Power grid load control method considering uncertainty of orderly power utilization

Abstract

A power grid load control method considering uncertainty of orderly power utilization belongs to the field of power systems, and the invention provides a power grid load control method under uncertainty of orderly power utilization, and the method comprises the steps: building a load control model and constructing constraint conditions according to consideration of uncertainty of controlled load response power; depicting the uncertainty of the load power responding to load control by using an uncertainty set; establishing a robust stochastic optimization scheduling model, and realizing conversion from a stochastic optimization model to a deterministic optimization model through a robust linear optimization theory under an uncertain set; through the effect of load control participating in optimization operation, the necessity of load control uncertainty and the coordination effect of a robustness index and a fluctuation quantity index on economy and robustness, the problem of multiple uncertainties in the power grid optimization operation process is solved; and theoretical support is provided for stable operation and efficient energy utilization of a power grid.

Description

Technical Field

[0001] The present invention belongs to the field of orderly power consumption power grids, and in particular relates to a power grid load control method taking into account the uncertainty of orderly power consumption. Background Art

[0002] As the cornerstone of urban and industrial development, the stability and security of the power grid are crucial to the smooth operation of society and economic development. With the continuous increase in electricity demand and the increasing complexity of the power grid architecture, how to optimize the operation of the power grid while ensuring power supply security has become one of the core issues in current power system research.

[0003] The power system is moving towards a highly intelligent and automated direction. Especially in the face of load peaks and emergencies, how to quickly and effectively adjust the load to reduce the risk of power grid operation and ensure its stability has become a core challenge in system operation. This puts forward more stringent requirements for the real-time dispatch and load management of the power system.

[0004] The existing load management data interaction and transmission functions are imperfect. Although the introduction of intelligent systems has improved the efficiency of load management, it has also brought information security risks, including the failure to fully utilize the value of load resources and the imperfect multi-party coordination mechanism. As a result, various uncertainties encountered in the optimized operation of the power grid will lead to a decrease in the stability of the power grid, an increase in the operating costs of the power grid, and problems such as unstable operation of the power grid and low energy efficiency. Summary of the invention

[0005] The present invention provides a power grid load control method taking into account the uncertainty of orderly power consumption, aiming to overcome the limitations of the existing technology, solve the multiple uncertainty problems faced in the process of power grid optimization operation, and provide technical support for the stable operation of the power grid and efficient energy utilization.

[0006] The present invention adopts the following technical solution:

[0007] A method for controlling a power grid load model considering uncertainty of orderly power consumption, characterized by comprising:

[0008] Step 1: Establish a load control model based on the uncertainty of the controlled load response power and construct the initial constraint conditions of the load control model;

[0009] Step 2: Use uncertainty sets to characterize the load control model, characterizing the uncertainty of the load power and wind and solar power predictions that respond to load control;

[0010] Step 3: Establish a robust stochastic optimization scheduling model. Through the robust linear optimization theory under uncertain sets, the transformation of the stochastic optimization model to the deterministic optimization model is realized.

[0011] Furthermore, constructing the initial constraint conditions of the load control model includes the following steps:

[0012] Construct the load model constraint conditions, which are expressed as follows:

[0013]

[0014] Where: t = 1, 2, .., N, N is the total number of scheduling periods; P LD,t and P L,t are the controlled afterload demand and the original load demand during period t respectively; P C,i is the power of each group of controlled loads in period i; d is the number of control periods of the controlled loads; g n is the compensation strategy coefficient; c t+1-i It is 1 when t+1-i≤d, otherwise it is 0; a0=max{1,t-d+1}; when t≥d+1, a1=maxt+ld-, a2=td otherwise, a1=0, a2=0; h is the number of compensation periods corresponding to the single-period control strategy.

[0015] The controlled load and rebound load upper limit constraints for each period are set as follows:

[0016]

[0017] Where: t = 1, 2, .., N; P X,max , P F,max They represent the maximum load reduction power and the maximum rebound load power in each period respectively.

[0018] Furthermore, uncertainty modeling of wind and solar power forecasting is constructed, including the following steps:

[0019]

[0020] Where: is the random parameter of the controlled load power; P DLC,max It is the upper limit of the controlled load power during the entire dispatch period.

[0021] Furthermore, a stochastic robust linear optimization model based on uncertainty sets is established, including the following steps:

[0022]

[0023] Where: x∈R n is the decision variable; c∈R n is the objective function coefficient vector; b∈R m , A∈R mnis the coefficient matrix of the inequality constraints, assuming that the random variables are only contained in the coefficient matrix A. The random parameter vector a in the i-th row of matrix A is i The actual range of variation is

[0024]

[0025] Where: a i is the random parameter of the i-th row of the coefficient matrix A, a ik for a i The kth element in ; is the volatility indicator, variable α ik By p 1,i Robustness index p 1,i Determine, when the robustness index p 1,i =|J i |, the optimization result is most robust, p 1,i The smaller it is, the weaker the robustness. 1,i When =0, the model becomes a deterministic model.

[0026] Furthermore, by controlling the variation range of the random parameters, the robustness and optimality of the stochastic robust linear optimization model of the uncertain set are adjusted, including the following steps:

[0027]

[0028] Where: z 1,i and q 1,ik is the new decision variable generated by the robust equivalence transformation. This formula is a deterministic linear programming problem and can be solved using conventional linear programming methods.

[0029] Furthermore, the transformation of the random optimization model to the deterministic optimization model is realized through the robust linear optimization theory under uncertainty sets, and the robust linear optimization model with multiple random parameters under uncertainty sets is expanded, including the following steps:

[0030] The linear programming model considering multiple random parameters is

[0031]

[0032] Added decision variables y and their coefficient matrices B, b ik is the element in row i and column k of B.

[0033] Furthermore, the uncertainty set is used to constrain the fluctuation range of wind and light power prediction, including the following steps:

[0034]

[0035] Where: are the predicted power expectation and actual power deviation of wind power and photovoltaic power in period t respectively; They are the upper and lower limits of the power deviation of wind and photovoltaic power generation respectively; They are the fluctuation indicators of the random parameters of wind and solar power prediction respectively.

[0036] Furthermore, considering the uncertainty of the response power of the controlled load, the response power fluctuation range of the controlled load is constrained, including the following steps:

[0037]

[0038] Where: is the expected value and deviation of the controlled load power in period t; The upper / lower limit of the controlled load power deviation; It is an indicator of the fluctuation of the random parameters of the controlled load power.

[0039] Furthermore, it can be transformed into a robust linear optimization model containing multiple random parameters, and then according to the multiple random parameter robust linear optimization method, by introducing dual variables, it is transformed into a robust equivalent model, including the following steps:

[0040]

[0041] Where p 1,t 、p 2,t is the robustness index, It is a volatility indicator, which can be controlled by p 1,t 、p 2,t and The size of is adjusted between robustness and optimality. The robust equivalent model of the stochastic optimization problem of micro energy grid is obtained through comprehensive arrangement, which can be solved by conventional linear programming method.

[0042] The present invention has the following beneficial effects:

[0043] On the basis of the role of load control in optimizing operation and the necessity of considering the uncertainty of load control, the transformation of random optimization model into deterministic optimization model can be realized. The coordination of economy and robustness by robustness index and fluctuation index can be utilized to solve the multiple uncertainty problems faced in the process of optimizing operation of power grid. The stability and security of power grid can be evaluated more accurately, which provides theoretical support for the stable operation of power grid and efficient energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the overall flow chart of the present invention;

[0045] Figure 2 It is a controlled load curve diagram under different robustness indicators of the present invention. DETAILED DESCRIPTION

[0046] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] In this specific example, direct load participation system evaluation is adopted in this example, the total number of scheduling time is selected as 24h, the unit scheduling time is 15min, the period of controlled load control is 06:00-24:00, and the power fluctuation ratio of controlled load is set to 20%. The mathematical model is established as mixed integer nonlinear optimization, and the percentage of the maximum value is used to represent the value of the robustness index. This example includes:

[0048] Step 1: Establish a load control model based on the uncertainty of the controlled load response power and construct the initial constraint conditions of the load control model;

[0049] Step 2: Use uncertainty sets to characterize the load control model, characterizing the uncertainty of the load power and wind and solar power predictions that respond to load control;

[0050] Step 3: Establish a robust stochastic optimization scheduling model and realize the transformation of the stochastic optimization model into a deterministic optimization model through the robust linear optimization theory under the (p, w)-norm uncertainty set.

[0051] The present invention adopts the following technical solution:

[0052] Step 1:

[0053] Considering the uncertainty of controlled load response power, a coordinated optimization load operation model is constructed and a multi-objective function is established.

[0054] Construct the load control model and the constraints as follows:

[0055]

[0056] Where: t = 1, 2, .., N, N is the total number of scheduling periods; P LD,t and P L,t are the controlled afterload demand and the original load demand during period t respectively; P C,i is the power of each group of controlled loads in period i; d is the number of control periods of the controlled loads; g n is the compensation strategy coefficient; c t+1-i It is 1 when t+1-i≤d, otherwise it is 0;

[0057] a0=max{1,t-d+1}; when t≥d+1, a1=maxt+ld-, a2=td otherwise, a1=0, a2=0; h is the number of compensation periods corresponding to the single-period control strategy.

[0058] The controlled load and rebound load upper limit constraints for each period are set as follows:

[0059]

[0060] Where: t = 1, 2, .., N; P X,max , P F,max They represent the maximum load reduction power and the maximum rebound load power in each period respectively.

[0061] Further build uncertainty modeling for wind and solar power forecasts

[0062]

[0063] Where: is the random parameter of the controlled load power; P DLC,max It is the upper limit of the controlled load power during the entire dispatch period.

[0064] Step 2: Use uncertainty sets to characterize the load control model, characterizing the uncertainty of the load power and wind and solar power forecasts that respond to load control;

[0065] A robust linear optimization model based on uncertainty sets is established as follows:

[0066]

[0067] Where: x∈R n is the decision variable; c∈R n is the objective function coefficient vector; b∈R m , A∈R mn is the coefficient matrix of the inequality constraints, assuming that the random variables are only contained in the coefficient matrix A. The random parameter vector a in the i-th row of matrix A is i The actual range of variation is

[0068]

[0069] Where: a i is the random parameter of the i-th row of the coefficient matrix A, a ik for a i The kth element in ; is the volatility indicator, variable α ik By p 1,i Robustness index p 1,i Determine, when the robustness index p 1,i =|Ji |, the optimization result is most robust, p 1,i The smaller it is, the weaker the robustness. 1,i When =0, the model becomes a deterministic model.

[0070] Furthermore, the robustness and optimality of the optimization model are adjusted to transform the linear programming model with random parameters into its robust counterpart.

[0071]

[0072] Where: z 1,i and q 1,ik is the new decision variable generated by the robust equivalence transformation. This formula is a deterministic linear programming problem and can be solved using conventional linear programming methods.

[0073] Step 3: Establish a robust stochastic optimization scheduling model and realize the transformation of the stochastic optimization model into a deterministic optimization model through the robust linear optimization theory under the (p, w)-norm uncertainty set.

[0074] The robust linear optimization model with multiple random parameters under the uncertainty set is as follows:

[0075]

[0076] Added decision variables y and their coefficient matrices B, b ik is the element in row i and column k in B.

[0077] Using uncertainty sets to constrain the fluctuation range of wind and light power prediction includes the following steps:

[0078]

[0079] Where: are the expected power of wind power and photovoltaic power in period t respectively

[0080] value and actual power deviation value; They are the upper and lower limits of the power deviation of wind and photovoltaic power generation respectively; They are the fluctuation indicators of the random parameters of wind and solar power prediction respectively.

[0081] The next steps are to verify the role of load control in optimizing operation and the necessity of considering the uncertainty of load control, as well as the coordination of robustness index and fluctuation index on economy and robustness.

[0082] The response power fluctuation range of the controlled load is constrained as follows:

[0083]

[0084] Where: is the expected value and deviation of the controlled load power in period t; The upper / lower limit of the controlled load power deviation; It is an indicator of the fluctuation of the random parameters of the controlled load power.

[0085] It is further transformed into a robust linear optimization model with multiple random parameters, and then transformed into a robust equivalent model by introducing dual variables according to the robust linear optimization method with multiple random parameters, as follows:

[0086]

[0087] Where p 1,t 、p 2,t is the robustness index, It is a volatility indicator, which can be controlled by p 1,t 、p 2,t and The size of is used to balance robustness and optimality.

[0088] The grid load control method for sequential power consumption uncertainty proposed in this invention constructs a load control model and sets corresponding constraints for the uncertainty of the controlled load response power. The model uses the (p, w)-norm uncertainty set to describe the power variation of the load when responding to the control command. Furthermore, we established a robust stochastic optimization scheduling model and successfully transformed the stochastic optimization problem into a deterministic optimization problem by using the robust linear optimization theory under the (p, w)-norm uncertainty set.

[0089] Figure 2 The robustness index of the random parameter of the controlled load power increases from 0 to 75%, and the load reduction power decreases. When the robustness index continues to increase from 75% to 100%, the controlled load curve does not change significantly, because the robustness index limits the controlled load power in the entire dispatching period. The present invention effectively responds to the multiple uncertain challenges encountered in the optimized operation of the power grid, and provides a solid theoretical support for the stable and efficient operation of the power grid.

[0090] The above technical solutions are only used to illustrate the specific implementation process of the present invention, rather than to limit the scope of the present invention. It should be understood by those skilled in the art that the technical solutions described in the embodiments can still be modified or replaced by equivalents, as long as these modifications or replacements do not deviate from the essence and scope of the present invention, they should be included in the protection scope of the present invention.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the embodiments of the present invention have been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid load model control method considering the uncertainty of orderly power consumption, characterized in that: include: Step 1: Considering the uncertainty of the controlled load response power, an uncertain load control model for constructing wind and solar power prediction power is established, and initial constraints of the load control model are constructed; Step 2: Use uncertainty sets to characterize the load control model, characterizing the uncertainty of the load power and wind and solar power predictions that respond to load control; Step 3: Establish a robust stochastic optimization scheduling model and realize the transformation of the stochastic optimization model into a deterministic optimization model through the robust linear optimization theory under uncertainty sets.

2. A method for controlling power grid load considering the uncertainty of orderly power consumption according to claim 1, characterized in that: Constructing the initial constraints of the load control model includes the following steps: Construct the load model constraint conditions, which are expressed as follows: Where: t = 1, 2, .., N, N is the total number of scheduling periods; P LD,t and P L,t are the controlled afterload demand and the original load demand during period t respectively; P C,i is the power of each group of controlled loads in period i; d is the number of control periods of the controlled loads; g n is the compensation strategy coefficient; c t+1-i It is 1 when t+1-i≤d, otherwise it is 0; a0=max{1,t-d+1}; when t≥d+1, a1=maxt+d, a2=td otherwise, a1=0, a2=0; h is the number of compensation periods corresponding to the single period control strategy; The controlled load and rebound load upper limit constraints for each period are set as follows: Where: t = 1, 2, .., N; P X,max , P F,max They represent the maximum load reduction power and the maximum rebound load power in each period respectively.

3. A method for controlling power grid load considering the uncertainty of orderly power consumption according to claim 1, characterized in that: Construct uncertainty modeling for wind and solar power forecasts, The following steps are involved: Where: is the random parameter of the controlled load power; P DLC,max It is the upper limit of the controlled load power during the entire dispatch period.

4. A method for controlling power grid load considering the uncertainty of orderly power consumption according to claim 1, characterized in that: Establishing a stochastic robust linear optimization model based on uncertain sets includes the following steps: Where: x∈R n is the decision variable; c∈R n is the objective function coefficient vector; b∈R m , A∈R mn is the coefficient matrix of the inequality constraints, assuming that the random variables are only contained in the coefficient matrix A. The random parameter vector a in the i-th row of matrix A is i The actual range of variation is Where: a i is the random parameter of the i-th row of the coefficient matrix A, a ik for a i The kth element in ; is the volatility indicator, variable α ik By p 1,i Robustness index p 1,i Determine, when the robustness index p 1,i =|J i |, the optimization result is most robust, p 1,i The smaller it is, the weaker the robustness. 1,i When =0, the model becomes a deterministic model.

5. The method for establishing a random robust linear optimization model based on an uncertainty set according to claim 4, characterized in that: By controlling the variation range of random parameters, the robustness and optimality of the stochastic robust linear optimization model of the uncertain set are adjusted, including the following steps: Where: z 1,i and q 1,ik is the new decision variable generated by the robust equivalence transformation. This formula is a deterministic linear programming problem and can be solved using conventional linear programming methods.

6. A method for controlling power grid load considering the uncertainty of orderly power consumption according to claim 4, characterized in that: Through the robust linear optimization theory under uncertainty, the transformation of random optimization model to deterministic optimization model is realized, and the robust linear optimization model with multiple random parameters under uncertainty is expanded, which includes the following steps: The robust linear optimization model with multiple random parameters under uncertainty is: Added decision variables y and their coefficient matrices B, b ik is the element in row i and column k of B.

7. A method for controlling power grid load considering the uncertainty of orderly power consumption according to claim 1, characterized in that: Using uncertainty sets to constrain the fluctuation range of wind and light power prediction includes the following steps: Where: are the predicted power expectation and actual power deviation of wind power and photovoltaic power in period t respectively; They are the upper and lower limits of the power deviation of wind and photovoltaic power generation respectively; They are the fluctuation indicators of the random parameters of wind and solar power prediction respectively.

8. A method for controlling power grid load considering the uncertainty of orderly power consumption according to claim 1, characterized in that: Considering the uncertainty of the response power of the controlled load, the response power fluctuation range of the controlled load is constrained, including the following steps: Where: is the expected value and deviation of the controlled load power in period t; The upper / lower limit of the controlled load power deviation; It is an indicator of the fluctuation of the random parameters of the controlled load power.

9. Based on the stochastic robust linear optimization model according to claim 4, the model is subjected to robust equivalent transformation, characterized in that: It can be transformed into a robust linear optimization model with multiple random parameters, and then transformed into a robust equivalent model by introducing dual variables according to the robust linear optimization method with multiple random parameters, including the following steps: Where p 1,t 、p 2,t is the robustness index, It is a volatility indicator, which can be controlled by p 1,t 、p 2,t and The size of is adjusted between robustness and optimality. The robust equivalent model of the stochastic optimization problem of micro energy grid is obtained through comprehensive arrangement, which can be solved by conventional linear programming method.

Images