Day-ahead joint optimization scheduling method and system considering multi-element resource inertia

Abstract

The present application relates to the technical field of safe and stable operation of power system, and particularly relates to a day-ahead joint optimization scheduling method and system considering inertia of multi-element resources. A model of inertia and primary frequency modulation auxiliary service participated by multi-element resources is constructed; a day-ahead optimization scheduling model of large power grid considering inertia and primary frequency modulation auxiliary service participated by multi-element resources is constructed; day-ahead optimization scheduling calculation considering inertia and primary frequency modulation auxiliary service participated by multi-element resources is carried out; and multi-element resource electric energy and various auxiliary service prices are calculated based on dual multipliers. The day-ahead joint optimization scheduling of inertia and primary frequency modulation auxiliary service for maintaining safe and stable operation of power grid under large disturbance is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safe and stable operation of power systems, and particularly relates to a day-ahead joint optimization scheduling method and system considering inertia of multi-element resources. BACKGROUND

[0002] With the increasing proportion of new energy in new power systems, the stable operation of power systems is facing unprecedented challenges. In order to maintain the stability and reliability of the system, frequency modulation auxiliary service has become an indispensable part of power system operation. The traditional frequency modulation service mainly relies on the regulation capacity of large generator units, which respond to the change of system frequency by adjusting the power generation, so as to maintain the stability of the system. However, this single-resource-dependent frequency modulation method has many limitations. In recent years, with the wide access of multi-element resources and the continuous development of power electronic devices, multi-element resources including wind power, photovoltaic, adjustable load and the like provide new possibilities for frequency modulation service of power systems. In view of the above phenomenon, in order to promote the development of multi-element resource frequency safety auxiliary service technology in China and promote the construction of new power system frequency safety auxiliary service market, it is necessary to study a day-ahead joint optimization scheduling method considering inertia and primary frequency modulation auxiliary service of multi-element resources. SUMMARY

[0003] In view of the problems existing in the prior art, the present application is proposed.

[0004] Therefore, the problem to be solved by the present application is how to solve the technical problem of maintaining the inertia and primary frequency modulation auxiliary service of the power grid in the case of large disturbance.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] In a first aspect, the embodiments of the present application provide a day-ahead joint optimization scheduling method considering inertia of multi-element resources, which comprises: constructing a model of inertia and primary frequency modulation auxiliary service of multi-element resources including thermal power units, wind power, photovoltaic, energy storage and frequency active support type load;

[0007] A day-ahead optimization scheduling model of large power grid considering inertia and primary frequency modulation auxiliary service of multi-element resources is constructed, the objective function is the sum of the energy and auxiliary service cost of various resources, and the constraint conditions include load balance, reserve capacity, line power flow and system frequency safety;

[0008] Day-ahead optimization scheduling calculation is performed, and the nonlinear terms in the frequency safety constraint are linearized;

[0009] Based on the Lagrange multiplier method, the dual problem of day-ahead optimization scheduling is solved, and the marginal price of energy, inertia and primary frequency modulation auxiliary service is obtained, which is used as the settlement basis of various resources.

[0010] As a preferred scheme of the day-ahead joint optimization scheduling method considering multi-resource inertia provided by the application, wherein: the construction of the model based on multi-resource participation inertia and primary frequency regulation auxiliary service includes the following steps:

[0011] The construction of the thermal power unit model includes thermal power unit operation constraints and thermal power unit provided primary frequency regulation capacity constraints; the thermal power unit operation constraints include upper and lower output constraints, ramp constraints and start-up and shutdown time constraints, which are respectively expressed as follows:

[0012] Thermal power unit output upper and lower limit constraints:

[0013] u i,t P i,min ≤P i,t ≤u i,t P i,max -R i,t

[0014] Thermal power unit ramp constraints:

[0015]

[0016] Thermal power unit start-up and shutdown time constraints:

[0017]

[0018] Wherein, P i,min , P i,max are the minimum and maximum active power output of the thermal power unit i; P i,t represents the active power of the thermal power unit i at time t; R i,t is the primary frequency regulation capacity of the thermal power unit i at time t; u i,t is a 0-1 variable describing the start-up and shutdown state of the thermal power unit i at time t, u i,t =1 indicates that the unit is running, and u i,t =0 indicates that the unit is shut down; P i,UR and P i,DR are the maximum increase and decrease values of the active power of the thermal power unit i at time t; X i,t is the number of continuous running (X i,t is a positive value) or continuous shutdown (X i,t is a negative value) of the unit i from time t; M i,GT and M i,DT are the minimum running time and minimum shutdown time of the unit i, respectively.

[0019] The thermal power unit provided primary frequency regulation capacity constraints adopt the following expression:

[0020]

[0021] wherein, represents the primary frequency regulation capacity ramping limit of the thermal power unit i;

[0022] In addition, the inertia time constant of the thermal power unit is related to the setting of the unit itself, and is usually an unchangeable constant.

[0023] As a preferred scheme of the day-ahead joint optimization scheduling method considering the inertia of multiple resources according to the present application, wherein: the construction of the model based on the inertia of multiple resources participating and the primary frequency regulation auxiliary service comprises the following steps:

[0024] The construction of the wind turbine model comprises wind turbine operation constraints, wind turbine provided primary frequency regulation capacity constraints and wind turbine provided inertia constraints, wherein the rotational inertia of the wind turbine transmission shaft can provide certain inertia support for the system, the wind power needs to reserve a certain capacity in the output power to provide primary frequency regulation service to the system, and the wind turbine operation constraints can be expressed as follows:

[0025]

[0026] wherein, P w,i,t and P respectively represent the active power and the predicted power of the wind power i at time t; R w,i,t represents the primary frequency regulation power capacity of the wind power i at time t;

[0027] The wind turbine provided primary frequency regulation capacity constraints can be expressed as follows:

[0028]

[0029] wherein, ε w,i represents the value proportion coefficient of the primary frequency regulation capacity of the wind turbine i.

[0030] The wind turbine provided inertia constraints can be expressed as follows:

[0031]

[0032] wherein, and respectively represent the inertia time constant and the set value of the wind power i at time t; is a 0-1 decision variable for describing the inertia support service provided by the wind power, represents that the wind power provides inertia support at time t, represents that it does not provide;

[0033] In addition, to avoid the secondary oscillation caused by the active power absorbed by the system during the process of restoring the speed of the fan, the time for restoring the speed of the fan is delayed by the control method until the primary frequency modulation of the system fully responds, the active power required by the fan is borne by all primary frequency modulation resources, and the maximum inertia support power required by the wind power is expressed as follows:

[0034]

[0035] wherein, represents the rated power of the wind power i; represents the maximum frequency change rate allowed by the system; f0 represents the rated frequency of the system;

[0036] The construction of the model based on the inertia and primary frequency modulation auxiliary service of the multi-element resource also includes the following steps: constructing a photovoltaic unit model, the photovoltaic unit model including photovoltaic operation constraints and photovoltaic primary frequency modulation capacity constraints, and the photovoltaic array providing virtual inertia and primary frequency modulation service through the configuration of energy storage; the photovoltaic operation constraint adopts the following expression:

[0037]

[0038] wherein, P v,i,t and respectively represent the active power and the predicted power of the photovoltaic i at time t;

[0039] The photovoltaic primary frequency modulation capacity constraint adopts the following expression:

[0040]

[0041] wherein, represents the rated discharge power of the photovoltaic i configured with energy storage; epsilon v,i represents the value proportion coefficient of the primary frequency modulation capacity of the photovoltaic i; R v,i,t represents the primary frequency modulation power capacity of the photovoltaic i at time t;

[0042] The inertia time constant of the photovoltaic i The value is defined by software and needs to be considered comprehensively according to the performance of the configured energy storage.

[0043] As a preferred scheme of the day-ahead joint optimization scheduling method considering the inertia of multi-element resources, wherein: the construction of the model based on the inertia and primary frequency modulation auxiliary service of the multi-element resource includes the following steps:

[0044] The construction of the energy storage model includes the operation constraints in the discharge state of the energy storage, the operation constraints in the charging state of the energy storage, the charging and discharging state constraints of the energy storage, and the state of charge constraints of the energy storage,

[0045] The operation constraint of the energy storage in discharging state can be expressed as follows:

[0046]

[0047] wherein, respectively represent the discharging power of the energy storage i at time t and the maximum value thereof; and R ess,i,max respectively represent the primary frequency modulation power capacity of the energy storage i at time t in the discharging state and the maximum primary frequency modulation capacity of the energy storage i; is a 0-1 variable for describing the discharging state of the energy storage, represents that the energy storage is in the discharging state, represents that the energy storage is in the charging state; represents the maximum inertia support power of the energy storage i in the discharging state; and respectively represent the inertia time constant of the energy storage i in the discharging state and the maximum inertia time constant of the energy storage i. is a continuous adjustable decision variable, and the charging state is the same;

[0048] The operation constraint of the energy storage in charging state can be expressed as follows:

[0049]

[0050] wherein, respectively represent the discharging power of the energy storage i at time t and the maximum value thereof; represents the primary frequency modulation power capacity provided by the energy storage i at time t in the charging state; is a 0-1 variable for modifying the charging state of the energy storage, represents that the energy storage is in the charging state, represents that the energy storage is in the discharging state; represents the maximum inertia support power of the energy storage i in the charging state; represents the inertia time constant of the energy storage i in the charging state;

[0051] The charging and discharging state constraint of the energy storage can be expressed as follows:

[0052]

[0053] The state of charge constraint of the energy storage can be expressed as follows:

[0054]

[0055] S ess,i,0 = S ess,i,T

[0056] wherein, S ess,i,tSoC i (t) is the state of charge of the energy storage i at time t; E i,ess SoC i (t) is the state of charge of the energy storage i at time t; E ess,i,min SoC i (t) is the state of charge of the energy storage i at time t; E ess,i,max SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E

[0057] SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E

[0058] SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E ess SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E SoC i (t) is the state of charge of the energy storage i at time t; E

[0059] The model based on multi-element resource participating in inertia and primary frequency modulation auxiliary service is constructed, and the model includes the following steps:

[0060] The frequency active support type load model includes frequency active support type load operation constraints and frequency active support type load providing primary frequency modulation capacity constraints.

[0061] The frequency active support type load operation constraints can be expressed as follows:

[0062]

[0063]

[0064] Wherein, T is the number of scheduling cycle periods; W ad,max,i W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P ad,min,i W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P ad,i,t W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P ad,i,max W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P W is the maximum and minimum value of the electric quantity of the frequency active support type load i after adjustment; P ad,i,tTo describe the 0-1 variable of the frequency active support type load up or down, η ad,i,t =0 indicates that the frequency active support type load is down, η ad,i,t =1 indicates that the frequency active support type load is up;

[0065] The frequency active support type load provides a primary frequency modulation capacity constraint, which can be expressed as follows:

[0066]

[0067] Where, R ad,i,t represents the primary frequency modulation power capacity of the frequency active support type load i at time t.

[0068] As a preferred scheme of the day-ahead joint optimization scheduling method considering the inertia of multiple resources, wherein: the day-ahead optimization scheduling model of the large power grid considering the inertia of multiple resources participating in primary frequency modulation and auxiliary service is constructed, comprising the following steps:

[0069] The objective function is constructed:

[0070] min F=F g +F w +F ess +F ad

[0071] Where, F is the overall purchase cost. F g , F w , F ess , F ad respectively represent the cost of thermal power units, wind power, photovoltaic, energy storage and frequency active support type load; Specifically, the following expressions are used:

[0072]

[0073] Where, N G , N s respectively represent the total number of thermal power units and the number of thermal power unit output segments; ρ i,t,k and respectively represent the kth segment of the thermal power unit i at time t The corresponding output of the offer; represents the primary frequency modulation capacity offer of the thermal power unit; and respectively represent the start-up and shut-down cost of the thermal power unit i, which can be expressed as follows:

[0074]

[0075] Where, N w represents the total number of wind power; represents the offer of the primary frequency modulation service of the wind power unit;

[0076]

[0077] where N ess represents the total number of energy storage; respectively represent the charging and discharging price of energy storage i for peak regulation service; respectively represent the price of primary frequency regulation service and inertia support service of energy storage i;

[0078]

[0079] where N ad represents the total number of frequency active support type load; respectively represent the up and down regulation price of frequency active support type load i for peak regulation service at time t; represents the price of primary frequency regulation service of frequency active support type load;

[0080] where the constraint conditions include system load power balance constraint, system positive and negative reserve capacity constraint, branch power flow safety constraint, and system frequency safety constraint;

[0081] The system load power balance constraint is expressed as follows:

[0082] The system needs to ensure that the load power balance demand is met during the operation period. For each period t, the load power balance constraint can be described as:

[0083]

[0084] where P i,t represents the winning power of thermal power unit i at time t; P load,t represents the predicted load of the system at time t; P i,t The analytical expression is as follows:

[0085]

[0086] The system positive and negative reserve capacity constraint is expressed as follows:

[0087]

[0088] where R L,t is the active reserve required for the load at time t; R w,t,up and R w,t,down are the positive and negative rotational reserve required for wind power at time t, respectively, where R L,t , R w,t,up , and R w,t,down are all taken as a certain proportion of the load output and wind power output;

[0089] The branch power flow safety constraint is expressed as follows:

[0090]

[0091] where N bus is the number of system nodes; N b is the number of lines; P l,max is the active power transmission limit of line l; G j,l is the power transfer factor of line l from node j; P g,j,t , P w,j,t , P v,j,t are the active power of conventional units, wind power units, and photovoltaic units at node j at time period t, respectively; are the charging and discharging power of energy storage at node j at time period t, respectively; P load,j,t and are the active load and frequency active support load at node j at time period t, respectively;

[0092] The system frequency safety constraint is expressed as follows:

[0093] To meet the demand for operation efficiency at a large grid scale, the frequency response of thermal power units and resources based on virtual synchronous machines, including wind power, photovoltaic, energy storage, and frequency active support load, is linearly approximated, and T g and T VSM represent the delivery time of primary frequency modulation service of thermal power units and multiple resources, respectively; t del,g and t del,VSM represent the response delay of thermal power units and multiple resources, respectively.

[0094] The RoCoF constraint is expressed as follows:

[0095]

[0096] where P L is the active power shortage when a disturbance occurs; H sys represents the total inertia of the system, and its expression is as follows:

[0097]

[0098] The frequency minimum point constraint is expressed as follows:

[0099]

[0100] where Δf max represents the maximum frequency deviation allowed by the system; and represent the total capacity of thermal power units and resources based on virtual synchronous machines participating in primary frequency modulation, respectively, and are expressed as follows:

[0101]

[0102] The quasi-steady constraint adopts the following expression:

[0103]

[0104] Wherein, ε PFR represents the proportion coefficient of the active power shortage to the primary frequency modulation capacity of demand.

[0105] As a preferred scheme of the day-ahead joint optimization scheduling method considering multi-resource inertia provided by the application, wherein: the day-ahead optimization scheduling model of the large power grid based on considering multi-resource inertia and primary frequency modulation auxiliary service for day-ahead optimization scheduling calculation, comprises the following steps:

[0106] For subsequent solution, the model needs to be linearized to become a mixed integer linear model; the frequency minimum point constraint is still a nonlinear constraint, and the following mainly aims at linearizing the constraint;

[0107] The frequency minimum point constraint is deformed to obtain the following expression:

[0108]

[0109] Wherein, is a three nonlinear term;

[0110] The term and The term first carries out binary conversion processing on , and the expression is as follows:

[0111]

[0112] Wherein, L represents the number of bits converted into binary, a reasonable value can be set according to the range of primary frequency modulation capacity of the thermal power unit in the example, and the setting of ε is the approximate processing of rounding off to an integer;

[0113] The term and The expression of the term and is as follows:

[0114]

[0115]

[0116] After conversion, the nonlinear term of the above formula only exists 0-1 decision variable term multiplied by 0-1 decision variable term and 0-1 decision variable term multiplied by continuous decision variable term, for the two terms, the following conversion method can be used for conversion:

[0117] max z y,z∈{0,1},y∈{R +}ory∈{0,1}

[0118] The above max z y is equivalent to:

[0119] max Yz

[0120] s.t.-(1-z)M≤Y z -y≤(1-z)M

[0121] -zM≤Y z ≤zM

[0122] y∈R + ,Y z ∈R

[0123] In the formula, M takes a large value, and the method converts the nonlinear expression of multiplying the 0-1 variable by the positive real variable into a linear expression by defining a new variable Y z .

[0124] The term is less than the active power shortage of the system under large disturbance due to the VSM providing a frequency modulation power L . It is assumed that P * ∈[0,P L ];

[0125] (P * ) 2 is monotonically increasing on 0 to P L , and P * is bounded, the piecewise linear approximation is performed on (P * ) 2 , the slope of each piece after linearization is k s , the constant term is b s , the total number of segments is N S , and the right side of the expression after transformation of the frequency minimum constraint can be converted to:

[0126]

[0127] At this point, the linearization is complete.

[0128] As a preferred scheme of the day-ahead joint optimization scheduling method considering multi-element resource inertia provided by the application, the method comprises the following steps of:

[0129] Convert the total inertia of the system into the second-order cone constraint at the lowest point, and the expression of the total inertia of the system is transformed into the following:

[0130]

[0131] Replacing the total inertia of the system with elements a, b, and c, we have:

[0132]

[0133] The frequency minimum point constraint can be transformed into:

[0134] ab≥c 2

[0135] right The expression to be binary converted is converted into the binary norm form:

[0136]

[0137] Define the dual variables in the first and second rows on the left as λ1 and λ2 respectively, and μ on the right;

[0138] Based on the objective function and The expression of binary conversion can be obtained as the Lagrangian function:

[0139]

[0140] based on The term expression can be used to obtain the inertia price of multiple resources based on virtual synchronous machine Electricity price Price of synchronous machine primary frequency modulation A frequency modulation price based on multiple resources of virtual synchronous machine

[0141]

[0142] In a second aspect, an embodiment of the present invention provides a day-ahead joint optimization scheduling system that considers the inertia of multiple resources. The system includes a building module that constructs a model for the participation of multiple resources, including thermal power units, wind power, photovoltaic power, energy storage, and frequency active support loads, in inertia and primary frequency regulation auxiliary services.

[0143] The constraint module constructs a large-scale power grid day-ahead optimization dispatch model that considers the inertia of multiple resources and primary frequency regulation ancillary services. The objective function is the sum of the electric energy of various resources and the cost of ancillary services. The constraints include load balance, reserve capacity, line flow, and system frequency security.

[0144] The processing module performs day-ahead optimization scheduling calculations and linearizes nonlinear terms in frequency security constraints.

[0145] The settlement module solves a dual problem of the day-ahead optimal scheduling based on a Lagrange multiplier method to obtain marginal electricity prices of electric energy, inertia and primary frequency auxiliary service as settlement basis of various resources.

[0146] In a third aspect, an embodiment of the present application provides a computer device comprising a memory and a processor, and the memory stores a computer program, wherein the computer program instructions are executed by the processor to implement the steps of the day-ahead joint optimization scheduling method considering inertia of multiple resources as described in the first aspect of the present application.

[0147] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program instructions are executed by the processor to implement the steps of the day-ahead joint optimization scheduling method considering inertia of multiple resources as described in the first aspect of the present application.

[0148] The present application has the beneficial effects that: the present application constructs a model of inertia and primary frequency auxiliary service participated by multiple resources, constructs a day-ahead optimal scheduling model of a large power grid considering inertia and primary frequency auxiliary service participated by multiple resources, performs day-ahead optimal scheduling calculation considering inertia and primary frequency auxiliary service participated by multiple resources, and finally calculates electric energy of multiple resources and prices of various auxiliary services based on dual multipliers. The present application considers a scheduling model including a thermal power unit, wind power, photovoltaic, energy storage and frequency actively supporting load providing frequency support. The present application considers capacity constraints and corresponding energy reservation of inertia and primary frequency service provided by energy storage in charging and discharging operation states, considers power compensation of a transmission shaft system of a doubly-fed wind power generator restoring a power frequency, establishes a model of frequency actively supporting load participating in peak shaving and primary frequency modulation, and establishes a multiple resource FSCUC joint optimization model.

[0149] On the basis of constructing a system frequency safety constraint, the present application determines frequency support capability required for guaranteeing system frequency safety. Based on this, in order to obtain deficiency inertia and primary frequency auxiliary service, inertia and auxiliary service provided by multiple resources are quoted to participate in market competition, which can more accurately reflect the value of inertia and primary frequency for system safety and stable operation compared with compensation.

[0150] The technical problem of obtaining inertia and primary frequency auxiliary service for maintaining safety and stability of a power grid in a large disturbance situation is solved. BRIEF DESCRIPTION OF DRAWINGS

[0151] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0152] Fig. 1 A flow chart of the day-ahead joint optimization scheduling method considering multi-resource inertia;

[0153] Fig. 2 A computer device diagram of the day-ahead joint optimization scheduling method considering multi-resource inertia. DETAILED DESCRIPTION

[0154] In order to make the above objectives, characteristics and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0155] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details given below. In other instances, well-known methods have not been described in detail in order to avoid unnecessarily obscuring the present application.

[0156] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or mutually exclusive with other embodiments.

[0157] Embodiment 1

[0158] Reference Figs. 1-2 For the first embodiment of the present application, the embodiment provides a day-ahead joint optimization scheduling method considering multi-resource inertia, comprising,

[0159] S100: Constructing a model of multi-resource participating in inertia and primary frequency modulation auxiliary service including thermal power units, wind power, photovoltaic, energy storage and frequency active support type load;

[0160] S101: Based on the construction of the model of multi-resource participating in inertia and primary frequency modulation auxiliary service, comprising the following steps:

[0161] The construction of the thermal power unit model includes thermal power unit operation constraints and thermal power unit provided primary frequency modulation capacity constraints; the thermal power unit operation constraints include output upper and lower limit constraints, climbing constraints and start-up and shutdown time constraints, which are respectively expressed as follows:

[0162] Thermal power unit output upper and lower limit constraints:

[0163] u i,t P i,min ≤P i,t ≤u i,t P i,max -R i,t

[0164] Climbing constraint of thermal power unit:

[0165]

[0166] Start-up and shut-down time constraint of thermal power unit:

[0167]

[0168] Wherein, P i,min , P i,max are minimum and maximum active power of thermal power unit i; P i,t represents active power of thermal power unit i at t time; R i,t is primary frequency modulation capacity of thermal power unit i at t time; u i,t is 0-1 variable describing start-up and shut-down state of thermal power unit i at t time, u i,t =1 indicates that the unit is running, u i,t =0 indicates that the unit is shut down; P i,UR and P i,DR are maximum rising value and falling value of active power of thermal power unit i at t period; X i,t is the number of periods of continuous operation (X i,t is positive) or continuous shutdown (X i,t is negative) of unit i to t time; M i,GT and M i,DT are minimum running time and minimum shutdown time of unit i;

[0169] The primary frequency modulation capacity constraint of thermal power unit is expressed as follows:

[0170]

[0171] Wherein, represents primary frequency modulation capacity climbing limit of thermal power unit i;

[0172] In addition, the inertia time constant of thermal power unit is related to the setting of the unit itself, and is usually an unchangeable constant.

[0173] S102: Construction of a model based on multi-element resources participating in inertia and primary frequency modulation auxiliary service, including the following steps:

[0174] Construction of a wind power unit model includes wind power unit operation constraints, wind power unit primary frequency modulation capacity constraints, and wind power unit inertia constraints, wherein the rotational inertia of the transmission shaft of the wind power unit can provide certain inertia support for the system, and the wind power needs to reserve a certain capacity in the output power to provide primary frequency modulation service to the system, and the wind power unit operation constraints can be expressed as follows:

[0175]

[0176] Among them, P w,i,t and They represent the active power and predicted power of wind power i at time t respectively; R w,i,t represents the primary frequency regulation power capacity of wind power i at time t;

[0177] The primary frequency regulation capacity constraint provided by the wind turbine can be expressed as follows:

[0178]

[0179] Among them, ε w,i It represents the proportional coefficient of the primary frequency regulation capacity of fan i.

[0180] The inertia constraint provided by the wind turbine can be expressed as follows:

[0181]

[0182] in, and They represent the inertia time constant of wind turbine i at time t and its set value respectively; A 0-1 decision variable describing the inertia support service provided by wind power. Indicates that wind power provides inertia support at time t, Indicates that it is not provided;

[0183] In addition, to avoid secondary oscillation caused by the absorption of active power from the system during the process of wind turbine speed recovery after providing inertia support, the control method is used to delay the time for wind turbine speed recovery until the system primary frequency regulation fully responds. The active power required for wind turbine recovery is shared by all primary frequency regulation resources. The maximum inertia support power required by wind power is expressed as follows:

[0184]

[0185] in, represents the rated power of wind power i; Indicates the maximum frequency change rate allowed by the system; f0 indicates the rated frequency of the system;

[0186] The construction of a model based on the participation of multiple resources in inertia and primary frequency regulation auxiliary services also includes the following steps: constructing a photovoltaic unit model, which includes photovoltaic operation constraints and photovoltaic primary frequency regulation capacity constraints. The photovoltaic array provides virtual inertia and primary frequency regulation services by configuring energy storage; the photovoltaic operation constraints are expressed as follows:

[0187]

[0188] wherein P v,i,t with respectively represent the active power and the predicted power of the photovoltaic i at time t;

[0189] The photovoltaic provides primary frequency modulation capacity constraint adopts the following expression:

[0190]

[0191] wherein, represents the rated discharge power of the photovoltaic i configured with energy storage; ε v,i represents the value proportion coefficient of the primary frequency modulation capacity of the photovoltaic i; R v,i,t represents the primary frequency modulation power capacity of the photovoltaic i at time t;

[0192] The inertia time constant of the photovoltaic i The value thereof is determined by software definition and needs to comprehensively consider the performance of the photovoltaic i configured with energy storage.

[0193] S103: Construction of a model based on multi-element resources participating in inertia and primary frequency modulation auxiliary services, including the following steps:

[0194] The construction of the energy storage model includes operation constraints in the discharge state of the energy storage, operation constraints in the charging state of the energy storage, charge-discharge state constraints of the energy storage, state of charge constraints of the energy storage,

[0195] The grid-type energy storage can reserve a part of capacity in the discharge state and reduce power in the charging state to realize the provision of inertia and the response of primary frequency modulation. Different from conventional units, the inertia time constant of the energy storage can be changed by adjusting the control parameters according to the scheduling needs.

[0196] The operation constraint of the energy storage in the discharge state can adopt the following expression:

[0197]

[0198] wherein, respectively represent the discharge power and the maximum value thereof of the energy storage i at time t; and R ess,i,max respectively represent the primary frequency modulation power capacity of the energy storage i at time t in the discharge state and the maximum value of the primary frequency modulation capacity of the energy storage i; is a 0-1 variable for describing the discharge state of the energy storage, represents that the energy storage is in the discharge state, represents that the energy storage is in the charging state; represents the maximum inertia support power of the energy storage i in the discharge state; and respectively represent the inertia time constant of the energy storage i in the discharge state and the maximum value of the inertia time constant of the energy storage i. is a continuous adjustable decision variable, and the state of charge is the same;

[0199] The operation constraint of the energy storage in the charging state can be expressed as follows:

[0200]

[0201] wherein, and represent the discharging power and its maximum value of the energy storage i at time t, respectively; represents the primary frequency modulation power capacity provided by the energy storage i at time t in the charging state; is a 0-1 variable for modifying the state of charge of the energy storage, represents that the energy storage is in the charging state, represents that the energy storage is in the discharging state; represents the maximum inertia support power of the energy storage i in the charging state; represents the inertia time constant of the energy storage i in the charging state;

[0202] The charging and discharging state constraint of the energy storage can be expressed as follows:

[0203]

[0204] The state of charge constraint of the energy storage can be expressed as follows:

[0205]

[0206] S ess,i,0 = S ess,i,T

[0207] wherein, S ess,i,t is the state of charge of the energy storage i at time t; E i,ess is the rated capacity of the energy storage i, in kw·h; S ess,i,min and S ess,i,max are the minimum and maximum capacities of the energy storage i under stable operation, respectively; and represent the charging and discharging efficiencies of the energy storage i, respectively; represents the reserved energy of the energy storage i at time t; is to ensure that the energy storage has enough energy to release to support frequency safety in the discharging state, and its expression is as follows:

[0208]

[0209] wherein, and represent the energy required to be reserved by the energy storage i at time t to provide inertia and primary frequency modulation services, respectively; T ess represents the delivery time of the primary frequency modulation service of the energy storage. Wherein, The integral of the response curve of the energy storage primary frequency modulation is linearly approximated in the present application;

[0210] Based on the model of the multi-element resource participating in the inertia and primary frequency modulation auxiliary service, the following steps are included:

[0211] The frequency active support type load model includes the frequency active support type load operation constraint and the frequency active support type load provided primary frequency modulation capacity constraint.

[0212] The frequency active support type load operation constraint can be expressed as follows:

[0213]

[0214] Wherein, T is the number of scheduling period; W ad,max,i and W ad,min,i are the maximum and minimum values of the power of the frequency active support type load i after adjustment; P ad,i,t , represent the load baseline and the power after scheduling of the frequency active support type load i at time t. and represent the up-regulation and down-regulation power of the frequency active support type load; P ad,i,max is the upper limit of the adjustment power of the frequency active support type load i at time t. and represent the maximum up-regulation and down-regulation power of the frequency active support type load i; η ad,i,t is a 0-1 variable describing the up-regulation or down-regulation of the frequency active support type load, η ad,i,t = 0 indicates the down-regulation of the frequency active support type load, and η ad,i,t = 1 indicates the up-regulation of the frequency active support type load.

[0215] The frequency active support type load provided primary frequency modulation capacity constraint can be expressed as follows:

[0216]

[0217] Wherein, R ad,i,t represents the primary frequency modulation power capacity of the frequency active support type load i at time t.

[0218] S200: A day-ahead optimal scheduling model of a large power grid considering multi-element resources participating in inertia and primary frequency modulation auxiliary service is constructed, the objective function is the sum of the electric energy of various resources and the auxiliary service cost, and the constraint conditions include load balance, reserve capacity, line power flow and system frequency safety.

[0219] S201: Based on the construction of the day-ahead optimal scheduling model of a large power grid considering multi-element resources participating in inertia and primary frequency modulation auxiliary service, the following steps are included:

[0220] Objective function is constructed as:

[0221] min F = F g +F w +F ess +F ad

[0222] Where F is the overall purchase cost. F g , F w , F ess , F ad represent the cost of thermal power units, wind power, photovoltaic, energy storage and frequency active support type load respectively; Specifically, the following expressions are adopted:

[0223]

[0224] Where N G , N s represent the total number of thermal power units and the number of thermal power unit output segments respectively; p i,t,k and represent the kth segment of thermal power unit i at time t and the corresponding output; represent the first frequency modulation capacity price of thermal power unit; and represent the start-up and shut-down cost of thermal power unit i, which adopts the following expression:

[0225]

[0226] Where N w represents the total number of wind power; represents the price of wind power unit first frequency modulation service;

[0227]

[0228] Where N ess represents the total number of energy storage; represent the charge and discharge price of energy storage i peak regulation service; represent the first frequency modulation service and inertia support service price of energy storage;

[0229]

[0230] Where N ad represents the total number of frequency active support type load; represent the up and down price of frequency active support type load i at time t for peak regulation service; represents the first frequency modulation service price of frequency active support type load;

[0231] The constraints include system load power balance constraints, system positive and negative reserve capacity constraints, branch power flow security constraints, and system frequency security constraints;

[0232] The system load power balance constraint is expressed as follows:

[0233] The system needs to ensure that the load power balance requirements are met during the operation period. For each period t, the load power balance constraint can be described as:

[0234]

[0235] Among them, P i,t represents the winning bid power of thermal power unit i at time t; P load,t represents the predicted load of the system at time t; P i,t The analytical expression is as follows:

[0236]

[0237] The system's positive and negative reserve capacity constraints are expressed as follows:

[0238]

[0239] Among them, R L,t is the active reserve required for the corresponding load at time t; R w,t,up With R w,t,down are the positive and negative spinning reserves required by wind power at time t, where R L,t 、R w,t,up and R w,t,down They are respectively determined by a certain ratio of load output and wind power output;

[0240] The branch power flow safety constraint is expressed as follows:

[0241]

[0242] Among them, N bus is the number of system nodes; N b is the number of lines; P l,max is the active power transmission limit of line l; G j,l is the power transfer factor of node j to line l; P g,j,t 、P w,j,t 、P v,j,t are respectively the active power of conventional units, wind turbines and photovoltaic units at node j in period t; are the energy storage charging and discharging power at node j during period t; P load,j,t and are the active load and frequency active support load at node j in period t respectively;

[0243] The system frequency safety constraint adopts the following expression:

[0244] To meet the demand for operation efficiency under large grid scale, the frequency response of thermal power units and resources based on virtual synchronous machines, including wind power, photovoltaic, energy storage and frequency active support type load, is linearly approximated, and T g and T VSM respectively represent the delivery time of the primary frequency modulation service of thermal power units and multiple resources; t del,g and t del,VSM respectively represent the response delay of thermal power units and multiple resources;

[0245] The RoCoF constraint adopts the following expression:

[0246]

[0247] Where, P L is the active power shortage when the disturbance occurs; H sys represents the total inertia of the system, and its expression is as follows:

[0248]

[0249] The frequency minimum constraint adopts the following expression:

[0250]

[0251] Where, Δf max represents the maximum frequency deviation allowed by the system; and respectively represent the total capacity of thermal power units and resources based on virtual synchronous machines participating in primary frequency modulation, which adopts the following expression:

[0252]

[0253] The quasi-steady-state constraint adopts the following expression:

[0254]

[0255] Where, ε PFR represents the proportion coefficient of the demand primary frequency modulation capacity to the active power shortage.

[0256] S300: Perform day-ahead optimal dispatching calculation, and linearize the nonlinear terms in the frequency safety constraint;

[0257] S301: Based on the large grid day-ahead optimal dispatching model considering the inertia and primary frequency modulation auxiliary service of multiple resources, perform day-ahead optimal dispatching calculation, including the following steps:

[0258] For subsequent solution needs, the model needs to be linearized to make it a mixed integer linear model; the frequency minimum point constraint is still a nonlinear constraint, and the following mainly focuses on the linearization of this constraint;

[0259] Transforming the frequency minimum point constraint yields the following expression:

[0260]

[0261] in, are three nonlinear terms;

[0262] Item and First of all, Perform binary conversion processing, the expression is as follows:

[0263]

[0264] Among them, L represents the number of bits converted to binary. A reasonable value can be set according to the range of the primary frequency regulation capacity of the thermal power unit in the example. The setting of ε is Perform approximate rounding to convert it into an integer;

[0265] Available Item and The term expression is as follows:

[0266]

[0267] After the transformation, the nonlinear terms in the above formula only include the 0-1 decision variable term multiplied by the 0-1 decision variable term and the 0-1 decision variable term multiplied by the continuous decision variable term. For these two terms, the following transformation method can be used:

[0268] max zy,z∈{0,1},y∈{R +}or y∈{0,1}

[0269] The above max zy is equivalent to:

[0270] max Y z

[0271] st-(1-z)M≤Y z -y≤(1-z)M

[0272] -zM≤Y z ≤zM

[0273] y∈R + ,Y z ∈R

[0274] In the formula, M takes a large value. This method defines a new variable Yz The nonlinear expression of multiplying the original 0-1 variable by a positive real variable is converted into a linear expression:

[0275] The term is less than the active power shortage of the system under large disturbance, P L is greater than Let P * ∈[0,P L ];

[0276] (P * ) 2 monotonically increasing on 0 to P L , and P * is bounded, the piecewise linear approximation is performed on (P * ) 2 , and the slope of each piece after linearization is k s , the constant term is b s , the total number of segments is N S , and the expression after transformation of the frequency minimum constraint can be converted to:

[0277]

[0278] At this point, the linearization is complete.

[0279] S400: Based on the Lagrange multiplier method, the dual problem of day-ahead optimal dispatch is solved to obtain the marginal price of electric energy, inertia and primary frequency auxiliary service as the basis for settlement of various resources.

[0280] S401: Based on the Lagrange multiplier method, the dual problem of day-ahead optimal dispatch is solved to obtain the marginal price of electric energy, inertia and primary frequency auxiliary service as the basis for settlement of various resources, including the following steps:

[0281] The total inertia of the system is converted into a minimum point second-order cone constraint, and the expression of the total inertia of the system is transformed as follows:

[0282]

[0283] The total inertia of the system is replaced by elements a, b, and c, which are:

[0284]

[0285] After transformation, the frequency minimum constraint can be converted to:

[0286] ab≥c 2

[0287] ​The expression subjected to binary conversion processing is converted into a two-norm form:

[0288]

[0289] The dual variables of the left side first row and second row are defined as λ1 and λ2 respectively, and the right side is μ;

[0290] Based on the objective function and The expression subjected to binary conversion processing can obtain a Lagrange function as follows:

[0291]

[0292] Based on The expression of the item can obtain a virtual synchronous machine multi-resource inertia price Electric energy price Synchronous machine primary frequency modulation price Virtual synchronous machine multi-resource primary frequency modulation price

[0293]

[0294] Further, the embodiment also provides a day-ahead joint optimization scheduling system considering multi-resource inertia, comprising,

[0295] A construction module is configured to construct a model of inertia and primary frequency modulation auxiliary service participated by multi-resource including thermal power units, wind power, photovoltaic, energy storage and frequency active support type load;

[0296] A constraint module is configured to construct a day-ahead optimization scheduling model of a large power grid considering inertia and primary frequency modulation auxiliary service participated by multi-resource, and a target function is a sum of electric energy and auxiliary service cost of various resources, and constraint conditions include load balance, reserve capacity, line power flow and system frequency safety;

[0297] A processing module is configured to perform day-ahead optimization scheduling calculation, and linearization processing is performed on a nonlinear item in frequency safety constraint;

[0298] A settlement module is configured to solve a dual problem of day-ahead optimization scheduling based on a Lagrange multiplier method, to obtain marginal electricity prices of electric energy, inertia and primary frequency modulation auxiliary service as a settlement basis of various resources.

[0299] The embodiment also provides a computer device suitable for the case of the day-ahead joint optimization scheduling method considering multi-resource inertia, comprising a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to realize the day-ahead joint optimization scheduling method considering multi-resource inertia proposed in the above embodiment.

[0300] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved by WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0301] The embodiment also provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the day-ahead joint optimization scheduling method considering multi-resource inertia as proposed in the above embodiment.

[0302] To sum up, the application constructs a model of multi-resource participating inertia and primary frequency regulation auxiliary service, constructs a day-ahead optimization scheduling model of a large power grid considering multi-resource participating inertia and primary frequency regulation auxiliary service, performs day-ahead optimization scheduling calculation considering multi-resource participating inertia and primary frequency regulation auxiliary service, and finally calculates multi-resource electric energy and the prices of various auxiliary services based on dual multipliers. The application considers modeling a scheduling model including a thermal power unit, wind power, photovoltaic, energy storage and frequency actively supporting load providing frequency support. The capacity constraints and corresponding energy reservation of energy storage in charging and discharging operation states providing inertia and primary frequency regulation service are considered, the power compensation of a double-fed wind turbine transmission shaft restoring a power frequency is considered, a frequency actively supporting load participating peak shaving and primary frequency regulation model is established, and a multi-resource FSCUC joint optimization model is established.

[0303] On the basis of constructing a system frequency safety constraint, the application determines the frequency support capability required to ensure system frequency safety. Based on this, in order to obtain the deficiency inertia and primary frequency regulation auxiliary service, the inertia and auxiliary service provided by the multi-resource are quoted to participate in market competition, which can more accurately reflect the value of inertia and primary frequency regulation for the safe and stable operation of the system compared with compensation.

[0304] The technical problem of obtaining inertia and primary frequency regulation auxiliary service for maintaining the safe and stable operation of the power grid under large disturbance is solved.

[0305] Embodiment 2

[0306] Reference Fig. 1 -Fig. 2 For the second embodiment of the application, the embodiment provides a day-ahead joint optimization scheduling method considering multi-element resource inertia. In order to verify the beneficial effects of the application, economic benefit calculation and simulation experiments are used for scientific demonstration.

[0307] In order to verify the effectiveness of the day-ahead joint optimization scheduling method considering multi-element resource inertia proposed in the application, the following simulation experiment is designed. Taking a regional power grid as an example, the regional power grid is under the jurisdiction of 3 municipal power grids, including 15 220kV substations and 40 110kV substations. The generator units include 6 300MW thermal power units, 8 200MW thermal power units, and a total installed capacity of 3200MW; the wind power installed capacity is 500MW, including 400MW of double-fed wind power and 100MW of direct-drive wind power; the photovoltaic power installed capacity is 300MW; the electrochemical energy storage is 100MW / 200MWh. The typical daily load shows obvious "double-peak" characteristics, with the maximum load of 2500MW, the minimum load of 1200MW, and the average load of 1900MW. The peak occurs around 10:00 and 19:00.

[0308] The parameters of each type of resource are set as follows: the inertia time constant of the thermal power unit is 6s, the frequency modulation response delay is 1s, the inertia time constant of the virtual inertia resource (wind power, photovoltaic, energy storage) is 0.5-2s, the maximum virtual inertia support time is 0.5s, and the virtual inertia response delay is taken as the sum of the measurement delay and the communication delay, about 0.2s. Combined with the delay difference of frequency response, the frequency modulation service delivery time is set to 10s and 1s respectively.

[0309] According to the above data, a typical day of 24h with multi-element resource grid-connected is simulated and analyzed. Three scenarios are developed: (1) traditional scenario, only thermal power units participate in day-ahead scheduling; (2) auxiliary service introduction scenario, on the basis of the traditional scenario, the inertia and frequency modulation auxiliary services provided by the thermal power units are increased; (3) the application scenario, further introducing the virtual inertia and fast frequency modulation provided by the wind power, photovoltaic and energy storage on the basis of scenario 2. The three scenarios carry out joint optimization scheduling of electric energy and auxiliary services at the same time in the day-ahead, and obtain the unit start-up plan. In order to verify the system frequency response effect, the load disturbance of 9:00 and 21:00 is simulated respectively, and the disturbance amplitude is set to 5% of the system load.

[0310] For brevity, the following is an example of 9:00 for comparative analysis of the optimization scheduling results under each scenario, as shown in Table 1. It can be seen that after the introduction of auxiliary services (scenarios 2 and 3), the inertia and total capacity of the system are significantly improved, the frequency stability margin is increased, and in scenario 3, the wind and light storage fast and flexible characteristics are more utilized, which effectively reduces the operating cost while ensuring the inertia level of the system. It is worth mentioning that after the introduction of new energy and energy storage, due to its low frequency modulation cost and flexible charging and discharging, the demand for deep peak shaving of thermal power can be significantly reduced, further saving costs.

[0311] Table 19:00 optimization scheduling results comparison

[0312]

[0313]

[0314] The following compares the frequency dynamic response of the system under each scenario after load disturbance,

[0315] In scenario 1 (traditional dispatching), due to the lack of sufficient inertia and frequency modulation support, the system frequency drops sharply after the disturbance occurs, the rate of change of frequency (RoCoF) is large, and the minimum frequency deviates from the rated value. The frequency recovery process is also relatively slow.

[0316] In scenario 2 (introduction of thermal power auxiliary services), the frequency response curve should be significantly improved compared to scenario 1. The rate and amplitude of frequency drop are reduced, the minimum frequency is increased, the overall waveform is relatively smoother, and the frequency recovery time is also shortened. This is because the rotational inertia and primary frequency modulation provided by thermal power units improve the system's ability to resist disturbances.

[0317] In scenario 3 (considering new energy auxiliary services), the system frequency response will be superior. The frequency drop rate is further slowed down, and the minimum frequency is closer to the rated value. At the same time, due to the fast response capability of new energy and energy storage, the recovery process after frequency drop is also significantly accelerated. The entire curve waveform will be the smoothest, with the smallest frequency fluctuation.

[0318] In summary, Fig. 1 Intuitively, it shows the process of continuously improving the dynamic frequency response quality of the system with the introduction of virtual inertia and other auxiliary services. Qualitatively, curve 1 is steep and fluctuates greatly; curve 2 is relatively flat compared to curve 1; curve 3 is the smoothest, with the smallest frequency change.

[0319] When the system is disturbed by load, the frequency of the system in the traditional scheduling mode deviates greatly, the RoCoF is as high as 0.5 Hz / s, and the minimum frequency is close to 49.3 Hz; after introducing the auxiliary service market, the frequency characteristics of the system are obviously improved, the RoCoF and the minimum frequency are optimized to 0.3 Hz / s and 49.6 Hz respectively; after further introducing the wind, light and storage resources, the frequency response quality is further improved, the RoCoF is less than 0.1 Hz / s, the minimum frequency is close to 49.8 Hz, and the frequency fluctuation is more smooth.

[0320] In summary, the day-ahead joint optimization scheduling method considering multi-resource inertia and frequency modulation service provided by the application can significantly reduce the system operation cost under the premise of ensuring the system frequency safety margin. The main innovations are as follows:

[0321] Overall, the multi-resource, especially the emerging resources such as new energy and energy storage, can simulate the characteristics of synchronous machines and provide virtual inertia through advanced control means, greatly enriching the system inertia source and improving the frequency safety level.

[0322] The inertia response, primary frequency modulation and other auxiliary service products are put up for bidding together with the electric energy, and the source flexibility resource is matched with the power grid frequency stability demand in the preventive day-ahead stage, so as to fully tap the regulation potential of multi-resource.

[0323] The differences and complementarities of thermal power units and virtual inertia resources in inertia response and frequency regulation are reasonably considered, and the coordination and interaction of the two are realized through fine modeling and flexible market mechanism, so as to meet higher frequency safety requirements at lower cost.

[0324] Unlike the inertia response measures for after-the-fact compensation, a forward-looking inertia auxiliary service market mechanism is proposed. Through price signal guidance, multi-resource including new energy is encouraged to actively participate in power grid frequency regulation, forming a new type of source-grid-load-storage collaborative power operation and dispatching paradigm.

[0325] Compared with the traditional scheduling mode which only relies on the regulation of thermal power units and post-accident treatment, the application examines the safe and stable and economic operation of the power system from a more open perspective. Under the background of large-scale new energy access, through the combination of technological innovation and mechanism innovation, the source, grid, load and storage and other multi-subjects are integrated in multiple time scales and spatial dimensions, so that the flexibility and intelligence level of power grid operation are greatly improved, which has important significance for promoting energy revolution and power system reform. At the same time, the application has a pioneering nature in theory and engineering application, and can provide key support for building a clean, low-carbon, safe and efficient new type of power system.

[0326] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A day-ahead joint optimization scheduling method considering the inertia of multiple resources, characterized by: include, Construct a model for the participation of multiple resources including thermal power units, wind power, photovoltaic power, energy storage and frequency active support loads in inertia and primary frequency regulation auxiliary services; The construction of a model based on the participation of multiple resources in inertia and primary frequency regulation auxiliary services includes the following steps: The construction of the thermal power unit model includes the thermal power unit operation constraints and the thermal power unit's primary frequency regulation capacity constraints; the thermal power unit operation constraints include output upper and lower limit constraints, ramp constraints, and start and stop time constraints, which are respectively expressed as follows: Upper and lower limits of thermal power unit output: ； Thermal power unit ramp constraints: ； Start and stop time constraints for thermal power units: ； in, 、 are the minimum and maximum active output of thermal power unit i respectively; represents the active power of thermal power unit i at time t; is the primary frequency regulation capacity of thermal power unit i at time t; is a 0-1 variable describing the start and stop status of thermal power unit i at time t, Indicates that the unit is running. Indicates that the unit is shut down; and are the maximum increase and decrease values ​​of active power of thermal power unit i in period t respectively; For unit i to t time continuous operation ( is a positive value) or continuous shutdown ( is a negative value); and are the minimum operating time and minimum downtime of unit i respectively; The primary frequency regulation capacity constraint provided by the thermal power unit is expressed as follows: ； in, Indicates the primary frequency regulation capacity ramp limit of thermal power unit i; In addition, the inertia time constant of the thermal power unit It is related to the setting of the unit itself and is usually an unchangeable constant; A large-scale power grid day-ahead dispatching model is constructed that considers the participation of multiple resources in inertia and primary frequency regulation ancillary services. The objective function is the sum of the energy consumption of various resources and the cost of ancillary services. The constraints include load balance, reserve capacity, line flow, and system frequency security. Perform day-ahead optimization scheduling calculations and linearize nonlinear terms in frequency security constraints; Based on the Lagrange multiplier method, the dual problem of day-ahead optimal scheduling is solved, and the marginal electricity prices of electric energy, inertia and primary frequency regulation auxiliary services are obtained as the basis for settlement of various resources.

2. The day-ahead joint optimization scheduling method considering the inertia of multiple resources according to claim 1, characterized in that: The construction of the model based on multi-resource participation in inertia and primary frequency regulation auxiliary services includes the following steps: The construction of the wind turbine model includes wind turbine operation constraints, wind turbine primary frequency regulation capacity constraints, and wind turbine inertia constraints. The rotational inertia of the wind turbine drive shaft system can provide a certain inertia support for the system. Wind power needs to reserve a certain capacity in the output power to provide primary frequency regulation services to the system. The wind turbine operation constraints can be expressed as follows: ； in, and They represent the active power and predicted power of wind power i at time t respectively; represents the primary frequency regulation power capacity of wind power i at time t; The primary frequency regulation capacity constraint provided by the wind turbine can be expressed as follows: ； in, Indicates the proportional coefficient of the primary frequency regulation capacity of fan i; The inertia constraint provided by the wind turbine can be expressed as follows: ； in, and They represent the inertia time constant of wind turbine i at time t and its set value respectively; A 0-1 decision variable describing the inertia support service provided by wind power. Indicates that wind power provides inertia support at time t, Indicates that it is not provided; In addition, to avoid secondary oscillation caused by the absorption of active power from the system during the process of wind turbine speed recovery after providing inertia support, the control method is used to delay the time for wind turbine speed recovery until the system primary frequency regulation fully responds. The active power required for wind turbine recovery is shared by all primary frequency regulation resources. The maximum inertia support power required by wind power is expressed as follows: ； in, represents the rated power of wind power i; Indicates the maximum frequency change rate allowed by the system; Indicates the system rated frequency; The construction of the model based on multi-resource participation in inertia and primary frequency regulation auxiliary services also includes the following steps: constructing a photovoltaic unit model, wherein the photovoltaic unit model includes photovoltaic operation constraints and photovoltaic primary frequency regulation capacity constraints, and the photovoltaic array provides virtual inertia and primary frequency regulation services by configuring energy storage; the photovoltaic operation constraints are expressed as follows: ； in, and They represent the active power and predicted power of photovoltaic i at time t respectively; The primary frequency regulation capacity constraint provided by photovoltaics is expressed as follows: ； in, Indicates the rated discharge power of photovoltaic i-configured energy storage; Indicates the proportional coefficient of the primary frequency modulation capacity of photoelectric i; represents the primary frequency modulation power capacity of photovoltaic i at time t; Inertia time constant of photovoltaic i Defined by software, its value needs to comprehensively consider the performance of the configured energy storage.

3. The day-ahead joint optimization scheduling method considering the inertia of multiple resources according to claim 2, characterized in that: The construction of the model based on multi-resource participation in inertia and primary frequency regulation auxiliary services includes the following steps: The construction of energy storage model includes operation constraints under energy storage discharge state, operation constraints under energy storage charging state, energy storage charge and discharge state constraints, and energy storage charge state constraints. The operating constraints under the energy storage discharge state can be expressed as follows: ； ； ； ； ； in, 、 They represent the discharge power and maximum value of energy storage i at time t respectively; and They represent the primary frequency modulation power capacity of energy storage i at time t when it is in the discharge state and the maximum primary frequency modulation capacity of energy storage i respectively; is a 0-1 variable describing the energy storage discharge state, Indicates that the energy storage is in the discharge state. Indicates that the energy storage is in charging state; represents the maximum inertial support power of energy storage i in the discharge state; and They represent the inertia time constant of energy storage i in the discharge state and the maximum inertia time constant of energy storage i respectively; It is a continuously adjustable decision variable, and the same is true for the charging state; The operating constraints under the energy storage charging state can be expressed as follows: ； ； ； ； in, 、 They represent the discharge power and maximum value of energy storage i at time t respectively; It represents the primary frequency modulation power capacity provided by energy storage i at time t in the charging state; A 0-1 variable that modifies the energy storage charge state. Indicates that the energy storage is in charging state. Indicates that the energy storage is in a discharging state; It represents the maximum inertial support power of energy storage i in the charging state; represents the inertia time constant of energy storage i in the charging state; The charge and discharge state constraints of energy storage can be expressed as follows: ； The energy storage state of charge constraint can be expressed as follows: ； ； ； in, is the state of charge of energy storage i at time t; is the rated capacity of energy storage i, unit ; and are the minimum and maximum capacities of energy storage i under stable operation, respectively; and Respectively represent the energy storage i charging and discharging efficiency; It is represented by the reserved energy of energy storage i at time t; This is to ensure that the energy storage has sufficient energy released to support frequency safety during the energy storage discharge state. The expression is as follows: ； ； ； in, and They represent the energy required to be reserved by energy storage i to provide inertia and primary frequency regulation services at time t respectively; Indicates the delivery time of the energy storage primary frequency regulation service; The origin of is the integration of the energy storage primary frequency modulation response curve, and this paper adopts linear approximation. The construction of the model based on multi-resource participation in inertia and primary frequency regulation auxiliary services includes the following steps: The construction of the frequency active support load model includes the frequency active support load operation constraints and the frequency active support load providing primary frequency regulation capacity constraints; The frequency active support load operation constraint can be expressed as follows: ； ； ； ； Where T is the number of scheduling period periods; and They are the maximum and minimum values ​​of power after frequency active support type load i is adjusted; 、 They represent the load baseline and dispatched power of frequency active support load i at time t respectively; and They represent the power increase and decrease of the frequency active support load respectively; Adjust the power upper limit for the frequency active support load i at time t; and They represent the maximum power values ​​allowed for upward and downward adjustment of the frequency active support load i; is a 0-1 variable describing the upward or downward adjustment of the frequency active support load, Indicates that the frequency active support type load is reduced, Indicates that the frequency active support type load is increased; The primary frequency regulation capacity constraint provided by the active frequency support load can be expressed as follows: ； in, It represents the primary frequency regulation power capacity of the frequency active support type load i at time t.

4. The day-ahead joint optimization scheduling method considering the inertia of multiple resources according to claim 3 is characterized by: The large power grid day-ahead optimization dispatch model based on building a model that considers the participation of multiple resources in inertia and primary frequency regulation auxiliary services includes the following steps: Construct the objective function: ； in, For overall purchase costs; 、 、 、 They represent the costs of thermal power units, wind power, photovoltaic power, energy storage and frequency active support loads respectively; the specific expressions are as follows: ； in, 、 Respectively represent the total number of thermal power units and the number of output stages of thermal power units; and They represent the k-th segment quotation and corresponding output of thermal power unit i at time t respectively; Indicates the quotation of primary frequency regulation capacity of thermal power units; and They represent the startup and shutdown costs of thermal power unit i, respectively, and are expressed as follows: ； in, represents the total amount of wind power; It represents the price quote for primary frequency regulation service of wind turbines; ； in, Indicates the total amount of stored energy; 、 They represent the charging and discharging quotes for the peak-shaving service of energy storage i respectively; 、 They represent the quotations for energy storage primary frequency regulation service and inertia support service respectively; ； in, Indicates the total number of frequency active support type loads; 、 They represent the upper and lower quotes for peak load shaving service of frequency active support type load i at time t respectively; Indicates the price quote for primary frequency regulation service for active frequency support loads; The constraints include system load power balance constraints, system positive and negative reserve capacity constraints, branch power flow security constraints, and system frequency security constraints; The system load power balance constraint is expressed as follows: The system needs to ensure that the load power balance requirements are met during the operation period. , the load power balance constraint can be described as: ； in, represents the winning bid power of thermal power unit i at time t; represents the predicted load of the system at time t; The analytical expression is as follows: ； The system's positive and negative reserve capacity constraints are expressed as follows: ； in, is the active reserve required for the corresponding load at time t; and are the positive and negative spinning reserves required by wind power at time t, respectively, where: 、 and They are respectively determined by a certain ratio of load output and wind power output; The branch power flow safety constraint is expressed as follows: ； in, is the number of system nodes; is the number of lines; For the line Active power transmission limit; For nodes Line The power transfer factor; 、 、 Respectively in Time period node Active power of conventional units, wind turbines and photovoltaics; 、 Respectively in Time period node Upper energy storage charging and discharging power; and Respectively in Time period node Active load and frequency active support load; The system frequency safety constraint is expressed as follows: In order to meet the demand for computational efficiency in large-scale power grids, the frequency responses of thermal power units and resources based on virtual synchronous machines are linearly approximated. The resources of the virtual synchronous machine include wind power, photovoltaic power, energy storage and frequency active support loads. and They represent the delivery time of thermal power units and multi-resource primary frequency regulation services respectively; and Respectively represent the response delays of thermal power units and multiple resources; Its RoCoF constraint is expressed as follows: ； in, is the active power shortage when the disturbance occurs; Represents the total inertia of the system, which is expressed as follows: ； The frequency minimum point constraint is expressed as follows: ； in, Indicates the maximum frequency deviation allowed by the system; and They represent the total capacity of thermal power units and resources based on virtual synchronous machines participating in primary frequency regulation, respectively, using the following expressions: ； ； The quasi-steady-state constraint is expressed as follows: ； in, It indicates the ratio coefficient of the required primary frequency regulation capacity to the active power shortage.

5. The day-ahead joint optimization scheduling method considering the inertia of multiple resources according to claim 4 is characterized in that: The large power grid day-ahead optimal dispatch model based on the consideration of multiple resource participation in inertia and primary frequency regulation auxiliary services performs day-ahead optimal dispatch calculation, including the following steps: For subsequent solution needs, the model needs to be linearized to make it a mixed integer linear model; the frequency minimum point constraint is still a nonlinear constraint, and the following mainly focuses on the linearization of this constraint; By modifying the frequency minimum point constraint, we can get the following expression: ； in, 、 、 are three nonlinear terms; Item and First of all, Perform binary conversion processing, the expression is as follows: ； Among them, L represents the number of bits converted to binary. A reasonable value can be set according to the range of the primary frequency regulation capacity of the thermal power unit in the example. The setting is Perform approximate rounding to convert it into an integer; Available Item and The term expression is as follows: ； ； After the transformation, the nonlinear terms in the above formula only include the 0-1 decision variable term multiplied by the 0-1 decision variable term and the 0-1 decision variable term multiplied by the continuous decision variable term. For these two terms, the following transformation method can be used: ； above is equivalent to: ； In the formula, M takes a large value. This method defines a new variable , convert the original nonlinear expression of 0-1 variables multiplied by positive real variables into a linear expression: Since the primary frequency regulation power provided by VSM is less than the active power shortage of the system under large disturbance, Greater than , let , ; From 0 to is monotonically increasing, and The value range is bounded, Perform piecewise linear approximation and make the slope of each segment after linearization be , the constant term is The total number of segments is , the right side of the expression after the frequency minimum point constraint is modified can be transformed into: ； At this point, the linearization process is completed.

6. The day-ahead joint optimization scheduling method considering the inertia of multiple resources according to claim 5, characterized in that: The method of solving the dual problem of day-ahead optimal scheduling based on the Lagrange multiplier method and obtaining the marginal electricity prices of electric energy, inertia, and primary frequency regulation auxiliary services as the settlement basis for various resources includes the following steps: Convert the total inertia of the system into the second-order cone constraint at the lowest point, and the expression of the total inertia of the system is transformed into the following: ； Replacing the total inertia of the system with elements a, b, and c, we have: ； The frequency minimum point constraint can be transformed into: right The expression to be binary converted is converted into the binary norm form: ； Define the dual variables of the first and second rows on the left as 、 , on the right ; Based on the objective function and The expression of binary conversion can be obtained as the Lagrangian function: ； based on The term expression can be used to obtain the inertia price of multiple resources based on virtual synchronous machine , electricity price , the price of synchronous machine primary frequency modulation , based on the price of frequency modulation of multiple resources of virtual synchronous machine : ； ； ； 。 7. A day-ahead joint optimization scheduling system considering the inertia of multiple resources, based on the day-ahead joint optimization scheduling method considering the inertia of multiple resources according to any one of claims 1 to 6, characterized in that: Also includes, Build a module to construct a model for the participation of multiple resources including thermal power units, wind power, photovoltaic power, energy storage and frequency active support loads in inertia and primary frequency regulation auxiliary services; The constraint module constructs a large-scale power grid day-ahead optimization dispatch model that considers the participation of multiple resources in inertia and primary frequency regulation ancillary services. The objective function is the sum of the power consumption of various resources and the cost of ancillary services. The constraints include load balance, reserve capacity, line flow, and system frequency security. The processing module performs day-ahead optimization scheduling calculations and linearizes nonlinear terms in frequency security constraints. The settlement module solves the dual problem of day-ahead optimal scheduling based on the Lagrange multiplier method, and obtains the marginal electricity price of electric energy, inertia and primary frequency regulation auxiliary services as the settlement basis for various resources.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the day-ahead joint optimization scheduling method considering the inertia of multiple resources according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the day-ahead joint optimization scheduling method considering the inertia of multiple resources according to any one of claims 1 to 6 are implemented.

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