Power control method, device and program product for distributed power supply of power distribution network

Abstract

The application relates to a power control method and device of a distributed power supply of a power distribution network, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring an operation cost corresponding to the distributed power supply; solving a dynamic economic dispatch function according to the operation cost corresponding to the distributed power supply, to obtain a target power of the distributed power supply corresponding to the operation cost; wherein the dynamic economic dispatch function is determined according to a voltage limit value corresponding to an active power of a distributed unit in the power distribution network; and controlling the output power of the distributed power supply according to the target power. The method can improve the scheduling accuracy.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a power control method, device, computer equipment, storage medium and computer program product for distributed power sources in power distribution networks. Background Technology

[0002] With the development of microgrid technology, microgrid technology provides a feasible route for small-scale integration of distributed generation, which to some extent solves the grid connection problem of distributed generation.

[0003] Typically, after distributed generation is extensively integrated into the distribution network, dynamic economic dispatch of new energy sources (distributed generation described later) is required. One approach is to employ a grid dynamic economic dispatch optimization method based on the high-dimensional dependence of wind farms. Specifically, this involves using a Copula function to describe the high-dimensional dependence of power output from multiple wind farms, obtaining the joint distribution of power output from these farms, and then introducing a two-stage compensated stochastic optimization algorithm to decouple and solve the conventional and random variables in the dynamic economic dispatch model. Furthermore, an improved two-stage compensated stochastic optimization algorithm is introduced using a recursive dynamic multiple linear regression method based on global least squares. Through dynamic updates of the expected compensation cost, the iterative solution of the two-stage model converges rapidly, thereby completing the grid dynamic economic dispatch.

[0004] However, the scheduling accuracy of the above methods is low. Summary of the Invention

[0005] Therefore, it is necessary to provide a power control method, device, computer equipment, computer-readable storage medium, and computer program product for distributed power sources in distribution networks that can improve dispatch accuracy in response to the above-mentioned technical problems.

[0006] In a first aspect, this application provides a power control method for distributed generation in a distribution network, comprising:

[0007] Obtain the operating cost of distributed power sources;

[0008] Based on the operating cost corresponding to the distributed power source, the dynamic economic dispatch function is solved to obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network;

[0009] The output power of the distributed power source is controlled according to the target power.

[0010] In one embodiment, the method for obtaining the dynamic economic scheduling function includes:

[0011] Obtain the objective function for the expected value of the full distribution across multiple regions;

[0012] Chance-constrained programming is performed on the objective function of the multi-region fully distributed expected value to obtain the objective constraint function;

[0013] The objective construction conditions are determined based on the objective constraint function and the multi-region fully distributed expected value objective function.

[0014] The dynamic economic scheduling function is determined based on the objective constraint function and the objective construction conditions.

[0015] In one embodiment, the dynamic economic scheduling function satisfies the following formula:

[0016] The dynamic economic dispatch function is minE[first value]; where E is the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed unit and the active power output constraint value of the distributed unit in each stage; the third value is the product of the sum of the fourth values ​​of each stage, the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-regional full distribution expected value objective function.

[0017] In one embodiment, the operating cost corresponding to the distributed power source is: the ratio of the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing the distributed power source to the actual cost coefficient of electricity purchase and sale in the upper-level power grid, and the corresponding operating cost in the area.

[0018] In one embodiment, the active power output constraint value of the distributed unit is: the ratio of a fifth value to a sixth value; the fifth value is the difference between the active power of the distributed unit and the voltage limit value corresponding to the active power of the distributed unit; the sixth value is the difference between the upper limit value and the lower limit value of the active power of the distributed unit.

[0019] In one embodiment, the method for obtaining the voltage limit value corresponding to the active power of the distributed unit includes:

[0020] Based on the relationship between the active power of the distributed generator set and its upper and lower limits, the voltage limit value corresponding to the active power of the distributed generator set is determined.

[0021] Secondly, this application provides a power control device for distributed generation in a power distribution network, the device comprising:

[0022] The first acquisition module is used to acquire the operating cost corresponding to the distributed power source.

[0023] The second acquisition module is used to solve the dynamic economic dispatch function based on the operating cost corresponding to the distributed power source, and obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network;

[0024] The control module is used to control the output power of the distributed power source according to the target power.

[0025] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0026] Obtain the operating cost of distributed power sources;

[0027] Based on the operating cost corresponding to the distributed power source, the dynamic economic dispatch function is solved to obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network;

[0028] The output power of the distributed power source is controlled according to the target power.

[0029] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0030] Obtain the operating cost of distributed power sources;

[0031] Based on the operating cost corresponding to the distributed power source, the dynamic economic dispatch function is solved to obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network;

[0032] The output power of the distributed power source is controlled according to the target power.

[0033] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0034] Obtain the operating cost of distributed power sources;

[0035] Based on the operating cost corresponding to the distributed power source, the dynamic economic dispatch function is solved to obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network;

[0036] The output power of the distributed power source is controlled according to the target power.

[0037] The aforementioned power control method, device, computer equipment, storage medium, and computer program product for distributed generation in the distribution network obtains the operating cost corresponding to the distributed generation, solves the dynamic economic dispatch function based on the operating cost, obtains the target power of the distributed generation corresponding to the operating cost, and then controls the output power of the distributed generation based on the target power. Compared with the prior art, the dynamic economic dispatch function of this application is determined based on the voltage limit value corresponding to the active power of the distributed unit. Therefore, when considering the power of the distributed unit, the economic dispatch of the distributed generation can improve the dispatch accuracy. Attached Figure Description

[0038] Figure 1 This is an application environment diagram of a power control method for distributed generation in a distribution network, as shown in one embodiment.

[0039] Figure 2 This is a flowchart illustrating a power control method for distributed generation in a distribution network in one embodiment.

[0040] Figure 3 This is a schematic diagram of the process for obtaining the target power according to an improved traditional particle swarm optimization algorithm in one embodiment;

[0041] Figure 4 This is a flowchart illustrating how the dynamic economic scheduling function is obtained in one embodiment;

[0042] Figure 5 This is a schematic diagram of the probability density function in one embodiment;

[0043] Figure 6 This is a structural block diagram of a power control device for distributed generation in a distribution network, as shown in one embodiment.

[0044] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] Currently, significant progress has been made in research on distributed generation. Microgrid technology offers a feasible route for small-scale integration of distributed generation. However, the control objectives of microgrids and distributed generation are inconsistent, and there are capacity limitations. Therefore, this technology cannot yet truly achieve large-scale integration of distributed generation. Continuous innovation in microgrid technology has led to significant advancements in power electronics and energy storage technologies, which have also addressed the grid connection issues of distributed generation to some extent. Nevertheless, the distribution network still faces problems such as insufficient absorption of intermittent energy sources, limited overall interaction between load, grid, and source, poor network optimization capabilities, unscientific dispatching methods, and weak compatibility, making large-scale utilization of renewable energy still very difficult.

[0047] Typically, after large-scale integration of distributed generation into the distribution network, dynamic economic dispatch of new energy sources (such as distributed generation) is required. One approach is to employ a grid dynamic economic dispatch optimization method based on the high-dimensional dependence of wind farms. Specifically, this involves using a Copula function to describe the high-dimensional dependence of power output from multiple wind farms, obtaining the joint distribution of power output from these farms, and then introducing a two-stage compensated stochastic optimization algorithm to decouple and solve the conventional and random variables in the dynamic economic dispatch model. Furthermore, a recursive dynamic multiple linear regression method based on global least squares is introduced to improve the two-stage compensated stochastic optimization algorithm. Through dynamic updates of the expected compensation cost, the iterative solution of the two-stage model converges rapidly, thereby completing the grid dynamic economic dispatch.

[0048] However, existing methods do not handle the out-of-bounds situation of state variables within the objective function during the dynamic economic dispatch of new energy sources, which leads to the voltage of the distribution network exceeding the upper limit. In other words, there is no limit on the voltage of distributed units, resulting in low accuracy of dynamic economic dispatch of the power grid.

[0049] The power control method for distributed generation in distribution networks provided in this application can be applied to multi-region, fully distributed distribution network scenarios. "Multi-region" refers to various target areas, including isolated power grids that are not connected to the main grid or have weak connections, as well as those operating globally. "Fully distributed" refers to complete distribution, specifically including aspects such as diesel generator ramping, line transmission, maximum interactive transmission power between the main grid and regional distribution networks, and energy storage units.

[0050] Specifically, such as Figure 1 As shown in the embodiments of this application, the power control method for distributed generation in a distribution network can be applied to, for example... Figure 1 The application environment shown is as follows. The distribution network includes a power distribution system, and the processor 104 in the power distribution system 102 can be used to implement economic dispatch of distributed power sources in the distribution network. Specifically, after obtaining the operating cost corresponding to the distributed power source, the processor 104 can solve the dynamic economic dispatch function based on the operating cost to obtain the target power of the distributed power source corresponding to the operating cost. Then, based on this target power, the processor 104 controls the output power of the distributed power source to achieve economic dispatch of the distributed power source.

[0051] In one embodiment, such as Figure 2 As shown, a power control method for distributed generation in a distribution network is provided, which is then applied to... Figure 1 Taking processor 104 as an example, the explanation includes the following steps:

[0052] S202, obtain the operating cost corresponding to the distributed power source.

[0053] In this embodiment, the operating cost corresponding to the distributed power source is: the ratio of the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing the distributed power source to the actual cost coefficient of electricity purchase and sale in the upper-level power grid, and the corresponding operating cost in the area.

[0054] For example, if I represents the operating cost of distributed power sources. SE C represents the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing distributed generation. PE This represents the actual cost coefficient of electricity purchase and sale by the upper-level power grid. This represents the corresponding operating cost within the region.

[0055] Among them, In this context, diesel generators only require calculations regarding fuel costs; however, energy storage batteries, photovoltaic cells, and wind power systems need to have their depreciation, operating, and maintenance costs calculated. For example, the relevant cost coefficients for energy storage batteries, wind power systems, and photovoltaic cells are as follows:

[0056] The actual operation and maintenance cost coefficient for energy storage batteries is 0.132 yuan / kW·h, and the installation cost is 16.9 million yuan / kW; the depreciation period for energy storage batteries is 15 years. The operation and maintenance cost coefficient for wind power generation systems is 0.045 yuan / kW·h; the actual installation cost for wind power generation systems is 23.75 million yuan / kW; the depreciation period for wind power generation systems is 10 years. The actual operation and maintenance cost coefficient for photovoltaic cells is 0.0096 yuan / kW·h; the actual installation cost for photovoltaic cells is 66.5 million yuan / kW; the depreciation period for photovoltaic cells is 20 years.

[0057] S204. Based on the operating cost of the distributed power source, solve the dynamic economic scheduling function to obtain the target power of the distributed power source corresponding to the operating cost.

[0058] In this embodiment, the constraints for economic dispatch can be selected using Monte Carlo simulation. These constraints include those related to diesel generator ramp-up, transmission line operation, maximum interactive transmission power between the main grid and regional distribution networks, and energy storage units. Furthermore, combining the selected constraints with the operating costs of distributed power sources, the dynamic economic dispatch function can be solved using an improved traditional particle swarm optimization (PSO) algorithm to obtain the target power. The improved PSO algorithm has a strong global optimization selection mechanism, avoiding interference from local minima, exhibiting strong robustness, ensuring algorithm convergence, facilitating accurate calculation of feasible solutions, and offering short computation time and ease of operation.

[0059] This application combines genetic algorithms with particle swarm optimization (PSO) to improve the traditional PSO algorithm. The steps for improving the traditional PSO algorithm include:

[0060] Step 1: After initializing the particle's attributes—size, position, velocity, inertial weight, and number of iterations—determine the particle's mutation rate.

[0061] Step 2: Determine the optimal point for each particle and indicate the "best" particle position;

[0062] Step 3: Replace the inertia weights with a contraction factor to update the particle weights;

[0063] Step 4: Update the particle mutation rate and update the position using a genetic algorithm;

[0064] Step 5: Calculate the particles and forces, recalculate the optimal position of the particles, compare the two optimal positions, and select the better position;

[0065] Step 6: Determine the stopping condition. If it is met, stop the iteration; otherwise, go to step 3 and continue iterating.

[0066] Furthermore, when using the improved traditional particle swarm optimization algorithm to solve the dynamic economic scheduling function, the parameters are first initialized to determine the optimal points of the particles and the population. Then, a genetic algorithm is used for updating. After that, the results are compared with the mutation rate to determine whether mutation is needed and whether the conditions are met. Then, it is decided whether to continue iterating or terminate the algorithm. The result after the algorithm is terminated is the target power.

[0067] In one embodiment, such as Figure 3As shown, a flowchart illustrating the process of obtaining the target power based on an improved traditional particle swarm optimization algorithm is provided, specifically as follows:

[0068] In this application, the collected information is the power of the distributed power source. Based on the collected power, the operating cost of the distributed power source can be determined. This operating cost is used as the input to the improved traditional particle swarm optimization algorithm. Particle swarm optimization (PSO) parameters are read in, and the particle swarm velocity and position are initialized.

[0069] By initializing the power flow calculation, the fitness function values ​​of each particle are determined, which in turn determines the constraints corresponding to the solution of the dynamic economic scheduling function. The quality of the particles is evaluated based on their initial fitness, and the initial individual optima and global optima are saved. Here, "particle" refers to the solution of the dynamic economic scheduling function. Evaluating the quality of particles based on their initial fitness and saving the initial individual optima and global optima evaluates the degree to which the solution of the dynamic economic scheduling function matches the function. By saving the initial solutions, iterative optimization can begin.

[0070] A new population is generated by selecting all particles using the Hybrid Particle Swarm Optimization (HPSO) algorithm. The velocity and position of the population are updated using a linearly decreasing weight and linearly adjusted learning factor strategy, and bounds checks are performed. Bounds checks are performed to verify whether the solution of the dynamic economic scheduling function satisfies the constraints.

[0071] Through power flow calculation, out-of-bounds penalties are applied, and the individual optimal position and global optimal value of each particle are updated. Particle fitness is then sorted; high-performing particles undergo minor perturbations, while low-performing particles are reinitialized, updating their individual and global optimal values. High-performing particles refer to those whose solutions to the dynamic economic scheduling function are close to the optimal solution. The updates to individual and global optimal values ​​pertain to the solution used for optimization iterations.

[0072] The algorithm determines whether the termination condition is met. If the termination condition is met, the target power is obtained. If the termination condition is not met, the algorithm continues to select all particles based on the HPSO algorithm to obtain a new population until the termination condition is met. The output result is the target power.

[0073] It can be understood that the above content on obtaining the target power based on the improved traditional particle swarm algorithm is actually the content on finding the optimal solution of the dynamic economic scheduling function using the improved traditional particle swarm algorithm, and this optimal solution is the target power.

[0074] S206 controls the output power of the distributed power source according to the target power.

[0075] In this application, the output power of the distributed power source can be adjusted to the target power, thereby realizing the economical dispatch of the distributed power source.

[0076] In summary, this application obtains the operating cost corresponding to the distributed power source, solves the dynamic economic dispatch function based on the operating cost, obtains the target power of the distributed power source corresponding to the operating cost, and then controls the output power of the distributed power source based on the target power. Compared with the prior art, the dynamic economic dispatch function of this application is determined based on the voltage limit value corresponding to the active power of the distributed unit. Therefore, when considering the power of the distributed unit, the economic dispatch of the distributed power source can improve the dispatch accuracy.

[0077] In one embodiment, such as Figure 4 The diagram illustrates a process for obtaining a dynamic economic scheduling function, including the following steps:

[0078] S402, Obtain the objective function for the expected value of the full distribution across multiple regions.

[0079] When performing opportunity-constrained planning in dynamic economic scheduling with multi-regional fully distributed properties, this application suggests that the extreme value problem in scheduling can be reduced to a problem of multi-regional fully distributed nonlinear programming, as specifically expressed in the first formula described below:

[0080]

[0081] Where x represents the decision, that is, the optimization objective corresponding to the dynamic economic dispatch of the active distribution network. Each optimization objective corresponds to an objective function. There can be multiple optimization objectives, such as total system cost, grid operation reliability, grid power quality, etc. In this application, the optimization objective is the power of distributed generation. Let represent the objective function of the multi-regional fully distributed expected value, ξ represent a random variable, α represent the given confidence level under constraints, β represent the confidence level corresponding to the multi-objective function of active distribution network dynamic economic dispatch, and f(x, ξ) represent... The corresponding extremum function, where i represents the actual number of objective functions, g i (x, ξ) represents the extreme value function corresponding to the multi-objective function of active distribution network dynamic economic dispatch at other confidence levels, Pr(·) represents the true probability of the event, and p represents the maximum value corresponding to the active distribution network dynamic economic dispatch multi-objective function when calculated according to the actual number of events. express The probability of an extreme function occurring exists when the confidence level is at least β.

[0082] According to the first formula, a necessary condition for decision x to be a feasible solution is: gi The probability that (x, ξ) ≤ 0 needs to be greater than α, i = 1, 2, ..., p. For x, f(x, ξ) is a random variable, and its function form and random parameters are independent. Figure 5 As shown, φ can be used f(x，ξ) Let f(x, ξ) be the probability density function of f(x, ξ), then it satisfies of The number is multiple. When for When solving, f(x, ξ) is The corresponding extremum function. Therefore, the objective function for the expected value of the multi-region fully distributed distribution can be directly equivalent to the second formula, which satisfies the following formula:

[0083]

[0084] in, Let f represent the objective function for the expected value of a multi-region universally distributed system. The corresponding extreme value function. Furthermore, the objective constraint function can be obtained by performing chance-constrained programming on the objective function of the fully distributed expected value in multiple regions.

[0085] S404. Determine the objective construction conditions based on the objective constraint function and the multi-region fully distributed expected value objective function.

[0086] Specifically, the conditions for constructing the target include the third formula and the fourth formula. The third formula satisfies the following equation:

[0087]

[0088] The fourth formula satisfies the following formula:

[0089]

[0090] in, Let E[·] denote the objective function for the expected value of the multi-region universal distribution, and let f(x, ξ) denote the objective constraint function. The corresponding extremum function, Let f represent the m-th equivalent objective function, and β1 and β2 represent f respectively. i The confidence level corresponding to (x, ξ).

[0091] S406. Determine the dynamic economic scheduling function based on the objective constraint function and the objective construction conditions.

[0092] In this embodiment, the cost of operation and maintenance of distributed power sources includes the cost of energy storage devices and the cost of dispatchable generators. Therefore, dynamic economic dispatch is implemented by considering the maintenance problem of distributed power sources and the optimization problem of active distribution networks. The dispatch aims to maximize the efficiency of renewable energy utilization and pay attention to the energy interaction costs between regional distribution networks and the main grid. Based on these considerations, a dynamic economic dispatch function is constructed under opportunity-constrained programming. Specifically, according to the objective constraint function and the objective construction conditions, a corresponding dynamic economic dispatch function is established, which satisfies the following formula: Dynamic economic dispatch function = minE [first value].

[0093] Where E[·] represents the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed unit and the active power output constraint value of the distributed unit in each stage; the third value is the sum of the fourth values ​​of each stage, the product of the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-region fully distributed expected value objective function.

[0094] Among them, the active power output constraint value of the distributed unit is: the ratio of the fifth value to the sixth value; the fifth value is: the difference between the active power of the distributed unit and the voltage limit value corresponding to the active power of the distributed unit; the sixth value is: the difference between the upper limit value and the lower limit value of the active power of the distributed unit.

[0095] Specifically, if p represents the active power output constraint value of the distributed unit. n This represents the active power of the distributed generation unit. This indicates the voltage limit value corresponding to the active power of the distributed generation unit. This can be the lower or upper limit of the voltage corresponding to the active power of the distributed generator unit. This represents the lower limit of the active power of the distributed generation unit. This represents the upper limit of the active power of the distributed unit. λ p Let N1 represent the penalty factor corresponding to the actual active power output constraint of the distributed generation unit, and N1 represent the total number of distributed generation units.

[0096] Specifically, the voltage limit value corresponding to the active power of the distributed generation unit can be determined based on the relationship between the active power of the distributed generation unit and its upper and lower limits. Satisfy the following formula:

[0097]

[0098] Specifically, f represents The corresponding extremum function, This represents the operating cost of distributed power sources. C PE This represents the actual cost coefficient of electricity purchase and sale by the upper-level power grid. I represents the corresponding operating cost within the region. SE This represents the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing distributed generation. This represents the coefficient of actual electricity purchase and sale cost from the upper-level power grid, corresponding to the operating costs within the region. The third value represents the actual cost coefficient of electricity purchase and sale in the region corresponding to the operating cost of the distribution network containing distributed generation. The third value then satisfies the following formula:

[0099]

[0100] Where N1 represents the total number of distributed generation, and T represents the grid dispatch level. The grid dispatch level can include first-level dispatch, second-level dispatch, third-level dispatch and fourth-level dispatch. First-level dispatch is the national power dispatch and communication center, second-level dispatch is the provincial dispatch agency, third-level dispatch is the regional dispatch agency, and fourth-level dispatch is the county-level dispatch agency.

[0101] Based on the above, the dynamic economic scheduling function satisfies the following formula:

[0102]

[0103]

[0104] It is understandable that in the dynamic economic scheduling function, the penalty function is used to increase... This is used to handle out-of-bounds situations of state variables within the objective function, preventing the voltage of the active distribution network from exceeding the upper limit and keeping the voltage within the upper limit, thereby improving the accuracy of dynamic economic dispatch of the active distribution network and ensuring the safe operation of the power grid.

[0105] In summary, Figure 4In the illustrated embodiment, by obtaining the objective function of the multi-region fully distributed expected value, and based on the objective constraint function and the objective function of the multi-region fully distributed expected value, the objective construction conditions are determined. Then, based on the objective constraint function and the objective construction conditions, the dynamic economic scheduling function is determined. Based on the operating cost corresponding to the distributed power source, the dynamic economic scheduling function is solved to obtain the target power of the distributed power source corresponding to the operating cost. Then, based on the target power, the output power of the distributed power source is controlled. Compared with the prior art, the dynamic economic scheduling function of this application is determined based on the voltage limit value corresponding to the active power of the distributed unit. Therefore, when considering the power of the distributed unit, the scheduling accuracy can be improved when realizing the economic scheduling of the distributed power source.

[0106] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0107] Based on the same inventive concept, this application also provides a power control device for a distributed power source in a distribution network, used to implement the power control method for the distributed power source in the distribution network described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the power control device for a distributed power source in a distribution network provided below can be found in the limitations of the power control method for the distributed power source in the above text, and will not be repeated here.

[0108] In one embodiment, such as Figure 6 As shown, a power control device for distributed generation in a power distribution network is provided, comprising: a first acquisition module 602, a second acquisition module 604, and a control module 606, wherein:

[0109] The first acquisition module 602 is used to acquire the operating cost corresponding to the distributed power source.

[0110] The second acquisition module 604 is used to solve the dynamic economic dispatch function based on the operating cost of the distributed power source to obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network.

[0111] The control module 606 is used to control the output power of the distributed power source according to the target power.

[0112] In one embodiment, the second acquisition module 604 is further configured to acquire a multi-region fully distributed expected value objective function; perform chance constraint programming on the multi-region fully distributed expected value objective function to obtain an objective constraint function; determine the objective construction conditions based on the objective constraint function and the multi-region fully distributed expected value objective function; and determine the dynamic economic scheduling function based on the objective constraint function and the objective construction conditions.

[0113] In one embodiment, the dynamic economic dispatch function satisfies the following formula: Dynamic economic dispatch function = minE[first value]; E is the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed units and the active power output constraint value of the distributed units in each stage; the third value is the product of the sum of the fourth values ​​of each stage, the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-region fully distributed expected value objective function.

[0114] In one embodiment, the operating cost corresponding to the distributed power source is the ratio of the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing the distributed power source to the actual cost coefficient of electricity purchase and sale in the upper-level power grid, and the corresponding operating cost in the area.

[0115] In one embodiment, the active power output constraint value of the distributed unit is: the ratio of the fifth value to the sixth value; the fifth value is the difference between the active power of the distributed unit and the voltage limit value corresponding to the active power of the distributed unit; the sixth value is the difference between the upper limit value and the lower limit value of the active power of the distributed unit.

[0116] In one embodiment, the second acquisition module 604 is further configured to determine the voltage limit value corresponding to the active power of the distributed unit based on the relationship between the active power of the distributed unit and the upper limit and lower limit of the active power of the distributed unit.

[0117] The various modules in the power control device of the aforementioned distributed power sources in the power distribution network can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0118] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores dynamic economic dispatch functions. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a power control method for distributed generation in a power distribution network.

[0119] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0120] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0121] Obtain the operating cost of distributed power sources;

[0122] Based on the operating cost of distributed generation, the dynamic economic dispatch function is solved to obtain the target power of distributed generation corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of distributed units in the distribution network;

[0123] Control the output power of the distributed power source according to the target power.

[0124] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a multi-region fully distributed expected value objective function; performing chance-constrained programming on the multi-region fully distributed expected value objective function to obtain an objective constraint function; determining the objective construction conditions based on the objective constraint function and the multi-region fully distributed expected value objective function; and determining the dynamic economic scheduling function based on the objective constraint function and the objective construction conditions.

[0125] In one embodiment, when the processor executes the computer program, it further implements the following steps: the dynamic economic scheduling function satisfies the following formula: dynamic economic scheduling function = minE[first value]; E is the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed unit and the active power output constraint value of the distributed unit in each stage; the third value is the product of the sum of the fourth values ​​of each stage, the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-region fully distributed expected value objective function.

[0126] In one embodiment, when the processor executes the computer program, it further implements the following steps: the operating cost corresponding to the distributed power source is: the ratio of the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing the distributed power source to the actual cost coefficient of electricity purchase and sale in the upper-level power grid, and the corresponding operating cost in the area.

[0127] In one embodiment, when the processor executes the computer program, it further implements the following steps: the active power output constraint value of the distributed unit is the ratio of the fifth value to the sixth value; the fifth value is the difference between the active power of the distributed unit and the voltage limit value corresponding to the active power of the distributed unit; the sixth value is the difference between the upper limit value and the lower limit value of the active power of the distributed unit.

[0128] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the voltage limit value corresponding to the active power of the distributed unit based on the relationship between the active power of the distributed unit and the upper limit and lower limit of the active power of the distributed unit.

[0129] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0130] Obtain the operating cost of distributed power sources;

[0131] Based on the operating cost of distributed generation, the dynamic economic dispatch function is solved to obtain the target power of distributed generation corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of distributed units in the distribution network;

[0132] Control the output power of the distributed power source according to the target power.

[0133] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a multi-region fully distributed expected value objective function; performing chance-constrained programming on the multi-region fully distributed expected value objective function to obtain an objective constraint function; determining the objective construction conditions based on the objective constraint function and the multi-region fully distributed expected value objective function; and determining the dynamic economic scheduling function based on the objective constraint function and the objective construction conditions.

[0134] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the dynamic economic scheduling function satisfies the following formula: dynamic economic scheduling function = minE[first value]; E is the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed unit and the active power output constraint value of the distributed unit in each stage; the third value is the product of the sum of the fourth values ​​of each stage, the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-region fully distributed expected value objective function.

[0135] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the operating cost corresponding to the distributed power source is: the ratio of the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing the distributed power source to the actual cost coefficient of electricity purchase and sale in the upper-level power grid, and the corresponding operating cost in the area.

[0136] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the active power output constraint value of the distributed unit is the ratio of the fifth value to the sixth value; the fifth value is the difference between the active power of the distributed unit and the voltage limit value corresponding to the active power of the distributed unit; the sixth value is the difference between the upper limit value and the lower limit value of the active power of the distributed unit.

[0137] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the voltage limit value corresponding to the active power of the distributed unit based on the relationship between the active power of the distributed unit and the upper limit and lower limit of the active power of the distributed unit.

[0138] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0139] Obtain the operating cost of distributed power sources;

[0140] Based on the operating cost of distributed generation, the dynamic economic dispatch function is solved to obtain the target power of distributed generation corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of distributed units in the distribution network;

[0141] Control the output power of the distributed power source according to the target power.

[0142] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a multi-region fully distributed expected value objective function; performing chance-constrained programming on the multi-region fully distributed expected value objective function to obtain an objective constraint function; determining the objective construction conditions based on the objective constraint function and the multi-region fully distributed expected value objective function; and determining the dynamic economic scheduling function based on the objective constraint function and the objective construction conditions.

[0143] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the dynamic economic scheduling function satisfies the following formula: dynamic economic scheduling function = minE[first value]; E is the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed unit and the active power output constraint value of the distributed unit in each stage; the third value is the product of the sum of the fourth values ​​of each stage, the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-region fully distributed expected value objective function.

[0144] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the operating cost corresponding to the distributed power source is: the ratio of the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing the distributed power source to the actual cost coefficient of electricity purchase and sale in the upper-level power grid, and the corresponding operating cost in the area.

[0145] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the active power output constraint value of the distributed unit is the ratio of the fifth value to the sixth value; the fifth value is the difference between the active power of the distributed unit and the voltage limit value corresponding to the active power of the distributed unit; the sixth value is the difference between the upper limit value and the lower limit value of the active power of the distributed unit.

[0146] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the voltage limit value corresponding to the active power of the distributed unit based on the relationship between the active power of the distributed unit and the upper limit and lower limit of the active power of the distributed unit.

[0147] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0148] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are 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.

[0149] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power control method for distributed generation in a power distribution network, characterized in that, include: Obtain the operating cost of distributed power sources; Based on the operating cost corresponding to the distributed power source, the dynamic economic dispatch function is solved to obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network; The output power of the distributed power source is controlled according to the target power. The method for obtaining the dynamic economic scheduling function includes: Obtain the objective function for the expected value of the full distribution across multiple regions; Chance-constrained programming is performed on the objective function of the multi-region fully distributed expected value to obtain the objective constraint function; The objective construction conditions are determined based on the objective constraint function and the multi-region fully distributed expected value objective function. The dynamic economic scheduling function is determined based on the objective constraint function and the objective construction conditions; the dynamic economic scheduling function satisfies the following formula: The dynamic economic dispatch function = minE[first value]; E[·] is the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed unit and the active power output constraint value of the distributed unit in each stage; the third value is the product of the sum of the fourth values ​​of each stage, the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-regional full distribution expected value objective function.

2. The method according to claim 1, characterized in that, The operating cost corresponding to the distributed power source is: the ratio of the actual cost coefficient of electricity purchase and sale in the corresponding area of ​​the distribution network containing the distributed power source to the actual cost coefficient of electricity purchase and sale in the upper-level power grid, and the corresponding operating cost in the area.

3. The method according to claim 1, characterized in that, The active power output constraint value of the distributed generator set is: the ratio of the fifth value to the sixth value; the fifth value is the difference between the active power of the distributed generator set and the voltage limit value corresponding to the active power of the distributed generator set; the sixth value is the difference between the upper limit value and the lower limit value of the active power of the distributed generator set.

4. The method according to claim 3, characterized in that, The method for obtaining the voltage limit value corresponding to the active power of the distributed generator set includes: Based on the relationship between the active power of the distributed generator set and its upper and lower limits, the voltage limit value corresponding to the active power of the distributed generator set is determined.

5. The method according to claim 1, characterized in that, The objective function for the multi-region fully distributed expected value satisfies the following formula: in, The decision represents the optimization objective corresponding to the dynamic economic dispatch of the active distribution network. Each optimization objective has a corresponding objective function. There can be multiple optimization objectives, and the optimization objective is the power of distributed generation. This represents the objective function for the expected value of a multi-regional, universally distributed system. Represents a random variable. This represents a given confidence level under constraints. This represents the confidence level corresponding to the multi-objective function of dynamic economic dispatching of the active distribution network. express The corresponding extremum function, This indicates the actual number of objective functions. This represents the extreme value function corresponding to the multi-objective function of active distribution network dynamic economic dispatch at other confidence levels. This represents the maximum value corresponding to the multi-objective function of dynamic economic dispatch of active distribution network when calculated according to the actual number of objectives. express At the lowest confidence level The probability that a corresponding extreme function occurs.

6. A power control device for distributed generation in a power distribution network, characterized in that, The device includes: The first acquisition module is used to acquire the operating cost corresponding to the distributed power source. The second acquisition module is used to solve the dynamic economic dispatch function based on the operating cost corresponding to the distributed power source, and obtain the target power of the distributed power source corresponding to the operating cost; wherein, the dynamic economic dispatch function is determined based on the voltage limit value corresponding to the active power of the distributed units in the distribution network; A control module is used to control the output power of the distributed power source according to the target power. The second acquisition module is further configured to: acquire a multi-region fully distributed expected value objective function; perform chance-constrained programming on the multi-region fully distributed expected value objective function to obtain an objective constraint function; determine objective construction conditions based on the objective constraint function and the multi-region fully distributed expected value objective function; and determine the dynamic economic scheduling function based on the objective constraint function and the objective construction conditions; the dynamic economic scheduling function satisfies the following formula: The dynamic economic dispatch function = minE[first value]; E[·] is the objective constraint function, the first value is the sum of the second value and the third value of each stage; the second value is the product of the penalty factor corresponding to the actual active power output constraint of the distributed unit and the active power output constraint value of the distributed unit in each stage; the third value is the product of the sum of the fourth values ​​of each stage, the actual cost coefficient of the power purchase and sale of the upper-level power grid corresponding to the operating cost in the region, and the actual cost coefficient of the power purchase and sale of the corresponding region of the distribution network containing distributed power sources corresponding to the operating cost in the region; the fourth value is the value obtained by solving the operating cost corresponding to the distributed power source according to the extreme value function corresponding to the multi-regional full distribution expected value objective function.

7. The apparatus according to claim 6, characterized in that, The second acquisition module is further configured to: Based on the relationship between the active power of the distributed generator set and its upper and lower limits, the voltage limit value corresponding to the active power of the distributed generator set is determined.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

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

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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