbon price. The fluctuation power range and response sensitivity of electric vehicle charging resources are obtained from the system's basic data to construct electric vehicle fluctuation power constraints. The cross-time period load transfer rate range and baseline load amount of smart terminal smart electricity resources are obtained from the system basic data, and smart terminal smart electricity transfer load constraints are constructed. The lower limit of self-consumption rate and total power generation of user-side distributed generation resources are obtained from the system basic data to construct user-side distributed generation self-consumption rate constraints. Obtain the lower limit of aggregator load agent coverage from the system's basic data, and construct aggregator load agent coverage constraints. The upper limit of the control power and the number of aggregator agent users for several types of power distribution resources are obtained from the system basic data to construct the total control load constraint; Obtain the regulation efficiency range of user-side energy storage resources from the system's basic data, and construct user-side energy storage regulation efficiency constraints; The deterministic response power constraint, the industrial and commercial interruptible load interruption ratio limit constraint, the comprehensive change of electricity price and carbon price constraint, the electric vehicle fluctuating power constraint, the smart terminal smart electricity transfer load constraint, the user-side distributed generation self-consumption rate constraint, the aggregator load proxy coverage rate constraint, the total load regulation constraint, and the user-side energy storage regulation efficiency constraint are integrated to form equipment regulation constraints.

3. The power distribution system dispatching method according to claim 2, characterized in that, The fluctuation power constraint is: -(P i,max -P i,min )≤ΔP i,j ≤P i,max -P i,min In the formula, ΔP i,j P represents the fluctuating power of the i-th type of power distribution resource at time j; i,max The upper limit of fluctuating power for the i-th type of power distribution resource; P i,min C represents the lower limit of fluctuating power for the i-th type of power distribution resources; L denoted as , where is the total controllable capacity of all types of resources; and 'n' is the quantity of resources.

4. The power distribution system dispatching method according to claim 3, characterized in that, Based on the aforementioned fluctuation power constraint, several uncertain fluctuation power groups are randomly sampled, including: Determine the probability distribution model of uncertain fluctuating power for each type of power distribution resource; For each type of power distribution resource, based on the uncertain fluctuation power probability distribution model, several uncertain fluctuation powers that meet the fluctuation power constraints are randomly sampled. Several uncertain fluctuation power corresponding to various types of power distribution resources are randomly combined to generate several uncertain fluctuation power groups; wherein, each uncertain fluctuation power group includes the uncertain fluctuation power corresponding to various types of power distribution resources.

5. The power distribution system dispatching method according to claim 4, characterized in that, The models for determining the probability distribution of uncertain fluctuating power for various types of power distribution resources include: The probability distribution model for uncertain fluctuating power of industrial high-energy-consuming resources is as follows: In the formula, The variable power of high-energy-consuming industrial resources at time j; N is a normal distribution; μ 1,j Let be the mean power of the uncertain fluctuation of industrial high-energy-consuming resources at time j; κ1 represents the basic variance of the uncertain fluctuation power of industrial high-energy-consuming load resources at time j; κ1 is the influence coefficient of load adjustment; P base,1,j This represents the baseline power of high-energy-consuming industrial resources at time j. Let κ be the deterministic response power at time j; 1,c C is the carbon price volatility coefficient. carbon,j Let be the carbon allowance trading price at time j; The average carbon price; The probability distribution model for uncertain fluctuating power of interruptible load resources in industry and commerce is as follows: In the formula, P represents the uncertain fluctuating power of interruptible load resources in industrial and commercial applications at time j; base,2,j X represents the baseline power of interruptible load resources in industrial and commercial applications at time j; 2,j n is the actual number of interrupt devices at time j; 2,j Let β be the number of interruptible devices at time j; 2,j Let be the slope parameter of the Sigmoid function for industrial and commercial interruptible load resources at time j; ΔC is the increment of emission reduction cost; e2 is the carbon emission per unit power of industrial and commercial interruptible load resources; Δt j δj represents the interval time at time j; δ2 represents the midpoint threshold of the interruption ratio of interruptible load resources in industrial and commercial sectors; where the actual number of interruptible devices follows a binomial distribution. The probability distribution model of uncertain fluctuating power of user-side energy storage resources is as follows: In the formula, Let δ represent the uncertain fluctuating power of smart electricity resources at time j; U represents a uniform distribution; δ 3,j The fluctuation coefficient of user-side energy storage resources at time j; SOC 3,j State of charge (SOC) of user-side energy storage resources at time j. min State of charge (SOC) is the limit of the state of charge of user-side energy storage resources. max ζ3 represents the upper limit of the state of charge of user-side energy storage resources; ζ3 is the amplification factor of carbon price fluctuations. The probability distribution model for uncertain fluctuating power of electric vehicle charging resources is as follows: In the formula, The uncertain fluctuating power of electric vehicle charging resources at time j; Truncated-N is a truncated normal distribution; μ 4,j The mean of the uncertain fluctuation power of the electric vehicle charging resources at time j; The variance of the uncertain fluctuating power of electric vehicle charging resources at time j; t dep,j Let be the electric vehicle's departure time at time j; j be the current time; τ4 be the characteristic decay constant of time distance on charging uncertainty; ξ4 be the perturbation coefficient of carbon price on user behavior; P total,4,j The upper limit of the uncertain fluctuation power of electric vehicle charging resources at time j; The probability distribution model for uncertain fluctuating power of user-side distributed generation resources is as follows: In the formula, Let be the uncertain fluctuating power of the user-side distributed generation resources at time j; The natural output of distributed generation resources on the user side at time j; I j Let I be the light intensity at time j; ΔI j Let I be the light intensity fluctuation at time j; ref For reference light intensity; θ 5,j e represents the adjustment coefficient of the user-side distributed generation resources at time j; grid,j Let e ​​be the average carbon emission factor of the power grid at time j; dg The carbon emission factor for distributed generation; ΔC carbon,j Let be the fluctuating carbon price at time j; where the fluctuating carbon price follows a normal distribution. The probability distribution model of uncertain fluctuating power for smart terminal power consumption resources is as follows: In the formula, Let be the uncertain fluctuating power of smart electricity resources at time j; Laplace represents the Laplace distribution; b 6,j λ represents the uncertainty fluctuation scale parameter of smart electricity resources at time j for intelligent terminals; 6,jk C represents the time-series elasticity coefficient of smart electricity resources for intelligent terminals between time j and time k; inc,k Let C be the incentive electricity price at time k; inc,j Let e ​​be the incentive electricity price at time j; grid,k Let be the average carbon emission factor of the power grid during time period k.

6. The power distribution system dispatching method according to claim 5, characterized in that, The method involves performing power correction processing on the initial adjustment power group based on several uncertain fluctuation power groups to obtain several post-fluctuation adjustment power groups, including: For each uncertain fluctuation power group, the uncertain fluctuation power in the uncertain fluctuation power group is superimposed with the initial regulation power of the same power distribution resource in the initial regulation power group to form several post-fluctuation regulation powers. The post-fluctuation adjustment power of each power is summarized to form a post-fluctuation adjustment power group.

7. The power distribution system dispatching method according to claim 6, characterized in that, The objective function for constructing the power distribution system scheduling model, based on the weighted integration of several post-fluctuation adjustable power groups and corresponding uncertain fluctuating power groups, includes: For each post-fluctuation adjustment power group, and in conjunction with the corresponding uncertain fluctuation power group, the revenue value of the power distribution system and the total carbon emissions of the power distribution system are calculated respectively; wherein, the revenue value of the power distribution system includes the revenue value of aggregators and the revenue value of various power distribution resources; the total carbon emissions of the power distribution system include resource-based carbon emissions and resource uncertainty carbon emission increments; The revenue value of the power distribution system and the total carbon emissions of the power distribution system are weighted and integrated to obtain the target value of the uncertain scenario of the adjusted power group after fluctuation. We weight and integrate the uncertain target values ​​of all fluctuating power adjustment groups to construct the objective function of the power distribution system scheduling model.

8. The power distribution system dispatching method according to claim 7, characterized in that, The aggregator's revenue is calculated using the following formula: In the formula, R A λ represents the aggregator's revenue. i C represents the agency weight of the i-th type of power distribution resource; base,j Let ΔP be the base electricity price at time j; i,j Let e ​​be the fluctuating power of the i-th type of power distribution resource at time j; i For the i-th type of power distribution resources, the carbon emissions per unit power are denoted as ; The revenue value of high-energy-consuming industrial resources is calculated using the following formula: In the formula, R1 is the revenue value of industrial high energy load resources; e1 is the carbon emission per unit power of industrial high energy load resources. The revenue value of interruptible load resources for industrial and commercial use is calculated using the following formula: R2=(C inc,j -C base,j )·ΔP 2,j +e2·ΔP 2,j ·Δt j ·C carbon,j In the formula, R2 represents the revenue value of interruptible load resources in industry and commerce; ΔP 2,j The fluctuating power of interruptible load resources in industry and commerce at time j; The revenue from user-side energy storage resources is calculated using the following formula: In the formula, R 3,chg The revenue value of energy storage charging for user-side energy storage resources; The charging power of the user-side energy storage resources at time j; e ren,j Let R be the carbon emission factor of renewable energy power generation at time j; 3,dsg The energy storage discharge revenue value of user-side energy storage resources; ηj represents the discharge power of the user-side energy storage resource at time j; η3 represents the regulation efficiency of the user-side energy storage resource. The revenue from electric vehicle charging resources is calculated using the following formula: In the formula, R4 represents the revenue value of electric vehicle charging resources; The charging power of the electric vehicle at time j; The revenue from user-side distributed generation resources is calculated using the following formula: In the formula, R5 represents the revenue value of distributed generation resources on the user side; Let J represent the total power generation of the user-side distributed generation resources at time j. This represents the self-consumption power generation of the user-side distributed generation resources at time j. The revenue value of smart electricity resources from intelligent terminals is calculated using the following formula: In the formula, R6 represents the revenue value of smart electricity resources from the intelligent terminal; P 6,k→j This refers to the load amount that is transferred from time k to time j for smart power consumption resources of intelligent terminals.

9. The power distribution system dispatching method according to claim 8, characterized in that, The resource-based carbon emissions are calculated using the following formula: E i =ΔP i,j ×e i In the formula, E i For the resource-based carbon emissions of the i-th type of electricity distribution resources; The incremental carbon emissions due to resource uncertainty are calculated using the following formula: In the formula, ΔE i,unc For the incremental carbon emissions due to resource uncertainty of the i-th type of power distribution resources; Let represent the uncertain power of the i-th type of power distribution resource.

10. A power distribution system dispatching device, characterized in that, include: The system includes a data acquisition module, a constraint construction module, a first sampling module, a second sampling module, a power correction module, a model construction module, a model solving module, and a power scheduling module. The data acquisition module is used to acquire the basic system data of the power distribution system; The constraint construction module is used to construct fluctuating power constraints and equipment control constraints based on the system's basic data. The first sampling module is used to randomly sample a set of initial adjustment power groups based on the equipment control constraints; wherein, the initial adjustment power group includes the initial adjustment power corresponding to several types of power distribution resources; The second sampling module is used to randomly sample several uncertain fluctuation power groups based on the fluctuation power constraint; The power correction module is used to perform power correction processing on the initial adjustment power group based on several uncertain fluctuation power groups to obtain several post-fluctuation adjustment power groups. The model building module is used to integrate several fluctuating power groups and corresponding uncertain fluctuating power groups with weights to construct the objective function of the power distribution system scheduling model. The model solving module is used to solve the objective function based on the equipment control constraints to obtain the optimal control power set; The power scheduling module is used to perform power scheduling on the power distribution system using the optimal adjustment power group.

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