Reactive power optimization method based on improved power flow model

Abstract

The invention relates to a reactive power optimization method based on an improved power flow model. The method comprises the following steps: acquiring power system parameters; constructing a multi-objective function; constructing a full-pure embedded power flow model according to the parameters of the power system; solving the multi-objective function through a multi-objective particle swarm algorithm and a full-pure embedded power flow model to obtain an optimal solution of reactive power optimization of the power system; and controlling the power system according to the reactive power optimization optimal solution of the power system. According to the invention, the multi-objective function is solved through the multi-objective particle swarm optimization algorithm and the full-pure embedding power flow model, the full-pure embedding power flow model combines the classical HELM and the classical HELM, and the classical HELM has the particle swarm optimization algorithm with the strong search capability and the rapid convergence capability. The efficiency and accuracy of load flow calculation and optimization target convergence can be obviously improved, all-pure embedding factors of classical HELM have practical physical significance, the system load level can be represented, and the analytic performance of the all-pure embedding method can be applied to the optimization solution process.

Description

Technical Field

[0001] The present disclosure relates to a reactive power optimization method based on an improved power flow model, and belongs to the technical field of reactive power optimization of power grids. Background Art

[0002] With the rapid development of new power systems, the integration of various distributed power sources into distribution networks has become a key path to a green, low-carbon energy transition. However, with the large-scale integration of distributed power sources, due to their inherent volatility and uncertainty, unabsorbed power can create a reverse flow, leading to power backflow within the grid and, in turn, power quality issues such as reactive power and voltage limits. This poses new challenges to the safe and economic operation of the grid. Furthermore, the integration of a large number of power electronic devices, including distributed power sources, will make the grid's operation even more flexible and dynamic. Summary of the Invention

[0003] In order to overcome the above problems, the present disclosure provides a reactive power optimization method based on an improved power flow model.

[0004] The technical solutions disclosed in this disclosure are as follows:

[0005] A reactive power optimization method based on an improved power flow model, comprising:

[0006] Obtain power system parameters;

[0007] Construct multi-objective functions;

[0008] Construct a holomorphic embedded power flow model based on power system parameters;

[0009] The multi-objective function is solved by using a multi-objective particle swarm optimization algorithm and a holomorphic embedded power flow model to obtain the optimal solution for reactive power optimization of the power system;

[0010] The power system is controlled according to the optimal solution of reactive power optimization of the power system.

[0011] Furthermore, the multi-objective function includes the total network loss of the system and the voltage deviation rate.

[0012] Furthermore, the multi-objective function is specifically:

[0013] f=min(w1f loss.p +w2f ΔV );

[0014] Among them, w1 and w2 are weight coefficients, f loss.p is the total network loss of the system, f ΔV is the voltage deviation rate.

[0015] Furthermore, the total network loss of the system f loss.p Obtained through:

[0016]

[0017] Among them, r ij is the resistance value of branch ij, I ij is the current value of branch ij, E is the branch set, and T is the total number of time periods.

[0018] Furthermore, the voltage deviation rate f ΔV Obtained through:

[0019]

[0020] Where N is the number of power system nodes, U i' spec is the desired node voltage amplitude, U i' is the voltage of the i'th node.

[0021] Furthermore, the multi-objective function needs to satisfy one or more of power system flow constraints, node voltage constraints, branch current constraints, node power constraints, distributed power supply node output constraints, and energy storage active and reactive output constraints.

[0022] Furthermore, a full-scale embedded power flow model is constructed based on the power system parameters, specifically:

[0023] The no-load state of the power system is taken as the initial solution of the classic HELM model;

[0024] The classical HELM model obtains the actual no-load state power flow solution of the power system at each time sequence based on the power generation state of the power system at each time sequence;

[0025] The actual no-load state power flow solution of the power system at each time sequence is used as the initial solution of the classical HELM model.

[0026] Furthermore, the multi-objective particle swarm optimization algorithm and the holomorphic embedded power flow model are used to solve the multi-objective function, and the optimal solution for reactive power optimization of the power system is obtained, which is specifically:

[0027] Initialize the parameters of the multi-objective particle swarm optimization algorithm;

[0028] Iterative multi-objective particle swarm optimization algorithm, in which the fitness of each particle is obtained by:

[0029] Update the population size and the status of each particle;

[0030] Inputting the updated particle swarm state into the holomorphic embedded power flow model to obtain an updated power system power flow distribution, and obtaining an updated voltage-power state of each node in the power system according to the updated power system power flow distribution;

[0031] The value of the objective function is obtained according to the voltage-power state of each node in the power system, and is used as the fitness of the particles in the multi-objective particle swarm algorithm;

[0032] The multi-objective particle swarm algorithm iteration is completed to obtain the optimal solution for reactive power optimization of the power system.

[0033] Furthermore, during the iteration of the multi-objective particle swarm algorithm, the particle velocity is updated as follows:

[0034] V i (t+1)=wV i (t)+c1rand1×(pbest i (t)-X i (t))+c2rand2×(gbest(t)-X i (t));

[0035] X i (t+1)=X i (t)+V i (t+1);

[0036] Among them, V i (t) is the particle velocity of the i-th particle after the t-th update, c1 and c2 are learning factors, w is the inertia factor, rand1 and rand2 are random numbers uniformly distributed in the interval [0,1], and pbest i (t) is the local best position of the i-th particle after the t-th update, gbest(t) is the global best position after the t-th update, X i (t) is the position velocity of the i-th particle after the t-th update.

[0037] Furthermore, the multi-objective particle swarm algorithm uses the charging and discharging power of the energy storage device in the distribution network as a basic particle.

[0038] The present disclosure has the following beneficial effects:

[0039] This paper solves multi-objective functions through a multi-objective particle swarm optimization algorithm and a holomorphic embedded power flow model. The classical HELM, a non-iterative, recursive, and computationally efficient power flow solution method independent of initial point selection, is organically combined with the particle swarm optimization algorithm, which boasts powerful search and rapid convergence capabilities. This significantly improves the efficiency and accuracy of power flow calculations and convergence of optimization objectives without significantly increasing the complexity of the algorithm. This achieves flexible resource optimization that balances operational economy and power quality. The classical HELM's holomorphic embedding factor has practical physical significance and can characterize system load levels. Its advantage lies in the ability to apply the analytical nature of the holomorphic embedding method to the optimization solution process.

[0040] This paper utilizes a holomorphic embedding method to replace conventional power flow calculation methods to solve the problem of optimizing the operating state of flexible resources in distribution networks. This method avoids the drawback of repeated iterative updates of the Jacobi matrix in traditional power flow calculation methods. Furthermore, it offers advantages in power flow calculation, such as non-iterative recursive solution of the power series coefficients of holomorphic functions and non-initial value dependence, using a given feasible solution with practical physical significance as the initial reference state. This method significantly reduces the difficulty of solving the power flow of the flexible resource operating state optimization model and improves the model's solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a method according to an embodiment of the present disclosure.

[0042] Figure 2 Schematic diagram of the process of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0044] Unless otherwise defined, the technical or scientific terms used in this disclosure should have the usual meanings understood by people with ordinary skills in the field to which this disclosure belongs. The "first", "second" and similar words used in this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In order to keep the following description of the embodiments of the present disclosure clear and concise, the present disclosure omits detailed descriptions of some known functions and known components.

[0045] The present disclosure will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] refer to Figure 1-2 , a reactive power optimization method based on an improved power flow model, comprising:

[0047] Obtain power system parameters;

[0048] Construct multi-objective functions;

[0049] Construct a holomorphic embedded power flow model based on power system parameters;

[0050] The multi-objective function is solved by using a multi-objective particle swarm optimization algorithm and a holomorphic embedded power flow model to obtain the optimal solution for reactive power optimization of the power system;

[0051] The power system is controlled according to the optimal solution of reactive power optimization of the power system.

[0052] In one embodiment of the present disclosure, the multi-objective function includes the total network loss of the system and the voltage deviation rate.

[0053] In one embodiment of the present disclosure, the multi-objective function is specifically:

[0054] f=min(w1f loss.p +w2f ΔV );

[0055] Among them, w1 and w2 are weight coefficients, f loss.p is the total network loss of the system, f ΔV is the voltage deviation rate.

[0056] In one embodiment of the present disclosure, the total network loss of the system f loss.p Obtained through:

[0057]

[0058] Among them, r ij is the resistance value of branch ij, I ij is the current value of branch ij, E is the branch set, and T is the total number of time periods.

[0059] In one embodiment of the present disclosure, the voltage deviation rate f ΔV Obtained through:

[0060]

[0061] Where N is the number of power system nodes, U i' spec is the desired node voltage amplitude, U i' is the voltage of the i'th node.

[0062] In one embodiment of the present disclosure, the multi-objective function needs to satisfy one or more of power system flow constraints, node voltage constraints, branch current constraints, node power constraints, distributed power supply node output constraints and energy storage active and reactive output constraints.

[0063] The power system flow constraints are as follows:

[0064] PQ node:

[0065] PV node:

[0066] Balance Node:

[0067] The node voltage constraints are as follows:

[0068] U i.min ≤U i ≤U i.max ;

[0069] Among them, U i.min 、U i.max are the upper and lower limits of the voltage at node i, U i is the voltage at node i.

[0070] The branch current constraints are as follows:

[0071] I ij.min ≤I ij ≤I ij.max ;

[0072] Among them, I ij.max , I ij.min are the upper and lower limits of the current of branch ij, I ij is the branch ij current.

[0073] The node power constraints are as follows:

[0074]

[0075] Among them, σ j and γ j is a pulse function. When node j accesses the flexibility resource, then σ j and γ j is 1, otherwise σ j and γ j is 0; m(j) refers to the set of end nodes of all branches in the distribution network that are the head end; n(j) refers to the set of end nodes of all branches in the distribution network that are the head end; P ESS.j When it is a positive value, the energy storage node is discharged, and when it is a negative value, the energy storage node is charged.

[0076] The output constraints of distributed power generation nodes are as follows:

[0077]

[0078] The constraints on the active and reactive output of energy storage are as follows:

[0079]

[0080] Among them, P ESS.j and Q ESS.j is the active power and reactive power injected into the energy storage device at the energy storage node j. and Indicates the amount of electricity stored in the system at time t and the next time t. cha is the charging efficiency of the energy storage system, η discha is the discharge efficiency of the energy storage system.

[0081] In one embodiment of the present disclosure, a holomorphic embedded power flow model is constructed based on power system parameters, specifically:

[0082] The no-load state of the power system is taken as the initial solution of the classic HELM model;

[0083] The classical HELM model obtains the actual no-load state power flow solution of the power system at each time sequence based on the power generation state of the power system at each time sequence;

[0084] The actual no-load state power flow solution of the power system at each time sequence is used as the initial solution of the classical HELM model.

[0085] The classical holomorphic embedded power flow algorithm model is as follows:

[0086] PQ node:

[0087] PV node:

[0088] Balance Node:

[0089] The classic holomorphic embedded power flow algorithm model is as follows:

[0090] PQ node:

[0091] PV node:

[0092] Balance Node: Where, i and k represent the node numbers; Y ik represents the (i,k)th element in the node admittance matrix, Y ik,tr and Y ik,sh are the line admittances about nodes i and k, and the ground self-admittance of node i; s is the holomorphic embedding factor. In the classical model, s can be represented as the overall load change level.

[0093] This implementation uses the readily available no-load state (zero current injection) as the initial solution of the classic HELM model. In the initial state s=0, the initial solution must satisfy:

[0094]

[0095] Define the holomorphic function W(s), let W(s) be equal to the reciprocal of V(s), and we have:

[0096]

[0097] (W[0]+W[1]s+W[2]s 2 +…)(V[0]+V[1]s+V[2]s 2 +…)=1;

[0098]

[0099]

[0100] ...;

[0101] According to the principle of equality of power series coefficients, the recursive formula of W(s) is obtained:

[0102]

[0103] The recursive solution derivation process of the classical HELM model is similar to that of the classic HELM model. Therefore, only the specific recursive equation of the classic HELM is given below, taking a 3-node busbar as an example:

[0104]

[0105] Among them, 1 is the balance node, 2 is the PV node, 3 is the PQ node, G ik 、B ik They are the node admittance matrices Y ik The real and imaginary parts, V k.re and V k.im The voltage V k The real and imaginary parts of .

[0106]

[0107] in, For a given voltage amplitude at the equilibrium node, δ ni is a pulse function. When n=i, δ ni =1; when n≠i, δ ni =0;W i (s) is a new holomorphic function, let W i (s) is equal to V i The reciprocal of (s),

[0108] In one embodiment of the present disclosure, the multi-objective function is solved by a multi-objective particle swarm optimization algorithm and a holomorphic embedded power flow model to obtain the optimal solution for reactive power optimization of the power system, specifically:

[0109] Initialize the parameters of the multi-objective particle swarm optimization algorithm;

[0110] Iterative multi-objective particle swarm optimization algorithm, in which the fitness of each particle is obtained by:

[0111] Update the population size and the status of each particle;

[0112] Inputting the updated particle swarm state into the holomorphic embedded power flow model to obtain an updated power system power flow distribution, and obtaining an updated voltage-power state of each node in the power system according to the updated power system power flow distribution;

[0113] The value of the objective function is obtained according to the voltage-power state of each node in the power system, and is used as the fitness of the particles in the multi-objective particle swarm algorithm;

[0114] The multi-objective particle swarm algorithm iteration is completed to obtain the optimal solution for reactive power optimization of the power system.

[0115] In one embodiment of the present disclosure, during the iteration of the multi-objective particle swarm algorithm, the particle velocity is updated as follows:

[0116] V i (t+1)=wV i (t)+c1rand1×(pbest i (t)-X i (t))+c2rand2×(gbest(t)-X i (t));

[0117] X i (t+1)=X i (t)+V i (t+1);

[0118] Among them, V i (t) is the particle velocity of the i-th particle after the t-th update, c1 and c2 are learning factors, w is the inertia factor, rand1 and rand2 are random numbers uniformly distributed in the interval [0,1], and pbest i (t) is the local best position of the i-th particle after the t-th update, gbest(t) is the global best position after the t-th update, X i (t) is the position velocity of the i-th particle after the t-th update.

[0119] In one embodiment of the present disclosure, the multi-objective particle swarm algorithm uses the charging and discharging power of the energy storage device in the distribution network as a basic particle.

[0120] Specifically:

[0121]

[0122] Among them, P is the particle swarm, X MN is the element in the Mth row and Nth column of the matrix, representing the charge and discharge power of the Mth particle of the energy storage device at the Nth time sequence. M(N+1) is the fitness value of the Mth particle in the matrix. In this step, the population is initialized, including the size of the particle population, the position of each particle (the charge and discharge power of the energy storage device), the speed (the direction and distance of the particle in the search space), and the state of the non-dominated solution.

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0125] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0126] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0127] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0128] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

[0129] Regarding this disclosure, the following points need to be explained:

[0130] (1) The drawings of the embodiments of the present disclosure only relate to the structures related to the embodiments of the present disclosure. Other structures may refer to conventional designs.

[0131] (2) In the absence of conflict, the embodiments of the present disclosure and the features therein may be combined with each other to form new embodiments.

[0132] The above descriptions are merely embodiments of the present disclosure and are not intended to limit the patent scope of the present disclosure. Any equivalent structures made using the contents of the present disclosure and the drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present disclosure.

Claims

1. A reactive power optimization method based on an improved power flow model, characterized in that: include: Obtain power system parameters; Construct multi-objective functions; Construct a holomorphic embedded power flow model based on power system parameters; The multi-objective function is solved by using a multi-objective particle swarm optimization algorithm and a holomorphic embedded power flow model to obtain the optimal solution for reactive power optimization of the power system; The power system is controlled according to the optimal solution of reactive power optimization of the power system.

2. The reactive power optimization method based on the improved power flow model according to claim 1, characterized in that: The multi-objective function includes the total network loss of the system and the voltage deviation rate.

3. The reactive power optimization method based on the improved power flow model according to claim 2, characterized in that: The multi-objective function is specifically: f=min(w1f loss.p +w2f ΔV ); Among them, w1 and w2 are weight coefficients, f loss.p is the total network loss of the system, f ΔV is the voltage deviation rate.

4. The reactive power optimization method based on the improved power flow model according to claim 1, characterized in that: Total network loss of the system f loss.p Obtained through: Among them, r ij is the resistance value of branch ij, I ij is the current value of branch ij, E is the branch set, and T is the total number of time periods.

5. The reactive power optimization method based on the improved power flow model according to claim 1, characterized in that: Voltage deviation rate f ΔV Obtained through: Where N is the number of power system nodes, U i' spec is the expected node voltage amplitude, U i' is the voltage of the i'th node.

6. The reactive power optimization method based on the improved power flow model according to claim 1, characterized in that: The multi-objective function needs to satisfy one or more of the following: power system flow constraints, node voltage constraints, branch current constraints, node power constraints, distributed power node output constraints, and energy storage active and reactive output constraints.

7. The reactive power optimization method based on the improved power flow model according to claim 1, characterized in that: A full-pure embedded power flow model is constructed based on the power system parameters, specifically: The no-load state of the power system is taken as the initial solution of the classic HELM model; The classical HELM model obtains the actual no-load state power flow solution of the power system at each time sequence based on the power generation state of the power system at each time sequence; The actual no-load state power flow solution of the power system at each time sequence is used as the initial solution of the classical HELM model.

8. The reactive power optimization method based on the improved power flow model according to claim 1, characterized in that: The multi-objective particle swarm optimization algorithm and the holomorphic embedded power flow model are used to solve the multi-objective function and obtain the optimal solution for reactive power optimization of the power system, which is: Initialize the parameters of the multi-objective particle swarm optimization algorithm; Iterative multi-objective particle swarm optimization algorithm, in which the fitness of each particle is obtained by: Update the population size and the status of each particle; Inputting the updated particle swarm state into the holomorphic embedded power flow model to obtain an updated power system power flow distribution, and obtaining an updated voltage-power state of each node in the power system according to the updated power system power flow distribution; The value of the objective function is obtained according to the voltage-power state of each node in the power system, and is used as the fitness of the particles in the multi-objective particle swarm algorithm; The multi-objective particle swarm algorithm iteration is completed to obtain the optimal solution for reactive power optimization of the power system.

9. The reactive power optimization method based on the improved power flow model according to claim 5, characterized in that: During the iteration of the multi-objective particle swarm algorithm, the particle velocity is updated as follows: V i (t+1)=wV i (t)+c1rand1×(pbest i (t)-X i (t))+c2rand2×(gbest(t)-X i (t)); X i (t+1)=X i (t)+V i (t+1); Among them, V i (t) is the particle velocity of the i-th particle after the t-th update, c1 and c2 are learning factors, w is the inertia factor, rand1 and rand2 are random numbers uniformly distributed in the interval [0,1], and pbest i (t) is the local best position of the i-th particle after the t-th update, gbest(t) is the global best position after the t-th update, X i (t) is the position velocity of the i-th particle after the t-th update.

10. The reactive power optimization method based on the improved power flow model according to claim 1, characterized in that: The multi-objective particle swarm algorithm uses the charging and discharging power of the energy storage device in the distribution network as the basic particle.

Images