Distributed light storage power distribution network dispatching method and device, storage medium and equipment

Abstract

The application discloses a kind of distributed light storage power distribution network scheduling method, device, storage medium and equipment.Therein, the method comprises: obtaining the network structure and operating parameter of power distribution network;Based on the above network structure and the above operating parameter, construct target scheduling model, wherein the target scheduling model is used for the power scheduling based on current electricity demand;Using target genetic algorithm to process the target scheduling model, determine target scheduling scheme;Based on the target scheduling scheme, carry out power distribution network scheduling.The present application solves the technical problems that the existing power distribution network scheduling method has low calculation efficiency and cannot obtain optimal results.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method, apparatus, storage medium, and equipment for dispatching a distributed photovoltaic-storage distribution network. Background Technology

[0002] With the increasing use of new energy power generation in power systems, current planning typically requires forecasting solar radiation and load demand over a certain time interval, followed by control of various distributed power sources via dispatch commands. This process necessitates consideration of distribution network load balancing, network losses, environmental impact, and economic efficiency. How to minimize economic losses from network losses, reduce environmental pollution, and increase photovoltaic utilization while ensuring distribution network voltage stability is a key research area.

[0003] However, existing research methods for renewable energy distribution networks often pay less attention to line losses and focus too much on renewable energy utilization and pollutant emissions. Most only consider generator active power constraints and output-load balance, and generally use approximate calculations for line losses instead of real-time power flow calculations. This leads to inconsistencies between the control layer and the actual line conditions, affecting the actual load balance and causing frequency and voltage problems in the distribution network. Furthermore, existing methods suffer from premature convergence, converging to local maxima instead of finding the global optimum.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a distributed photovoltaic-storage distribution network scheduling method, apparatus, storage medium, and equipment to at least solve the technical problems of low computational efficiency and inability to obtain optimal results in existing distribution network scheduling methods.

[0006] According to one aspect of the present invention, a distributed photovoltaic-storage distribution network scheduling method is provided, comprising: acquiring the network structure and operating parameters of the distribution network; constructing a target scheduling model based on the network structure and operating parameters, wherein the target scheduling model is used for power scheduling based on current power demand; processing the target scheduling model using a target genetic algorithm to determine a target scheduling scheme; and performing distribution network scheduling based on the target scheduling scheme.

[0007] Optionally, the acquisition of the network structure and operating parameters of the distribution network includes: acquiring first capacity information and first location information of photovoltaic equipment in the distribution network, wherein the first capacity information is used to characterize the photovoltaic unit capacity of the photovoltaic equipment in the photovoltaic-storage distribution network, and the first location information is used to characterize the photovoltaic unit location of the photovoltaic equipment in the photovoltaic-storage distribution network; acquiring second capacity information and second location information of energy storage equipment in the distribution network, wherein the second capacity information is used to characterize the energy storage unit capacity of the energy storage equipment in the photovoltaic-storage distribution network, and the second location information is used to characterize the energy storage unit location of the energy storage equipment in the photovoltaic-storage distribution network; determining the network structure based on the first location information and the second location information; and determining the operating parameters based on the first capacity information and the second capacity information.

[0008] Optionally, the above-mentioned construction of the target scheduling model based on the above-mentioned network structure and the above-mentioned operating parameters includes: obtaining the daily electricity demand of the target area corresponding to the above-mentioned distribution network; determining the power supply data based on the above-mentioned network structure and the above-mentioned operating parameters; if the above-mentioned power supply data meets the above-mentioned daily electricity demand, then constructing a day-ahead scheduling model based on the above-mentioned power supply data and the above-mentioned daily electricity demand; and determining the above-mentioned day-ahead scheduling model as the above-mentioned target scheduling model.

[0009] Optionally, the above-mentioned day-ahead scheduling model, based on the above-mentioned power supply data and the above-mentioned daily electricity demand, includes: determining the optimization function based on the above-mentioned power supply data and the above-mentioned daily electricity demand; determining the constraints based on the above-mentioned network structure and the above-mentioned operating parameters; and determining the above-mentioned day-ahead scheduling model based on the above-mentioned optimization function and the above-mentioned constraints.

[0010] Optionally, the above-mentioned scheduling model based on the above-mentioned network structure and the above-mentioned operating parameters further includes: if the above-mentioned power supply data does not meet the above-mentioned daily power demand, then based on the above-mentioned power supply data and the above-mentioned daily power demand, a short-term scheduling model is constructed; and the above-mentioned short-term scheduling model is determined as the above-mentioned target scheduling model.

[0011] Optionally, the above-mentioned target scheduling model is processed using a target genetic algorithm to determine the target scheduling scheme, including: initializing the photovoltaic equipment and energy storage equipment in the distribution network to generate an initial population; selecting the initial population using a preset selection algorithm to obtain multiple target individuals; performing crossover and mutation processing on the multiple target individuals using a preset crossover and mutation algorithm to obtain crossover and mutated individuals; and performing power flow calculation on the crossover and mutated individuals to determine the target scheduling scheme.

[0012] According to another aspect of the present invention, a distributed photovoltaic-storage distribution network scheduling device is also provided, comprising: an acquisition module for acquiring the network structure and operating parameters of the distribution network; a construction module for constructing a target scheduling model based on the network structure and operating parameters, wherein the target scheduling model is used for power scheduling based on current power demand; a determination module for processing the target scheduling model using a target genetic algorithm to determine a target scheduling scheme; and a scheduling module for performing distribution network scheduling based on the target scheduling scheme.

[0013] According to another aspect of the present invention, a non-volatile storage medium is also provided, which stores a plurality of instructions adapted for a processor to load and execute any one of the above-described distributed photovoltaic-storage distribution network scheduling methods.

[0014] According to another aspect of the present invention, a processor is also provided, which is used to run a program, wherein the program is configured to execute any of the above-described distributed photovoltaic-storage-distribution network scheduling methods during runtime.

[0015] According to another aspect of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to execute any of the above-described distributed photovoltaic-storage-distribution network scheduling methods.

[0016] In this embodiment of the invention, the network structure and operating parameters of the distribution network are obtained; a target scheduling model is constructed based on the network structure and operating parameters, wherein the target scheduling model is used for power dispatching based on current power demand; a target genetic algorithm is used to process the target scheduling model to determine the target scheduling scheme; and distribution network dispatching is carried out based on the target scheduling scheme. This achieves the goal of establishing a mathematical model and obtaining an optimized solution through a genetic algorithm as the basis for optimal dispatching, thereby improving the accuracy of the optimization results and effectively reducing the time and economic losses caused by network losses in the dispatching of new energy distribution networks. This solves the technical problems of low computational efficiency and inability to obtain optimal results in existing distribution network dispatching methods. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a distributed photovoltaic-storage-distribution network scheduling method according to an embodiment of the present invention;

[0019] Figure 2This is a flowchart of an optional day-ahead optimization scheduling method for a distributed photovoltaic-storage distribution network according to an embodiment of the present invention;

[0020] Figure 3 This is a flowchart of an optional genetic algorithm according to an embodiment of the present invention;

[0021] Figure 4 This is an optional simulation object IEEE 33 system structure diagram according to an embodiment of the present invention;

[0022] Figure 5 This is a day-ahead planning curve of the active power output of the photovoltaic unit and the energy storage unit under an optional day-ahead optimization scheduling method according to an embodiment of the present invention;

[0023] Figure 6 This is a flowchart of an optional short-time optimization scheduling method for distributed photovoltaic-storage distribution networks according to an embodiment of the present invention;

[0024] Figure 7 This is a schematic diagram of the voltage of each node under an optional short-time optimization scheduling method for an IEEE 33 system at a certain moment, according to an embodiment of the present invention.

[0025] Figure 8 This is a flowchart of an optional distributed photovoltaic-storage distribution network optimization scheduling method according to an embodiment of the present invention;

[0026] Figure 9 This is a schematic diagram of the structure of a distributed photovoltaic energy storage distribution network dispatching device according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] According to an embodiment of the present invention, an embodiment of a distributed photovoltaic-storage distribution network scheduling method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] Figure 1 This is a flowchart of a distributed photovoltaic-storage-distribution network scheduling method according to an embodiment of the present invention, as follows: Figure 1 As shown, the method includes the following steps:

[0032] Step S102: Obtain the network structure and operating parameters of the distribution network;

[0033] Step S104: Based on the above network structure and the above operating parameters, construct a target scheduling model, wherein the target scheduling model is used to perform power scheduling based on the current power demand;

[0034] Step S106: Use the target genetic algorithm to process the above target scheduling model and determine the target scheduling scheme;

[0035] Step S108: Perform distribution network scheduling based on the above target scheduling scheme.

[0036] In this embodiment of the invention, the execution entity of the distributed photovoltaic-storage distribution network scheduling method provided in steps S102 to S108 is the distribution network scheduling system. The system is used to obtain the network structure and operating parameters of the distribution network. Based on the network structure and operating parameters, a target scheduling model is constructed, wherein the target scheduling model is used to perform power scheduling based on the current power demand. A target genetic algorithm is used to process the target scheduling model to determine the target scheduling scheme. Distribution network scheduling is performed based on the target scheduling scheme.

[0037] As an optional embodiment, such as Figure 2 The flowchart shown is a day-ahead optimization scheduling method for distributed photovoltaic-storage distribution networks. It obtains the configuration parameters of the distribution network system, performs photovoltaic output prediction, and constructs an economic and environmental day-ahead optimization scheduling model for the photovoltaic-storage distribution network. An improved genetic algorithm is used to solve the above optimization scheduling model, obtain the global optimal solution of the solved model, and complete the optimization scheduling of the distribution network system.

[0038] Through the embodiments of the present invention, based on the obtained distribution network structure and various operating parameters, with the objectives of minimizing the economic losses caused by network losses, maximizing the utilization rate of new energy sources, and minimizing the economic cost of scheduling, a mathematical model is established considering the power flow distribution and node voltage limitations of the actual distribution network, and an optimized solution is obtained through an improved genetic algorithm as the basis for optimal scheduling.

[0039] In one optional embodiment, the acquisition of the network structure and operating parameters of the distribution network includes: acquiring first capacity information and first location information of photovoltaic equipment in the distribution network; acquiring second capacity information and second location information of energy storage equipment in the distribution network; determining the network structure based on the first location information and the second location information; and determining the operating parameters based on the first capacity information and the second capacity information.

[0040] It should be noted that the first capacity information is used to characterize the photovoltaic unit capacity of the photovoltaic equipment in the photovoltaic-storage distribution network, and the first location information is used to characterize the location of the photovoltaic unit of the photovoltaic equipment in the photovoltaic-storage distribution network; the second capacity information is used to characterize the energy storage unit capacity of the energy storage equipment in the photovoltaic-storage distribution network, and the second location information is used to characterize the location of the energy storage unit of the energy storage equipment in the photovoltaic-storage distribution network.

[0041] As an optional embodiment, the acquired photovoltaic-storage distribution network operating parameters include: photovoltaic unit capacity and installation location; energy storage unit capacity and installation location; energy storage unit charge parameters; node location parameters; distribution network structure, including branch impedance, branch allowable capacity, and other branch parameters; node load parameters; maximum and minimum active and reactive power that generator nodes can withstand; maximum and minimum voltage that conventional nodes can withstand; time-of-use electricity price of the main grid to which the distribution network is connected; and local solar irradiance conditions.

[0042] In one optional embodiment, the above-mentioned construction of the target scheduling model based on the above-mentioned network structure and the above-mentioned operating parameters includes: obtaining the daily electricity demand of the target area corresponding to the above-mentioned distribution network; determining the power supply data based on the above-mentioned network structure and the above-mentioned operating parameters; if the above-mentioned power supply data meets the above-mentioned daily electricity demand, then constructing a day-ahead scheduling model based on the above-mentioned power supply data and the above-mentioned daily electricity demand; and determining the above-mentioned day-ahead scheduling model as the above-mentioned target scheduling model.

[0043] In one optional embodiment, the above-mentioned construction of the day-ahead scheduling model based on the above-mentioned power supply data and the above-mentioned daily electricity demand includes: determining an optimization function based on the above-mentioned power supply data and the above-mentioned daily electricity demand; determining constraints based on the above-mentioned network structure and the above-mentioned operating parameters; and determining the above-mentioned day-ahead scheduling model based on the above-mentioned optimization function and the above-mentioned constraints.

[0044] As an optional embodiment, the objective function of the aforementioned day-ahead optimization scheduling model for the photovoltaic-storage distribution network includes the economic losses caused by network losses, photovoltaic power consumption, and scheduling economic costs. The constraints include line power flow constraints, power balance constraints, and node voltage amplitude constraints. In this embodiment, optimization scheduling is performed at 1-hour time intervals.

[0045] Optionally, optimization objective 1 is to minimize the economic loss caused by network losses. Distribution network losses are obtained by solving the power flow equations:

[0046]

[0047]

[0048] Optionally, the network loss is then:

[0049]

[0050] Optionally, the total economic loss caused by network failure is:

[0051]

[0052] Optionally, optimization objective 2 is to maximize photovoltaic (PV) absorption. When PV absorption is maximized, the corresponding unutilized PV power is minimized.

[0053]

[0054] Optionally, optimization objective 3 is to minimize the economic cost of dispatching. The economic cost of dispatching consists of the electricity purchase behavior of the distribution network from the main grid and the dispatching cost of the energy storage power station unit.

[0055]

[0056] Optional constraints include line power flow constraints:

[0057] S bus,i,t ≤S bus,i,max i = 1, ..., N bus (7)

[0058] Power balance constraints:

[0059]

[0060] Node voltage amplitude constraints:

[0061] U i,min ≤U i,t ≤U i,max i = 1, ..., N bus (9)

[0062] Among them, P i,t Q i,t Let U be the line power at time t. i,t and U j,t These are the voltage magnitudes at node i and node j at time t, respectively, G ij and B ij θ represents the conductance and susceptance of the distribution network branch connecting nodes i and j. ij,t P is the voltage phase difference between node i and node j at time t. loss,t and P d,t These represent the distribution network loss and total distribution network load at time t, respectively, N. bus N pv and N ESS P represents the number of nodes in the distribution network, the number of photovoltaic units in the distribution network, and the number of energy storage units in the distribution network, respectively. pv,i,t and P pv,i,MPPT,t α and β represent the actual active power and the maximum active power under MPPT of the i-th photovoltaic unit at time t, respectively. i P represents the economic cost coefficient of the distribution network purchasing electricity from the main grid and the cost coefficient of the power regulation of the i-th energy storage unit, respectively. 0,t and Q 0,t P represents the power transmitted on the connection lines between the distribution network and the main grid at time t, i.e., the power between the slack node and the node connected to the slack node. ESS,i,t and Q ESS,i,t Let U represent the active power and reactive power of the i-th energy storage unit at time t, respectively. i,min and U i,max Let S represent the minimum and maximum allowable node voltages of the i-th node, respectively. bus,i,t S represents the apparent power of line i at time t; bus,i,max Let be the maximum power capacity of line i.

[0063] Optionally, considering the overall objective function and constraints, if the control signals of photovoltaic and energy storage in the distribution network are denoted as column vector X, then the day-ahead scheduling model for economic and environmental optimization of the photovoltaic-energy storage distribution network can be expressed as:

[0064] Min[M loss (X),PV waste (X),SE(X)]

[0065] st:e i(X)=0,i=1,…,M1,

[0066] h j (X)≤0,j=1,…,M2, (10)

[0067] Among them, e i (X) and h j (X) represents the i-th equality constraint and the j-th inequality constraint involved, respectively, and M1 and M2 are the number of equality constraints and inequality constraints, respectively.

[0068] Optionally, the economic and environmental optimization scheduling model of the photovoltaic-storage distribution network can be solved based on the objective function and constraints. Multi-objective optimization differs from single-objective optimization. While a single-objective optimization can find an optimal solution, multiple objectives may be competing with each other, such as minimizing economic losses from grid losses and maximizing photovoltaic power consumption in this embodiment. In such conflicting situations, the optimization algorithm often fails to obtain an optimal solution. In this case, multiple optimization objectives can be merged into a single optimization objective by setting weight coefficients for different objectives.

[0069] F(X)=ω1M loss (X)+ω2PV waste (X)+ω3SE(X) (11)

[0070] Among them, ω1, ω2, and ω3 are the weight values ​​of the three optimization objectives, which are adjusted according to the actual needs of the distribution network.

[0071] In an optional embodiment, the above-mentioned target scheduling model is processed using a target genetic algorithm to determine the target scheduling scheme, including: initializing the photovoltaic equipment and energy storage equipment in the distribution network to generate an initial population; selecting the initial population using a preset selection algorithm to obtain multiple target individuals; performing crossover and mutation processing on the multiple target individuals using a preset crossover and mutation algorithm to obtain crossover mutated individuals; and performing power flow calculation on the crossover mutated individuals to determine the target scheduling scheme.

[0072] As an optional embodiment, such as Figure 3 The flowchart of the genetic algorithm shown illustrates the specific solution process of the improved genetic algorithm for the economic and environmental optimization scheduling model of the photovoltaic-storage distribution network: First, determine the weights of the optimization objectives and synthesize multiple optimization objectives into a single objective. Second, initialize the population: initialize the relevant parameters of the improved genetic algorithm, and simultaneously initialize the power of the energy storage units and photovoltaic units in the distribution network, representing possible solutions to the problem. Third, define the output of the energy storage units and photovoltaic units using real-number encoding, setting all photovoltaic capacity to emit active power, and energy storage units to emit both active and reactive power. The control signal, denoted as column vector X, can then be represented as:

[0073] [x pv,1 ,…,x pv,n ;x ESS,1,P ,…,x ESS,n,P ;x ESS,1,Q ,…,x ESS,n,Q ] T (12)

[0074] Where, x pv,i x represents the ratio of the active power output of the i-th photovoltaic unit in each time interval to the active power of the photovoltaic unit at that time during the MPPT. ESS,i,P and x ESS,i,Q This represents the ratio of the active power and reactive power output by the i-th energy storage unit to its energy storage capacity in each time interval.

[0075] Furthermore, the fitness of the initial population is calculated, with constraints serving as a penalty function in the fitness calculation. Since some variables in the constraints, such as the node voltages of the distribution network, are intermediate variables, they cannot be constrained through initialization or forced assignment once they exceed the limits, unlike variables such as the power output values ​​of energy storage stations and photovoltaic power sources. Constraints can only be implemented indirectly. For example, directly discarding individuals that do not meet the constraints, and setting their fitness function to zero once an individual exceeds the voltage limit, making them no longer possible to be selected, is effective, but it eliminates the excellent characteristics of many individuals outside the feasible region, reducing population diversity and decreasing convergence speed and quality. Alternatively, specific genetic operators can be used to ensure that all individuals are within the feasible region, but this method can only handle linear constraints, while the distribution network voltage optimization problem is nonlinear in both its objective function and constraints. Therefore, a penalty function is chosen to implement constraints. Once an individual exceeds the voltage limit, it is penalized by reducing its fitness function value to decrease the probability of that individual being selected. Therefore, the fitness function is defined as follows:

[0076] Fit(X)=-F(X)-εE(X) (13)

[0077] Where ε is the adaptive penalty coefficient, which increases with the number of algorithm iterations. E(X) is the penalty function, which is a positive constant when the current solution does not meet the constraints.

[0078] Furthermore, a selection operation is performed on the population to obtain the individuals eligible for the next step: the selection adopts a combination of tournament and elite selection: the two individuals with the best fitness are retained, and the remaining individuals are selected through a tournament to obtain the individuals that can proceed to the next step.

[0079] Furthermore, crossover and mutation operations are performed on the population to obtain the next generation of individuals. In this embodiment, the crossover and mutation operations are performed using an adaptive simulated binary crossover method and an adaptive random mutation method, respectively. Simulated Binary Crossover (SBX) mainly simulates the working principle of single-point crossover in a binary string and implements it on a chromosome composed of real numbers. SBX has performed well in seeking local optima in multiple tests, especially in solving optimization problems with high dimensionality. Since the voltage optimization problem of the distribution network has many variables, SBX is suitable as a crossover operator.

[0080] First, determine the distribution factor:

[0081]

[0082] Secondly, the offspring are calculated using the distribution coefficient and the parent generation:

[0083]

[0084] Where p1 and p2 are the parent generations to be crossed, c1 and c2 are the generated offspring, β is the distribution factor, and u c η is a random number between 0 and 1. c This is the cross-distribution index.

[0085] Optionally, to improve the adaptability of the algorithm and avoid excessively long convergence time, the crossover distribution index is determined adaptively to ensure that the search can be carried out with optimal efficiency in both the early and late stages of evolution, thereby improving the convergence speed and accuracy of the algorithm.

[0086] In this embodiment, the adaptive method uses the ratio of the number of generations in the population to the number of generations in the threshold. When the ratio is small, it means that the genetic algorithm has just started, and the crossover index should be larger to speed up the convergence speed in the early stage of the algorithm; while when the ratio is large, it means that the algorithm is nearing its end, and the crossover index should be reduced to avoid failing to find the global optimal solution due to the excessively large search step size.

[0087] Optionally, this embodiment uses a random mutation method to operate on the offspring after crossover.

[0088] c1 = p1 + u m *η m (16)

[0089] Optionally, the offspring c1 that undergoes mutation is determined by the parent p1 and a random number u between 0 and 1. m With adaptive coefficient of variation η m The product is obtained by multiplying and then adding directly. Using this simplest mutation operator can speed up the algorithm's computation during mutation operations without affecting mutation efficiency.

[0090] Optionally, to improve the adaptability of the algorithm and avoid excessively long convergence time, the coefficient of variation is also determined adaptively to ensure that the search can be carried out with optimal efficiency in both the early and late stages of evolution, thereby improving the convergence speed and accuracy of the algorithm.

[0091] In this embodiment, the adaptive mutation coefficient is similar to the crossover index, and the adaptive method uses the ratio of the population generation to the threshold generation. When the ratio is small, it means that the genetic algorithm has just started, and the mutation coefficient should be larger to speed up the convergence speed in the early stage of the algorithm; while when the ratio is large, it means that the algorithm is nearing its end, and the mutation coefficient should be reduced to avoid failing to find the global optimal solution due to an excessively large search step size.

[0092] Furthermore, based on the offspring obtained from crossover and mutation, the fitness of all individuals in the new population is calculated. This process is repeated for all individuals in the new population, calculating and determining the fitness of each individual according to the constraints and power flow. The selection-crossover-mutation steps are repeated until a solution satisfying the convergence condition is obtained.

[0093] Furthermore, the dispatch instructions for photovoltaic and energy storage in the distribution network are determined according to the solution.

[0094] In this embodiment, the distribution network states and operating parameters used in the scheduling method can be actual grid parameters or arbitrary simulated distribution network parameters, such as the IEEE 33 system. This simulation example uses the IEEE 33 system, and adds 6 energy storage units and 6 photovoltaic units to it, such as... Figure 4 The diagram shows the system structure of the simulation object I EEE33. When using... Figure 4 As an example, the output is as follows Figure 5 The results show the day-ahead planning curves of the active power output of the photovoltaic unit and the energy storage unit under the day-ahead optimization scheduling method.

[0095] In this embodiment, based on an improved genetic algorithm and the obtained distribution network structure and various operating parameters, with the goals of minimizing economic losses caused by network losses, maximizing the utilization rate of new energy sources, and minimizing greenhouse gas emissions, a mathematical model is established considering the power flow distribution and node voltage limitations of the actual distribution network. The improved genetic algorithm is used to obtain an optimized solution as the basis for the optimal day-ahead scheduling plan. This accelerates the convergence speed of the optimization algorithm and improves the accuracy of the optimization results, effectively reducing the time required for new energy distribution network scheduling and the economic losses caused by network losses.

[0096] As an optional implementation, based on the obtained distribution network structure and various operating parameters, with the goals of minimizing real-time network loss, maximizing renewable energy utilization, and minimizing scheduling economic costs, a mathematical model is established, taking into account the actual power flow distribution and node voltage limitations of the distribution network, and an optimized solution is obtained through an improved genetic algorithm as the basis for optimal scheduling.

[0097] Optional, such as Figure 6 The method for short-time optimal scheduling of a distributed photovoltaic-storage distribution network includes: obtaining the configuration parameters of the distribution network system and constructing a short-time optimal scheduling model for the economic and environmental aspects of the photovoltaic-storage distribution network; using an improved genetic algorithm to solve the above-mentioned optimal scheduling model, obtaining the global optimal solution of the solved model, and completing the short-time optimal scheduling of the distribution network system.

[0098] In this embodiment, the simulation example used is the IEEE 33 system. Under this example, the simulation results of the node voltages are as follows: Figure 7 As shown.

[0099] In one alternative embodiment, such as Figure 8 The flowchart of the distributed photovoltaic-storage distribution network optimization scheduling method shown above, based on the above network structure and the above operating parameters, constructs a scheduling model, and further includes: if the above power supply data does not meet the above daily electricity demand, then based on the above power supply data and the above daily electricity demand, a short-term scheduling model is constructed; and the above short-term scheduling model is determined as the above target scheduling model.

[0100] Through the above steps, an improved genetic algorithm can be used to obtain an optimized solution based on the obtained distribution network structure and various operating parameters. The goal is to minimize real-time network losses, maximize the utilization rate of new energy sources, and minimize greenhouse gas emissions. The algorithm takes into account the power flow distribution and node voltage limitations of the actual distribution network, establishes a mathematical model, and obtains the optimized solution through the improved genetic algorithm as a supplement when day-ahead planning fails, effectively improving the safety of the method.

[0101] Example 2

[0102] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described distributed photovoltaic-storage-distribution network scheduling method is also provided. Figure 9 This is a schematic diagram of the structure of a distributed photovoltaic-storage-distribution network dispatching device according to an embodiment of the present invention, as shown below. Figure 9 As shown, the above-mentioned device includes: an acquisition module 90, a construction module 92, a determination module 94, and a scheduling module 96, wherein:

[0103] Module 90 is used to acquire the network structure and operating parameters of the power distribution network.

[0104] Module 92 is used to construct a target scheduling model based on the above network structure and the above operating parameters, wherein the target scheduling model is used to perform power scheduling based on the current power demand;

[0105] Module 94 is used to process the above target scheduling model using a target genetic algorithm to determine the target scheduling scheme;

[0106] The scheduling module 96 is used to perform distribution network scheduling based on the above-mentioned target scheduling scheme.

[0107] It should be noted that the above-mentioned acquisition module 90, construction module 92, determination module 94 and scheduling module 96 correspond to steps S102 to S108 in embodiment 1. The four modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in embodiment 1 above.

[0108] It should be noted that the preferred implementation of this embodiment can be found in the relevant description in Embodiment 1, and will not be repeated here.

[0109] According to embodiments of the present invention, an embodiment of a computer-readable storage medium is also provided. Optionally, in this embodiment, the computer-readable storage medium can be used to store the program code executed by the distributed photovoltaic-storage distribution network scheduling method provided in Embodiment 1.

[0110] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0111] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining the network structure and operating parameters of the distribution network; constructing a target scheduling model based on the network structure and operating parameters, wherein the target scheduling model is used for power dispatching based on current power demand; processing the target scheduling model using a target genetic algorithm to determine a target scheduling scheme; and performing distribution network dispatching based on the target scheduling scheme.

[0112] Optionally, the aforementioned computer-readable storage medium is configured to store program code for performing the following steps: obtaining first capacity information and first location information of photovoltaic equipment in the aforementioned distribution network, wherein the first capacity information is used to characterize the photovoltaic unit capacity of the photovoltaic equipment in the photovoltaic-storage distribution network, and the first location information is used to characterize the photovoltaic unit location of the photovoltaic equipment in the photovoltaic-storage distribution network; obtaining second capacity information and second location information of energy storage equipment in the aforementioned distribution network, wherein the second capacity information is used to characterize the energy storage unit capacity of the energy storage equipment in the photovoltaic-storage distribution network, and the second location information is used to characterize the energy storage unit location of the energy storage equipment in the photovoltaic-storage distribution network; determining the network structure based on the first location information and the second location information, and determining the operating parameters based on the first capacity information and the second capacity information.

[0113] Optionally, the aforementioned computer-readable storage medium is configured to store program code for performing the following steps: obtaining the daily electricity demand of the target area corresponding to the aforementioned distribution network; determining power supply data based on the aforementioned network structure and the aforementioned operating parameters; if the aforementioned power supply data meets the aforementioned daily electricity demand, then constructing a day-ahead scheduling model based on the aforementioned power supply data and the aforementioned daily electricity demand; and determining the aforementioned day-ahead scheduling model as the aforementioned target scheduling model.

[0114] Optionally, the aforementioned computer-readable storage medium is configured to store program code for performing the following steps: determining an optimization function based on the aforementioned power supply data and the aforementioned daily electricity demand; determining constraints based on the aforementioned network structure and the aforementioned operating parameters; and determining the aforementioned day-ahead scheduling model based on the aforementioned optimization function and the aforementioned constraints.

[0115] Optionally, the aforementioned computer-readable storage medium is configured to store program code for performing the following steps: if the aforementioned power supply data does not meet the aforementioned daily electricity demand, then based on the aforementioned power supply data and the aforementioned daily electricity demand, a short-term scheduling model is constructed; and the aforementioned short-term scheduling model is determined as the aforementioned target scheduling model.

[0116] Optionally, the aforementioned computer-readable storage medium is configured to store program code for performing the following steps: initializing the photovoltaic equipment and energy storage equipment in the aforementioned distribution network to generate an initial population; selecting the aforementioned initial population using a preset selection algorithm to obtain multiple target individuals; performing crossover and mutation processing on the multiple aforementioned target individuals using a preset crossover and mutation algorithm to obtain crossover and mutated individuals; and performing power flow calculation on the aforementioned crossover and mutated individuals to determine the aforementioned target scheduling scheme.

[0117] According to embodiments of the present invention, an embodiment of a processor is also provided. Optionally, in this embodiment, the computer-readable storage medium described above can be used to store the program code executed by the distributed photovoltaic-storage distribution network scheduling method provided in Embodiment 1 above.

[0118] This application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: obtaining the network structure and operating parameters of the distribution network; constructing a target scheduling model based on the network structure and operating parameters, wherein the target scheduling model is used for power dispatching based on current power demand; processing the target scheduling model using a target genetic algorithm to determine a target scheduling scheme; and performing distribution network dispatching based on the target scheduling scheme.

[0119] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: obtaining the network structure and operating parameters of the distribution network; constructing a target scheduling model based on the network structure and operating parameters, wherein the target scheduling model is used for power dispatching based on current power demand; processing the target scheduling model using a target genetic algorithm to determine a target scheduling scheme; and performing distribution network dispatching based on the target scheduling scheme.

[0120] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0121] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0123] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0126] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for dispatching a distributed photovoltaic-storage distribution network, characterized in that, include: Obtain the network structure and operating parameters of the power distribution network; Based on the network structure and the operating parameters, a target scheduling model is constructed, wherein the target scheduling model is used to perform power scheduling based on the current power demand; The target scheduling model is processed using a target genetic algorithm to determine the target scheduling scheme. Distribution network scheduling is performed based on the target scheduling scheme. The step of obtaining the network structure and operating parameters of the distribution network includes: obtaining first capacity information and first location information of photovoltaic equipment in the distribution network, wherein the first capacity information is used to characterize the photovoltaic unit capacity of the photovoltaic equipment in the photovoltaic-storage distribution network, and the first location information is used to characterize the photovoltaic unit location of the photovoltaic equipment in the photovoltaic-storage distribution network; obtaining second capacity information and second location information of energy storage equipment in the distribution network, wherein the second capacity information is used to characterize the energy storage unit capacity of the energy storage equipment in the photovoltaic-storage distribution network, and the second location information is used to characterize the energy storage unit location of the energy storage equipment in the photovoltaic-storage distribution network; determining the network structure based on the first location information and the second location information; and determining the operating parameters based on the first capacity information and the second capacity information. The step of constructing a target scheduling model based on the network structure and the operating parameters includes: obtaining the daily electricity demand of the target area corresponding to the distribution network; determining power supply data based on the network structure and the operating parameters; if the power supply data meets the daily electricity demand, determining an optimization function based on the power supply data and the daily electricity demand; determining constraints based on the network structure and the operating parameters; determining a day-ahead scheduling model based on the optimization function and the constraints; and determining the day-ahead scheduling model as the target scheduling model; if the power supply data does not meet the daily electricity demand, constructing a short-term scheduling model based on the power supply data and the daily electricity demand; and determining the short-term scheduling model as the target scheduling model.

2. The method according to claim 1, characterized in that, The step of using a target genetic algorithm to process the target scheduling model and determine the target scheduling scheme includes: The photovoltaic and energy storage devices in the power distribution network are initialized to generate an initial population. A preset selection algorithm is used to select from the initial population to obtain multiple target individuals; A preset crossover mutation algorithm is used to perform crossover mutation processing on multiple target individuals to obtain crossover mutated individuals; Power flow calculations are performed on the crossover variant individuals to determine the target scheduling scheme.

3. A distributed photovoltaic-storage-distribution network dispatching device, characterized in that, include: The acquisition module is used to acquire the network structure and operating parameters of the power distribution network. A construction module is used to construct a target scheduling model based on the network structure and the operating parameters, wherein the target scheduling model is used to perform power scheduling based on the current power demand; The determination module is used to process the target scheduling model using a target genetic algorithm to determine the target scheduling scheme; The scheduling module is used to perform distribution network scheduling based on the target scheduling scheme; The acquisition module is further configured to acquire first capacity information and first location information of photovoltaic equipment in the distribution network, wherein the first capacity information is used to characterize the photovoltaic unit capacity of the photovoltaic equipment in the photovoltaic-storage distribution network, and the first location information is used to characterize the photovoltaic unit location of the photovoltaic equipment in the photovoltaic-storage distribution network; acquire second capacity information and second location information of energy storage equipment in the distribution network, wherein the second capacity information is used to characterize the energy storage unit capacity of the energy storage equipment in the photovoltaic-storage distribution network, and the second location information is used to characterize the energy storage unit location of the energy storage equipment in the photovoltaic-storage distribution network; determine the network structure based on the first location information and the second location information, and determine the operating parameters based on the first capacity information and the second capacity information; The construction module is further configured to: acquire the daily electricity demand of the target area corresponding to the distribution network; determine power supply data based on the network structure and the operating parameters; if the power supply data meets the daily electricity demand, determine an optimization function based on the power supply data and the daily electricity demand; determine constraints based on the network structure and the operating parameters; determine a day-ahead scheduling model based on the optimization function and the constraints; determine the day-ahead scheduling model as the target scheduling model; if the power supply data does not meet the daily electricity demand, construct a short-term scheduling model based on the power supply data and the daily electricity demand; and determine the short-term scheduling model as the target scheduling model.

4. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the distributed photovoltaic-storage distribution network scheduling method according to any one of claims 1 to 2.

5. A processor, characterized in that, The processor is used to run a program, wherein the program is configured to execute the distributed photovoltaic energy storage distribution network scheduling method according to any one of claims 1 to 2 when running.

6. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to execute the distributed photovoltaic-storage-distribution network scheduling method according to any one of claims 1 to 2.

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