CN114447924A

CN114447924A - Distributed differential privacy ADMM (advanced data mm) energy management and control method and system for smart grid

Info

Publication number: CN114447924A
Application number: CN202210054425.5A
Authority: CN
Inventors: 李岩; 赵大端; 彭超; 曹向阳; 张承慧
Original assignee: Shandong University
Current assignee: Shandong University
Priority date: 2022-01-18
Filing date: 2022-01-18
Publication date: 2022-05-06

Abstract

The invention belongs to the field of smart grids, and provides a distributed differential privacy ADMM energy management and control method and system for a smart grid. The method comprises the steps of obtaining relevant parameters of heterogeneous power generation equipment and a demand response unit in the smart grid; obtaining optimized output power of the heterogeneous power generation equipment and consumption power of the demand response unit based on the obtained parameters, the distributed differential privacy ADMM algorithm and a pre-constructed objective function; the objective function is maximized in social welfare and is constructed based on the power generation cost function of the heterogeneous power generation equipment, the utility function of the demand response unit, the constraint condition and the transmission loss of the heterogeneous power generation equipment and the demand response unit.

Description

Distributed differential privacy ADMM (advanced data mm) energy management and control method and system for smart grid

Technical Field

The invention belongs to the field of smart power grids, and particularly relates to a distributed differential privacy ADMM energy management and control method and system for a smart power grid.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

The intelligent power grid is a main component of a third-generation power grid, is used as a typical physical-information-social system, not only has the physical characteristics of the traditional power grid, but also integrates the information and social attributes of the future power grid, and is a highly automatic system integrating control, communication and calculation. The safe and healthy development of the intelligent power grid has very important significance for reasonably configuring an energy structure. However, the unstable and uncontrollable nature of wind power generation presents significant challenges to the energy management of the power grid.

As the scale of the power grid increases, the related equipment is not controlled by a single entity, and the traditional centralized energy management optimization method is not applicable any more. Due to the distributed power generation characteristic of the smart grid, the existing research widely applies the distributed optimization algorithm to the energy management of the smart grid. Compared with a centralized algorithm, the distributed algorithm is divided into autonomous and cooperative design principles, so that the robustness, reliability and expansibility of the algorithm are improved, and the communication traffic among nodes is greatly reduced. However, the inventor finds that frequent communication and communication of the distributed power generation units and continuous updating and upgrading of a network attack technology make data privacy extremely easy to steal, and the existing research on the distributed energy management problem of the smart grid ignores the requirement on privacy protection.

Disclosure of Invention

In order to solve the technical problems in the background art, the invention provides a distributed differential privacy ADMM energy management and control method and a system for a smart grid, and aims to realize management and control on the optimal generated power and the optimal consumed power of a power consumption load of power supply equipment in the smart grid.

In order to achieve the purpose, the invention adopts the following technical scheme:

the invention provides a distributed differential privacy (ADMM) energy management and control method for a smart grid, which comprises the following steps:

acquiring relevant parameters of heterogeneous power generation equipment and a demand response unit in the smart grid;

obtaining optimized output power of the heterogeneous power generation equipment and consumption power of the demand response unit based on the obtained parameters, the distributed differential privacy ADMM algorithm and a pre-constructed objective function;

the objective function is maximized in social welfare and is constructed based on the power generation cost function of the heterogeneous power generation equipment, the utility function of the demand response unit, the constraint condition and the transmission loss of the heterogeneous power generation equipment and the demand response unit.

As one implementation, a pre-constructed objective function is converted into an equivalent static sub-optimization model.

As one embodiment, the iterative update rule of the distributed differential privacy ADMM algorithm comprises a P-iterative update rule, a Y-iterative update rule and a u-iterative update rule.

As an embodiment, the termination criteria of the distributed differential privacy ADMM algorithm include termination criteria on the time axis and the iteration axis.

As an embodiment, laplacian noise with a fading variance is introduced in the internal power state in the objective function.

The second aspect of the present invention provides a distributed differential privacy ADMM energy management and control system for a smart grid, including:

the parameter acquisition module is used for acquiring relevant parameters of heterogeneous power generation equipment and a demand response unit in the smart grid;

the energy management and control module is used for obtaining the optimized output power of the heterogeneous power generation equipment and the consumption power of the demand response unit based on the obtained parameters, the distributed differential privacy ADMM algorithm and a pre-constructed objective function;

A third aspect of the present invention provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps in the distributed differential privacy ADMM energy management method of a smart grid as described above.

A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the steps in the method for energy management and control of distributed differential privacy ADMM for a smart grid as described above.

Compared with the prior art, the invention has the beneficial effects that:

(1) the distributed optimization problem of the social welfare maximization of the smart grid is further decomposed, an equivalent static sub-optimization model is established, heterogeneous power supply equipment and transmission loss are considered, and a solution is provided for solving the complex optimization problem of the smart grid.

(2) The ADMM optimization method based on the distributed differential privacy can enable the intelligent power grid to meet the energy supply and demand balance and constraint conditions of each node and achieve optimal energy management.

(3) The distributed differential privacy ADMM algorithm designed by the invention can not only protect privacy information in the communication interaction process of each unit of the smart grid, but also gradually realize an optimization target. The algorithm has better flexibility, reliability and expandability, and provides a new idea for realizing the energy management of privacy protection in the smart grid.

Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.

Fig. 1 is a diagram of a distributed differential privacy ADMM energy management and control principle of a smart grid according to an embodiment of the present invention;

FIG. 2 is a generated power variation curve for each power plant of an embodiment of the present invention;

FIG. 3 is a graph illustrating the variation of the power mismatch function of each device in the system according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of a distributed differential privacy ADMM energy management and control system of a smart grid according to an embodiment of the present invention.

Detailed Description

The invention is further described with reference to the following figures and examples.

It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

Example one

As shown in fig. 1, the embodiment provides a distributed differential privacy ADMM energy management and control method for a smart grid, which specifically includes the following steps:

s101: and acquiring relevant parameters of heterogeneous power generation equipment and a demand response unit in the smart grid.

For a smart grid consisting of traditional power generation units, wind power generation units, battery storage systems, demand response units, and the like, each individual has a corresponding cost function or utility function. Suppose that the smart grid consists of m thermal power generators, n battery energy storage systems, o wind power generators and q local loads. Related parameters rho, phi, mu and pi of improved optimization control algorithm under privacy protection requirement_i、κ_i、

The initial scheduling time τ is 0, the initial iteration number k is 1, and the initial iteration state P of each variable_i ¹、Y_i ¹、

Coefficient alpha in cost function of each power generation equipment and response unit_i、β_i、γ_i. Coefficient of power loss χ_h、χ_l、χ_r、χ_s. Tolerance threshold P_i ^min、P_i ^max、ε^τ、ε^pri、ε^dual。

The cost of power generation function, the output limit, for each individual can be modeled as follows:

(1) thermal power generator

Cost function:

wherein alpha is_h＞0，β_h，γ_hAnd > 0 represents the cost coefficient of the h-th thermal generator respectively.

Output limit:

wherein the content of the first and second substances,

respectively representing the minimum output and the maximum output of the h-th thermal generator. In general, the minimum output is usually set to zero, i.e. the minimum output is set to

(2) Battery energy storage system

Cost function:

wherein alpha is_lRepresenting the cost factor of the l-th battery energy storage unit.

Output limit:

wherein the content of the first and second substances,

represents the maximum discharge power of the battery,

representing the maximum charging power of the battery. For energy storage systems, when P_B,l(t) < 0, indicating that the battery is in a charged state, when P_B,l(t) > 0, indicating that the battery is in a discharged state.

(3) Wind power generator

Cost function:

wherein alpha is_r、β_r、γ_rRespectively representing direct, penalty, reserve cost functions. Due to the stochastic nature of wind power generation, the underestimated term in the above equation

Overestimated term

Can be calculated by the following equation

Wherein the content of the first and second substances,

indicating rated power of wind generator, f_P(p) available for Weber-distribution-compliant applicationsProbability density function of wind power.

Output limit:

wherein the content of the first and second substances,

respectively representing the minimum and maximum output power of the wind turbine.

(4) Demand response unit

Utility function:

wherein alpha is_s、β_sRespectively representing preset parameters of the s-th load.

And (4) energy consumption limit:

wherein the content of the first and second substances,

respectively representing the minimum and maximum power consumption of the load.

According to the optimal energy management of heterogeneous power generation equipment and a demand response unit in the smart grid, namely the problem of social welfare maximization, modeling is carried out on the social welfare of the smart grid.

The welfare function expression brought to the society by the thermal power generator is established as follows:

W_G,h(P_G,h(t))＝η(t)(1-χ_h)P_G,h(t)-C_h(P_G,h(t)) (10)

the welfare function expression brought to the society by the battery energy storage system is established as follows:

W_B,l(P_B,l(t))＝η(t)(1-χ_l)P_B,l(t)-C_l(P_B,l(t) (11)

the welfare function expression brought to the society by the wind driven generator is established as follows:

W_R,r(P_R,r(t))＝η(t)(1-χ_r)P_R,r(t)-C_r(P_R,r(t) (12)

the welfare function expression brought to the society by the demand response unit is established as follows:

W_D,s(P_D,s(t))＝U_s(P_D,s(t))-η(t)(1-χ_s)P_D,s(t (13)

considering the transmission loss problem among the social welfare maximization problems, an expression of a transmission energy loss function is established as follows:

in the above function, χ_h、χ_l、χ_r、χ_sThe transmission electric quantity loss coefficients of all units in the system are respectively expressed, and eta (t) represents the electricity price.

S102: and obtaining the optimized output power of the heterogeneous power generation equipment and the consumption power of the demand response unit based on the obtained parameters, the distributed differential privacy ADMM algorithm and the pre-constructed objective function.

The original optimization problem containing the objective function and constraints can be modeled as follows:

further decomposing the dynamic optimization problem of the social welfare maximization, and establishing an equivalent static sub-optimization model:

the shorthand form is:

C_i(P_i)∈{C_h(P_G,h),C_l(P_B,l),C_r(P_R,r),-U_s(P_D,s)}

where N ═ m + N + o + q denotes the total number of all power generating equipment and power consuming loads in the system, and the social welfare function W_i(·)∈{W_G,h(·),W_B,l(·),W_R,r(·),W_D,s(. -) power P_i(t)∈{P_G,h(t),P_B,l(t),P_R,r(t),P_D,s(t) }, force limit P_i ^min∈{P_h ^min,P_l ^min,P_r ^min,P_s ^min}、P_i ^max∈{P_h ^max,P_l ^max,P_r ^max,P_s ^max}。

For the purposes of the following description, the following two convex sets are defined: j is a unit of₁＝{P(t)∈R^N| (16) } and J₂＝{Y(t)∈R^N|(16)}。

The laplacian noise with the variance of attenuation is introduced in the internal power state as follows:

wherein the mean value of the random variable W is mu, and the variance is 2 sigma². The purpose of protecting the privacy information in the communication interaction process of all the intelligent agents in the power grid is achieved.

The simplified static sub-optimization model is further converted into the following optimization problem:

wherein the content of the first and second substances,

the augmented Lagrangian function that gives the optimization problem (18) is as follows:

and designing three iteration updating rules and a termination criterion of the distributed differential privacy ADMM algorithm according to optimization problems and privacy protection requirements.

P-iterative update rule:

wherein

The above optimization problem is converted into the equivalent form:

wherein the power loss coefficient vector is

χ＝[1-χ₁,...,1-χ_N-q,-(1+χ_N-q+1),...,-(1+χ_N)]

Designing a distributed differential privacy algorithm to solve the optimization problem:

wherein w_i(τ)～Lap(σ_i(τ)) are independent identically distributed random variables generated by the embedded noise generator,

in order to inject the power after the noise,

in order to be a time-varying variance,

representing the gradient of the objective function.

Y-iterative update rule

The distributed form is as follows:

u-iterative update rule

u^k+1＝u^k+P^k+1-Y^k+1 (25)

The distributed form is as follows:

the stopping criteria on the time axis and the iteration axis are given as follows:

||P(τ+1)-P(τ)||₂≤ε^τ

P^k+1＝P(τ+1 (27)

and

real-time pricing mechanism

Wherein

Is the optimal lagrangian multiplier of the distributed optimization problem (21).

The following calculations were started with the distributed differential privacy ADMM algorithm:

step 1: starting the iteration times k of the algorithm to be k + 1;

step 2: starting scheduling time iteration tau is tau + 1;

step 2.1: generating Laplace noise w_i(τ)～Lap(σ_i(τ))；

Step 2.2: injecting noise into power

Carrying out privacy protection on communication data of each power generation device and each response unit;

step 2.3: calculating the gradient of an objective function

Step 2.4: using distributed discrete time differential privacy convergence gradient descent method

Calculating the power P_i(τ)；

Step 2.5: checking whether a stop condition | | P (τ +1) -P (τ) | luminance is satisfied₂≤ε^τIf yes, updating the generated power P_i ^k+1＝P_i(tau +1), if not, returning to the step 3 for continuing;

and step 3: according to

Updating Y_i ^k+1；

And 4, step 4: according to

Updating

And 5: checking whether a stop condition is satisfied, if | | | r₁ ^k||₂≤ε^priAnd

the current optimum output/consumed power is output. Otherwise, returning to the step 2 for continuing;

step 6: calculating pricing of each power generation equipment according to a real-time pricing mechanism

Calculating pricing for response units

FIG. 2 shows a generated power curve for each power plant; fig. 3 shows the variation curve of the power mismatch function of each device in the system.

Compared with the optimization model designed in the previous research result, the optimization model established by the invention considers the bidirectional energy flow characteristic of the battery energy storage system and the random characteristic of the wind driven generator, so that the optimization model is more in line with the actual application scene.

Example two

As shown in fig. 4, the embodiment provides a distributed differential privacy ADMM energy management and control system for a smart grid, which specifically includes the following modules:

the energy management and control module is used for obtaining optimized output power of the heterogeneous power generation equipment and consumption power of the demand response unit based on the obtained parameters, the distributed differential privacy ADMM algorithm and a pre-constructed objective function;

the objective function is maximized in social welfare and is constructed on the basis of a power generation cost function of the heterogeneous power generation equipment, a utility function and constraint conditions of the demand response unit and transmission losses of the heterogeneous power generation equipment and the demand response unit.

And in the process of solving the pre-constructed objective function, converting the pre-constructed objective function into an equivalent static sub-optimization model.

In the objective function, laplacian noise with a fading variance is introduced in the internal power state. Thus, the privacy protection is performed on the communication data.

It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described herein again.

EXAMPLE III

The present embodiment provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps in the distributed differential privacy ADMM energy management method of a smart grid as described above.

Example four

The embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the method for managing and controlling distributed differential privacy ADMM energy of a smart grid as described above.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed differential privacy (ADMM) energy management and control method for a smart grid is characterized by comprising the following steps:

2. The distributed differential privacy (ADMM) energy management and control method of the smart grid according to claim 1, characterized in that a pre-constructed objective function is converted into an equivalent static sub-optimization model.

3. The distributed differential privacy ADMM energy management and control method of a smart grid according to claim 1, wherein the iterative update rules of the distributed differential privacy ADMM algorithm include P-iterative update rules, Y-iterative update rules and u-iterative update rules.

4. The distributed differential privacy ADMM energy management and control method of a smart grid as claimed in claim 1, wherein termination criteria of the distributed differential privacy ADMM algorithm include termination criteria on a time axis and an iteration axis.

5. The distributed differential privacy, ADMM, energy management and control method of a smart grid according to claim 1, characterized in that in the objective function Laplace noise with decaying variance is introduced in the internal power states.

6. The utility model provides a distributed difference privacy ADMM energy management and control system of smart power grids which characterized in that includes:

7. The distributed differential privacy ADMM energy management and control system for smart grids of claim 6, wherein a pre-constructed objective function is converted into an equivalent static sub-optimization model.

8. The distributed differential privacy, ADMM, energy management and control system of a smart grid according to claim 6, wherein in the objective function Laplace noise with a decaying variance is introduced in internal power states.

9. A computer readable storage medium, having stored thereon a computer program, characterized in that the program, when being executed by a processor, realizes the steps in the distributed differential privacy, ADMM, energy management method of a smart grid according to any one of claims 1-5.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the distributed differential privacy, ADMM, energy management method for a smart grid according to any one of claims 1-5.