Method for predicting photovoltaic power consumption capacity of ac-dc distribution network based on harris hawk algorithm

Abstract

The application discloses a kind of based on Harris eagle algorithm AC-DC distribution network photovoltaic consumption capacity prediction method, comprising the following steps: input the basic parameters of AC-DC distribution network;Establish AC-DC hybrid distribution network photovoltaic consumption capacity prediction model, set the objective function and constraint condition of this model;Using Harris eagle algorithm to solve photovoltaic consumption capacity prediction model with the maximum objective function value as the target and combining constraint condition, the maximum value of photovoltaic consumption capacity, i.e. the Harris eagle position of optimal fitness is obtained;The AC-DC distribution network photovoltaic consumption capacity prediction method based on Harris eagle algorithm can realize the photovoltaic consumption capacity of AC-DC hybrid distribution network is predicted, the maximum value of consumption capacity is obtained, avoids the occurrence of light abandonment phenomenon, and guarantees the power quality of AC-DC hybrid distribution network, provides technical support for promoting the consumption of photovoltaic.

Description

Technical Field

[0001] This invention relates to the field of AC / DC hybrid distribution network technology, and in particular to a method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm. Background Technology

[0002] Driven by the "dual carbon" goals, the proportion of distributed photovoltaic (PV) power grids connected to distribution networks is increasing year by year. This necessitates forecasting the PV absorption capacity of distribution networks to avoid excessive PV integration affecting power quality and voltage control, while also preventing curtailment. This provides guidance for PV integration. The DC power supply technology in AC / DC hybrid distribution networks can effectively solve power quality problems such as harmonics and three-phase imbalance. Constructing AC / DC hybrid distribution networks is the future trend of distribution network development. Currently, there is some research on PV absorption capacity in AC distribution networks both domestically and internationally. How to forecast PV absorption capacity in AC / DC hybrid distribution networks has become an important research topic for power industry professionals.

[0003] This invention takes into account the characteristics of AC / DC hybrid distribution networks and proposes a method for predicting the absorption capacity of AC / DC hybrid distribution networks based on the Harris Eagle algorithm, combined with swarm intelligence algorithms. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm.

[0005] Therefore, the technical solution of the present invention is as follows:

[0006] This invention provides a method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm, comprising the following steps:

[0007] Step 1: Input the basic parameters of the AC / DC distribution network, including rated voltage, rated capacity, network topology, node conventional load, line parameters, and photovoltaic output data;

[0008] Step 2: Establish a prediction model for the photovoltaic absorption capacity of the AC / DC hybrid distribution network, and set the objective function and constraints of the model. The objective function is:

[0009]

[0010] In the formula, P PV,m N represents the amount of photovoltaic power connected to the m-th node. PV This represents the total number of photovoltaic nodes connected to the system.

[0011] Step 3: The Harris Eagle algorithm is used to solve the photovoltaic absorption capacity prediction model with the objective function value as the goal and the constraints as the result, so as to obtain the Harris Eagle position with the best fitness, which is the maximum value of photovoltaic absorption capacity.

[0012] Furthermore, the constraints include AC-side power flow constraints, DC-side power flow constraints, and operational safety constraints;

[0013] The power flow constraint on the AC side is:

[0014]

[0015] In the formula, P i ,P i+1 With Q i Q i+1 P represents the active and reactive power flowing through nodes i and i+1, respectively. Li With Q Li P represents the active and reactive power of the load at node i, respectively. PVi For the active power output of distributed photovoltaic system at node i, R i With X i R represents the resistance and impedance between node i-1 and node i, respectively. i+1 With X i+1 These represent the resistance and reactance values ​​between node i and node i+1, respectively, U i-1 with U i+1 These are the voltage values ​​at node i-1 and node i+1, respectively;

[0016] The DC-side power flow constraint is as follows:

[0017]

[0018] In the formula, Ω ij Ω represents the set of branches whose final node is DC node j. jk P represents the set of branches whose first node is DC node j. dc.jk With P dc.ij I represents the active power of DC branch jk and DC branch ij, respectively. dc.ij R represents the current in the DC branch ij. dc.ij U represents the resistance of the DC branch ij. dc.i with U dc.j These are the voltage values ​​at DC nodes i and j, respectively;

[0019] The operational safety constraints include system short-circuit capacity constraints, photovoltaic power generation capacity constraints, voltage deviation constraints, voltage fluctuation constraints, and line thermal stability constraints; specifically as follows:

[0020]

[0021] In the formula, I scmax S represents the maximum short-circuit current value specified in the national standard. PV,i S represents the photovoltaic power generation capacity of the i-th node.PV,max U represents the upper limit of the photovoltaic access capacity of the i-th node. N The nominal voltage value of the system is represented by ε1 and ε2, which represent the allowable voltage deviation rate specified by the national standard. max N1 represents the maximum value of the voltage fluctuation range specified by the national standard, and N1 represents the number of lines.

[0022] Furthermore, the specific method for solving the model using the Harris Eagle algorithm is as follows:

[0023] 1) Determine the upper and lower limits of the Harris Eagle algorithm based on the system's operational constraints;

[0024] 2) Obtain the number of photovoltaic grid connection points D, and within the boundary, use the tent chaotic mapping to obtain N D-dimensional Harris Eagle positions, whose values ​​are the photovoltaic access volume corresponding to each photovoltaic grid connection point, where N is the population size.

[0025] 3) Perform the first-generation position calculation according to the Harris Eagle algorithm;

[0026] 4) Update the Harris Hawk position, check whether the updated position is out of bounds according to the constraints, and adjust the position of the Harris Hawk that is out of bounds to the boundary value of the search space.

[0027] 5) Calculate the fitness value of the position of each Harris Hawk in the current Harris Hawk flock, compare it with the fitness value of the best Harris Hawk in the previous generation, and retain the position of the best Harris Hawk individual.

[0028] 6) Determine if the algorithm has reached the total number of iterations T. If the total number of iterations is met, the algorithm ends and proceeds to step 7). If the total number of iterations is not met, return to step 4) and begin execution.

[0029] 7) Obtain the Harris Eagle position with the best fitness, which is the maximum value of photovoltaic absorption capacity.

[0030] Furthermore, the method for improving the population initialization of the Harris Eagle algorithm using the tent chaotic mapping is as follows:

[0031] Randomly generate a D-dimensional vector X1(t);

[0032] Obtain N chaotic sequences X i (t), obtained in the following way:

[0033]

[0034] Where i = 1, 2, ..., N represents the population size; t = 1, 2, ..., T represents the number of iterations;

[0035] Given N chaotic sequences X i(t) is inversely mapped to the search space using the following formula to obtain the initialized population;

[0036] Y i (t)=lb i +(ub i -lb i )X i (t) (6)

[0037] Among them, ub i With lb i X i (t) The upper and lower bounds of the search.

[0038] This invention also provides a computer device, including...

[0039] Memory and processor;

[0040] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein the processor executes the computer-executable instructions to implement the above-mentioned method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm.

[0041] This invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the above-described method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm.

[0042] Compared with existing technologies, this method for predicting the photovoltaic (PV) absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm can predict the PV absorption capacity of AC / DC hybrid distribution networks, determine the maximum absorption capacity, avoid curtailment, and ensure power quality, thus providing technical support for promoting PV absorption. This method improves the Harris Eagle algorithm through chaotic mapping, resulting in a more efficient algorithm with greater advantages in ergodicity, uniformity, regularity, and iteration speed, making it easier to obtain the global optimum. Furthermore, the improved Harris Eagle algorithm is applied to the assessment of PV absorption capacity in AC / DC hybrid distribution networks, laying a technical foundation for analyzing and predicting the integration of new energy sources into AC / DC distribution systems. Attached Figure Description

[0043] Figure 1 The flowchart shows the photovoltaic absorption capacity prediction method for AC / DC distribution networks based on the Harris Eagle algorithm provided by this invention. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the following embodiments are by no means intended to limit the present invention.

[0045] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms, encompassing any or all possible combinations of one or more associated listed items.

[0046] This invention provides a method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm, comprising the following steps:

[0047] Step 1: Input the basic parameters of the AC / DC distribution network, including rated voltage, rated capacity, network topology, node conventional load, line parameters, and photovoltaic output data;

[0048] Step 2: Establish a prediction model for the photovoltaic absorption capacity of the AC / DC hybrid distribution network, and set the objective function and constraints of the model. The objective function is:

[0049]

[0050] In the formula, P PV,m N represents the amount of photovoltaic power connected to the m-th node. PV This represents the total number of photovoltaic nodes connected to the system.

[0051] It should be noted that the constraints include AC power flow constraints, DC power flow constraints, and operational safety constraints.

[0052] The power flow constraint on the AC side is:

[0053]

[0054] In the formula, P i ,P i+1 With Q i Q i+1 P represents the active and reactive power flowing through nodes i and i+1, respectively. Li With Q Li P represents the active and reactive power of the load at node i, respectively. PVi For the active power output of distributed photovoltaic system at node i, R i With X i R represents the resistance and impedance between node i-1 and node i, respectively. i+1 With X i+1 These represent the resistance and reactance values ​​between node i and node i+1, respectively, U i-1 with U i+1 These are the voltage values ​​at node i-1 and node i+1, respectively;

[0055] The DC-side power flow constraint is as follows:

[0056]

[0057] In the formula, Ω ij Ω represents the set of branches whose final node is DC node j. jk P represents the set of branches whose first node is DC node j. dc.jk With P dc.ij I represents the active power of DC branch jk and DC branch ij, respectively. dc.ij R represents the current in the DC branch ij. dc.ij U represents the resistance of the DC branch ij. dc.i with U dc.j These are the voltage values ​​at DC nodes i and j, respectively;

[0058] The operational safety constraints include system short-circuit capacity constraints, photovoltaic power generation capacity constraints, voltage deviation constraints, voltage fluctuation constraints, and line thermal stability constraints; specifically as follows:

[0059]

[0060] In the formula, I scmax S represents the maximum short-circuit current value specified in the national standard. PV,i S represents the photovoltaic power generation capacity of the i-th node. PV,max U represents the upper limit of the photovoltaic access capacity of the i-th node. N The nominal voltage value of the system is represented by ε1 and ε2, which represent the allowable voltage deviation rate specified by the national standard. max N1 represents the maximum value of the voltage fluctuation range specified by the national standard, and N1 represents the number of lines.

[0061] Step 3: The Harris Eagle algorithm is used to solve the photovoltaic absorption capacity prediction model with the objective function value as the goal and the constraints as the result, so as to obtain the Harris Eagle position with the best fitness, which is the maximum value of photovoltaic absorption capacity.

[0062] The specific method for solving the model using the Harris Eagle algorithm is as follows:

[0063] 1) Determine the upper and lower limits of the Harris Eagle algorithm based on the system's operational constraints;

[0064] 2) Obtain the number of photovoltaic grid-connected points D. Within the boundary, use the tent chaotic mapping to obtain N D-dimensional Harris Eagle positions, whose values ​​represent the photovoltaic grid connection amount corresponding to each photovoltaic grid-connected point, where N is the population size. The method for improving the population initialization of the Harris Eagle algorithm using the tent chaotic mapping is as follows:

[0065] Randomly generate a D-dimensional vector X1(t);

[0066] Obtain N chaotic sequences X i (t), obtained in the following way:

[0067]

[0068] Where i = 1, 2, ..., N represents the population size; t = 1, 2, ..., T represents the number of iterations;

[0069] Given N chaotic sequences X i (t) is inversely mapped to the search space using the following formula to obtain the initialized population;

[0070] Y i (t)=lb i +(ub i -lb i )X i (t) (6)

[0071] Among them, ub i With lb i X i (t) The upper and lower bounds of the search.

[0072] 3) Perform the first-generation position calculation according to the Harris Eagle algorithm;

[0073] 4) Update the Harris Hawk position, check whether the updated position is out of bounds according to the constraints, and adjust the position of the Harris Hawk that is out of bounds to the boundary value of the search space.

[0074] 5) Calculate the fitness value of the position of each Harris Hawk in the current Harris Hawk flock, compare it with the fitness value of the best Harris Hawk in the previous generation, and retain the position of the best Harris Hawk individual.

[0075] 6) Determine if the algorithm has reached the total number of iterations T. If the total number of iterations is met, the algorithm ends and proceeds to step 7). If the total number of iterations is not met, return to step 4) and begin execution.

[0076] 7) Obtain the Harris Eagle position with the best fitness, which is the maximum value of photovoltaic absorption capacity.

[0077] The Harris Hawk algorithm is a functional optimization algorithm that simulates the behavior of the Harris Hawk. It mainly consists of three parts: the search phase, the search-development transition, and the development phase.

[0078] I. Search Phase

[0079] Harris eagles randomly roost in a location and use two strategies to find prey:

[0080]

[0081] In the formula, r1, r2, r3, r4, q are random numbers within the interval (0, 1); X rand (t) represents an individual randomly selected from the t-th generation of the population; X i (t) represents the i-th individual in the t-th generation of the population; X rabbit (t) represents the globally optimal individual obtained up to generation t; ub and lb represent the upper and lower bounds of the population position at this stage, respectively, X m (t) represents the average position of the population in generation t, as shown in the following formula:

[0082]

[0083] II. Conversion between Search and Development

[0084] The HHO algorithm switches between search and different exploitation behaviors based on the prey's escape energy, where E represents the prey's escape energy, and the calculation formula is as follows:

[0085] E=2E0(1-t / T) (9)

[0086] In the formula, E0 is a random number between (-1, 1), representing the initial energy of the prey's escape, t is the current iteration number, and T represents the total number of iterations.

[0087] III. Development Phase

[0088] The behavior of Harris eagles hunting prey was simulated. Based on the different values ​​of the escape energy E and the random number r on (0,1), the population was updated according to the following four mechanisms.

[0089] When r≥0.5 and |E|≥0.5, the eagle flock adopts a soft encirclement strategy, as shown in the following formula:

[0090] X i (t+1)=X rabbit (t)-X i (t)-E|JX rabbit (t)-X i (t)| (10)

[0091] In the formula, J = 2(1-r5) represents the jumping intensity of the prey, and r5 is a random number between (0,1).

[0092] When r ≥ 0.5 and |E| < 0.5, the eagle flock adopts a hard encirclement strategy, as shown in the following formula:

[0093] X i (t+1)=X rabbit (t)-E|X rabbit (t)-X i (t)| (11)

[0094] When r < 0.5 and |E| ≥ 0.5, the eagle flock adopts a gradual, rapid dive-and-surround strategy, as shown in the following formula:

[0095]

[0096] In the formula, F represents the optimal fitness value, which is the maximum value of photovoltaic absorption capacity. The formulas for Y and Z are as follows:

[0097] Y = X rabbit (t)-E|JX rabbit (t)-X i (t)| (13)

[0098] Z=Y+S×LF(D) (14)

[0099] In the formula, D represents the spatial dimension, S represents a random vector of size D×1 in the (0,1) direction, and LF is the Levy flight function, calculated as follows:

[0100]

[0101] In the formula, μ and v are random numbers between (0,1), and β = 1.5.

[0102] When r < 0.5 and |E| < 0.5, the eagle flock adopts a gradual, rapid dive-and-surround strategy, as shown in the following formula:

[0103]

[0104] The formulas for calculating Y and Z are as follows:

[0105] Y = X rabbit (t)-E|JX rabbit (t)-X m (t)| (17)

[0106] Z=Y+S×LF(D) (18)

[0107] This invention also provides a computer device, including...

[0108] Memory and processor;

[0109] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein the processor executes the computer-executable instructions to implement the above-mentioned method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm.

[0110] The present invention also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, provide the above-described method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm.

[0111] This is an illustrative embodiment of a computer-readable storage medium. It should be noted that the technical solution of this storage medium and the technical solution of the data recovery method described above belong to the same concept. Details not described in detail in the technical solution of the storage medium can be found in the description of the technical solution of the data recovery method described above.

[0112] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

Claims

1. A method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm, characterized in that, Includes the following steps: Step 1: Input the basic parameters of the AC / DC distribution network, including rated voltage, rated capacity, network topology, node conventional load, line parameters, and photovoltaic output data; Step 2: Establish a prediction model for the photovoltaic absorption capacity of the AC / DC hybrid distribution network, and set the objective function and constraints of the model. The objective function is: （1） In the formula, This represents the amount of photovoltaic power connected to the m-th node. This represents the total number of photovoltaic nodes connected to the system. Step 3: The Harris Eagle algorithm is used to solve the photovoltaic absorption capacity prediction model with the objective function value as the goal and the constraints as the result, to obtain the Harris Eagle position with the best fitness, which is the maximum value of photovoltaic absorption capacity. The constraints include AC power flow constraints, DC power flow constraints, and operational safety constraints. The power flow constraint on the AC side is: （2） In the formula, , and , These represent the active and reactive power flowing through nodes i and i+1, respectively. and These represent the active and reactive power of the load at node i, respectively. The active power output of distributed photovoltaic system at node i. and Let be the resistance and impedance between node i-1 and node i, respectively. and These are the resistance and reactance values ​​between node i and node i+1, respectively. and These are the voltage values ​​at node i-1 and node i+1, respectively; The DC-side power flow constraint is as follows: （3） In the formula, This represents the set of branches whose final node is DC node j. This represents the set of branches whose first node is DC node j. and Let J and J represent the active power of DC branch Jk and DC branch Jij, respectively. This represents the current in the DC branch ij. The resistance of the DC branch ij is represented. and These are the voltage values ​​at DC nodes i and j, respectively; The operational safety constraints include system short-circuit capacity constraints, photovoltaic power generation capacity constraints, voltage deviation constraints, voltage fluctuation constraints, and line thermal stability constraints; specifically as follows: （4） In the formula, This represents the maximum short-circuit current value specified in the national standard. This represents the photovoltaic power generation capacity of the i-th node. This represents the upper limit of the photovoltaic (PV) access capacity of the i-th node. This represents the system's nominal voltage value. and This represents the allowable voltage deviation rate specified in the national standard. This represents the maximum value of the voltage fluctuation range specified by the national standard. This represents the number of lines.

2. The method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm according to claim 1, characterized in that, The specific method for solving the model using the Harris Eagle algorithm is as follows: 1) Determine the upper and lower limits of the Harris Eagle algorithm based on the system's operational constraints; 2) Obtain the number of photovoltaic grid connection points D, and within the boundary, use the tent chaotic mapping to obtain N D-dimensional Harris Eagle positions, whose values ​​are the photovoltaic access volume corresponding to each photovoltaic grid connection point, where N is the population size. 3) Perform first-generation position calculations using the Harris Eagle algorithm; 4) Update the Harris Hawk position, check whether the updated position is out of bounds according to the constraints, and adjust the position of the Harris Hawk that is out of bounds to the boundary value of the search space. 5) Calculate the fitness value of the position of each Harris Hawk in the current Harris Hawk flock, compare it with the fitness value of the best Harris Hawk in the previous generation, and retain the position of the best Harris Hawk individual. 6) Determine if the algorithm has reached the total number of iterations T. If the total number of iterations is met, the algorithm ends and proceeds to step 7). If the total number of iterations is not met, return to step 4) and begin execution. 7) Obtain the Harris Eagle position with optimal fitness, which is the maximum value of photovoltaic absorption capacity.

3. The method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm according to claim 2, characterized in that, The method for improving the population initialization of the Harris Eagle algorithm using the tent chaotic mapping is as follows: Randomly generate D-dimensional vectors ; Obtain N chaotic sequences The method of obtaining it is as follows: （5） Where i = 1, 2, ..., N represents the population size; t = 1, 2, ..., T represents the number of iterations; Given N chaotic sequences The initial population is obtained by inverse mapping to the search space using the following formula; （6） in, and They are respectively Upper and lower bounds of the search.

4. A computer device, characterized in that, include Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the processor executes the computer-executable instructions, it implements the method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm as described in claims 1-3.

5. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed by a processor, implement the method for predicting the photovoltaic absorption capacity of AC / DC distribution networks based on the Harris Eagle algorithm as described in claims 1-3.

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