Distributed differential privacy admm energy management and control method and system for smart grid

Abstract

The application 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 following steps: obtaining relevant parameters of heterogeneous power generation equipment and demand response units in the smart grid; based on the obtained parameters, a distributed differential privacy ADMM algorithm and a pre-constructed objective function, obtaining optimized output power of the heterogeneous power generation equipment and consumption power of the demand response units; wherein the objective function is constructed based on a power generation cost function of the heterogeneous power generation equipment, an utility function of the demand response units, a constraint condition and transmission loss of the heterogeneous power generation equipment and the demand response units, and maximizes social welfare.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of smart grid, and particularly relates to a distributed differential privacy ADMM energy management and control method and system of a smart grid. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] The smart grid is the main component of the third generation power grid, as a typical physical-information-social system, it 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 automated system integrating control, communication and calculation. The safe and healthy development of the smart grid is of great significance for the rational allocation of energy structure. However, the unstable and uncontrollable characteristics of wind power generation bring great challenges to the energy management of the power grid.

[0004] With the increase of the scale of the power grid, the related equipment is not controlled by a single entity, and the traditional centralized energy management optimization method is no longer applicable. Due to the distributed power generation characteristics of the smart grid, the existing research widely applies distributed optimization algorithms to the energy management of the smart grid. Compared with the centralized algorithm, the design principles of the distributed algorithm of autonomy and collaborative cooperation improve the robustness, reliability and scalability of the algorithm, and greatly reduce the communication amount between nodes. However, the inventors found that the frequent communication and exchange of distributed power generation units and the continuous updating and upgrading of network attack technology make the data privacy extremely easy to be stolen, and the existing research on the distributed energy management problem of the smart grid ignores the demand for privacy protection. SUMMARY

[0005] In order to solve the technical problems existing in the background art, the present application provides a distributed differential privacy ADMM energy management and control method and system of a smart grid, which aims to realize the optimal generation of power of power supply equipment and the optimal consumption of power of power consumption load in the smart grid.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] The first aspect of the present application provides a distributed differential privacy ADMM energy management and control method of a smart grid, which comprises:

[0008] obtaining the related parameters of the heterogeneous power generation equipment and the demand response unit in the smart grid;

[0009] obtaining the output power of the optimized 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;

[0010] The target function is constructed based on a generation cost function of the heterogeneous generation equipment, an utility function of the demand response unit, a constraint condition, and transmission loss of the heterogeneous generation equipment and the demand response unit.

[0011] As an implementation form, the pre-constructed target function is converted into an equivalent static sub-optimization model.

[0012] As an implementation form, the iteration update rule of the distributed differential privacy ADMM algorithm comprises a P-iteration update rule, a Y-iteration update rule, and a u-iteration update rule.

[0013] As an implementation form, the termination criterion of the distributed differential privacy ADMM algorithm comprises a termination criterion on a time axis and an iteration axis.

[0014] As an implementation form, in the target function, Laplace noise with a decaying variance is introduced in the internal power state.

[0015] A second aspect of the present application provides a distributed differential privacy ADMM energy management system of a smart grid, comprising:

[0016] a parameter acquisition module configured to acquire relevant parameters of the heterogeneous generation equipment and the demand response unit in the smart grid;

[0017] an energy management module configured to obtain optimized output power of the heterogeneous generation equipment and consumption power of the demand response unit based on the acquired parameters, the distributed differential privacy ADMM algorithm, and the pre-constructed target function.

[0018] The target function is constructed based on a generation cost function of the heterogeneous generation equipment, an utility function of the demand response unit, a constraint condition, and transmission loss of the heterogeneous generation equipment and the demand response unit.

[0019] A third aspect of the present application provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps in the distributed differential privacy ADMM energy management method of a smart grid as described above.

[0020] A fourth aspect of the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the distributed differential privacy ADMM energy management method of a smart grid as described above when executing the program.

[0021] Compared with the prior art, the present application has the following beneficial effects:

[0022] (1) The application further decomposes the distributed optimization problem of maximizing the social welfare of the smart grid, establishes an equivalent static sub-optimization model, and considers heterogeneous power supply equipment and transmission loss, thereby providing a solution to the complex optimization problem of the smart grid.

[0023] (2) The ADMM optimization method based on distributed differential privacy of the application can make the smart grid meet the energy supply and demand balance and the constraint conditions of each node while realizing optimal energy management.

[0024] (3) The distributed differential privacy ADMM algorithm designed by the application can not only protect the privacy information in the communication and interaction process of each unit of the smart grid, but also gradually realize the optimization goal. The algorithm has better flexibility, reliability and scalability, and provides a new idea for realizing privacy protection energy management in the smart grid.

[0025] The advantages of the additional aspects of the application will be partially given in the following description, partially become obvious from the following description, or be known through the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0026] The drawings accompanying the specification of the application are used to provide further understanding of the application, the illustrative embodiments of the application and the description thereof are used to explain the application, and do not constitute undue limitation on the application.

[0027] Figure 1 is the distributed differential privacy ADMM energy management principle diagram of the smart grid of the embodiment of the application;

[0028] Figure 2 is the generation power change curve of each power generation equipment of the embodiment of the application;

[0029] Figure 3 is the power mismatch degree function change curve of each equipment in the system of the embodiment of the application;

[0030] Figure 4 is the distributed differential privacy ADMM energy management system structure schematic diagram of the smart grid of the embodiment of the application. DETAILED DESCRIPTION

[0031] The application will be further described below in combination with the drawings and embodiments.

[0032] It should be pointed out that the following detailed description is exemplary and is intended to provide further description of the application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the application belongs.

[0033] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components and / or combinations thereof, but do not preclude the presence or addition of one or more other features, steps, operations, devices, components and / or combinations thereof.

[0034] Embodiment One

[0035] As shown in the figure, the embodiment provides a distributed differential privacy ADMM energy management method for a smart grid, which specifically includes the following steps: Figure 1

[0036] S101: Obtain relevant parameters of heterogeneous power generation equipment and demand response units in the smart grid.

[0037] For a smart grid composed of traditional power generation units, wind power generation units, battery storage systems, demand response units, etc., each individual has a corresponding cost function or utility function. Assume that the smart grid is composed of m thermal power generators, n battery energy storage systems, o wind power generators, and q local loads. The related parameters of the improved optimization control algorithm under privacy protection requirements are ρ, φ, μ, π i , κ i ,

[0038] Initialize the scheduling time τ = 0, the initial iteration number k = 1, and the initial iteration state of each variable P i 1 , Y i 1 , The coefficients α i , β i , γ i in the cost function of each power generation equipment and response unit. Power loss coefficients χ h , χ l , χ r , χ s . Allowable threshold P i min , P i max , ε τ , ε pri , ε dual .

[0039] The power generation cost function and output limit of each individual can be modeled as follows:

[0040] (1) Thermal power generator

[0041] Cost function:​

[0042]

[0043] where, a h > 0, b h > 0, g h > 0 are the cost coefficients of the hth thermal generator.

[0044] Output limits:

[0045]

[0046] where, are the minimum and maximum output of the hth thermal generator, respectively. Generally, the minimum output is set to zero, i.e.

[0047] (2) Battery Energy Storage System

[0048] Cost function:

[0049]

[0050] where, a l is the cost coefficient of the lth battery energy storage unit.

[0051] Output limits:

[0052]

[0053] where, is the maximum discharging power of the battery, is the maximum charging power of the battery. For the energy storage system, when P B,l (t) < 0, it indicates that the battery is in charging state, when P B,l (t) > 0, it indicates that the battery is in discharging state.

[0054] (3) Wind Turbine

[0055] Cost function:

[0056]

[0057] where, a r , b r , g r are the direct, penalty, and reserve cost functions, respectively. Due to the stochastic nature of wind power generation, the under-estimation term in the above equation can be calculated by

[0058] ​

[0059] where, Pfrepresents the rated power of the wind turbine, f P (p) represents the probability density function of the available wind power subject to Weibull distribution.

[0060] Output limit:

[0061]

[0062] where, Pminand Pmaxrepresent the minimum and maximum output power of the wind turbine, respectively.

[0063] (4) Demand response unit

[0064] Utility function:

[0065]

[0066] where, α s and β s represent the preset parameters of the s-th load, respectively.

[0067] Energy consumption limit:

[0068]

[0069] where, Pminand Pmaxrepresent the minimum and maximum energy consumption of the load, respectively.

[0070] According to the optimal energy management of heterogeneous power generation equipment and demand response units in the smart grid, i.e., the social welfare maximization problem, the social welfare is modeled.

[0071] The expression of the welfare function brought by the thermal power generator to the society is as follows:

[0072] W G,h (P G,h (t)) = η(t)(1 - χ h )P G,h (t) - C h (P G,h (t)) (10)

[0073] The expression of the welfare function brought by the battery energy storage system to the society is as follows:

[0074] W B,l (P B,l (t)) = η(t)(1 - χ l )P B,l (t) - C l (P B,l (t) (11)

[0075] The expression of the welfare function brought by the wind power generator to the society is as follows:

[0076] W R,r (P R,r (t))=η(t)(1-χ r )P R,r (t)-C r (P R,r (t) (12)

[0077] The expression of the welfare function brought by the demand response unit to the society is as follows:

[0078] W D,s (P D,s (t))=U s (P D,s (t))-η(t)(1-χ s )P D,s (t (13)

[0079] Considering the transmission loss in the problem of maximizing the social welfare, the expression of the transmission energy loss function is as follows:

[0080]

[0081] In the above functions, χ h , χ l , χ r , χ s respectively represent the transmission power loss coefficients of each unit in the system, and η(t) represents the electricity price.

[0082] S102: Based on the obtained parameters, the distributed differential privacy ADMM algorithm and the pre-constructed objective function, the optimized output power of the heterogeneous power generation equipment and the consumption power of the demand response unit are obtained.

[0083] The objective function 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.

[0084] The original optimization problem containing the objective function and the constraint condition can be modeled as follows:

[0085]

[0086]

[0087] The dynamic optimization problem of maximizing the social welfare is further decomposed to establish an equivalent static sub-optimization model:

[0088]

[0089] In short form:

[0090]

[0091]

[0092] C i (P i )∈{C h (P G,h ),C l (P B,l ),C r (P R,r ),-U s (P D,s )}

[0093] where N = m + n + o + q represents the total number of all power generation devices and power consumption loads in the system, the social welfare function W i (·)∈{W G,h (·),W B,l (·),W R,r (·),W D,s (·)}, the power P i (t)∈{P G,h (t),P B,l (t),P R,r (t),P D,s (t)}, the output 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}.

[0094] For the convenience of the following description, define the following two convex sets: J1 = {P(t) ∈ R N |(16)} and J2 = {Y(t) ∈ R N |(16)}.

[0095] Introduce the Laplace noise with decaying variance in the internal power state as follows:

[0096]

[0097] where the mean of the random variable W is μ and the variance is 2σ 2 . To achieve the purpose of protecting the privacy information in the communication interaction process of all agents in the power grid.

[0098] The simplified static sub-optimization model is further converted into the following optimization problem:

[0099]

[0100] where,

[0101]

[0102]

[0103]

[0104] The augmented Lagrangian function of the optimization problem (18) is given as follows:

[0105]

[0106] According to the optimization problem and the privacy protection requirement, three iteration update rules and termination criteria of the distributed differential privacy ADMM algorithm are designed.

[0107] P-iteration update rule:

[0108]

[0109] where The above optimization problem is converted into the following equivalent form:

[0110]

[0111] where the power loss coefficient vector is

[0112] χ = [1 - χ1,..., 1 - χ N-q , -(1 + χ N-q+1 ),..., -(1 + χ N )]

[0113] A distributed differential privacy algorithm is designed to solve the above optimization problem:

[0114]

[0115] where w i (τ) ~ Lap(σ i (τ)) is an independent and identically distributed random variable generated by an embedded noise generator, is the power after injecting noise, For time-varying variance, This represents the gradient of the objective function.

[0116] Y-Iterative Update Rules

[0117]

[0118] Its distributed form is as follows:

[0119]

[0120] u-Iterative update rules

[0121] u k+1 =u k +P k+1 -Y k+1 (25)

[0122] Its distributed form is as follows:

[0123]

[0124] The stopping criteria on the time axis and iteration axis are given below:

[0125] ||P(τ+1)-P(τ)||2≤ε τ

[0126] P k+1 =P(τ+1 (27)

[0127] as well as

[0128]

[0129]

[0130] Real-time pricing mechanism

[0131]

[0132] in It is the optimal Lagrange multiplier for the distributed optimization problem (21).

[0133] The following calculations will be performed using the Distributed Differential Privacy (ADMM) algorithm:

[0134] Step 1: Start the algorithm iteration count k = k + 1;

[0135] Step 2: Start the scheduling time iteration τ = τ + 1;

[0136] Step 2.1: Generate Laplace noise w i (τ)~Lap(σ i (τ));

[0137] Step 2.2: Inject noise into power The communication data of each power generation device and response unit is privacy protected;

[0138] Step 2.3: Calculate the gradient of the objective function

[0139] Step 2.4: Use the distributed discrete-time differential privacy convergent gradient descent method, i.e.

[0140]

[0141] Calculate the power P i (τ);

[0142] Step 2.5: Check whether the stop condition ||P(τ+1)-P(τ)||2≤ε τ is met, if met, update the power generation power P i k+1 =P i (τ+1), if not met, return to step 3 to continue;

[0143] Step 3: Update Y

[0144]

[0145] according to i k+1 ;

[0146] Step 4: Update according to

[0147] Step 5: Check whether the stop condition is met, if ||r1 k ||2≤ε pri and , output the current optimal output / consumption power. Otherwise, return to step 2 to continue;

[0148] Step 6: According to the real-time pricing mechanism, calculate the pricing of each power generation device

[0149]

[0150] Calculate the pricing of each response unit

[0151]

[0152] Figure 2 The generation power change curve of each power generation device is given; Figure 3 The power mismatch degree function change curve of each device in the system is given.

[0153] Compared with the optimization model designed in the previous research results, the optimization model established in the application considers the bidirectional energy flow characteristics of the battery energy storage system and the random characteristics of the wind turbine, and is more in line with the actual application scene.

[0154] Embodiment two

[0155] As shown in the figure, the embodiment provides a distributed differential privacy ADMM energy management and control system of a smart grid, which specifically includes the following modules: Figure 4

[0156] A parameter acquisition module for acquiring related parameters of heterogeneous power generation equipment and demand response units in a smart grid;

[0157] An energy management and control module for obtaining optimized output power of the heterogeneous power generation equipment and consumption power of the demand response units based on the acquired parameters, the distributed differential privacy ADMM algorithm, and a pre-constructed objective function;

[0158] The objective function 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.

[0159] In the process of solving the pre-constructed objective function, the pre-constructed objective function is converted into an equivalent static sub-optimization model.

[0160] In the objective function, Laplace noise with decaying variance is introduced in the internal power state. In this way, the communication data is protected.

[0161] It should be noted that each module in the embodiment corresponds to each step in Embodiment One, and the specific implementation process is the same, which will not be repeated here.

[0162] Embodiment three

[0163] The embodiment provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps in the distributed differential privacy ADMM energy management and control method of a smart grid as described above.

[0164] Embodiment four

[0165] The embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the steps in the distributed differential privacy ADMM energy management and control method of a smart grid as described above.

[0166] ​The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments of the present application. In this regard, each flowchart block and / or block in the Figures can represent a module, segment, or portion of code, which comprises one or more executable Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0167] The specific embodiments of the present application have been shown and described in detail so that they can be better understood and fully appreciated. While the application has been described and illustrated in connection with certain embodiments, those skilled in the art will understand that the application is capable of further modifications. This application is intended to cover any variations or equivalents of the application included within the spirit and scope of the application.

Claims

1. A distributed differential privacy ADMM energy management method for a smart grid, characterized in that, The method comprises: acquiring parameters of heterogeneous power generation devices and demand response units in a smart grid; based on the acquired parameters, a distributed differential privacy ADMM algorithm, and a pre-constructed objective function, obtaining optimized output power of the heterogeneous power generation devices and consumption power of the demand response units; wherein the pre-constructed objective function is converted into an equivalent static sub-optimization model; The original optimization problem containing the objective function and the constraint condition is modeled as follows: ； Wherein, m represents the number of thermal power generators, n represents the number of battery energy storage systems, o represents the number of wind power generators, and q represents the number of local loads; , respectively represent the minimum and maximum output of the mth thermal power generator; represents the maximum discharge power of the battery, represents the maximum charging power of the battery; represents the rated power of the wind power generator; , respectively represent the minimum and maximum output power of the wind power generator; , respectively represent the minimum and maximum consumption power of the load;​ The dynamic optimization problem of maximizing social welfare is further decomposed, and an equivalent static sub-optimization model is established: ； The simplified static sub-optimization model is further converted into the following optimization problem: ； ； wherein denotes the total number of all power generation facilities and power consumption loads in the system, the social welfare function , power ; , , , denote the transmission power loss coefficients of each unit in the system, respectively wherein, ； wherein the objective function is constructed based on a power generation cost function of the heterogeneous power generation devices, an utility function of the demand response units, the constraint condition, and transmission loss of the heterogeneous power generation devices and the demand response units, and the Laplace noise with decaying variance is introduced in the internal power state. ； ； ； The method comprises: In the distributed differential privacy ADMM algorithm, the termination criterion includes a termination criterion on a time axis and an iteration axis; and the iteration update rule of the distributed differential privacy ADMM algorithm includes an iteration update rule, an iteration update rule and an iteration update rule.

2. A distributed differential privacy ADMM energy management system for a smart grid, the system comprising: a parameter acquisition module, configured to acquire parameters of heterogeneous power generation devices and demand response units in a smart grid; an energy management module, configured to, based on the acquired parameters, a distributed differential privacy ADMM algorithm, and a pre-constructed objective function, obtain optimized output power of the heterogeneous power generation devices and consumption power of the demand response units; wherein the pre-constructed objective function is converted into an equivalent static sub-optimization model; The original optimization problem containing the objective function and the constraint condition is modeled as follows: The dynamic optimization problem of maximizing social welfare is further decomposed, and an equivalent static sub-optimization model is established: ； Wherein, m represents the number of thermal power generators, n represents the number of battery energy storage systems, o represents the number of wind power generators, and q represents the number of local loads; , respectively represent the minimum output and the maximum output of the mth thermal power generator; represents the maximum discharge power of the battery, represents the maximum charging power of the battery; represents the rated power of the wind power generator; , respectively represent the minimum and maximum output power of the wind power generator; , respectively represent the minimum and maximum consumption power of the load;​ The simplified static sub-optimization model is further converted into the following optimization problem: ； wherein, ； ； wherein represents the total number of all power generation devices and power consumption loads in the system, the social welfare function , power ; , , , respectively represent the transmission power loss coefficients of each unit in the system; wherein the objective function is constructed based on a power generation cost function of the heterogeneous power generation devices, an utility function of the demand response units, the constraint condition, and transmission loss of the heterogeneous power generation devices and the demand response units, and the Laplace noise with decaying variance is introduced in the internal power state. ； The program is executed by the processor to implement the steps in the distributed differential privacy ADMM energy management method of the smart grid as claimed in claim 1. ； ； ； The processor executes the program to implement the steps in the distributed differential privacy ADMM energy management method of the smart grid as claimed in claim 1. The termination criterion of the distributed differential privacy ADMM algorithm includes a termination criterion on a time axis and an iteration axis; the iteration update rule of the distributed differential privacy ADMM algorithm includes an iteration update rule, an iteration update rule and an iteration update rule.

3. A computer-readable storage medium having stored thereon a computer program, characterized in that, ​ 4. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, ​

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