Reactive power optimization control method, system and equipment for regional power grid and storage medium

Abstract

The invention relates to the technical field of power grid reactive power optimization, and discloses a regional power grid reactive power optimization control method, system and device and a storage medium, and the method comprises the steps: predicting the active power output of a photovoltaic power station according to the illumination intensity, and carrying out the simulation and load flow calculation of the load fluctuation in a regional power grid; the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station are obtained; constructing a three-layer optimization control model according to the system network loss and the voltage deviation; solving the system-level optimization control model by adopting an improved particle swarm algorithm to obtain a system optimization reference value, and performing data processing on the system optimization reference value through a data processing model to obtain a reactive power output reference value of the photovoltaic power station; and solving the station level optimization control model according to the reactive power output reference value to obtain an optimal output scheme of each group of photovoltaic power generation units and the static var generator in the photovoltaic power station. According to the method, the photovoltaic power station participates in fine control of power grid reactive power optimization, so that the safety and stability of power grid operation are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid reactive power optimization, and in particular to a method, system, device and storage medium for regional power grid reactive power optimization control. Background Art

[0002] With the continuous expansion of the scale of power systems and the rapid development of renewable energy, the stability and security of power systems have received increasing attention. Reactive power plays an important role in maintaining voltage stability, improving system power factor and reducing transmission losses in power systems. As an important part of the power system, the reactive power optimization of regional power grids is of great significance for improving system operation efficiency and reducing operating costs.

[0003] At present, regional power grids are facing challenges brought by load fluctuations and output fluctuations of renewable energy sources such as wind power and photovoltaics. The uncertainty of load fluctuations and the volatility of renewable energy will affect the reactive power demand of the power system, and thus affect the stability and reliability of the power grid. However, traditional reactive power optimization strategies often fail to fully consider the impact of these fluctuations on the system, thus affecting the stability of power grid operation. Therefore, how to achieve reactive power optimization of regional power grids while considering load fluctuations has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a regional power grid reactive power optimization control method, system, device and storage medium, so as to overcome the influence of the randomness and volatility of photovoltaic power generation and load on the stability of power grid operation, so as to ensure the safety and stability of regional power grid operation.

[0005] In a first aspect, the present invention provides a method for optimizing reactive power control in a regional power grid, the method comprising: The active output of the photovoltaic power station is predicted based on the light intensity, and the load fluctuation in the regional power grid is simulated and the power flow is calculated to obtain the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station; According to the system network loss and the voltage deviation, a three-layer optimization control model is constructed, wherein the three-layer optimization control model includes a system-level optimization control model, a data processing model, and a station-level optimization control model; The improved particle swarm algorithm is used to solve the system-level optimization control model to obtain a system optimization reference value, and the system optimization reference value is processed by the data processing model to obtain a reactive power output reference value of the photovoltaic power station; The station-level optimization control model is solved according to the reactive output reference value to obtain the optimal output plan of each group of photovoltaic power generation units and static reactive generators in the photovoltaic power station, and reactive power optimization control is performed on the photovoltaic power station according to the optimal output plan.

[0006] Furthermore, the step of predicting the active output of the photovoltaic power station according to the light intensity includes: The light intensity prediction curve is divided into segments, and based on the area equivalence principle, the equivalent light intensity in the time interval corresponding to each segment is calculated; According to the equivalent light intensity, the active output prediction value of the photovoltaic power station corresponding to each optimized control period is determined.

[0007] Furthermore, the step of constructing a three-layer optimization control model according to the system network loss and the voltage deviation includes: Taking the minimization of the network loss of the system as the first objective function, taking the transformer ratio of each substation, the number of capacitor switching groups, the static VAR generator, and the reactive power output of the photovoltaic inverter of the photovoltaic power station as the control variables, and taking the power flow constraint, the node voltage constraint, the node power constraint, the power output constraint, the reactive compensation constraint, the transformer tap position constraint, the capacitor unit operation quantity constraint, the transformer tap operation constraint and the capacitor unit operation constraint as the first constraint condition, the system-level optimization control model is constructed; Based on the power flow calculation of the regional power grid, construct the data processing model; The station-level optimization control model is constructed by taking the minimization of the active power loss of the photovoltaic power station and the voltage deviation as the second objective function, and taking the reactive output constraint of the photovoltaic power generation unit and the access point voltage constraint as the second constraint condition.

[0008] Furthermore, the step of using the improved particle swarm algorithm to solve the system-level optimization control model to obtain the system optimization reference value includes: The chaos algorithm is used to optimize the initial particle swarm parameters of the system-level optimization control model, and the inertia weight coefficient and the learning factor are set according to the inverse sigmoid function and the trigonometric function; The particle swarm is iteratively calculated, and during the iterative calculation process, the position of the particle is updated based on the rounding of discrete variables, and the current optimal particle is perturbed through Cauchy mutation until the preset iteration stop condition is reached to obtain the system optimization reference value.

[0009] Furthermore, the optimization calculation is expressed by the following formula: Where, T i,m represents the mth chaotic variable of the ith particle, and a represents the control variable; The inertia weight coefficient is expressed by the following formula: In the formula, k represents the number of iterations, kmax represents the maximum number of iterations, and r represents a random number; The learning factor is expressed by the following formula: In the formula, c1 represents the individual acceleration factor, c2 represents the social acceleration factor; The following formula is used to represent the rounding of discrete variables: In the formula, x(i,j) represents the position of the i-th particle in the j-th dimension, round represents the rounding function, and v(i,j) represents the velocity of the i-th particle in the j-th dimension; The Cauchy variation is expressed by the following formula: In the formula, x i represents the position of the ith particle, Cauchy(0,1) represents the standard Cauchy perturbation random value, x min and x max Indicates the lower and upper limits of the particle position.

[0010] Further, In a second aspect, the present invention provides a regional power grid reactive power optimization control system, the system comprising: The data processing module is used to predict the active output of the photovoltaic power station according to the light intensity, simulate the load fluctuation in the regional power grid and calculate the power flow, and obtain the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station; A model building module, used to build a three-layer optimization control model according to the system network loss and the voltage deviation, wherein the three-layer optimization control model includes a system-level optimization control model, a data processing model, and a station-level optimization control model; An optimization control module, used for solving the system-level optimization control model by using an improved particle swarm algorithm to obtain a system optimization reference value, and performing data processing on the system optimization reference value by using the data processing model to obtain a reactive power output reference value of the photovoltaic power station; The station-level optimization control model is solved according to the reactive output reference value to obtain the optimal output plan of each group of photovoltaic power generation units and static reactive generators in the photovoltaic power station, and reactive power optimization control is performed on the photovoltaic power station according to the optimal output plan.

[0011] Furthermore, the model building module is also used to take the minimization of the system network loss as the first objective function, take the transformer ratio of each substation, the number of capacitor switching groups, the static VAR generator, and the reactive power output of the photovoltaic inverter of the photovoltaic power station as the control variables, and take the power flow constraint, the node voltage constraint, the node power constraint, the power output constraint, the reactive compensation constraint, the transformer tap position constraint, the capacitor unit operation quantity constraint, the transformer tap operation constraint and the capacitor unit operation constraint as the first constraint condition to build the system-level optimization control model; Based on the power flow calculation of the regional power grid, construct the data processing model; The station-level optimization control model is constructed by taking the minimization of the active power loss of the photovoltaic power station and the voltage deviation as the second objective function, and taking the reactive output constraint of the photovoltaic power generation unit and the access point voltage constraint as the second constraint condition.

[0012] Furthermore, the optimization control module also includes an improved particle swarm algorithm module; The improved particle swarm algorithm module is used to optimize the initial particle swarm parameters of the system-level optimization control model using a chaos algorithm, and set the inertia weight coefficient and the learning factor according to the inverse sigmoid function and the trigonometric function; The particle swarm is iteratively calculated, and during the iterative calculation process, the position of the particle is updated based on the rounding of discrete variables, and the current optimal particle is perturbed through Cauchy mutation until the preset iteration stop condition is reached to obtain the system optimization reference value.

[0013] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0015] The present invention provides a method, system, device and storage medium for optimizing reactive power of a regional power grid. By means of the method, photovoltaic power stations are involved in the reactive power optimization of the power grid, which can effectively deal with the impact of the randomness and volatility of photovoltaic power generation and load on the stability of the power grid, thereby ensuring the safety and stability of the operation of the regional power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a method for optimizing reactive power control of a regional power grid in an embodiment of the present invention; Figure 2Schematic diagram of the distribution of Pareto optimal solutions in a simulation experiment in an embodiment of the present invention; Figure 3 is a schematic diagram of access point voltage in a simulation experiment in an embodiment of the present invention; Figure 4 is a schematic diagram of the structure of a regional power grid reactive power optimization control system according to an embodiment of the present invention; Figure 5 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 The first embodiment of the present invention provides a method for optimizing reactive power control in a regional power grid, which includes steps S10 to S40: Step S10, predicting the active output of the photovoltaic power station according to the light intensity, simulating the load fluctuation in the regional power grid and calculating the power flow, and obtaining the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station; Step S20, constructing a three-layer optimization control model according to the system network loss and the voltage deviation, wherein the three-layer optimization control model includes a system-level optimization control model, a data processing model, and a station-level optimization control model; Step S30, using an improved particle swarm algorithm to solve the system-level optimization control model to obtain a system optimization reference value, and performing data processing on the system optimization reference value through the data processing model to obtain a reactive power output reference value of the photovoltaic power station; Step S40, solving the station-level optimization control model according to the reactive output reference value to obtain the optimal output plan of each group of photovoltaic power generation units and static reactive generators in the photovoltaic power station, and performing reactive optimization control on the photovoltaic power station according to the optimal output plan.

[0019] The present invention provides a method for optimizing reactive power control of a regional power grid taking into account load fluctuation characteristics and uncertainty in the output of new energy sources. In this embodiment, the active output of a photovoltaic power station is first predicted according to the light intensity. The specific steps include: The light intensity prediction curve is divided into segments, and based on the area equivalence principle, the equivalent light intensity in the time interval corresponding to each segment is calculated; According to the equivalent light intensity, the active output prediction value of the photovoltaic power station corresponding to each optimized control period is determined.

[0020] In this embodiment, the idea of ​​curve segmentation is adopted. Aiming at the time-varying nature of illumination, the area equivalence principle is used to segment the sunlight intensity curve. The constant illumination intensity is used instead of the constantly changing illumination intensity for calculation. The dynamic optimization is converted into a static optimization problem at multiple time points, and the dynamic optimization of the reactive voltage of the photovoltaic power station is realized, so as to obtain the equivalent illumination intensity in each time interval. Then, the active output prediction value of the photovoltaic power station corresponding to each optimization control period can be determined according to the equivalent illumination intensity. The mathematical expression of the active output prediction value of the photovoltaic power station is: in, In the formula, Indicates the predicted active output value of the photovoltaic power station; Indicates the number of inverters in the photovoltaic power station; Indicates the efficiency of the photovoltaic inverter; Indicates the number of solar panels connected in series and parallel for each photovoltaic inverter; Indicates the maximum power point voltage of the photovoltaic panel under actual working conditions; Indicates the maximum power point current of the photovoltaic panel under actual working conditions; Indicates the maximum power point voltage of the photovoltaic panel under rated conditions; represents the Euler number; Indicates the actual light intensity; Indicates equivalent light intensity, its value is 1000 ; Indicates the maximum power point current of the photovoltaic panel under rated conditions.

[0021] The Monte Carlo method is used to simulate the load fluctuation in the regional power grid. Specifically, a large number of random numbers are generated to simulate the future load conditions. The Monte Carlo simulation formula is as follows: In the formula, Load(t) represents the load at time t, Load(t-1) represents the load at time t-1, and ε is a random number that follows a certain random distribution and is used to simulate load fluctuations. By continuously iterating this formula, the load fluctuation at future time points can be obtained.

[0022] By predicting the active output of the photovoltaic power station and simulating the load fluctuation of the regional power grid, the load value of each node and the size of the photovoltaic output can be obtained for 24 hours. Then, the load and photovoltaic power station are regarded as PQ nodes, and the power flow is calculated through Matpower to obtain the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station.

[0023] Based on the system network loss and voltage deviation, an optimization control model of the regional power grid is constructed. The specific steps include: Taking the minimization of the network loss of the system as the first objective function, taking the transformer ratio of each substation, the number of capacitor switching groups, the static VAR generator, and the reactive power output of the photovoltaic inverter of the photovoltaic power station as the control variables, and taking the power flow constraint, the node voltage constraint, the node power constraint, the power output constraint, the reactive compensation constraint, the transformer tap position constraint, the capacitor unit operation quantity constraint, the transformer tap operation constraint and the capacitor unit operation constraint as the first constraint condition, the system-level optimization control model is constructed; Based on the power flow calculation of the regional power grid, construct the data processing model; The station-level optimization control model is constructed by taking the minimization of the active power loss of the photovoltaic power station and the voltage deviation as the second objective function, and taking the reactive output constraint of the photovoltaic power generation unit and the access point voltage constraint as the second constraint condition.

[0024] In this embodiment, a three-layer optimization control framework is used to construct an optimization control model, wherein the first-layer optimization control model is a system-level optimization control model of the regional power grid, the second-layer optimization control module is a data processing model, and the third-layer optimization control model is a site-level optimization control module of the photovoltaic power station.

[0025] Specifically, the system-level optimization control model is a global reactive power optimization for the regional power grid. The model takes the minimization of system network loss as the objective function, and takes the transformer ratio, capacitor switching group number, static VAR generator SVG, and reactive power output of the photovoltaic inverter of each substation as the control variables, and takes the flow constraint, node voltage constraint, node power constraint, power output constraint, reactive power compensation constraint, transformer tap position constraint, capacitor unit operation quantity constraint, transformer tap operation constraint, and capacitor unit operation constraint as the constraint conditions. The data processing model further processes the output data of the system-level optimization control model, and the processed data will be passed to the station-level optimization control model. The station-level optimization control model is a station reactive power optimization for photovoltaic power stations, and the optimization results of the station-level optimization control model will be passed back to the system-level optimization control model, thereby realizing iterative optimization control of the regional power grid.

[0026] Among them, the objective function of the system-level optimization control model can be expressed as: In the formula, g ij and θ ij They represent the branch admittance and voltage phase difference between node i and node j, respectively, U i and U j They represent the voltages of node i and node j respectively, and N represents the number of nodes.

[0027] The reactive power optimization constraints of the system-level optimization control model can be divided into two types: equality and inequality. The equality constraint is the power flow constraint: Where P i and Q i Represent the active power and reactive power of node i respectively, G ij represents the conductance from node i to node j, B ij represents the susceptance from node i to node j, and H represents the set of nodes j connected to node i.

[0028] The inequality constraints include node voltage constraints, node power constraints, power output constraints, reactive power compensation constraints, transformer tap position constraints, capacitor unit operation quantity constraints, transformer tap operation constraints, and capacitor unit operation constraints. Node voltage constraints: Node power constraints: Power output constraints: Reactive power compensation constraints: Where U i,min and U i,max They represent the minimum and maximum voltages of node i, respectively. i,min and P i,max They represent the minimum and maximum active power of node i, Q i,min and Q i,max Respectively represent the minimum and maximum value of the reactive power of node i, P Gi represents the active power of the power supply connected to node i, P Gi,min and P Gi,max They represent the minimum and maximum active power of the power supply connected to node i, Qc,min and Q c,max Respectively represent the minimum and maximum values ​​of reactive power compensation capacity, Q c Indicates the reactive compensation capacity of the reactive compensation device.

[0029] Transformer tap OLTC location constraints: Capacitor units CBs commissioning quantity constraints: Transformer tap OLTC operation constraints: Capacitor units CBs operating constraints: In the formula, Indicates the lower limit of the transformer tap position, Indicates the transformer tap position. represents the upper limit of the transformer tap position, represents the lower limit of the number of capacitor units put into operation at node i, represents the number of capacitor units put into operation at node i, represents the upper limit of the number of capacitor units put into operation at node i, Indicates the transformer tap position at time t, Indicates the maximum gear difference allowed for the transformer tap position change within a single control cycle. represents the number of capacitor units put into operation at time t, Indicates the maximum number difference of capacitor units allowed to be put into operation in a single control cycle.

[0030] In this embodiment, the transformer ratio and the number of capacitor switching groups are obtained by day-ahead optimization, and a set of 24-hour SVG and photovoltaic inverter output reactive power are obtained by real-time optimization using an improved particle swarm algorithm to minimize the system network loss. The steps of using the improved particle swarm algorithm to solve the model include: The chaos algorithm is used to optimize the initial particle swarm parameters of the system-level optimization control model, and the inertia weight coefficient and the learning factor are set according to the inverse sigmoid function and the trigonometric function; The particle swarm is iteratively calculated, and during the iterative calculation process, the position of the particle is updated based on the rounding of discrete variables, and the current optimal particle is perturbed through Cauchy mutation until the preset iteration stop condition is reached to obtain the system optimization reference value.

[0031] Before explaining the above solution steps, we first briefly introduce the solution steps of the traditional particle swarm algorithm for the system-level optimization control model: (1) Input the original data, obtain the system node information and branch information, obtain the number of control variables and their respective value ranges, obtain the particle swarm size m, and the maximum number of iterations iter max And other parameters; (2) Calculate the objective function value. When the number of iterations iter = 0, each particle in the population is randomly assigned an initial position x under the control variable constraint. i and initial velocity v i For each particle in the population, the Newton-Raphson iteration method is used to calculate the power flow and network loss, and to determine whether the constraints such as node voltage and generator reactive output are violated.

[0032] (3) According to the flow calculation results, evaluate the fitness value F(x i ); Find the best individual of each particle, recorded as pBest, and the best individual in pBest is gBest; (4) Update counter iter=iter+1; (5) Update the inertia factor w; (6) Update the flight speed v, if v>v max , then v=v max ; If v<−v max , then v=−v max ; (7) Calculate the position of each particle. If a particle exceeds the search space in one dimension, restrict the particle to the boundary of the search space; (8) Apply the Newton-Raphson iteration method to calculate power flow and network loss, re-evaluate the fitness value of each particle, and determine whether to update the pBest of each particle and the optimal particle gBest of the entire population based on the fitness value of each particle; (9) Determine whether iter has reached the preset maximum number of iterations iter max , if yes, go to (10), otherwise go to (4); (10) Output the optimal solution (i.e., gBest after the last iteration), including the value of the control variables of the number of compensation capacitor groups and SVG compensation reactive capacity, as well as the network loss before and after optimization.

[0033] The reason why the improved particle swarm algorithm is used to solve the model in this embodiment is that the above-mentioned traditional particle swarm algorithm adopts a random selection method for the population position and velocity initialization, which cannot guarantee the traversal and diversity of all particles, and it is easy to fall into the dilemma of local optimal solution, resulting in the inability to obtain the best data for the optimization problem, affecting the operation effect of the model. Therefore, in this embodiment, the initial particle parameters are processed in combination with a chaotic sequence with good traversal to seek a better population for traversal. When initializing the particle swarm parameters, the chaotic algorithm is used for the first optimization, and the calculation equation is: Where, T i,m represents the mth chaotic variable of the ith particle, and a represents the control variable.

[0034] The improved particle swarm algorithm in this embodiment also processes discrete variables such as transformer ratio and capacitor switching group number, that is, rounding operation is performed when updating particles of discrete variables after the flight speed is updated to improve the operation speed: Where x(i,j) represents the position of the i-th particle in the j-th dimension, round represents the rounding function, and v(i,j) represents the velocity of the i-th particle in the j-th dimension.

[0035] Since the inertia weight coefficient and the learning factor will directly affect the update speed and accuracy of the particle swarm iteration, this embodiment takes into account that the standard particle swarm algorithm cannot take into account both the optimization speed and the optimization accuracy, and improves the inertia weight coefficient and the learning factor.

[0036] For the inertia weight coefficient, the larger the inertia weight coefficient w, the greater the particle speed, which is conducive to global optimization. The smaller the inertia weight, the smaller the particle speed, which is conducive to local optimization. Therefore, the inertia weight coefficient w should be kept larger in the early stage and smaller in the later stage, so as to converge quickly and accurately.

[0037] The inverse Sigmoid function refers to taking the inverse of the independent variable in the Sigmoid function, so the inverse Sigmoid function can be expressed as: Where k represents the function variable.

[0038] It can be seen that the value range of the inverse Sigmoid function is [0.5, 1]. The value is close to 1 in the early stage, the value decreases slowly, the value decreases rapidly in the middle stage, and tends to be stable in the later stage.

[0039] Considering that the algorithm falls into the local optimal problem in the later stage, the random number part is added to the formula. The numerical characteristics of trigonometric functions can be used to limit the value of the random number to prevent convergence. Therefore, in this embodiment, the inertia weight coefficient is set to: In the formula, k represents the number of iterations, k max represents the maximum number of iterations, and r represents a random number; The learning factor is set to: In the formula, c1 represents the individual acceleration factor, c2 represents the social acceleration factor; The range of learning factors c1 and c2 is [1,2]. This setting method is beneficial to the global traversal of particles in the early stage and to the convergence of the algorithm in the later stage, thus effectively ensuring the efficiency of the algorithm.

[0040] At the same time, in order to prevent particles from falling into a premature convergence state, this embodiment also introduces a Cauchy mutation operation to maintain particle diversity, wherein the particle perturbation method is as follows: In the formula, x i represents the position of the ith particle, Cauchy(0,1) represents the standard Cauchy perturbation random value, rand represents a random number, x min and x max Indicates the lower and upper limits of the particle position.

[0041] The improved particle swarm algorithm mentioned above is used to solve the system-level optimization scheduling model, thereby obtaining a set of 24-hour transformer ratios, capacitor switching groups, static VAR generator SVG and photovoltaic inverter output reactive power solutions that minimize system network losses. The output system optimization reference values ​​will be passed to the data processing model. Based on these system optimization reference values, the data processing model uses Matpower to perform power flow calculations on the regional power grid to obtain the reactive output reference values ​​of each photovoltaic power station. The data output by the data processing model will continue to be passed to the site-level optimization control model.

[0042] In this embodiment, the reactive output reference value of the photovoltaic power station is used as a known quantity, and the field-level optimization control model takes the minimization of the active power loss and voltage deviation of the photovoltaic power station as the objective function, that is, the minimum mean square error of the voltage at the access point of each group of photovoltaic power generation units PVGU and the minimum active power loss of the collector line in the station as the objective function: Where P loss Indicates the active power loss of the collector line in a photovoltaic power station.

[0043] The constraints include reactive power output constraints and access point voltage constraints: In the formula, represents the reactive power output of the ith photovoltaic power generation unit, and They represent the minimum and maximum reactive power output of the ith photovoltaic power generation unit, represents the access point voltage of the i-th photovoltaic power generation unit, and They respectively represent the lower limit and upper limit of the access point voltage of the i-th group of photovoltaic power generation units.

[0044] It can be seen that the above-mentioned station-level optimization control model is essentially a multi-objective optimization model. Therefore, this embodiment adopts a multi-objective genetic algorithm to solve and obtain a set of Pareto optimal solutions. In the actual process, it is also necessary to select the optimal compromise solution from a set of Pareto optimal solutions. Therefore, this embodiment also solves the optimal compromise solution based on the closest distance to the origin, that is, the reactive power output plan of each group of photovoltaic power generation units PVGU and static VAR generator SVG inside the photovoltaic power station during the period to be optimized. Notifying the implementation of the reactive power output plan for each group of photovoltaic power generation units PVGU and static VAR generator SVG in the photovoltaic power station can realize the reactive power optimization control of the photovoltaic power station, thereby realizing the refined control of the reactive power optimization of the regional power grid.

[0045] The following simulation experiment verifies the safety and economy of the reactive power optimization control method of the photovoltaic power station proposed in this paper. In this simulation experiment, for the regional power grid system, Matlab is used to optimize the reactive compensation of the improved IEEE9-node system, and MATPOWER6.1 is used to calculate the network loss by flow calculation. The benchmark capacity is 100MVA, and the capacity of SVG is set at 1 location with 50MVar, and the capacity of capacitors is set at 1 location with a total of three groups, each with a capacity of 10MVar. Then the particle swarm algorithm is used to solve the system. The 24h active network loss and voltage deviation of the system with photovoltaic power station participating in the reactive power optimization of the system and photovoltaic power station not participating in the reactive power optimization are compared. And the reactive power reference value of the photovoltaic power station is solved.

[0046] For the photovoltaic power station model, the capacity is set to 40MW and 4 collection lines. That is, it includes 40 groups of 1MW PVGUs, each group of PVGUs is composed of 2 groups of 500kW photovoltaic inverters in parallel, connected to the 10kV collection line through a 0.29kV / 10kV box transformer, and then stepped up by a 10kV / 110kV main transformer, and connected to the regional power grid through a long-distance transmission line.

[0047] For the above model, two scenarios are set. Scenario 1 is that the photovoltaic power station does not participate in the system reactive power optimization, and scenario 2 is that the photovoltaic power station participates in the system reactive power optimization. By comparing the solution results of whether photovoltaic power station participates in the reactive power optimization of the regional power grid, it can be obtained that the participation of photovoltaic power station in reactive power optimization can effectively reduce the system active network loss and node deviation. The optimization comparison results are shown in Table 1: Table 1 Comparison of optimization results in different scenarios Then, the grid states at 3:00, 9:00, 13:00 and 17:00 are used as the basic states for optimization calculation. After optimization and solution, the system-level reactive power voltage optimization control results are shown in Table 2 below: Table 2 System-level reactive power optimization control results The data of 13 points are taken as the basic data for station-level reactive power optimization. In order to simplify the calculation, one collector line is taken for analysis and it is considered that the reactive power is evenly distributed on the four collector lines. NSGA-Ⅱ is used to solve the problem and a set of Figure 2 The Pareto optimal solution shown in the figure is then selected, and then the extreme solution with the minimum voltage deviation and the minimum active network loss and the compromise solution in the Pareto optimal solution set are selected, as shown in Table 3: Table 3 Station-level reactive power optimization results In order to verify the effectiveness of the method provided by the present invention, two schemes were used for comparison in this simulation experiment. Scheme 1 adopts the method provided by the present invention, takes the minimum voltage deviation of the access point and the minimum active network loss as the optimization goals, and optimizes the upper reactive power reference value output by the system-level optimization control model and distributes it to each group of PVGU and SVG; Scheme 2 reserves the SVG capacity, and the reactive power is evenly distributed to PVGU first, and then to SVG when the PVGU capacity is insufficient.

[0048] The station active network losses obtained by the above two schemes are 0.247MW and 0.262MW respectively. The voltage per unit value of the PVGU access point of a single collector line is as follows: Figure 3 As shown, it can be seen that the reactive power optimization control method provided by the present invention can effectively reduce the active network loss and the voltage deviation of the PVGU access point.

[0049] The present embodiment provides a method for reactive power optimization control of a regional power grid. By studying the reactive power optimization problem of the regional power grid and taking into account the influence of load fluctuation and new energy output, the photovoltaic power station is involved in the reactive power optimization of the power grid, and a three-layer optimization control model is established. The refined control of the photovoltaic power station participating in the reactive power optimization of the power grid is achieved, thereby ensuring the safety and stability of the operation of the regional power grid.

[0050] See also Figure 4 Based on the same inventive concept, a second embodiment of the present invention proposes a regional power grid reactive power optimization control system, comprising: The data processing module 10 is used to predict the active power output of the photovoltaic power station according to the light intensity, simulate the load fluctuation in the regional power grid and calculate the power flow, and obtain the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station; A model building module 20, configured to build a three-layer optimization control model according to the system network loss and the voltage deviation, wherein the three-layer optimization control model includes a system-level optimization control model, a data processing model, and a station-level optimization control model; The optimization control module 30 is used to solve the system-level optimization control model by using an improved particle swarm algorithm to obtain a system optimization reference value, and to perform data processing on the system optimization reference value by using the data processing model to obtain a reactive power output reference value of the photovoltaic power station; The station-level optimization control model is solved according to the reactive output reference value to obtain the optimal output plan of each group of photovoltaic power generation units and static reactive generators in the photovoltaic power station, and reactive power optimization control is performed on the photovoltaic power station according to the optimal output plan.

[0051] Furthermore, the model building module 20 is also used to take the minimization of the system network loss as the first objective function, take the transformer ratio of each substation, the number of capacitor switching groups, the static VAR generator, and the reactive power output of the photovoltaic inverter of the photovoltaic power station as the control variables, and take the power flow constraint, the node voltage constraint, the node power constraint, the power output constraint, the reactive compensation constraint, the transformer tap position constraint, the capacitor unit operation quantity constraint, the transformer tap operation constraint and the capacitor unit operation constraint as the first constraint condition to build the system-level optimization control model; Based on the power flow calculation of the regional power grid, construct the data processing model; The station-level optimization control model is constructed by taking the minimization of the active power loss of the photovoltaic power station and the voltage deviation as the second objective function, and taking the reactive output constraint of the photovoltaic power generation unit and the access point voltage constraint as the second constraint condition.

[0052] The optimization control module 30 also includes an improved particle swarm algorithm module 31; The improved particle swarm algorithm module 31 is used to optimize the initial particle swarm parameters of the system-level optimization control model using a chaos algorithm, and set the inertia weight coefficient and the learning factor according to the inverse sigmoid function and the trigonometric function; The particle swarm is iteratively calculated, and during the iterative calculation process, the position of the particle is updated based on the rounding of discrete variables, and the current optimal particle is perturbed through Cauchy mutation until the preset iteration stop condition is reached to obtain the system optimization reference value.

[0053] The technical features and technical effects of the regional power grid reactive power optimization control system proposed in the embodiment of the present invention are the same as the method proposed in the embodiment of the present invention, and are not described in detail here. Each module in the above-mentioned regional power grid reactive power optimization control system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0054] In addition, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0055] See also Figure 5 , an internal structure diagram of a computer device in an embodiment, the computer device may specifically be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for optimizing the reactive power of a regional power grid is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, a trackball or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0056] It can be understood by those skilled in the art that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0057] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0058] In summary, an embodiment of the present invention proposes a method, system, device and storage medium for reactive power optimization control of a regional power grid. The method predicts the active output of a photovoltaic power station according to light intensity, simulates load fluctuations in the regional power grid and calculates power flow, and obtains the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station; constructs a three-layer optimization control model based on the system network loss and the voltage deviation, and the three-layer optimization control model includes a system-level optimization control model, a data processing model and a station-level optimization control model; uses an improved particle swarm algorithm to solve the system-level optimization control model to obtain a system optimization reference value, and uses the data processing model to process the system optimization reference value to obtain a reactive output reference value of the photovoltaic power station; solves the station-level optimization control model based on the reactive output reference value to obtain the optimal output plan of each group of photovoltaic power generation units and static reactive generators in the photovoltaic power station, and performs reactive power optimization control on the photovoltaic power station based on the optimal output plan. The present invention involves photovoltaic power stations in reactive power optimization of power grids, and establishes a three-layer optimization control model, thereby achieving refined control of photovoltaic power stations participating in reactive power optimization of power grids, thereby ensuring the safety and stability of regional power grid operation.

[0059] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for optimizing reactive power control of a regional power grid, characterized in that: include: The active output of the photovoltaic power station is predicted based on the light intensity, and the load fluctuation in the regional power grid is simulated and the power flow is calculated to obtain the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station; According to the system network loss and the voltage deviation, a three-layer optimization control model is constructed, wherein the three-layer optimization control model includes a system-level optimization control model, a data processing model, and a station-level optimization control model; The improved particle swarm algorithm is used to solve the system-level optimization control model to obtain a system optimization reference value, and the system optimization reference value is processed by the data processing model to obtain a reactive power output reference value of the photovoltaic power station; The station-level optimization control model is solved according to the reactive output reference value to obtain the optimal output plan of each group of photovoltaic power generation units and static reactive generators in the photovoltaic power station, and reactive power optimization control is performed on the photovoltaic power station according to the optimal output plan.

2. The method for optimizing reactive power control of a regional power grid according to claim 1, characterized in that: The step of predicting the active output of the photovoltaic power station according to the light intensity includes: The light intensity prediction curve is divided into segments, and based on the area equivalence principle, the equivalent light intensity in the time interval corresponding to each segment is calculated; According to the equivalent light intensity, the active output prediction value of the photovoltaic power station corresponding to each optimized control period is determined.

3. The method for optimizing reactive power control of a regional power grid according to claim 1, characterized in that: The step of constructing a three-layer optimization control model according to the system network loss and the voltage deviation includes: Taking the minimization of the network loss of the system as the first objective function, taking the transformer ratio of each substation, the number of capacitor switching groups, the static VAR generator, and the reactive power output of the photovoltaic inverter of the photovoltaic power station as the control variables, and taking the power flow constraint, the node voltage constraint, the node power constraint, the power output constraint, the reactive compensation constraint, the transformer tap position constraint, the capacitor unit operation quantity constraint, the transformer tap operation constraint and the capacitor unit operation constraint as the first constraint condition, the system-level optimization control model is constructed; Based on the power flow calculation of the regional power grid, construct the data processing model; The station-level optimization control model is constructed by taking the minimization of the active power loss of the photovoltaic power station and the voltage deviation as the second objective function, and taking the reactive output constraint of the photovoltaic power generation unit and the access point voltage constraint as the second constraint condition.

4. The method for optimizing reactive power control of a regional power grid according to claim 1, characterized in that: The step of using the improved particle swarm algorithm to solve the system-level optimization control model to obtain the system optimization reference value includes: The chaos algorithm is used to optimize the initial particle swarm parameters of the system-level optimization control model, and the inertia weight coefficient and the learning factor are set according to the inverse sigmoid function and the trigonometric function; The particle swarm is iteratively calculated, and during the iterative calculation process, the position of the particle is updated based on the rounding of discrete variables, and the current optimal particle is perturbed through Cauchy mutation until the preset iteration stop condition is reached to obtain the system optimization reference value.

5. The method for optimizing reactive power control of a regional power grid according to claim 4, characterized in that: The optimization calculation is expressed by the following formula: Where, T i,m represents the mth chaotic variable of the ith particle, and a represents the control variable; The inertia weight coefficient is expressed by the following formula: In the formula, k represents the number of iterations, k max represents the maximum number of iterations, and r represents a random number; The learning factor is expressed by the following formula: In the formula, c1 represents the individual acceleration factor, c2 represents the social acceleration factor; The following formula is used to represent the rounding of discrete variables: In the formula, x(i,j) represents the position of the i-th particle in the j-th dimension, round represents the rounding function, and v(i,j) represents the velocity of the i-th particle in the j-th dimension; The Cauchy variation is expressed by the following formula: In the formula, x i represents the position of the ith particle, Cauchy(0,1) represents the standard Cauchy perturbation random value, rand represents a random number, x min and x max Indicates the lower and upper limits of the particle position.

6. A regional power grid reactive power optimization control system, characterized in that: include: The data processing module is used to predict the active output of the photovoltaic power station according to the light intensity, simulate the load fluctuation in the regional power grid and calculate the power flow, and obtain the system network loss of the regional power grid and the voltage deviation of the photovoltaic power station; A model building module, used to build a three-layer optimization control model according to the system network loss and the voltage deviation, wherein the three-layer optimization control model includes a system-level optimization control model, a data processing model, and a station-level optimization control model; An optimization control module, used for solving the system-level optimization control model by using an improved particle swarm algorithm to obtain a system optimization reference value, and performing data processing on the system optimization reference value by using the data processing model to obtain a reactive power output reference value of the photovoltaic power station; The station-level optimization control model is solved according to the reactive output reference value to obtain the optimal output plan of each group of photovoltaic power generation units and static reactive generators in the photovoltaic power station, and reactive power optimization control is performed on the photovoltaic power station according to the optimal output plan.

7. The regional power grid reactive power optimization control system according to claim 6, characterized in that: The model building module is further used to take the minimization of the system network loss as the first objective function, take the transformer ratio of each substation, the number of capacitor switching groups, the static VAR generator, and the reactive power output of the photovoltaic inverter of the photovoltaic power station as the control variables, and take the power flow constraint, the node voltage constraint, the node power constraint, the power output constraint, the reactive compensation constraint, the transformer tap position constraint, the capacitor unit operation quantity constraint, the transformer tap operation constraint and the capacitor unit operation constraint as the first constraint condition to build the system-level optimization control model; Based on the power flow calculation of the regional power grid, construct the data processing model; The station-level optimization control model is constructed by taking the minimization of the active power loss of the photovoltaic power station and the voltage deviation as the second objective function, and taking the reactive output constraint of the photovoltaic power generation unit and the access point voltage constraint as the second constraint condition.

8. The regional power grid reactive power optimization control system according to claim 6, characterized in that: The optimization control module also includes an improved particle swarm algorithm module; The improved particle swarm algorithm module is used to optimize the initial particle swarm parameters of the system-level optimization control model using a chaos algorithm, and set the inertia weight coefficient and the learning factor according to the inverse sigmoid function and the trigonometric function; The particle swarm is iteratively calculated, and during the iterative calculation process, the position of the particle is updated based on the rounding of discrete variables, and the current optimal particle is perturbed through Cauchy mutation until the preset iteration stop condition is reached to obtain the system optimization reference value.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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