Load aggregator transaction method based on power grid flexibility demand

Abstract

The invention relates to the technical field of electric power markets, in particular to a load aggregator transaction method based on power grid flexibility demand, which comprises the following steps: S1, constructing an upper layer model: establishing the upper layer model, and measuring and calculating demand side flexibility resource demand by taking minimum demand side flexibility resource calling cost as an objective function; s2, construction of power supply and demand balance constraints: constructing multiple constraints; s3, constructing a lower-layer model: taking the maximum total benefit of the load aggregator as a target function, and measuring and calculating the aggregation response potential of the load aggregator; s4, constructing load constraints: constructing transferable load constraints, reducible load constraints and important load constraints; s5, calculating the response potential of the load aggregator, wherein the response potential of the load aggregator is obtained by summing the demand response amount of the aggregation user of the load aggregator; the method is realized in a centralized bidding and unified clearing mode, and the flexibility and competitiveness of the electricity market are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a load aggregator trading method based on power grid flexibility demand. Background Art

[0002] As the proportion of renewable energy in the power system continues to increase, the demand for grid flexibility is also gradually increasing. The high proportion of renewable energy access has brought greater uncertainty and complexity to the distribution network, and posed new challenges to the safe and stable operation of the power grid. In this context, how to improve the flexibility of the system and achieve integrated and coordinated operation of sources, networks, loads and storage has become the key to building a new power system.

[0003] Existing technologies mainly rely on traditional power sources and energy storage equipment to adjust the supply and demand balance of the power grid. However, as the flexible adjustment capacity of conventional power sources approaches its limit, the construction of energy storage equipment faces difficulties such as high cost and long payback period. It is becoming increasingly difficult to rely on these means to meet the flexibility needs of the power grid. In addition, although demand-side flexibility resources have the advantages of wide distribution and large number, their single-unit capacity is small, making it difficult to directly and effectively participate in the regulation of the power grid. Therefore, existing technologies are insufficient in adjusting the flexibility supply and demand balance.

[0004] The purpose of the present invention is to propose a load aggregator trading method based on the flexibility demand of the power grid, make full use of demand-side resources to alleviate the pressure of power supply and demand balance, and establish a two-layer optimization model. The upper model aims to minimize the demand-side flexibility resource call cost and evaluate the flexibility demand of the power grid. The lower model aims to maximize the profit of the load aggregator. By aggregating the reducible and transferable loads, the response potential of the load aggregator is calculated, and unified optimization and clearing are performed through centralized bidding, so as to effectively meet the flexibility demand of the upper-level power grid. Summary of the invention

[0005] The present invention provides a load aggregator trading method based on power grid flexibility demand.

[0006] A load aggregator trading method based on power grid flexibility demand includes the following steps:

[0007] S1, construction of the upper model: when the flexibility resources on the power supply side are insufficient to support the safe and economic operation of the power grid, the upper model is established to minimize the cost of calling the flexibility resources on the demand side as the objective function, and the demand for flexibility resources on the demand side is calculated;

[0008] S2, construction of power supply and demand balance constraints: construction of multiple constraints including power supply and demand balance constraints, thermal power unit operation constraints, new energy unit output constraints, energy storage device constraints, demand response capacity constraints, system reserve constraints, power supply guarantee and consumption promotion constraints;

[0009] S3, construction of the lower-level model: the upper-level power grid releases the flexibility demand to the load aggregator. The lower-level model takes the maximum total benefit of the load aggregator as the objective function and measures the aggregation response potential of the load aggregator while satisfying the flexibility resource shortage of the upper-level model;

[0010] S4, construction of load constraints: construction of transferable load constraints, curtailable load constraints, and important load constraints;

[0011] S5, Calculation of load aggregator's response potential: The load aggregator's response potential is calculated by summing up the demand response amounts of its aggregated users.

[0012] Optionally, the objective function of the upper model in S1 is expressed as:

[0013]

[0014] Among them, P z,t , P f,t are the shortfall of valley-filling demand response and peak-shaving demand response at time t, respectively. t , C x They are the compensation prices for unit peak shaving demand response and unit valley filling demand response respectively.

[0015] Optionally, the construction of the power supply and demand balance constraint in S2 includes:

[0016] S21, power supply and demand balance constraint: The constraint condition of power supply and demand balance constraint is expressed as:

[0017]

[0018] Among them, N g 、N re 、N c 、N d are the collection of thermal power units, new energy units, energy storage, and loads in the region, respectively. load,i,t is the typical daily baseline load power of the ith user at time t, P z,i,t is the valley-filling demand response power of the i-th user at time t, P f,i,t is the peak-shaving demand response power of the i-th user at time t, P g,i,t is the actual output of thermal power unit i at time t, P wind,v,t , P pv,v,t The actual output of wind power and photovoltaic units v at each moment in the operation simulation at time t, is the discharge power of energy storage node i at time t, is the charging power of energy storage node i at time t, P d,t is the power shortage load in the system at time t;

[0019] S22, thermal power unit operation constraints: The constraints of thermal power unit operation constraints are expressed as:

[0020]

[0021] Among them, g on,t , g off,t are the total capacity of thermal power units increased and decreased in the system at time t, O g,t is the total online capacity of thermal power units in the system at time t, T on , T off are the shortest on / off time intervals of thermal power units, are respectively the allowed up and down ramp powers of thermal power unit i;

[0022] S23, new energy unit output constraint: The constraint condition of new energy unit output constraint is expressed as:

[0023]

[0024] in, is the predicted maximum output value of wind power and photovoltaic unit v at time t, P wind,v,t , P pv,v,t is the wind power and photovoltaic power abandonment value of wind power and photovoltaic unit v at time t;

[0025] S24, energy storage device constraint: The constraint condition of the energy storage device constraint is expressed as:

[0026]

[0027] Among them, ΔT is the step length, the model value is 1h, η d , η u , is the energy storage charging and discharging efficiency, E es,i,t is the charge state of energy storage system i at time t, E es,t＝0 、E es,t＝T Indicates the stored energy at the initial and final states, E es,i,t is the charging and discharging power of energy storage system i at time t, is the upper limit of energy storage charge and discharge power, μ is the minimum discharge coefficient set considering the energy storage life, and its value is 0.1;

[0028] S25, demand response capacity constraint: The constraint condition of demand response capacity constraint is expressed as:

[0029]

[0030] in, The demand is the maximum value of the typical day load, and α is the percentage coefficient of the planned capacity of valley filling demand response and peak shaving demand response to the maximum load value;

[0031] S26, system backup constraint: The constraint condition of the system backup constraint is expressed as:

[0032]

[0033] in, is the spinning reserve capacity required by the system at time t, is the system's reserve capacity demand in direction A due to the prediction error of new energy unit i at time t, is the system's reserve capacity demand in direction A due to load forecast error at time t, A∈{+,-}, which can be divided into upward / downward adjustment;

[0034] S27, power supply guarantee and consumption promotion constraints: The constraints of power supply guarantee and consumption promotion are expressed as:

[0035]

[0036] in, is the power generation power of the new energy unit v at time t, P re,v,t is the actual output of the new energy unit v in the operation simulation at time t, λ1 and λ2 are the target power abandonment rate and power shortage rate respectively, and λ3 is the percentage coefficient of the load shedding power in the system at time t to the maximum load value.

[0037] Optionally, the objective function of the lower model in S3 is expressed as:

[0038] maxL = F1 - F2 - F3;

[0039] F1=f x +f t ;

[0040] F2=max(0.9·Q0-Q,0)·C0;

[0041] F3=Q·C y ;

[0042] f x =Q x ·p·C cx ·T x ;

[0043] f t =Q t ·p·C ct ·T t ;

[0044] C0=1.1C;

[0045] Among them, L is the profit of the load aggregator, F1 is the demand response compensation fee paid by the upper power grid to the load aggregator, F2 is the assessment fee, which is used to assess the part of the effective response capacity that is less than 90% of the winning response capacity. The assessment fee is calculated on an hourly basis, C0 is the assessment price, F3 is the user compensation fee, and C u Compensate users with a fixed price, f x 、f t are the peak shaving and valley filling demand response compensation fees, Q x , Q t are the effective peak shaving response and the effective valley filling response, p is the subsidy coefficient, T x , T t are the peak shaving and valley filling response times, C cx , C ct are the clearing prices of unit peak shaving demand response and unit valley filling demand response respectively, C is the clearing price of the power grid for load aggregators performing demand response at time t, Q0 is the winning bid capacity, and Q is the actual response capacity.

[0046] Optionally, the construction of the load constraint in S4 includes:

[0047] S41, transferable load constraint: The transferable load constraint requires ensuring the continuity of load transfer during the load transfer period, setting the minimum continuous operation time of load transfer and the maximum and minimum power range allowed;

[0048] S42, load reduction constraint: The load reduction constraint is based on the load model to ensure that when reducing user load, the basic electricity demand of users is fully considered. The load reduction upper limit is limited by the load model, and the minimum and maximum continuous reduction time and reduction times are set to ensure user satisfaction and the effective response capability of load aggregators.

[0049] S43, Important load constraints: Important load constraints ensure that during the demand response process, the load does not participate in demand-side reduction or transfer.

[0050] Optionally, the constraint condition of the transferable load constraint in S41 is expressed as:

[0051]

[0052] Where τ is the start time, The maximum and minimum values ​​of load transfer power are respectively allowed. are the amount of responses transferred before and after the i-th user at time t, is the minimum continuous running time.

[0053] Optionally, the load model in S42 is expressed as:

[0054]

[0055] in, are the load of the jth user after reducing the response at time t, u j,t is the reduction factor of the load that can be reduced at node j at time t;

[0056] The constraint condition of the load reduction constraint is expressed as:

[0057]

[0058] in, is the minimum continuous reduction time, is the maximum continuous reduction time, N max is the maximum number of reductions.

[0059] Optionally, the constraint condition of the important load constraint in S43 is expressed as:

[0060]

[0061] Among them, P im,t It is the important load at time t and is the load that does not participate in demand-side reduction or transfer.

[0062] Optionally, the response potential calculation of the load aggregator in S5 is expressed as:

[0063]

[0064] Among them, ΔP up,max,t ΔP down,max,t are the maximum valley-filling and peak-cutting powers that the load aggregator can achieve at time t, respectively.

[0065] Beneficial effects of the present invention:

[0066] The present invention constructs a two-layer optimization model to rationally mobilize demand-side flexibility resources to participate in grid regulation. The upper model aims to minimize the cost of calling demand-side flexibility resources, ensure the balance of power supply and demand, and maintain safe and stable operation of the system. The lower model aims to maximize the total benefit of load aggregators, and fully mobilizes demand-side resources to participate in grid flexibility supply by aggregating reducible loads and transferable loads. In this trading method, the response potential of load aggregators is realized through centralized bidding and unified clearing, which improves the flexibility and competitiveness of the power market.

[0067] The present invention, by accurately setting the constraints of transferable loads, reducible loads and important loads, ensures that in the process of flexible resource calling, the power grid demand can be effectively met without unnecessary impact on the user's electricity demand, thereby realizing an economical and efficient solution for balancing electricity supply and demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0069] Figure 1 A schematic diagram of a transaction method flow chart of an embodiment of the present invention;

[0070] Figure 2 A schematic diagram of constructing a power supply and demand balance constraint according to an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0072] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0073] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0074] like Figure 1-Figure 2As shown, a load aggregator trading method based on power grid flexibility demand includes the following steps:

[0075] (1) Upper-level goals:

[0076] The upper model starts from the perspective of optimizing the overall flexibility of the system. Under the constraints of the supply capacity of various flexible resources, operation constraints, power supply guarantee, consumption guarantee and system power balance, when the flexible resources on the power supply side cannot meet the safe and economic operation of the power grid, the objective function is to minimize the cost F of calling the flexible resources on the demand side during the simulation period. The decision variables are the valley filling / peak shaving demand response amount on the system demand side in each period, such as:

[0077]

[0078] Where, P z,t , P f,t are the shortfall of valley-filling demand response and peak-shaving demand response at time t, respectively. t , C x They are the compensation prices for unit peak shaving demand response and unit valley filling demand response respectively.

[0079] a) Power supply and demand balance constraints:

[0080] The power supply and demand balance constraint formula is:

[0081]

[0082] Where, N g 、N re 、N c 、N d are the collection of thermal power units, new energy units, energy storage, and loads in the region; P load,i,t is the typical daily baseline load power of the ith user at time t; P z,i,t is the valley-filling demand response power of the i-th user at time t; P f,i,t is the peak-shaving demand response power of the i-th user at time t; P g,i,t is the actual output of thermal power unit i at time t; P wind,v,t , P pv,v,t The actual output of wind power and photovoltaic unit v at each moment in the operation simulation at time t; is the discharge power of energy storage node i at time t; is the charging power of energy storage node i at time t, P d,t is the power shortage load in the system at time t.

[0083] b) Operation constraints of thermal power units:

[0084] The operating constraint formula of thermal power units is:

[0085]

[0086] In the formula, g on,t , g off,t are the total capacity of thermal power units increased and decreased in the system at time t, O g,t is the total online capacity of thermal power units in the system at time t, T on , T off are the shortest on / off time intervals of thermal power units, They are respectively the allowed up and down climbing powers of thermal power unit i.

[0087] c) Output constraints of new energy units:

[0088] The output constraint formula of new energy units is:

[0089]

[0090] In the formula, is the predicted maximum output value of wind power and photovoltaic unit v at time t, P wind,v,t , P pv,v,t are the wind power and photovoltaic power curtailment values ​​of wind power and photovoltaic unit v at time t.

[0091] d) Energy storage device constraints:

[0092] The constraint formula of energy storage device is:

[0093]

[0094] In the formula, ΔT is the step length, and the model value is 1h; η d , η u , is the energy storage charging and discharging efficiency; E es,i,t is the charge state of energy storage system i at time t, E es,t＝0 、E es,t＝T Indicates the stored energy at the initial and final states of energy storage; E es,i,t is the charging and discharging power of energy storage system i at time t; is the upper limit of energy storage charging and discharging power; μ is the minimum discharge coefficient set considering the energy storage life, and its value is generally 0.1.

[0095] e) Demand response capacity constraints:

[0096] The demand response capacity constraint formula is:

[0097]

[0098] In the formula, It needs to be the maximum value of the typical day load; α is the percentage coefficient of the planned capacity of valley filling demand response and peak shaving demand response to the maximum load value.

[0099] f) System backup constraints:

[0100] The system reserve constraint formula is:

[0101]

[0102] In the formula, is the spinning reserve capacity required by the system at time t, is the system's reserve capacity demand in direction A due to the prediction error of new energy unit i at time t, is the system's reserve capacity demand in direction A due to load forecast error at time t, where A∈{+,-}, which is divided into upward / downward adjustment.

[0103] g) Constraints on ensuring power supply and promoting consumption:

[0104] The constraint formula for ensuring power supply and promoting consumption is:

[0105]

[0106] In the formula, is the power generation power of the new energy unit v at time t, P re,v,t is the actual output of the new energy unit v in the operation simulation at time t, λ1 and λ2 are the target power abandonment rate and power shortage rate respectively, and λ3 is the percentage coefficient of the load shedding power in the system at time t to the maximum load value.

[0107] (2) Lower level goals:

[0108] The revenue model of load aggregators is:

[0109] maxL=F1-F2-F3 (9)

[0110] Where F1 = f x +f t ,F2=max(0.9·Q0-Q,0)·C0,F3=Q·C y In the formula, f x =Q x ·p·C cx ·T x ,f t =Q t ·p·C ct ·T t , C0=1.1C. L is the profit of the load aggregator, F1 is the demand response compensation fee paid by the upper power grid to the load aggregator; F2 is the assessment fee, which is used to assess the effective response capacity that is less than 90% of the winning response capacity. The assessment fee is calculated by hour, C0 is the assessment price, F3 is the user compensation fee, C yCompensate users with a fixed price, f x 、f t are the peak shaving and valley filling demand response compensation fees, Q x , Q t are the effective peak shaving response and the effective valley filling response, p is the subsidy coefficient, T x , T t are the peak shaving and valley filling response times, C cx , C ct are the clearing prices of unit peak shaving demand response and unit valley filling demand response respectively, C is the clearing price of the power grid for load aggregators performing demand response at time t, Q0 is the winning bid capacity, and Q is the actual response capacity.

[0111] a) Transferable load constraints:

[0112] β l,t Indicates transferable load The transfer state of time period τ, that is, β l,t =1 means In the τ period, β l,t =0 means No transfer in the τ period; To avoid the load being transferred to multiple discontinuous periods, that is, the frequent start and stop of the equipment, the minimum continuous running time of the transferred load needs to be constrained, that is:

[0113]

[0114] In the formula, τ is the starting time, The maximum and minimum values ​​of load transfer power are respectively allowed. are the amount of responses before and after the transfer of the i-th user at time t, is the minimum continuous running time.

[0115] b) Reducible load constraints:

[0116] γ j,τ Indicates that load can be reduced The reduction state of the period τ, i.e., γ j,τ =1 means is reduced in the τ period, γ j,τ =0 means If no reduction is made, when the load aggregator signs a contract with the user, it should take into account the user's basic electricity demand, so a certain percentage of power reduction cap should be set, that is, the load model is:

[0117]

[0118] In the formula, are the load of the jth user after reducing the response at time t, u j,tis the load reduction factor of node j at time t.

[0119] Considering user satisfaction, it is necessary to constrain the minimum and maximum continuous reduction time and the number of reductions, that is,

[0120]

[0121] in, is the minimum continuous reduction time, is the maximum continuous reduction time, N maX is the maximum number of reductions.

[0122] c) Important load constraints:

[0123] The important load constraints are:

[0124]

[0125] Where, P im,t It is the important load at time t and is the load that does not participate in demand-side reduction or transfer.

[0126] d) Demand-side response:

[0127] Based on the two types of load models mentioned above, assuming that N users belong to the transferable load type and M users belong to the curtailable load type, the potential of the load aggregator is obtained by summing up the regulation potential of all users it aggregates. The potential of the load aggregator to participate in demand-side response can be obtained as:

[0128]

[0129] In the formula, ΔP up,max,t ΔP down,max,t are the maximum valley-filling and peak-cutting powers of the load aggregator at time t, respectively. The maximum potential shown in the above formula only indicates the upper and lower adjustment boundary values ​​that the load aggregator may reach. The load aggregator will form a dynamic adjustment interval based on the minimum deviation between the response load and the flexibility resource shortage of the upper model.

[0130] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0131] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A load aggregator trading method based on power grid flexibility demand, characterized in that: The following steps are involved: S1, construction of the upper model: when the flexibility resources on the power supply side are insufficient to support the safe and economic operation of the power grid, the upper model is established to minimize the cost of calling the flexibility resources on the demand side as the objective function, and the demand for flexibility resources on the demand side is calculated; S2, construction of power supply and demand balance constraints: construction of multiple constraints including power supply and demand balance constraints, thermal power unit operation constraints, new energy unit output constraints, energy storage device constraints, demand response capacity constraints, system reserve constraints, power supply guarantee and consumption promotion constraints; S3, construction of the lower-level model: the upper-level power grid releases the flexibility demand to the load aggregator. The lower-level model takes the maximum total benefit of the load aggregator as the objective function and measures the aggregation response potential of the load aggregator while satisfying the flexibility resource shortage of the upper-level model; S4, construction of load constraints: construction of transferable load constraints, curtailable load constraints, and important load constraints; S5, Calculation of load aggregator's response potential: The load aggregator's response potential is calculated by summing up the demand response amounts of its aggregated users.

2. A load aggregator trading method based on power grid flexibility demand according to claim 1, characterized in that: The objective function of the upper model in S1 is expressed as: Among them, P z,t , P f,t are the shortfall of valley-filling demand response and peak-shaving demand response at time t, respectively. t , C x They are the compensation prices for unit peak shaving demand response and unit valley filling demand response respectively.

3. A load aggregator trading method based on power grid flexibility demand according to claim 2, characterized in that: The construction of the power supply and demand balance constraint in S2 includes: S21, power supply and demand balance constraint: The constraint condition of power supply and demand balance constraint is expressed as: Among them, N g 、N re 、N c 、N d are the collection of thermal power units, new energy units, energy storage, and loads in the region, respectively. load,i，t is the typical daily baseline load power of the ith user at time t, P z，i，t is the valley-filling demand response power of the i-th user at time t, P f，i，t is the peak-shaving demand response power of the i-th user at time t, P g，i，t is the actual output of thermal power unit i at time t, P wind，v，t , P pv，v，t The actual output of wind power and photovoltaic units v at each moment in the operation simulation at time t, is the discharge power of energy storage node i at time t, is the charging power of energy storage node i at time t, P d，t is the power shortage load in the system at time t; S22, thermal power unit operation constraints: The constraints of thermal power unit operation constraints are expressed as: Among them, g on，t , g off，t are the total capacity of thermal power units increased and decreased in the system at time t, O g，t is the total online capacity of thermal power units in the system at time t, T on , T off are the shortest on / off time intervals of thermal power units, are respectively the allowed up and down ramp powers of thermal power unit i; S23, new energy unit output constraint: The constraint condition of new energy unit output constraint is expressed as: in, is the predicted maximum output value of wind power and photovoltaic unit v at time t, P wind,v，t , P pv，v，t is the wind power and photovoltaic power abandonment value of wind power and photovoltaic unit v at time t; S24, energy storage device constraint: The constraint condition of the energy storage device constraint is expressed as: Among them, ΔT is the step length, the model value is 1h, η d , η u , is the energy storage charging and discharging efficiency, E es,i，t is the charge state of energy storage system i at time t, E es,t＝0 、E es,t＝T Indicates the stored energy at the initial and final states, E es,i，t is the charging and discharging power of energy storage system i at time t, is the upper limit of energy storage charge and discharge power, μ is the minimum discharge coefficient set considering the energy storage life, and its value is 0.1; S25, demand response capacity constraint: The constraint condition of demand response capacity constraint is expressed as: in, The demand is the maximum value of the typical day load, and α is the percentage coefficient of the planned capacity of valley filling demand response and peak shaving demand response to the maximum load value; S26, system backup constraint: The constraint condition of the system backup constraint is expressed as: in, is the spinning reserve capacity required by the system at time t, is the system's reserve capacity demand in direction A due to the prediction error of new energy unit i at time t, is the system's reserve capacity demand in direction A due to load forecast error at time t, A∈{+,-}, divided into upward / downward adjustment; S27, power supply guarantee and consumption promotion constraints: The constraints of power supply guarantee and consumption promotion are expressed as: in, is the power generation power of the new energy unit v at time t, P re，v，t is the actual output of the new energy unit v in the operation simulation at time t, λ1 and λ2 are the target power abandonment rate and power shortage rate respectively, and λ3 is the percentage coefficient of the load shedding power in the system at time t to the maximum load value.

4. A load aggregator trading method based on power grid flexibility demand according to claim 3, characterized in that: The objective function of the lower model in S3 is expressed as: maxL = F1 - F2 - F3; F1=f x +f t ; F3=max(0.9·Q0-Q,0)·C0; F3=Q·C y ; f x =Q x ·p·C cx ·T x ; f t =Q t ·p·C ct ·T t ; C0=1.1C; Among them, L is the profit of the load aggregator, F1 is the demand response compensation fee paid by the upper power grid to the load aggregator, F2 is the assessment fee, which is used to assess the part of the effective response capacity that is less than 90% of the winning response capacity. The assessment fee is calculated on an hourly basis, C0 is the assessment price, F3 is the user compensation fee, and C y Compensate users with a fixed price, f x 、f t are the peak shaving and valley filling demand response compensation fees, Q x , Q t are the effective peak shaving response and the effective valley filling response, p is the subsidy coefficient, T x , T t are the peak shaving and valley filling response times, C cx , C ct are the clearing prices of unit peak shaving demand response and unit valley filling demand response respectively, C is the clearing price of the power grid for load aggregators performing demand response at time t, Q0 is the winning bid capacity, and Q is the actual response capacity.

5. A load aggregator trading method based on power grid flexibility demand according to claim 4, characterized in that: The construction of the load constraints in S4 includes: S41, transferable load constraint: The transferable load constraint requires ensuring the continuity of load transfer during the load transfer period, setting the minimum continuous operation time of load transfer and the maximum and minimum power range allowed; S42, load reduction constraint: The load reduction constraint is based on the load model to ensure that when reducing user load, the basic electricity demand of users is fully considered. The load reduction upper limit is limited by the load model, and the minimum and maximum continuous reduction time and reduction times are set to ensure user satisfaction and the effective response capability of load aggregators. S43, Important load constraints: Important load constraints ensure that during the demand response process, the load does not participate in demand-side reduction or transfer.

6. A load aggregator trading method based on power grid flexibility demand according to claim 5, characterized in that: The constraint condition of the transferable load constraint in S41 is expressed as: Where T is the start time, The maximum and minimum values ​​of load transfer power are respectively allowed. are the amount of responses transferred before and after the i-th user at time t, is the minimum continuous running time.

7. A load aggregator trading method based on power grid flexibility demand according to claim 6, characterized in that: The load model in S42 is expressed as: in, are the load of the jth user after reducing the response at time t, u j，t is the reduction factor of the load that can be reduced at node j at time t; The constraint condition of the load reduction constraint is expressed as: in, is the minimum continuous reduction time, is the maximum continuous reduction time, N max is the maximum number of reductions.

8. A load aggregator trading method based on power grid flexibility demand according to claim 7, characterized in that: The constraint condition of the important load constraint in S43 is expressed as: Among them, P im,t It is the important load at time t and is the load that does not participate in demand-side reduction or transfer.

9. A load aggregator trading method based on power grid flexibility demand according to claim 8, characterized in that: The response potential calculation of the load aggregator in S5 is expressed as: Where ΔP up,max,t ΔP down,max,t are the maximum valley-filling and peak-cutting powers that the load aggregator can achieve at time t, respectively.

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