Distribution method, device and equipment of power grid load, medium and product

Abstract

The invention discloses a power grid load distribution method, device and equipment, a medium and a product. The method comprises the following steps: acquiring output limit data of each generator set in a target power grid, and generating an input population according to the output limit data of each generator set; performing a preset number of iteration operations on individuals in the input population, and determining an output population of each iteration; and if the number of iterations reaches a preset number of iterations, stopping iteration, and determining a load distribution result of the target power grid according to the output population obtained by the last iteration. According to the technical scheme, the problem of nonlinear power grid load distribution is solved, the fitness function is constructed based on the fire coal characteristics, the valve point effect and the power balance constraint of each generator set in the target power grid, and the load distribution result is determined through the differential evolution model, so that the calculation time is saved, and the effectiveness and the reliability of the distribution result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and in particular to a method, device, equipment, medium and product for distributing power grid load. Background Art

[0002] With the development of industrialization and the improvement of people's living standards, electricity, as the main energy source for industrial production and daily life, is increasingly in demand. Under the condition of high demand for electricity, it is urgent to adjust load distribution, improve the imbalance of electricity at different times, and maximize the utilization of resources.

[0003] At present, the existing power grid load distribution methods mainly include power grid load distribution based on mathematical programming and power grid load distribution based on modern heuristic algorithms. The existing technology usually uses quadratic functions to describe the objective function of power grid load distribution, and solves the objective function based on Lagrange method, linear programming, interior point method and dynamic programming method to obtain the power grid load distribution result. Although the existing technology is efficient, it is limited to solving continuous and differentiable objective functions and cannot handle nonlinear power grid load distribution problems. At the same time, the existing power grid load distribution method has a long calculation time and slow convergence speed, and is prone to fall into a local optimal solution. Therefore, a power grid load distribution method is needed to solve the nonlinear problem in power grid load distribution, save calculation time, and improve the effectiveness and reliability of the distribution results. Summary of the invention

[0004] The present invention provides a method, device, equipment, medium and product for distributing power grid load to solve the problem of nonlinear power grid load distribution. A fitness function is constructed based on the coal-fired characteristics, valve point effect and power balance constraints of each generator set in the target power grid. The load distribution result is determined through a differential evolution model, which is beneficial to saving calculation time and improving the effectiveness and reliability of the distribution result.

[0005] According to one aspect of the present invention, a method for distributing power grid load is provided, the method comprising:

[0006] Obtain output limit data of each generator set in the target power grid, and generate an input population according to the output limit data of each generator set;

[0007] Performing a preset number of iteration operations on individuals in the input population to determine the output population of each iteration; wherein the iteration operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal combustion characteristics, valve point effect and power balance constraints of each generator set;

[0008] If the number of iterations reaches the preset number of iterations, the iteration is terminated, and the load distribution result of the target power grid is determined according to the output population obtained in the last iteration.

[0009] According to another aspect of the present invention, there is provided a device for distributing loads of a power grid, the device comprising:

[0010] An input population generation module is used to obtain output limit data of each generator set in the target power grid, and generate an input population according to the output limit data of each generator set;

[0011] An output population determination module is used to perform a preset number of iterative operations on individuals in the input population to determine the output population of each iteration; wherein the iterative operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal-fired characteristics, valve point effect and power balance constraints of each generator set;

[0012] The allocation result determination module is used to terminate the iteration if the number of iterations reaches a preset number of iterations, and determine the load allocation result of the target power grid according to the output population obtained from the last iteration.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for distributing power grid loads as described in any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for distributing power grid loads described in any embodiment of the present invention when executed.

[0016] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for distributing power grid loads according to any embodiment of the present invention is implemented.

[0017] The technical solution of the embodiment of the present invention is to obtain the output limit data of each generator set in the target power grid, and generate an input population according to the output limit data of each generator set; perform a preset number of iteration operations on the individuals in the input population to determine the output population of each iteration; wherein the iteration operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal-fired characteristics, valve point effects and power balance constraints of each generator set; if the number of iterations reaches the preset number of iterations, the iteration is terminated, and the load distribution result of the target power grid is determined according to the output population obtained from the last iteration. This technical solution solves the nonlinear power grid load distribution problem, constructs a fitness function based on the coal-fired characteristics, valve point effects and power balance constraints of each generator set in the target power grid, and determines the load distribution result through a differential evolution model, which is conducive to saving calculation time and improving the effectiveness and reliability of the distribution result.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a flow chart of a method for distributing power grid loads provided according to Embodiment 1 of the present invention;

[0021] Figure 2 is a structural schematic diagram of a power grid load distribution device provided according to Embodiment 2 of the present invention;

[0022] Figure 3 It is a structural schematic diagram of an electronic device for implementing the method for distributing power grid loads according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0025] Embodiment 1

[0026] Figure 1 A flowchart of a method for distributing power grid loads is provided for the first embodiment of the present invention. This embodiment is applicable to load distribution scenarios of generator sets in a power grid. The method can be executed by a power grid load distribution device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0027] S110, obtaining output limit data of each generator set in the target power grid, and generating an input population according to the output limit data of each generator set.

[0028] This solution can be executed by electronic devices such as computers and servers, and the electronic devices can obtain the output limit data of each generator set in the target power grid. The output limit data of the generator set can be determined based on the inherent properties of the generator set, for example, it can include the power upper limit, power lower limit and continuous startup time of the generator set.

[0029] In a feasible solution, the generator set output limit data may include generator set output constraint, ramp capacity constraint and start-stop time constraint. Based on the above solution, the generator set output constraint can be expressed as:

[0030]

[0031] The ramp capacity constraints include:

[0032]

[0033] The start-stop time constraints include:

[0034] [ton (i,t-1)-UT i ]×[u(i,t-1)-u(i,t)]≥0;

[0035] [t off (i,t-1)-DT i ]×[u(i,t)-u(i,t-1)]≥0;

[0036] Where i represents the generator group index, N represents the set of generator groups in the target power grid, t represents the time period index, T represents the set of time periods in a cycle, P(i,t) represents the active output of generator group i in time period t, P min (i, t) represents the lower limit of active output of generator set i in period t, P max (i,t) represents the upper limit of active output of generator set i in period t, U pi Indicates the upper limit of the upward climbing rate of generator set i, SU pi represents the starting ramp rate of generator set i, x on (i,t) indicates whether generator set i is started in period t, x on (i, t) takes the value 0 or 1, D pi Indicates the upper limit of the downward climbing rate of generator set i, SD pi The shutdown ramp rate of generator set i, x off (i,t) indicates whether generator set i is shut down in period t, x off (i,t) takes the value of 0 or 1, u(i,t) represents the start and stop status of generator set i in time period t, t on (i,t-1) represents the continuous operation time of generator set i in period t-1, t off (i, t-1) represents the continuous shutdown time of generator set i in period t-1, UT i Indicates the lower limit of the startup time of generator set i, DT i Indicates the lower limit of the shutdown time of generator set i.

[0037] After obtaining the output limit data of each generator set, the electronic device can use the output limit data of each generator set as an individual constraint, randomly generate a preset number of individuals that meet the individual constraints, and form an input population for the initial iteration. The preset number can be determined based on the number of generator sets in the target power grid, for example, it can be an integer multiple of the number of generator sets. In a specific example, the preset number can be 5-10 times the number of generator sets in the target power grid. The individuals in the input population can be used to characterize the output of different generator sets. Each individual can be represented by a vector, the dimension of the vector is equal to the number of generator sets in the target power grid, and each element in the vector represents the output of a generator set.

[0038] It is understandable that the generator set output constraint, climbing capacity constraint, and start-stop time constraint and other generator set output constraint data are associated with time. Therefore, the electronic device can generate input populations that match different time periods according to the generator set output constraint, climbing capacity constraint, and start-stop time constraint, and then obtain load distribution results that match different time periods, which is conducive to the dynamic distribution of power grid load. Specifically, the electronic device can pre-set the update cycle, generate the input population that matches the current cycle according to the generator set output constraint, climbing capacity constraint, and start-stop time constraint, and then obtain the load distribution result that matches the current cycle to meet the real-time requirements of the target power grid load distribution.

[0039] S120, performing a preset number of iterative operations on the individuals in the input population to determine the output population of each iteration; wherein the iterative operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal combustion characteristics, valve point effects and power balance constraints of each generator set.

[0040] The electronic device can perform a preset number of iterative operations on the individuals in the input population based on the differential evolution model to determine the output population of each iteration. It is easy to understand that the iterative operation may include a mutation operation, a crossover operation and a selection operation. In each iteration process, the mutation operation, the crossover operation and the selection operation can be performed sequentially. The mutation operation is an operation of mutating a preset number of individuals in the input population according to a preset mutation strategy to generate new individuals. The crossover operation is an operation of hybridizing individuals in the input population with new individuals generated by the mutation operation. The selection operation is an operation of screening individuals in the input population after the crossover based on a pre-established fitness function.

[0041] In this embodiment, optionally, the mutation strategy is one of the first mutation strategy, the second mutation strategy, the third mutation strategy, the fourth mutation strategy or the fifth mutation strategy;

[0042] Wherein, the first mutation strategy is:

[0043] V j,k =X r1,k +α(X r2,k -X r3,k );

[0044] The second mutation strategy is:

[0045] V j,k =X j,k +β(X best,k -X rj,k )+γ(X r1,k -X r2,k);

[0046] The third mutation strategy is:

[0047] V j,k =X best,k +δ(X r1,k -X r2,k );

[0048] The fourth mutation strategy is:

[0049] V j,k =X best,k +ε(X r1,k -X r2,k +X r3,k -X r4,k );

[0050] The fifth mutation strategy is:

[0051] V j,k =X r1,k +μ(X r2,k -X r3,k +X r4,k -X r5,k );

[0052] Where k is the iteration index, j is the individual index of the kth iteration, r1, r2, r3, r4 and r5 are different random integers that are not equal to k, and X r1,k represents the r1th individual in the input population of the kth iteration, X r2,k represents the r2th individual in the input population of the kth iteration, X r3,k represents the r3th individual in the input population of the kth iteration, X r4,k represents the r4th individual in the input population of the kth iteration, X r5,k represents the r5th individual in the input population of the kth iteration, X best,t represents the individual with the smallest fitness in the input population at the kth iteration, X j,k represents the jth individual in the input population of the kth iteration, V j,k represents the jth individual reproduced in the kth iteration, and α, β, γ, δ, ε and μ represent mutation factors.

[0053] It can be understood that the mutation factor is a parameter that controls the probability of mutation. The smaller the mutation factor, the smaller the probability of mutation, the fewer mutated individuals, and the faster the model converges, but it is also easy to be limited to the local optimal solution. The larger the mutation factor, the greater the possibility of sacrificing the convergence speed in exchange for the global optimal solution. The mutation factor usually takes values ​​in the interval (0,1]. The mutation operation can reproduce new individuals based on the individuals in the input population, increase the diversity of the population, and increase the probability of obtaining the optimal solution.

[0054] Optionally, the crossover operation is a binomial crossover or an exponential crossover. The binomial crossover expression can be expressed as:

[0055]

[0056] The exponential crossover can be expressed as:

[0057]

[0058] Among them, m represents the individual index, k represents the iteration index, n represents the element index in the individual vector, CR represents the crossover probability, and rand n (0,1) represents a random number between 0 and 1 generated by the kth iteration, v mn,k represents the nth element in the individual vector of the mth individual generated by the kth iteration mutation operation, x mn,k represents the nth element in the individual vector of the mth individual in the input population at the kth iteration, a represents a random integer in [1,D], D represents the dimension of the individual vector, b represents a random integer in (1,D), and rand(n) represents a random integer in [1,D].

[0059] In contrast to the mutation factor, the smaller the crossover probability, the slower the model converges, and the larger the crossover probability, the faster the model converges. Because the more individuals cross-reproduce, the more offspring individuals are produced, and the greater the probability of an individual meeting the fitness requirement. The crossover probability usually takes values ​​in the interval (0,1).

[0060] After the crossover operation, the individuals in the input population are amplified, including the original individuals and the crossover mutation individuals, wherein the original individuals are the individuals contained in the input population before the mutation operation and the crossover operation are performed, and the crossover mutation individuals are the new individuals generated after the mutation operation and the crossover operation are performed on the input population. The electronic device can calculate the fitness of the individuals in the input population in sequence according to the fitness function, and select a preset number of individuals from the input population according to the fitness size to form the output population.

[0061] The fitness function is constructed based on the coal-fired characteristics, valve point effect and power balance constraints of each generator set. In this solution, the fitness function is:

[0062]

[0063] Among them, i represents the generator group index, n represents the number of generator groups in the target power grid, and α i , β i and γ i represents the consumption characteristic constant of generator set i, P i represents the active output of generator set i, ei and h i represents the valve point effect constant of generator set i, represents the lower limit of the output of generator set i, M represents the penalty factor, P lo Indicates the total power of the load in the target power grid.

[0064] Understandably, It can be used to characterize the coal-fired characteristic curve of the generator set. The differential increase rate represents the differential of the input consumption and output power of the generator set, that is, The valve point effect is the phenomenon of wire drawing when the steam inlet valve of the generator set is opened, which causes a discontinuous pulse to be superimposed on the coal-fired characteristic curve of the generator set. i sin[h i (P i -P i min )]|Used to indicate the added consumption on the coal-fired characteristic curve of a generating unit.

[0065] The penalty function method is an auxiliary constraint processing method for the sequential unconstrained minimization technique. Constructing a penalty function is to add a composite function with a specific meaning on the basis of the original function, which can improve the efficiency of solving unconstrained optimization problems. According to the classification of constraints in the optimization problem: equality, inequality, and mixed equality and inequality, the interior point penalty function method can only solve inequality constraint problems, while the exterior point penalty function can solve all problems. The expression of the exterior point penalty function is p(x)=f(x)+M[h(x)] 2 , f(x) represents the original function, h(x) represents the equality constraint, and M represents the penalty factor. For the external point penalty function, it can be solved based on Newton's method. If the unconstrained optimal point of the penalty function in the two iterations is very small, it converges, otherwise the penalty factor M is multiplied by a coefficient to amplify it. For the power grid load distribution problem, the target power grid total power balance can be used as an equality constraint, constructed in the form of an external point penalty function and integrated into the objective function In the example, the absolute value of the difference between the sum of the outputs of each generator set and the target total load of the power grid is taken as a penalty value and added to the objective function to construct an external point penalty function. The electronic device can use the external point penalty function as a fitness function.

[0066] In this scheme, the selection operation can be expressed as:

[0067]

[0068] Among them, m represents the individual index, k represents the iteration index, n represents the element index in the individual vector, f(·) represents the fitness function, and x mn,krepresents the nth element in the individual vector of the mth original individual in the kth iteration input population, u n,k represents the nth element in the individual vector of the mth crossover mutation individual in the kth iteration input population, x mn,k+1 It represents the nth element in the individual vector of the mth individual in the output population of the kth iteration, that is, the nth element in the individual vector of the mth individual in the input population of the k+1th iteration.

[0069] S130: If the number of iterations reaches a preset number of iterations, the iterations are terminated, and a load distribution result of the target power grid is determined according to the output population obtained in the last iteration.

[0070] After each iteration, the electronic device can determine whether the current iteration has reached a preset number of iterations. If the preset number of iterations has not been reached, the output population of the current iteration is used as the input population of the next iteration, and the input population is returned to perform mutation operations, crossover operations, and selection operations until the number of iterations reaches the preset number of iterations. If the number of iterations reaches the preset number of iterations, the iteration is terminated, and the load distribution result of the target power grid is determined based on the output population obtained in this iteration.

[0071] Specifically, the electronic device can select the individual with the smallest fitness in the output population obtained in the last iteration as the target individual, and use the value of each element in the target individual as the output value of each generator set in the target power grid, thereby completing the load distribution of the target power grid.

[0072] The technical solution of the embodiment of the present invention is to obtain the output limit data of each generator set in the target power grid, and generate an input population according to the output limit data of each generator set; perform a preset number of iteration operations on the individuals in the input population to determine the output population of each iteration; wherein the iteration operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal-fired characteristics, valve point effects and power balance constraints of each generator set; if the number of iterations reaches the preset number of iterations, the iteration is terminated, and the load distribution result of the target power grid is determined according to the output population obtained from the last iteration. This technical solution solves the nonlinear power grid load distribution problem, constructs a fitness function based on the coal-fired characteristics, valve point effects and power balance constraints of each generator set in the target power grid, and determines the load distribution result through a differential evolution model, which is conducive to saving calculation time and improving the effectiveness and reliability of the distribution result.

[0073] Embodiment 2

[0074] Figure 2 A schematic diagram of the structure of a power grid load distribution device provided in Embodiment 2 of the present invention.

[0075] like Figure 2 As shown, the device comprises:

[0076] An input population generation module 210 is used to obtain output limit data of each generator set in the target power grid, and generate an input population according to the output limit data of each generator set;

[0077] An output population determination module 220 is used to perform a preset number of iteration operations on individuals in the input population to determine the output population of each iteration; wherein the iteration operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal combustion characteristics, valve point effect and power balance constraints of each generator set;

[0078] The allocation result determination module 230 is used to terminate the iteration if the number of iterations reaches a preset number of iterations, and determine the load allocation result of the target power grid according to the output population obtained in the last iteration.

[0079] In this solution, the fitness function is:

[0080]

[0081] Among them, i represents the generator group index, n represents the number of generator groups in the target power grid, and α i , β i and γ i represents the consumption characteristic constant of generator set i, P i represents the active output of generator set i, e i and h i represents the valve point effect constant of generator set i, P i min represents the lower limit of the output of generator set i, M represents the penalty factor, P lo Indicates the total power of the load in the target power grid.

[0082] In a feasible solution, the generator set output limitation data includes generator set output constraints, ramp capacity constraints, and start-up and shutdown time constraints.

[0083] Based on the above scheme, the generator set output constraints include:

[0084]

[0085] The ramp capacity constraints include:

[0086]

[0087] The start-stop time constraints include:

[0088] [ton (i,t-1)-UT i ]×[u(i,t-1)-u(i,t)]≥0;

[0089] [t off (i,t-1)-DT i ]×[u(i,t)-u(i,t-1)]≥0;

[0090] Where i represents the generator group index, N represents the set of generator groups in the target power grid, t represents the time period index, T represents the set of time periods in a cycle, P(i,t) represents the active output of generator group i in time period t, P min (i, t) represents the lower limit of active output of generator set i in period t, P max (i,t) represents the upper limit of active output of generator set i in period t, U pi Indicates the upper limit of the upward climbing rate of generator set i, SU pi represents the starting ramp rate of generator set i, x on (i,t) indicates whether generator set i is started in period t, x on (i, t) takes the value 0 or 1, D pi Indicates the upper limit of the downward climbing rate of generator set i, SD pi The shutdown ramp rate of generator set i, x off (i,t) indicates whether generator set i is shut down in period t, x off (i,t) takes the value of 0 or 1, u(i,t) represents the start and stop status of generator set i in time period t, t on (i,t-1) represents the continuous operation time of generator set i in period t-1, t off (i, t-1) represents the continuous shutdown time of generator set i in period t-1, UT i Indicates the lower limit of the startup time of generator set i, DT i Indicates the lower limit of the shutdown time of generator set i.

[0091] In this embodiment, optionally, the mutation operation is an operation of mutating a preset number of individuals in the input population according to a preset mutation strategy to generate new individuals; the mutation strategy is one of the first mutation strategy, the second mutation strategy, the third mutation strategy, the fourth mutation strategy or the fifth mutation strategy;

[0092] Wherein, the first mutation strategy is:

[0093] V j,k =X r1,k +α(X r2,k -X r3,k );

[0094] The second mutation strategy is:

[0095] V j,k =X j,k +β(X best,k -X rj,k )+γ(X r1,k -X r2,k );

[0096] The third mutation strategy is:

[0097] V j,k =X best,k +δ(X r1,k -X r2,k );

[0098] The fourth mutation strategy is:

[0099] V j,k =X best,k +ε(X r1,k -X r2,k +X r3,k -X r4,k );

[0100] The fifth mutation strategy is:

[0101] V j,k =X r1,k +μ(X r2,k -X r3,k +X r4,k -X r5,k );

[0102] Where k is the iteration index, j is the individual index of the kth iteration, r1, r2, r3, r4 and r5 are different random integers that are not equal to k, and X r1,k represents the r1th individual in the input population of the kth iteration, X r2,k represents the r2th individual in the input population of the kth iteration, X r3,k represents the r3th individual in the input population of the kth iteration, X r4,k represents the r4th individual in the input population of the kth iteration, X r5,k represents the r5th individual in the input population of the kth iteration, X best,t represents the individual with the smallest fitness in the input population at the kth iteration, X j,k represents the jth individual in the input population of the kth iteration, V j,k represents the jth individual reproduced in the kth iteration, and α, β, γ, δ, ε and μ represent mutation factors.

[0103] Optionally, the crossover operation is a binomial crossover or an exponential crossover.

[0104] The power grid load distribution device provided in the embodiment of the present invention can execute the power grid load distribution method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0105] Embodiment 3

[0106] Figure 3 A schematic diagram of the structure of an electronic device 310 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] like Figure 3 As shown, the electronic device 310 includes at least one processor 311, and a memory connected to the at least one processor 311 in communication, such as a read-only memory (ROM) 312, a random access memory (RAM) 313, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 311 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 312 or the computer program loaded from the storage unit 318 to the random access memory (RAM) 313. In the RAM 313, various programs and data required for the operation of the electronic device 310 can also be stored. The processor 311, the ROM 312, and the RAM 313 are connected to each other via a bus 314. An input / output (I / O) interface 315 is also connected to the bus 314.

[0108] A number of components in the electronic device 310 are connected to the I / O interface 315, including: an input unit 316, such as a keyboard, a mouse, etc.; an output unit 317, such as various types of displays, speakers, etc.; a storage unit 318, such as a disk, an optical disk, etc.; and a communication unit 319, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 319 allows the electronic device 310 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0109] The processor 311 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 311 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 311 executes the various methods and processes described above, such as a method for distributing power grid loads.

[0110] In some embodiments, the method for distributing the load of the power grid may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 318. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 310 via the ROM 312 and / or the communication unit 319. When the computer program is loaded into the RAM 313 and executed by the processor 311, one or more steps of the method for distributing the load of the power grid described above may be performed. Alternatively, in other embodiments, the processor 311 may be configured to execute the method for distributing the load of the power grid by any other appropriate means (e.g., by means of firmware).

[0111] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] The computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable grid load distribution device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0113] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0114] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0115] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0116] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0117] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0118] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for distributing power grid load, characterized in that: The method comprises: Obtain output limit data of each generator set in the target power grid, and generate an input population according to the output limit data of each generator set; Performing a preset number of iteration operations on individuals in the input population to determine the output population of each iteration; wherein the iteration operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal combustion characteristics, valve point effect and power balance constraints of each generator set; If the number of iterations reaches the preset number of iterations, the iteration is terminated, and the load distribution result of the target power grid is determined according to the output population obtained in the last iteration.

2. The method according to claim 1, characterized in that The fitness function is: Among them, i represents the generator group index, n represents the number of generator groups in the target power grid, and α i , β i and γ i represents the consumption characteristic constant of generator set i, P i represents the active output of generator set i, e i and h i represents the valve point effect constant of generator set i, represents the lower limit of the output of generator set i, M represents the penalty factor, P lo Indicates the total power of the load in the target power grid.

3. The method according to claim 1, characterized in that: The generator set output limitation data includes generator set output constraint, ramp capacity constraint and start-up and shutdown time constraint.

4. The method according to claim 3, characterized in that: The generator set output constraints include: The ramp capacity constraints include: The start-stop time constraints include: [t on (i,t-1)-UT i ]×[u(i,t-1)-u(i,t)]≥0; [t off (i,t-1)-DT i ]×[u(i,t)-u(i,t-1)]≥0; Where i represents the generator group index, N represents the set of generator groups in the target power grid, t represents the time period index, T represents the set of time periods in a cycle, P(i,t) represents the active output of generator group i in time period t, P min (i, t) represents the lower limit of the active output of generator set i in period t, P max (i,t) represents the upper limit of active output of generator set i in period t, U pi Indicates the upper limit of the upward climbing rate of generator set i, SU pi represents the starting ramp rate of generator set i, x on (i,t) indicates whether generator set i is started in period t, x on (i, t) takes the value 0 or 1, D pi Indicates the upper limit of the downward climbing rate of generator set i, SD pi The shutdown ramp rate of generator set i, x off (i,t) indicates whether generator set i is shut down in period t, x off (i,t) takes the value of 0 or 1, u(i,t) represents the start and stop status of generator set i in time period t, t on (i,t-1) represents the continuous operation time of generator set i in period t-1, t off (i, t-1) represents the continuous shutdown time of generator set i in period t-1, UT i Indicates the lower limit of the startup time of generator set i, DT i Indicates the lower limit of the shutdown time of generator set i.

5. The method according to claim 1, characterized in that The mutation operation is an operation of generating new individuals by mutating a preset number of individuals in the input population according to a preset mutation strategy; the mutation strategy is one of the first mutation strategy, the second mutation strategy, the third mutation strategy, the fourth mutation strategy or the fifth mutation strategy; Wherein, the first mutation strategy is: V j,k =X r1,k +α(X r2,k -X r3,k ); The second mutation strategy is: V j,k =X j,k +β(X best,k -X rj,k )+γ(X r1,k -X r2,k ); The third mutation strategy is: V j,k =X best,k +δ(X r1,k -X r2,k ); The fourth mutation strategy is: V j,k =X best,k +ε(X r1,k -X r2,k +X r3,k -X r4,k ); The fifth mutation strategy is: V j,k =X r1,k +μ(X r2,k -X r3,k +X r4,k -X r5,k ); Where k is the iteration index, j is the individual index of the kth iteration, r1, r2, r3, r4 and r5 are different random integers that are not equal to k, and X r1,k represents the r1th individual in the input population of the kth iteration, X r2,k represents the r2th individual in the input population of the kth iteration, X r3,k represents the r3th individual in the input population of the kth iteration, X r4,k represents the r4th individual in the input population of the kth iteration, X r5,k represents the r5th individual in the input population of the kth iteration, X best,t represents the individual with the smallest fitness in the input population at the kth iteration, X j,k represents the jth individual in the input population of the kth iteration, V j,k represents the jth individual reproduced in the kth iteration, and α, β, γ, δ, ε and μ represent mutation factors.

6. The method according to claim 1, characterized in that The crossover operation is a binomial crossover or an exponential crossover.

7. A distribution device for power grid load, characterized in that: The device comprises: An input population generation module is used to obtain output limit data of each generator set in the target power grid, and generate an input population according to the output limit data of each generator set; An output population determination module is used to perform a preset number of iterative operations on individuals in the input population to determine the output population of each iteration; wherein the iterative operation includes a mutation operation, a crossover operation and a selection operation; the selection operation performs individual screening based on a pre-established fitness function, and the fitness function is constructed based on the coal-fired characteristics, valve point effect and power balance constraints of each generator set; The allocation result determination module is used to terminate the iteration if the number of iterations reaches a preset number of iterations, and determine the load allocation result of the target power grid according to the output population obtained from the last iteration.

8. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for distributing power grid loads as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for distributing power grid loads according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for distributing power grid loads according to any one of claims 1 to 6.

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