Coordinated Control Method for Active Power Command Tracking and Reactive Power Voltage Support in New Energy Bases

Abstract

The application discloses a kind of new energy base active instruction tracking and reactive voltage support coordination control method, comprising: obtaining new energy base grid-connected point output active power instruction and new energy base inner station operating parameter, constructs new energy base grid-connected power flow model;According to the new energy base grid-connected power flow model, determine optimization target variable, target and constraint condition, establish new energy base active instruction tracking and reactive voltage support coordination optimization model;According to the new energy base active instruction tracking and reactive voltage support coordination optimization model, optimization model is solved using hybrid intelligent algorithm, obtains the optimal active, reactive power output coefficient of each station in new energy base;According to the optimal active, reactive power output coefficient, the active instruction tracking and reactive voltage support coordination of new energy base is controlled.

Description

Technical Field

[0001] This application relates to the field of new energy grid connection control technology, and in particular to a method for coordinated control of active power command tracking and reactive power voltage support in a new energy base. Background Technology

[0002] The large-scale grid connection of new energy bases, represented by wind and solar power, presents a severe challenge to the stable operation of the power grid due to the intermittent and fluctuating nature of their power output. To meet the power supply demands of the load side, the automatic generation control system of the power grid needs to issue frequent active power dispatch commands to the new energy bases. However, rapid adjustments in active power can significantly alter the power flow distribution of the power grid, leading to drastic voltage fluctuations at the grid connection point. Traditionally, new energy power plants have relied on the commissioning of on-site power electronic reactive power compensation devices to stabilize voltage. However, this passive response mode is insufficient to cope with the dynamic voltage problems caused by active power dispatch commands and ignores the reactive power regulation potential of the new energy power plants themselves, weakening the safety and economy of the new energy base operation. Therefore, while achieving active power command tracking, it is necessary to further explore the reactive power voltage support capabilities of the new energy bases themselves. How to coordinate the active and reactive power outputs of various power plants within a new energy base to achieve rapid response to active power dispatch commands while maximizing reactive power voltage support capabilities is the core of the coordinated control of active power command tracking and reactive power voltage support in new energy bases. Currently, there are several issues to be addressed in this research: (1) how to construct a collaborative optimization model that can simultaneously and quickly respond to active power commands and grid reactive power and voltage demands; (2) the power output allocation problem of the total active power commands received by the new energy base in each station; and (3) how to quickly and accurately solve high-dimensional nonlinear optimization problems and generate executable coordinated control commands under the premise of satisfying multiple constraints.

[0003] Most research methods employ a rigorous power flow calculation method for problem (1). Although this method can accurately calculate the active and reactive power output of nodes, it is still an open-loop control method, which makes it difficult to respond quickly to active power commands and achieve coordinated control of reactive power. For problem (2), existing methods often treat the new energy base as a whole and ignore the influence of internal impedance on active and reactive power. In addition, the power control of the stations in the new energy base is usually uniform, which cannot give full play to the reactive power regulation potential of the new energy itself. For problem (3), facing high-dimensional nonlinear optimization problems, a single solution algorithm is still used, which has the problem of substituting local optima for global optima, resulting in large solution deviations. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method for coordinated control of active power command tracking and reactive power voltage support at new energy bases, thereby resolving the technical problem in related technologies where new energy bases cannot quickly and collaboratively respond to active power commands and grid reactive power voltage demands.

[0005] According to a first aspect of the embodiments of this application, a method for coordinated control of active power command tracking and reactive power voltage support in a new energy base is provided, comprising:

[0006] Obtain the active power output command from the grid connection point of the new energy base and the operating parameters of the substations within the new energy base, and construct a grid connection power flow model for the new energy base;

[0007] Based on the grid-connected power flow model of the new energy base, the active and reactive power output coefficients of each station in the new energy base are used as the optimization target variables. The objectives are active power command tracking and maximizing reactive power output. The constraints are station capacity constraints, node voltage amplitude constraints, line power flow constraints, and power balance constraints in the new energy base. A coordinated optimization model for active power command tracking and reactive power voltage support in the new energy base is established.

[0008] Based on the coordinated optimization model of active power command tracking and reactive power voltage support in the new energy base, a hybrid intelligent algorithm is used to solve the optimization model to obtain the optimal active and reactive power output coefficients of each station in the new energy base.

[0009] Based on the optimal active and reactive power output coefficients, the active power command tracking and reactive power voltage support coordination of the new energy base are controlled.

[0010] Optionally, the active power output command of the new energy base grid connection point includes the active power dispatch command for the next 24 hours given by the grid dispatch.

[0011] The operating parameters of the stations within the new energy base include: the type and capacity of each station within the new energy base, the wind and sunlight conditions within the new energy base on that day, the geographical location parameters of each station within the new energy base, the connection line parameters between stations within the new energy base, and the maximum and minimum voltage that each station node within the new energy base can withstand. The connection line parameters between stations within the new energy base include line impedance and the allowable capacity of the line.

[0012] Optionally, the grid-connected power flow model of the new energy base is used for receiving active power commands output from the grid connection point; calculating the power of each station within the new energy base; and determining the operating conditions and modes of each station within the new energy base.

[0013] Optionally, a hybrid intelligent algorithm is used to solve the optimization model, including:

[0014] The diversity of the initial population is increased by using an intelligent and diversified initialization strategy. By combining four methods—historical optimal solution, physical heuristic, Latin hypercube sampling, and random initialization—the global convergence and convergence speed of the algorithm are improved.

[0015] By employing adaptive crossover and adaptive mutation strategies in the hybrid algorithm, uniform crossover and large-scale mutation are used in the early stage to maintain diversity, while arithmetic crossover and small-scale mutation are used in the later stage to enhance convergence and improve the convergence speed.

[0016] By using a dynamic penalty function to handle voltage constraints, combined with Lévy flight perturbation, elite local search, and population diversity maintenance mechanisms, the optimization efficiency and solution quality of the algorithm are improved.

[0017] According to a second aspect of the embodiments of this application, a coordinated control device for active power command tracking and reactive power voltage support in a new energy base is provided, comprising:

[0018] The modeling module is used to acquire the active power output command of the grid connection point of the new energy base and the operating parameters of the stations in the new energy base, and to construct the grid connection power flow model of the new energy base.

[0019] The modeling module is used to establish a coordinated optimization model for active power command tracking and reactive power voltage support of the new energy base based on the grid-connected power flow model of the new energy base, with the active and reactive power output coefficients of each station in the new energy base as the optimization target variables, active power command tracking and maximizing reactive power output as the objectives, and station capacity constraints, node voltage amplitude constraints, line power flow constraints and power balance constraints in the new energy base as the constraints.

[0020] The solution module is used to solve the optimization model based on the active power command tracking and reactive power voltage support coordination optimization model of the new energy base, and to obtain the optimal active and reactive power output coefficients of each station in the new energy base.

[0021] The control module is used to control the active power command tracking and reactive power voltage support coordination of the new energy base according to the optimal active and reactive power output coefficients.

[0022] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising:

[0023] One or more processors;

[0024] Memory, used to store one or more programs;

[0025] When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0026] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0027] The technical solutions provided by the embodiments of this application may include the following beneficial effects:

[0028] As can be seen from the above embodiments, this application constructs a grid-connected power flow model for the new energy base based on the obtained active power command output from the grid connection point and the operating parameters of the substations within the new energy base. Then, it constructs an objective function based on active power command tracking and maximizing reactive power output, using substation capacity constraints, node voltage amplitude constraints, line power flow constraints, and power balance constraints within the new energy base as constraints. A hybrid intelligent algorithm is used to solve the coordinated optimization model of active power command tracking and reactive power voltage support in the new energy base. This determines the active and reactive power output coefficients of each substation within the new energy base that can accurately track active power dispatch commands and maximize reactive power output. It can find higher-quality solutions in a shorter time, effectively improving the reactive power voltage support capability of the new energy base while achieving accurate active power command tracking.

[0029] This invention provides a method for coordinated control of active power command tracking and reactive power voltage support in new energy bases. While accurately tracking active power dispatch commands, it maximizes the reactive power voltage support capability of new energy bases and can provide a basis for operation control strategies for new energy bases.

[0030] By establishing a coordinated optimization model for active power command tracking and reactive power voltage support in new energy bases, the active and reactive power outputs of each station within the new energy base can be independently controlled, improving the accuracy of active power tracking while maximizing the potential of reactive power support from new energy sources.

[0031] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0033] Figure 1 This is a flowchart illustrating a coordinated control method for active power command tracking and reactive power voltage support in a new energy base, according to an exemplary embodiment.

[0034] Figure 2 This is a comparison chart of reactive power output between hybrid intelligent algorithm control and conventional control, according to an exemplary embodiment.

[0035] Figure 3 This is a comparison diagram of active power command tracking between hybrid intelligent algorithm control and conventional control, according to an exemplary embodiment.

[0036] Figure 4 This is a comparison chart of the tracking error of the active power command output between hybrid intelligent algorithm control and conventional control, according to an exemplary embodiment.

[0037] Figure 5 This is a block diagram illustrating a coordinated control device for active power command tracking and reactive power voltage support in a new energy base, according to an exemplary embodiment. Detailed Implementation

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0039] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0040] Figure 1 This is a flowchart illustrating a coordinated control method for active power command tracking and reactive power voltage support in a new energy base, according to an exemplary embodiment. Figure 1 As shown, this method, when applied to a terminal, may include the following steps:

[0041] S1: Obtain the active power output command from the grid connection point of the new energy base and the operating parameters of the stations within the new energy base, and construct the grid connection power flow model of the new energy base;

[0042] Specifically, based on the active power command output from the grid connection point of the new energy base and the operating parameters of the power stations within the new energy base, a topological connection relationship is established between the nodes of each power station within the new energy base. The grid connection point of the new energy base is set as the balancing node, and each power station node within the new energy base is set as a PQ node, thus constructing a grid power flow model for the new energy base.

[0043] The active power output command of the new energy base grid connection point includes the active power dispatch command given by the power grid dispatch for the next 24 hours.

[0044] The operating parameters of the stations within the new energy base include: the type and capacity of each station within the new energy base, the wind and sunlight conditions within the new energy base on that day, the geographical location parameters of each station within the new energy base, the connection line parameters between stations within the new energy base, and the maximum and minimum voltage that each station node within the new energy base can withstand. The connection line parameters between stations within the new energy base include line impedance and the allowable capacity of the line.

[0045] S2: Based on the grid-connected power flow model of the new energy base, the active and reactive power output coefficients of each station in the new energy base are used as the optimization target variables. The objectives are active power command tracking and maximizing reactive power output. The constraints are station capacity constraints, node voltage amplitude constraints, line power flow constraints, and power balance constraints in the new energy base. A coordinated optimization model for active power command tracking and reactive power voltage support in the new energy base is established.

[0046] Specifically, the coordinated optimization model for active power command tracking and reactive power voltage support in the new energy base uses the active power output coefficient and reactive power output coefficient of each power station within the new energy base as optimization variables to establish a multi-objective optimization model. The objective function is active power command tracking and maximizing reactive power output, and the constraints include power station capacity constraints, node voltage amplitude constraints, line power flow constraints, and power balance constraints.

[0047] 1. Optimization Objectives: Active power command tracking, maximizing reactive power output.

[0048] The objective function for tracking active power commands in the new energy base is expressed as:

[0049] (1)

[0050] in, f 1( X ) represents the active power instruction tracking objective function for the new energy base. X To optimize variables, α i For the first i The active power output coefficient of each station P i,max For the first i The maximum contribution of each station P ref The active power command given to the power grid dispatcher; n is the number of power generation stations in the new energy base.

[0051] The objective function for maximizing reactive power output of the new energy base is expressed as:

[0052] (2)

[0053] in, f 2( X Let be the objective function for maximizing the reactive power output of the new energy base. X To optimize variables, β i For the first i The reactive power output coefficient of each power station Q i,max For the first i The maximum reactive power output of each station.

[0054] Furthermore, the multi-objective optimization function is expressed using a weighted method as follows:

[0055] (3)

[0056] in, F ( X () is the overall multi-objective optimization function. X To optimize variables, w 1 , w 2 These are the weighting coefficients for active power instruction tracking and reactive power output maximization, respectively.

[0057] 2. Constraints;

[0058] Station capacity constraints:

[0059] (4)

[0060] (5)

[0061] in, S i,max For the first i The maximum apparent power capacity of each station.

[0062] Power balance constraints:

[0063] (6)

[0064] (7)

[0065] in, P i , Q i The first i The merits and demerits of each station P loss , Q loss These represent the system's active and reactive power losses, respectively. P load , Q load These represent the active and reactive power demands of the load, respectively.

[0066] Node voltage amplitude constraints:

[0067] (8)

[0068] in, U i For the first i The voltage amplitude at each node, U i,minand U i,max The first i The minimum and maximum allowable voltage amplitude for each node.

[0069] Power flow constraints on the line:

[0070] (9)

[0071] in, P ij , Q ij They are nodes i To the node j The active and reactive power of the line, S ij,max For nodes i To the node j Line capacity limitations.

[0072] Therefore, considering the objective function and constraints, if the active and reactive power output coefficients of each power station within the new energy base are denoted as a column vector X=[α]... 1 ,α 2 ,...,α n ,β 1 ,β 2 ,...,β n ]ᵀ, then the coordinated optimization model of active power command tracking and reactive power voltage support in the new energy base can be expressed as:

[0073] min F ( X )

[0074] st: g i ( X ) = 0, i =1,…, M 1, (10)

[0075] h j ( X ) ≤ 0, j =1,…, M 2,

[0076] in, e=g i ( X )and h j ( X ) respectively represent the number involved i The equality constraints and the first j Inequality constraints M 1 and M2 represents the number of equality constraints and inequality constraints, respectively.

[0077] The advantages of establishing the above-mentioned active power command tracking and reactive power voltage support coordination optimization model for new energy bases are: (1) using the output coefficient α i β i As an optimization variable, its value is normalized to the interval [0,1], which facilitates the unified scheduling of stations with different capacities and can intuitively reflect the output margin of each station, which is conducive to the rapid convergence of the optimization algorithm. (2) Through the weighting coefficient w 1 w 2 By flexibly adjusting the priority of active power command tracking and reactive power output maximization, it can accurately respond to dispatch commands to ensure power balance, and fully tap the potential of reactive power regulation to enhance voltage support, thereby achieving coordinated optimization of active and reactive power.

[0078] S3: Based on the active power command tracking and reactive power voltage support coordination optimization model of the new energy base, a hybrid intelligent algorithm is used to solve the optimization model to obtain the optimal active power and reactive power output coefficients of each station in the new energy base. Based on the optimization results, the active power command tracking and reactive power voltage support coordination control of the new energy base is completed.

[0079] Specifically, the solution process for the coordinated optimization model of active power command tracking and reactive power voltage support in new energy bases using a hybrid intelligent algorithm is as follows:

[0080] (1) Intelligent diversification initialization stage: Construct an initial population of size N. X =[ X 1 , X 2 ,… X N ] T ,in X i For the first i Population locations. Four initialization methods are used to generate the initial population, increasing its diversity:

[0081] 1. Initialization of historical optimal solutions: The operation of the power system is continuous, and the optimal solution at the current moment is often near the historical optimal solution. This characteristic can be used to accelerate convergence.

[0082] (11)

[0083] in, X hist The optimal solution vector is found in the historical operational data. X i This is the initial population vector generated based on the historical best solution. δ It represents a small random disturbance.

[0084] 2. Physically-inspired initialization: Initialization is performed based on the physical characteristics of the new energy base.

[0085] , , (12)

[0086] in, X i The initial population vector is generated based on physical heuristic initialization. α i The heuristic value of the active power output coefficient. P ref For reference active power, P i,max For the first i The maximum active power output of each station β i is the heuristic value for reactive power output coefficient, and rand(0,1) is a random number in the interval 0 to 1.

[0087] 3. Latin hypercube sampling initialization: Ensures that each dimension is uniformly divided and sampled, avoids clustering of random sampling, and improves the spatial coverage of the initial population.

[0088] (13)

[0089] in, X ij for X i The j One portion, X min,j Let j be the minimum value of the j-th component. X max,j For the maximum value of the j-th component, LHS( i,j () represents the Latin hypercube sampling value.

[0090] 4. Random initialization: Maintain the randomness of the population and expand the exploration range.

[0091] (14)

[0092] in, X ij Let j be the value of the j-th dimension variable for the i-th individual. X min,j Let j be the minimum value of the j-th component. X max,j Let be the maximum value of the j-th component, and rand(0,1) be a random number in the interval 0 to 1.

[0093] (2) Construct a fitness function for multi-objective evaluation, specifying:

[0094] (15)

[0095] (16)

[0096] (17)

[0097] (18)

[0098] in, P out (X i ) is X i The corresponding new energy bases have actually contributed their efforts. P ref The command value for contributing to the new energy base. Q out (X i ) is X i The corresponding new energy base actually has no active power output. λ V This is the voltage over-limit penalty coefficient. M This represents the total number of nodes in the new energy trend model. U k For the first k Actual voltage at each node V min and V max The minimum and maximum voltage constraint values ​​corresponding to each node.

[0099] (3) PSO update phase

[0100] PSO updates are performed only in the early stages of an iteration; let the current iteration number be . t ,when t ≤0.7 t max To perform a PSO update, follow these steps:

[0101] 1. Adaptive inertia weights, dynamically adjusted according to individual fitness;

[0102] (19)

[0103] in, For the first t Average fitness of the population in each iteration.

[0104] 2. Velocity update including Levy flight disturbance;

[0105] (20)

[0106] in, That is, it follows a uniform distribution in the interval [0,1] and survives independently in each iteration; c 1 , c 2 Levy (the individual learning factor coefficient and the social learning factor coefficient) D ) is a sample from the Lévy distribution.

[0107] (3) GA operation phase;

[0108] Regardless of the iteration phase, GA operations are performed to optimize the population, with the following steps:

[0109] 1. Elite Preservation Mechanism: The population is sorted in ascending order of fitness. f ( X (1) )≤ f ( X (2) )≤…, f ( X (N) ), take the previous N elite Each individual serves as an elite member of the new population;

[0110] 2. Tournament selection mechanism: For the remaining population of the new species N - N elite Each position is randomly selected each time. K From a set of individuals, the individual with the lowest fitness is selected as the parent, resulting in a parent pair ( X p1 , X p2 );

[0111] 3. Adaptive crossover: Through parent pairing ( X p1 , X p2 ) to obtain the offspring team ( X c1 , X c2 )

[0112] In the early stages of iteration, a uniform crossover strategy is used to maintain population diversity, whereby...

[0113] (twenty one)

[0114] Arithmetic crossover is used in the later stages of the iteration to enhance convergence, where

[0115] (twenty two)

[0116] 4. Adaptive mutation

[0117] Large-scale mutations are used in the early stages of the iteration:

[0118] (twenty three)

[0119] Small-scale mutations are used in the later stages of the iteration:

[0120] (twenty four)

[0121] in, After mutation, it must meet the following requirements. ;

[0122] 5. New Population Construction: Offspring resulting from crossover mutation are assigned to a new population; offspring growth rate... V new,i Inheritance from the father V p1 and V p2 Mean.

[0123] (4) Elite Local Search Strategy: Perform a fine-grained local search on the current optimal solution.

[0124] (25)

[0125] in, X imp For local search values ​​of elite populations, X elite As the current elite population, ε This is the local search step size, which decreases with the number of iterations.

[0126] (5) Population diversity maintenance: by calculating population diversity indicators D Maintaining population diversity

[0127] 1. Diversity Calculation: The population diversity index is defined as the mean of the standard deviations of each variable.

[0128] (26)

[0129] in, Let be the mean of the j-th dimension variable in the t-th iteration;

[0130] 2. Diversity regulation: D is specified th As a diversity threshold, if D(t) < D th Then, the population will be reinitialized with 20 individuals:

[0131] (6) Convergence Criterion

[0132] 1. When the number of iterations reaches the maximum value

[0133] 2. Fitness values ​​tend to stabilize: iteration number t>30 and (27);

[0134] in, This represents the average of the globally optimal fitness over the last 30 iterations. This is the convergence accuracy threshold.

[0135] (7) Repeat steps (2) to (6) above until a solution that satisfies the convergence condition is obtained.

[0136] (8) Determine the active and reactive power output coefficients of each station in the new energy base according to the solution.

[0137] By applying a hybrid intelligent algorithm, which deeply integrates the globally efficient search capability of Particle Swarm Optimization (PSO) with the locally refined optimization characteristics of Genetic Algorithm (GA), the inherent shortcomings of single intelligent algorithms are effectively overcome. It leverages the PSO algorithm's collaborative optimization mechanism to rapidly traverse the high-dimensional solution space, avoiding getting trapped in local optima, while the GA algorithm's crossover mutation and elite retention strategies enhance the refined exploration of the optimal solution's neighborhood, significantly improving solution accuracy. Furthermore, by combining collaborative strategies such as intelligent diversified initialization, adaptive parameter adjustment, Lévy flight perturbation, and population diversity maintenance, the algorithm's robustness and convergence efficiency in multi-objective and multi-constraint scenarios are greatly enhanced. It can quickly and stably output optimal active and reactive power control coefficients that meet engineering requirements in complex power system operating environments, balancing the synergy of optimization objectives with the feasibility of solutions, providing reliable technical support for the safe and economical operation of new energy bases.

[0138] from Figure 2 As can be seen, the application of hybrid intelligent algorithms (“algorithm control” in the figure) can effectively improve the reactive power output of the new energy base, effectively leveraging its reactive voltage support capability. From Figure 3 and Figure 4 It can be seen that both the application of the hybrid intelligent algorithm (“algorithm control” in the figure) and conventional control have good active power tracking performance, but considering the tracking error, it can be seen that the application of the hybrid intelligent algorithm has higher tracking accuracy.

[0139] S4: Based on the optimal active and reactive power output coefficients, control the active power command tracking and reactive power voltage support coordination of the new energy base.

[0140] Specifically, based on the active and reactive power output coefficients obtained from the hybrid intelligent algorithm, control commands are sent to various power stations within the new energy base to achieve coordinated control of active power command tracking and reactive power voltage support.

[0141] As can be seen from the above technical solution, this embodiment is based on a hybrid intelligent algorithm. By acquiring the active power output command from the grid connection point of the new energy base and the operating parameters of the substations within the new energy base, and taking active power command tracking and maximizing reactive power output as the objective function, it considers the capacity constraints of the substations, node voltage amplitude constraints, line power flow constraints, and power balance constraints within the new energy base. A mathematical model is established, and the optimized solution is obtained through the hybrid intelligent algorithm as the basis for the active and reactive power output coefficients of each substation within the new energy base. This method deeply integrates the fast global search capability of the particle swarm optimization algorithm with the fine local optimization capability of the genetic algorithm, and introduces mechanisms such as intelligent initialization, adaptive parameter adjustment, Lévy flight, and diversity monitoring, effectively improving the convergence speed and solution quality of the algorithm. While achieving accurate tracking of active power commands, it maximizes the reactive power voltage support capability of the new energy base, providing a scientific theoretical basis and technical support for the safe and economical operation of the new energy base.

[0142] Corresponding to the aforementioned embodiments of the active power command tracking and reactive power voltage support coordination control method for new energy bases, this application also provides embodiments of the active power command tracking and reactive power voltage support coordination control device for new energy bases.

[0143] Figure 5 This is a block diagram illustrating a coordinated control device for active power command tracking and reactive power voltage support in a new energy base, according to an exemplary embodiment. (Refer to...) Figure 5 The device includes:

[0144] Modeling module 1 is used to acquire the active power output command of the grid connection point of the new energy base and the operating parameters of the stations in the new energy base, and to construct the grid connection power flow model of the new energy base.

[0145] Modeling module 2 is used to establish a coordinated optimization model for active power command tracking and reactive power voltage support of the new energy base based on the grid-connected power flow model of the new energy base, with the active and reactive power output coefficients of each station in the new energy base as the optimization target variables, active power command tracking and maximizing reactive power output as the objectives, and station capacity constraints, node voltage amplitude constraints, line power flow constraints and power balance constraints in the new energy base as the constraints.

[0146] Solution module 3 is used to solve the optimization model based on the active power command tracking and reactive power voltage support coordination optimization model of the new energy base, and to obtain the optimal active and reactive power output coefficients of each station in the new energy base.

[0147] Control module 4 is used to control the active power command tracking and reactive power voltage support coordination of the new energy base according to the optimal active and reactive power output coefficients.

[0148] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0149] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0150] Accordingly, this application also provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described coordinated control method for active power instruction tracking and reactive power voltage support in new energy bases.

[0151] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the above-described method for coordinated control of active power instruction tracking and reactive power voltage support in a new energy base.

[0152] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0153] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for coordinated control of active power command tracking and reactive power voltage support in a new energy base, characterized in that, include: Obtain the active power output command from the grid connection point of the new energy base and the operating parameters of the substations within the new energy base, and construct a grid connection power flow model for the new energy base; Based on the grid-connected power flow model of the new energy base, the active and reactive power output coefficients of each station in the new energy base are used as the optimization target variables. The objectives are active power command tracking and maximizing reactive power output. The constraints are station capacity constraints, node voltage amplitude constraints, line power flow constraints, and power balance constraints in the new energy base. A coordinated optimization model for active power command tracking and reactive power voltage support in the new energy base is established. Based on the coordinated optimization model of active power command tracking and reactive power voltage support in the new energy base, a hybrid intelligent algorithm is used to solve the optimization model to obtain the optimal active and reactive power output coefficients of each station in the new energy base. Based on the optimal active and reactive power output coefficients, the active power command tracking and reactive power voltage support coordination of the new energy base are controlled. A hybrid algorithm framework integrating particle swarm optimization and genetic algorithms is constructed, wherein: In the early iteration stage of the particle swarm optimization algorithm update, the diversity of the initial population is increased by intelligent diversification initialization strategy. The initial population is generated by combining four methods: historical best solution, physical heuristic, Latin hypercube sampling and random initialization. In the particle swarm optimization algorithm update phase, adaptive inertial weights are adopted, and Levy flight perturbation is incorporated into the velocity update. During the genetic algorithm operation phase of the entire iterative process, the population is evolved through adaptive crossover and adaptive mutation strategies. Uniform crossover and large-scale mutation are used in the early stage, and arithmetic crossover and small-scale mutation are used in the later stage. After the genetic algorithm operation phase, an elite local search is performed on the current optimal solution, and population diversity is maintained by calculating the population diversity index. When the diversity is lower than the threshold, some individuals are reinitialized. By using a dynamic penalty function to handle voltage constraints, the constraints are transformed into penalty terms of the fitness function.

2. The method for coordinated control of active power command tracking and reactive power voltage support in a new energy base as described in claim 1, characterized in that, The active power output command of the new energy base grid connection point includes the active power dispatch command given by the power grid dispatch for the next 24 hours. The operating parameters of the stations within the new energy base include: the type and capacity of each station within the new energy base, the wind and sunlight conditions within the new energy base on that day, the geographical location parameters of each station within the new energy base, the connection line parameters between stations within the new energy base, and the maximum and minimum voltage that each station node within the new energy base can withstand. The connection line parameters between stations within the new energy base include line impedance and the allowable capacity of the line.

3. The method for coordinated control of active power command tracking and reactive power voltage support in a new energy base as described in claim 1, characterized in that, The grid-connected power flow model of the new energy base is used for receiving active power commands output from the grid connection point; calculating the power of each station within the new energy base; and determining the operating conditions and modes of each station within the new energy base.

4. A coordinated control device for active power command tracking and reactive power voltage support in a new energy base, characterized in that, include: The modeling module is used to acquire the active power output command of the grid connection point of the new energy base and the operating parameters of the stations in the new energy base, and to construct the grid connection power flow model of the new energy base. The modeling module is used to establish a coordinated optimization model for active power command tracking and reactive power voltage support of the new energy base based on the grid-connected power flow model of the new energy base, with the active and reactive power output coefficients of each station in the new energy base as the optimization target variables, active power command tracking and maximizing reactive power output as the objectives, and station capacity constraints, node voltage amplitude constraints, line power flow constraints and power balance constraints in the new energy base as the constraints. The solution module is used to solve the optimization model based on the active power command tracking and reactive power voltage support coordination optimization model of the new energy base, and to obtain the optimal active and reactive power output coefficients of each station in the new energy base. The control module is used to control the active power command tracking and reactive power voltage support coordination of the new energy base according to the optimal active and reactive power output coefficients. A hybrid algorithm framework integrating particle swarm optimization and genetic algorithms is constructed, wherein: In the early iteration stage of the particle swarm optimization algorithm update, the diversity of the initial population is increased by intelligent diversification initialization strategy. The initial population is generated by combining four methods: historical best solution, physical heuristic, Latin hypercube sampling and random initialization. In the particle swarm optimization algorithm update phase, adaptive inertial weights are adopted, and Levy flight perturbation is incorporated into the velocity update. During the genetic algorithm operation phase of the entire iterative process, the population is evolved through adaptive crossover and adaptive mutation strategies. Uniform crossover and large-scale mutation are used in the early stage, and arithmetic crossover and small-scale mutation are used in the later stage. After the genetic algorithm operation phase, an elite local search is performed on the current optimal solution, and population diversity is maintained by calculating the population diversity index. When the diversity is lower than the threshold, some individuals are reinitialized. By using a dynamic penalty function to handle voltage constraints, the constraints are transformed into penalty terms of the fitness function.

5. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-3.

6. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-3.

Images