Power distribution internet of things optimal economic dispatch method considering uncertainty

Abstract

The application provides a power distribution Internet of Things optimal economic dispatch method considering uncertainty, and relates to the technical field of power grids.The power distribution Internet of Things optimal economic dispatch method considering uncertainty comprises the following steps: S1, a mathematical model of each power generation unit in the power distribution Internet of Things is established; S2, the operation strategy of the power distribution Internet of Things is determined, and the expected minimum of the operation cost of the power distribution Internet of Things is taken as an objective function to establish an optimization target; S3, according to the characteristics that the objective function is a convex function, a piecewise linear optimization algorithm is used to approximate the objective function; S4, constraints are constructed according to the actual characteristics of the mathematical model, an optimization model is established, and the optimization model is solved to obtain a day-ahead economic dispatch strategy of the power distribution Internet of Things.The method directly obtains the minimum value of the cost expectation of the power distribution Internet of Things in a dispatch period by reasonably modeling the uncertainty of wind and light power generation, using a convex optimization method for optimization, and the optimization speed is fast, and the global optimal solution can be solved.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and more specifically, to an optimal economic dispatch method for distribution Internet of Things that takes into account uncertainty. Background Technology

[0002] With the surge in distributed energy resources, power grid companies need to find a way to balance supply and demand across complex, dynamic, and multidirectional networks, increasing the flexibility of the distribution Internet of Things (IoT). The International Renewable Energy Agency's (IRENA) Global Renewable Energy Outlook, released at the end of April, also emphasizes that flexibility is a key factor for future high-proportion renewable energy power systems. In the flexibility planning phase of the distribution IoT, although renewable energy generation is greener and more environmentally friendly, its uncertainty poses a significant challenge to the flexibility of the distribution IoT. How to maximize economic benefits while flexibly dispatching uncertain renewable energy output has become a crucial objective.

[0003] Currently, the commonly used optimization method for economic dispatch of distribution networks under uncertainties in new energy sources mainly uses chance-constrained programming (CCP). However, CCP requires a large number of sampling iterations to obtain an approximate solution using stochastic simulation experiments, which is time-consuming and computationally slow. The physical meaning of the result of CCP is the minimum cost based on a certain probability, and its application scenarios are limited. If a deterministic equivalent form is used, heuristic algorithms are often required, which are slow and may not find the global optimum. Summary of the Invention

[0004] The purpose of this invention is to provide an optimal economic dispatch method for distribution Internet of Things (IoT) that takes into account uncertainty. This method reasonably models the uncertainty of wind and solar power generation, uses convex optimization methods for optimization, directly obtains the minimum expected cost of distribution IoT for a dispatch cycle, has a fast optimization speed, and can solve for the global optimal solution.

[0005] The embodiments of the present invention can be implemented as follows:

[0006] This embodiment provides an optimal economic dispatch method for distribution IoT considering uncertainty. The optimal economic dispatch method for distribution IoT considering uncertainty includes:

[0007] S1. Establish mathematical models for each power generation unit within the power distribution Internet of Things;

[0008] S2. Determine the operation strategy of the distribution IoT, and establish an optimization objective with the expected minimum operation cost of the distribution IoT as the objective function;

[0009] S3. Based on the characteristic that the objective function is a convex function, a piecewise linear optimization algorithm is used to approximate the objective function;

[0010] S4. Based on the actual characteristics of the mathematical model, construct constraints, establish an optimization model, and solve the optimization model to obtain the day-ahead economic dispatch strategy of the distribution Internet of Things.

[0011] In an optional embodiment, the mathematical model includes a wind turbine model and an energy storage battery model.

[0012] In an optional embodiment, the formula for the wind turbine model is as follows:

[0013]

[0014] Among them, P WT (t), P N Let v and vt be the actual power and rated power of the wind turbine at time t, respectively, without considering probability distribution; vt, vt in v N v out These are the actual wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed of the wind turbine, respectively.

[0015] In an optional embodiment, the actual power of the wind turbine It is expressed as follows:

[0016]

[0017] Among them, P WindForecast (t) is the predicted power value of the wind turbine calculated based on wind speed, P WindForecast (t)=P WT (t); It is a random disturbance superimposed on the power; The actual wind turbine power is a random variable considering the probability distribution of wind speed.

[0018] In an optional embodiment, the formula for the energy storage battery model is as follows:

[0019]

[0020] Among them, S soc (t), S soc (t-1) represent the charge of the energy storage battery at time t and time t-1, respectively; P Bat (t) represents the power of the energy storage battery at time t. A value greater than 0 indicates discharging, a value less than 0 indicates charging, and a value equal to 0 indicates neither charging nor discharging. Δt represents a time interval.

[0021] E B This represents the total capacity of the battery.

[0022] In an optional embodiment, in S2, the objective function is:

[0023]

[0024] Where C is the operating cost; T is the number of time periods divided in a scheduling cycle; C(t) is the operating cost at time t, and it is a random variable that follows a certain probability distribution; E(C(t)) is the expected value of C(t) at time t.

[0025] In an optional embodiment, in S2, S3 includes:

[0026] Piecewise linearization is used to transform a convex function into multiple linear functions.

[0027] In an optional embodiment, S3 includes:

[0028] Determine the cost function E(C(t)) at time t with respect to the battery discharge power P. Bat Relationship curve;

[0029] Divide the relationship curve into n segments, and use a straight line k in each segment. i P Bati To approximate, where k i Let P represent the slope of the i-th straight line segment. Bati The independent variable E(C(t)) representing the segment is approximated using the following formula:

[0030]

[0031] According to P Bati And its boundary constraints, solve P Bat The boundary constraints are shown in the following formula:

[0032] 0≤P Bati ≤P BatStep

[0033] P Bat The calculation formula is as follows:

[0034]

[0035] Among them, P BatStep It is every P Bati The upper limit.

[0036] In an optional embodiment, in S4, the constraints include energy storage battery constraints, which are shown in the following formula:

[0037]

[0038] in, Let be the minimum and maximum values ​​of the battery's charge capacity at time t, respectively. S represents the minimum and maximum power of the energy storage battery at time t, respectively; soc (t) represents the charge of the energy storage battery at time t; P BAT (t) represents the power of the energy storage battery at time t.

[0039] In an optional embodiment, in S2 and S4, the optimal output of the wind turbine at each time step is solved in the optimization model using the following formula:

[0040]

[0041] The following formula is used to calculate the power purchased from the grid at each time step in the optimization model:

[0042]

[0043] in, P represents the actual power output of the wind turbine at time t. Bat (t) represents the power of the energy storage battery at time t; C Wind (t) represents the price per kilowatt-hour of electricity calculated by amortizing the investment in the wind turbine; P Load (t) represents the load power at time t; C Grid (t) represents the electricity price of the power grid at time t.

[0044] The beneficial effects of the optimal economic dispatch method for distribution IoT considering uncertainties provided in this embodiment include:

[0045] By reasonably modeling the uncertainties of wind power generation and using piecewise linear optimization for approximation, a solution can be found in polynomial time. Mathematically, it can approach the global optimum in a very short time. Piecewise linear optimization can approximate convex optimization at any desired precision, and the optimization result can approach the global optimum at any precision. This enables a very fast solution to the almost globally minimum expected value of the operating cost of the distribution IoT. The expected value is directly solved, making it more widely applicable. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of the optimal economic dispatch method for power distribution Internet of Things considering uncertainties provided in the embodiments of the present invention;

[0048] Figure 2Let the cost function E(C(t)) at time t be related to P. Bat The relationship curve. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0051] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0052] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0053] Please refer to Figure 1 This embodiment provides an optimal economic dispatch method for power distribution Internet of Things that considers uncertainty, including the following steps:

[0054] S1. Establish mathematical models for each power generation unit within the power distribution Internet of Things.

[0055] The mathematical models include wind turbine models and energy storage battery models.

[0056] 1. The formula for the wind turbine model is as follows:

[0057]

[0058] Among them, P WT (t), P N Let v and vt be the actual power and rated power of the wind turbine at time t, respectively, without considering probability distribution; vt, vt in v N v out These are the actual wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed of the wind turbine, respectively.

[0059] In practice, since wind speed cannot be accurately predicted, it is considered as a superposition of a predicted value and a random disturbance. Similarly, according to the formula of the wind turbine model, the actual power of the wind turbine also becomes a superposition of a predicted value and a random disturbance. The actual power of the wind turbine is expressed as follows:

[0060]

[0061] Among them, P WindForecast (t) is the predicted power value of the wind turbine calculated based on wind speed, R WindForecast (t)=P WT (t); It is a random disturbance superimposed on the power, which follows a random distribution with a mean of μ and a standard deviation of σ; The actual wind turbine power is a random variable considering the probability distribution of wind speed.

[0062] 2. The formula for the energy storage battery model is as follows:

[0063]

[0064] Among them, S soc (t), S soc (t-1) represent the charge of the energy storage battery at time t and time t-1, respectively; P Bat (t) represents the power of the energy storage battery at time t; a value greater than 0 indicates discharging, a value less than 0 indicates charging, and a value equal to 0 indicates neither charging nor discharging; Δt is a time interval; E B This represents the total capacity of the battery.

[0065] S2. Determine the operation strategy of the distribution IoT and establish an optimization objective with the expected minimum operation cost of the distribution IoT as the objective function.

[0066] Specifically, assuming a scheduling cycle is divided into T time periods, and the battery discharge power is P in each time period. Bat (t), then there are the following cases:

[0067] 1. When the wind power price is lower than the grid price, it is necessary to purchase wind power. If wind power is sufficient, the operating cost during this period is as follows:

[0068] C(t)=C Bat P Bat (t)+C Wind (t)(P Load (t)-P Bat (t))

[0069] Where C(t) is the operating cost at time t, and it is a random variable that follows a certain probability distribution; P Bat C Wind (t) represents the price of charging and discharging the battery and the price of the wind turbine investment per kilowatt-hour, respectively; P Load (t) represents the load power at time t.

[0070] 2. When the wind power price is lower than the grid power price, it is necessary to purchase wind power. If wind power is insufficient, it is necessary to purchase grid power. The operating costs during this period are as follows:

[0071]

[0072] 3. When the wind power price is higher than the grid power price, it is necessary to purchase grid power. The operating cost during this period is as follows:

[0073] C(t)=C Bat P Bat (t)+C Grid (t)(P Load (t)-P Bat (t))

[0074] In summary, the cost over a given period can be expressed as a piecewise function as follows:

[0075]

[0076] Simplifying the above formula, we get the following formula:

[0077]

[0078] Where F(x,y) is a piecewise function, which holds the following formula:

[0079]

[0080] This cost is a random variable. The expected value of a function is given by the following formula:

[0081] This expected formula is about P Bat The function in this formula contains only the variable P. Bat It is an unknown quantity, and the above formula applies to P. Bat The second derivative yields the following formula:

[0082]

[0083] Since the above formula is always greater than zero, E(C(t)) is the expected value of the cost C(t) at time t, which is a convex function and can be solved using convex optimization.

[0084] Therefore, the objective function for optimizing the model is:

[0085]

[0086] Where C represents operating costs.

[0087] S3. Based on the characteristic that the objective function is a convex function, a piecewise linear optimization algorithm is used to approximate the objective function.

[0088] Specifically, for an optimization where the objective function is convex, piecewise linearization can be used to transform the convex function into multiple linear functions.

[0089] Suppose that the cost function E(C(t)) at time t is related to P Bat Relationship curves as follows Figure 2 As shown.

[0090] We can consider dividing it into n segments, and using a straight line k in each segment. i P Bati To approximate, k i Let P represent the slope of the i-th straight line segment. Bati The independent variable represents the segmentation. Figure 2 The distance between the two vertical lines is P. Bati Therefore, E(C(t)) can be approximated using the following formula:

[0091]

[0092] E(C(t))′ here is not exactly equal to E(C(t)), but the solution for the minimum value of E(C(t))′ can approximate the solution for the minimum value of E(C(t)).

[0093] After using piecewise linear programming, the P value for each segment... Bati Each has a boundary constraint, and P Bat P can be used Bati The result is shown in the following formula:

[0094] 0≤P Bati ≤P BatStep

[0095]

[0096] Among them, P BatStep It is every P Bati The upper limit.

[0097] S4. Based on the actual characteristics of the mathematical model, construct constraints, establish an optimization model, and solve the optimization model to obtain the day-ahead economic dispatch strategy of the distribution Internet of Things.

[0098] The constraints include energy storage battery constraints, which are shown in the following formula:

[0099]

[0100] in, Let be the minimum and maximum values ​​of the battery's charge capacity at time t, respectively. Let t represent the minimum and maximum power of the energy storage battery at time t, respectively.

[0101] Based on the objective function and constraints described above, this model can be optimized using linear optimization tools. Since the optimization model is linear, it exhibits the characteristic of converging to the global optimum in polynomial time.

[0102] The optimization results can yield P at each time step. Bat (t), then the following formula can be used:

[0103]

[0104] Find the optimal output of the wind turbine at each moment.

[0105] Use the formula:

[0106]

[0107] It can calculate the power purchased from the grid at each moment.

[0108] in, P represents the actual power output of the wind turbine at time t. Bat (t) represents the power of the energy storage battery at time t; C Wind (t) represents the price per kilowatt-hour of electricity calculated by amortizing the investment in the wind turbine; P Load (t) represents the load power at time t; C Grid (t) represents the electricity price of the power grid at time t.

[0109] The beneficial effects of the optimal economic dispatch method for distribution IoT considering uncertainties provided in this embodiment include:

[0110] 1. The expected value model proposed in this method can directly solve for the expected revenue of a scheduling cycle. As the usage time increases, the final revenue will converge to the calculated expected value. Moreover, the expected value model used is a convex function, which is convenient for solving using piecewise linear optimization, and the optimization results are more accurate and faster.

[0111] 2. By reasonably modeling the uncertainties of wind power generation and using piecewise linear optimization for approximation, the solution can be obtained in polynomial time. Mathematically, it can approach the global optimum in a very short time. Piecewise linear optimization can approximate convex optimization at any desired precision, and the optimization result can approach the global optimum at any precision. This enables a very fast solution to the almost globally minimum expected cost of the distribution IoT, directly solving for the expected value, which has a wider range of applications.

[0112] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimal economic dispatch of power distribution Internet of Things considering uncertainty, characterized in that, The optimal economic dispatch method for the distribution Internet of Things that considers uncertainty includes: S1. Establish mathematical models for each power generation unit within the power distribution Internet of Things; the mathematical models include wind turbine models and energy storage battery models; S2. Determine the operation strategy of the power distribution Internet of Things (IoT), and establish an optimization objective with the expected minimum operation cost of the power distribution IoT as the objective function; The objective function is: in, The cost is the operating cost; T is the number of time periods divided into a scheduling cycle. Let be the operating cost at time t, and it is a random variable that follows a certain probability distribution; The operating cost at time t Expectations; in, The price for charging and discharging batteries; The cost of the wind turbine at time t is spread across each kilowatt-hour of electricity. Let be the power of the load at time t; Let t be the power of the energy storage battery at time t; Let be the electricity price of the power grid at time t; For a piecewise function, when When the value is 1, The value is 0 at that time; It is the predicted power of the wind turbine calculated based on the wind speed at time t; It is a random disturbance superimposed on the power, which follows a mean of Standard deviation is The random distribution; S3. Based on the characteristic that the objective function is a convex function, a piecewise linear optimization algorithm is used to approximate the objective function, transforming the convex function into multiple linear functions, including: Determine the expected cost function at time t Regarding the discharge power of energy storage batteries Relationship curve; Divide the relationship curve into n segments, and use a straight line for each segment. To approximate, among which, Indicates the first The slope of the straight line connecting the first and last points of a curve segment. Indicates the first The magnitude of the independent variable corresponding to the segment curve. Approximate using the following formula: in, and They are not exactly equal, find the solution. The minimum value is approximated by the solution. The solution to the minimum value; according to and its boundary constraints, solve The boundary constraints are shown in the following formula: The calculation formula is as follows: in, It is every The upper limit; S4. Based on the actual characteristics of the mathematical model, construct constraints, establish an optimization model, and solve the optimization model to obtain the day-ahead economic dispatch strategy of the power distribution Internet of Things.

2. The optimal economic dispatch method for distribution IoT considering uncertainty according to claim 1, characterized in that, The formula for the wind turbine model is as follows: in, , These represent the actual power and rated power of the wind turbine at time t, without considering probability distribution. , , , These are the actual wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed of the wind turbine, respectively.

3. The optimal economic dispatch method for distribution IoT considering uncertainty according to claim 2, characterized in that, The actual power of the wind turbine at time t after considering the probability distribution of wind speed It is expressed as follows: in, This is the predicted power output of the wind turbine at time t, calculated based on the wind speed. ; It is a random disturbance superimposed on the power; The actual power of the wind turbine at time t is calculated after considering the probability distribution of wind speed.

4. The optimal economic dispatch method for distribution IoT considering uncertainty according to claim 1, characterized in that, The formula for the energy storage battery model is as follows: in, , These are the charge quantities of the energy storage battery at time t and time t-1, respectively. Let t be the power of the energy storage battery at time t. A value greater than 0 indicates discharging, a value less than 0 indicates charging, and a value equal to 0 indicates neither charging nor discharging. A time period of a single moment; This represents the total capacity of the energy storage battery.

5. The optimal economic dispatch method for distribution IoT considering uncertainty according to claim 1, characterized in that, In S4, the constraints include energy storage battery constraints, which are shown in the following formula: in, , Let be the minimum and maximum charge values ​​of the energy storage battery at time t, respectively. , Let represent the minimum and maximum power of the energy storage battery at time t, respectively; Let be the charge of the energy storage battery at time t; Let t be the power of the energy storage battery at time t.

6. The optimal economic dispatch method for distribution IoT considering uncertainty according to claim 1, characterized in that, In S4, the optimization model solves for the optimal output of the wind turbine at each time step using the following formula: The optimization model calculates the power purchased from the grid at each time step using the following formula: in, The actual power of the wind turbine at time t after considering the probability distribution of wind speed; Let t be the power of the energy storage battery at time t; The cost of the wind turbine at time t is spread across each kilowatt-hour of electricity. Let be the power of the load at time t; Let t be the electricity price of the power grid at time t.

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