CN114925546B - Method, device and storage medium for decomposing medium of cold chain load aggregation participating in power grid regulation and control - Google Patents

Abstract

The present invention discloses a method, device and storage medium for decomposing the winning bid amount of cold chain load aggregation participating in power grid regulation, which includes: obtaining the constraints related to the aggregator and the cold chain load users during the peak-shaving response period of the trading day; obtaining the maximum profit optimization objective function of the aggregator; and calculating the equipment operation plan of each cold chain load user according to the constraints and the maximum profit optimization objective function of the aggregator. The present invention can reasonably decompose the winning bid amount of the aggregator to each cold chain load user, formulate the equipment operation plan of each cold chain load user, and improve the accuracy of power grid peak-shaving.

Description

Method, device and storage medium for decomposing the winning bid amount of cold chain load aggregation participating in power grid regulation

Technical Field

The present invention relates to a method, a device and a storage medium for decomposing a winning bid amount of cold chain load aggregation participating in power grid regulation, and belongs to the technical field of power grid peak regulation.

Background technique

With the rapid development of new energy sources such as wind power and photovoltaic power in my country, the proportion of new energy power generation has increased year by year. Due to the uncertainty of new energy power generation, it is very difficult to connect new energy to the grid on a large scale. Controllable loads on the demand side are considered to be an effective resource for stabilizing the volatility of new energy and promoting the consumption of new energy. With the continuous introduction of controllable load resources on the demand side, the power system is changing from the traditional "supply follows demand" to the "source-load interaction" model.

As a kind of controllable load, cold chain load has the characteristics of random load and high power consumption. The scale of cold chain is constantly increasing, which undoubtedly aggravates the shortage of electricity in cities. The huge load generated randomly has become a burden on the power grid. Through the participation of cold chain load resources in the power peak-shaving market, the orderly management and control of cold chain load resources can be achieved, which can be used as an effective measure to smooth the volatility of the power grid. Cold chain load users need to participate in the power peak-shaving market with load aggregators as market entities. After the aggregators participate in the market and win the bid, how to reasonably allocate the winning bid to each cold chain load user is an urgent problem to be solved for cold chain load resources to participate in the power peak-shaving market.

Summary of the invention

In order to solve the problems existing in the prior art, the present invention proposes a method, device and storage medium for decomposing the winning bid quantity of cold chain load aggregation participating in power grid regulation, which realizes the function of decomposing the winning bid quantity of the aggregator to cold chain load users based on market rules and production environment requirements.

In order to solve the above technical problems, the present invention adopts the following technical means:

In the first aspect, the present invention proposes a method for decomposing the winning bid amount of cold chain load aggregation participating in power grid regulation, comprising the following steps:

Obtain constraints related to aggregators and cold chain load users during the peak load response period of the trading day;

Obtain the maximum profit optimization objective function of the aggregator;

The equipment operation plan for each cold chain load user is calculated based on the constraints and the aggregator's maximum profit optimization objective function.

In combination with the first aspect, further, the constraints related to the aggregator and the cold chain load users during the peak-shaving response period of the trading day include: the effective response load constraints of the aggregator during the peak-shaving response period of the trading day, the winning bid constraints of the aggregator during the peak-shaving response period of the trading day, the production environment temperature constraints of the cold chain load users and the mandatory startup time constraints of the cold chain load users.

Combined with the first aspect, further, the effective response load constraints of the aggregator during the peak load response period of the trading day are as follows:

Where _xn,t is the equipment status of cold chain load user n in period t, _Pn is the rated power of cold chain load user n, P0 ^,max is the maximum baseline load in the peak load response period, is the average load of the cold chain load response during the peak load response period, is the average value of the baseline load during the peak load response period, n = 1, 2, ..., N, and N is the total number of cold chain load users.

Combined with the first aspect, further, the constraints on the number of bids aggregators can win during the peak load response period of a trading day are as follows:

in, is the baseline load of the aggregator, is the clearing winning bid of the load aggregator in period t.

Combined with the first aspect, further, the production environment temperature constraints of cold chain load users are as follows:

in, is the minimum cold chain temperature preset by cold chain load user n in time period t, Tn _,t is the temperature of cold chain load user n in time period t, The maximum cold chain temperature preset for cold chain load user n in time period t;

The relationship between the production environment temperature of cold chain load users is:

Where Tn _,t+1 represents the temperature of cold chain load user n in time period t+1, _xn,t represents the equipment status of cold chain load user n in time period t, is the maximum startup time of cold chain load user n, is the maximum adjustable downtime of cold chain load user n.

In combination with the first aspect, further, the constraint condition for the cold chain load user to be powered on during the period is: x _n,t =1.

Combined with the first aspect, further, the expression of the aggregator's maximum profit optimization objective function is as follows:

in, is the market clearing price in period t, α _t ={0,1}, α _t indicates whether period t is a demand response period, x _n,t is the equipment status of cold chain load user n in period t, P _n is the rated power of cold chain load user n, is the baseline load value of cold chain load user n in period t, n = 1, 2, ..., N, N is the total number of cold chain load users, t = 1, 2, ..., T, T is the total peak load response period of the trading day, and ε is a preset constant.

Combined with the first aspect, further, a bid decomposition model for aggregator users to participate in grid regulation is constructed based on constraints and the aggregator's maximum profit optimization objective function. The mixed integer linear programming method is used to solve the model and calculate the equipment operation plan for each cold chain load user.

In a second aspect, the present invention proposes a bid quantity decomposition device for cold chain load aggregation participating in power grid regulation, including a processor and a storage medium;

The storage medium is used to store instructions;

The processor is used to operate according to the instructions to execute the steps of the method described in the first aspect.

In a third aspect, the present invention proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

The following advantages can be obtained by using the above technical means:

The present invention proposes a method, device and storage medium for decomposing the winning bid quantity of cold chain load aggregation participating in power grid regulation. A winning bid quantity decomposition model is constructed through multiple constraints related to aggregators and cold chain load users during the peak-shaving response period of trading days and the aggregator's maximum profit optimization goal. The winning bid quantity is reasonably allocated during the peak-shaving response period of trading days, and an equipment operation plan for each cold chain load user is obtained, thereby laying a foundation for cold chain load resources to participate in power grid peak-shaving. Based on the equipment operation plan of cold chain load users, cold chain load resources are orderly managed and controlled, thereby improving the accuracy of power grid peak-shaving and improving the stability of the power grid.

Under the premise of meeting the requirements of feasibility, safety, efficiency and reliability, the present invention can decompose the winning bid of the aggregator to each cold chain load user in the process of cold chain load aggregation participating in power grid peak regulation, and formulate an equipment operation plan for each cold chain load user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart showing the steps of a method for decomposing a winning bid for cold chain load aggregation participating in power grid regulation according to the present invention.

Detailed ways

The technical solution of the present invention is further described below in conjunction with the accompanying drawings:

Embodiment 1:

The present invention proposes a method for decomposing the winning bid amount of cold chain load aggregation participating in power grid regulation, as shown in FIG1 , which specifically includes the following steps:

Step A: Obtain constraints related to aggregators and cold chain load users during the peak load response period of the trading day.

In an embodiment of the present invention, the constraints related to the aggregator and the cold chain load users during the peak-shaving response period of the trading day mainly include: the effective response load constraints of the aggregator during the peak-shaving response period of the trading day, the winning bid constraints of the aggregator during the peak-shaving response period of the trading day, the production environment temperature constraints of the cold chain load users and the necessary startup time constraints of the cold chain load users.

Step A01: The effective response load constraints of the aggregator during the peak load response period of the trading day include the maximum load constraint and the average load constraint. The maximum load constraint is: during the demand response period, the actual load is less than the maximum value of the historical baseline load; the average load constraint is: during the demand response period, the average load of the actual load is less than the average value of the historical baseline load, as follows:

Where _xn,t is the equipment status of cold chain load user n in period t, _Pn is the rated power of cold chain load user n, P0 ^,max is the maximum baseline load in the peak load response period, is the average load of the cold chain load response during the peak load response period, is the average value of the baseline load during the peak load response period, n = 1, 2, …, N, and N is the total number of cold chain load users.

Step A02: Constraints on the winning bid amount of the aggregator during the peak load response period of the trading day: During the demand response period, the actual load must be less than the historical baseline load minus the winning bid amount, that is, the actual load adjustment amount is greater than or equal to the winning bid amount, as follows:

in, is the aggregator’s load, is the baseline load of the aggregator, is the clearing winning bid of the load aggregator in period t.

Step A03, cold chain load user production environment temperature constraints: The cold storage temperature must be within the preset temperature range, as follows:

in, is the minimum cold chain temperature preset by cold chain load user n in time period t, Tn _,t is the temperature of cold chain load user n in time period t, is the maximum cold chain temperature preset by cold chain load user n in time period t. In this embodiment of the present invention, -22℃, It is -18℃.

In the embodiment of the present invention, the relationship between the temperature change of the production environment of the cold chain load user is:

Where T _n,t+1 represents the temperature of cold chain load user n in time period t+1, is the maximum startup time of cold chain load user n, is the maximum adjustable downtime of cold chain load user n.

Step A04, Constraints for the time period during which cold chain load users must turn on the machine: Users may require that the refrigeration equipment must be turned on during certain time periods. For example, during the process of taking goods from a cold storage, the cold storage door is open. Generally, the refrigeration equipment will be turned on at this time to prevent the cold storage temperature from rising too quickly and failing to meet the temperature requirements. Therefore, during the period of taking goods from the cold storage, the refrigeration equipment must be turned on. Constraints for the time period during which cold chain load users must turn on the machine are: x _n,t = 1. Assuming that cold chain load user 1 must turn on the machine during time period 10, then x _1,10 = 1.

Step B: Taking the maximum profit of the aggregator as the optimization goal, obtain the optimization objective function of the maximum profit of the aggregator, which is expressed as follows:

in, is the market clearing price in period t, α _t = {0, 1}, when α _t = 1, it means period t is the demand response period, otherwise α _t = 0, is the baseline load value of cold chain load user n in period t, t = 1, 2, …, T, T is the total peak load response period of the trading day, and ε is a preset constant.

Step C: Taking formulas (7)-(11) as constraints and maximizing the aggregator's profit as the optimization goal, that is, formula (12), a bid decomposition model for aggregator users to participate in power grid regulation is constructed.

Step D: Based on the established bid decomposition model of aggregator users participating in power grid regulation, the equipment operation plan _xn,t of each cold chain load user is calculated.

In the embodiment of the present invention, a mixed integer linear programming method is used to solve the model and calculate the equipment operation plan of each cold chain load user.

The model solving process is as follows:

Assume decision variables x, y, and z, where x = [x ₁ x ₂ … x ₂₄ ] ^T , y = [y ₁ y ₂ … y ₄₄ ] ^T , and z = [z ₁ z ₂ …z ₂₄ ] ^T , respectively representing the on/off status of cold chain load users 1, 2, and 3 in 24 hours.

Then the aggregator's maximum profit optimization objective function can be written as:

minimize M ₁ x+M ₂ y+M ₃ z (13)

Where, _Mn = [ _{mn,1mn ,2} …mn _,24 ],

Among them, n∈{1,2,3}, t∈{1,2,…,24}.

(1) Temperature limit constraints

According to formula (11), the high temperature limit constraint is:

I ₁ A ₁ x≤B ₁ (15)

I ₁ A ₂ y≤B ₂ (16)

I ₁ A ₃ z≤B ₃ (17)

in,

B _n =[b _n,1 b _n,2 ... b _n,24 ] ^T (19)

is the initial temperature of cold chain load user n.

The low temperature limit constraint is:

I ₂ A ₁ x≤D ₁ (23)

I ₂ A ₂ y≤D ₂ (24)

I ₂ A ₃ z≤D ₃ (25)

in,

D _n =[d _n,1 d _n,2 ... d _n,24 ] ^T (26)

(2) Load aggregators effectively respond to load constraints

The maximum load during the load aggregator response period should be less than the baseline maximum load:

C ₁ x+C ₂ y+C ₃ z≤P ^0,max (29)

Among them, C _n =[c _n,1 c _n,2 ... c _n,24 ], and c _n,t =P _n α _t .

The average load during the load aggregator response period should be less than the baseline average load:

Among them, E _n =[e _n,1 e _n,2 ... e _n,24 ],

(3) Constraints on the number of bids received by aggregators

The scalar quantity constraint in the response period aggregator is:

G ₁ x+G ₂ y+G ₃ z≤H (32)

Among them, G _n =[g _n,1 g _n,2 ... g _n,24 ], H = [h ₁ h ₂ ... h ₂₄ ] ^T ,

g _n,t =P _n α _n,t (33)

The above is to convert the constructed model of the present invention into the standard form of the linear programming problem, and then use the matlab mixed integer linear programming solution method to calculate the decision variables x, y, z, and then get the start and stop status of each cold chain load user.

Embodiment 2:

The present invention also proposes a device for decomposing the winning quantity of cold chain load aggregation participating in power grid regulation, including a processor and a storage medium; wherein the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the winning quantity decomposition method in Example 1.

Embodiment 3:

The present invention also proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the scalar decomposition method in Example 1 are implemented.

Compared with the prior art, the present invention reasonably allocates the winning bids within the peak-shaving response period of the trading day through multiple constraints related to the aggregator and the cold-chain load users within the peak-shaving response period of the trading day and the optimization goal of the aggregator's maximum profit, and obtains the equipment operation plan for each cold-chain load user, thereby laying a foundation for the cold-chain load resources to participate in the peak-shaving of the power grid. Based on the equipment operation plan of the cold-chain load users, the cold-chain load resources are orderly managed and controlled, thereby improving the accuracy of the peak-shaving of the power grid and improving the stability of the power grid.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

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 technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims (4)

1. The medium-scale decomposition method for the cold chain load aggregation participating in the regulation and control of the power grid is characterized by comprising the following steps of:

Acquiring constraint conditions related to a cold chain load user by an aggregator in a transaction day peak shaving response period;

obtaining the maximum benefit optimization objective function of the aggregator;

Calculating the equipment operation plan of each cold chain load user according to the constraint conditions and the maximum profit optimization objective function of the aggregator;

the constraint conditions related to the cold chain load users by the aggregator in the trade day peak shaving response period include: the trade day peak regulation response period aggregator effectively responds to load constraint conditions, the trade day peak regulation response period aggregator bid amount constraint conditions, cold chain load user production environment temperature constraint conditions and cold chain load user power-on-demand period constraint conditions;

the trade day peak shaver response period aggregator effective response load constraint conditions are as follows:

Where x _n,t is the device status of cold chain load user n during period t, P _n is the rated power of cold chain load user n, P ^0,max is the maximum baseline load during peak shaver response period, To peak shaver the cold chain load response average load during the response period,For the average value of the baseline load in the peak shaver response period, n=1, 2, …, N is the total number of cold chain load users;

The trade day peak shaver response period aggregate bid constraint conditions are as follows:

Wherein, For the baseline load of the aggregator,A clearing scalar for the period t load aggregator;

the cold chain load user production environment temperature constraints are as follows:

Wherein, For a minimum cold chain temperature preset for the cold chain load user n during the period T, T _n,t is the temperature of the cold chain load user n during the period T,The maximum cold chain temperature preset for the cold chain load user n in the period t;

the change relation of the cold chain load user production environment temperature is as follows:

wherein T _n,t+1 represents the temperature of the cold chain load user n in the period t+1, x _n,t is the device state of the cold chain load user n in the period T, For the maximum on-time of the cold chain load user n,Maximum mediation time for the cold chain load user n;

The constraint conditions of the cold chain load user on-demand time period are as follows: x _n,t = 1;

the expression of the aggregate maximum benefit optimization objective function is as follows:

Wherein, For the price of the market for period t, α _t＝{0,1},α_t represents whether period t is a demand response period, x _n,t is the device status of cold chain load user n during period t, P _n is the rated power of cold chain load user n,For the baseline load value of the cold chain load user N in the period T, n=1, 2, …, N is the total number of the cold chain load users, t=1, 2, …, T is the total peak regulation response period of the trade day, and epsilon is a preset constant.

2. The method for decomposing the medium bid amount of the cold chain load aggregation participating in the power grid regulation according to claim 1, wherein a medium bid amount decomposition model of the aggregate user participating in the power grid regulation is constructed according to constraint conditions and an aggregate maximum benefit optimization objective function, and a mixed integer linear programming method is adopted to solve the model, so that a device operation plan of each cold chain load user is calculated.

3. The medium-scale decomposition device for the cold chain load aggregation participating in the regulation and control of the power grid is characterized by comprising a processor and a storage medium;

the storage medium is used for storing instructions;

the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-2.

4. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-2.