Low-frequency load shedding method, system and equipment for power grid containing new energy, and medium

Abstract

The invention discloses a low-frequency load shedding method, system, equipment and medium for a power grid containing new energy, and the method comprises the steps: firstly, considering the load shedding qualified rate, the load shedding deviation rate and the control stability, and putting forward a low-frequency load shedding method evaluation index system; then, by taking the minimum total load shedding amount and the minimum low-frequency load shedding evaluation index as targets, constructing a low-frequency load shedding setting model of the power grid containing the new energy; and finally, introducing chaotic mapping and a self-adaptive weight optimization Hereum optimization algorithm, and solving the model by adopting an improved Hereum optimization algorithm. According to the low-frequency load shedding method for the power grid containing the new energy, the load shedding amount and the dynamic frequency deviation are relatively smaller, the frequency recovery stability capability is higher, and the safe and stable operation level after the power grid breaks down is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-frequency load shedding, and in particular to a method, system, equipment and medium for low-frequency load shedding in a power grid containing new energy. Background Art

[0002] As the scale of grid interconnection continues to expand and the penetration rate of clean energy power generation continues to increase, the economic efficiency of power system operation has improved while its complexity has increased. The risk of large power shortages at the receiving end has increased. When large-capacity units shed load or DC transmission is urgently locked, power shortages will affect frequency stability. Low-frequency load shedding is the third line of defense for the power grid. When a large power shortage occurs, it quickly and orderly removes part of the load to achieve a new power balance between power generation and consumption, preventing rapid frequency drops or even large-scale power outages.

[0003] Faced with the new situation of power grid development, relevant research is mainly carried out on the low-frequency load reduction scheme of provincial regional power grid, with the goal of improving practicality, economy and reliability. At present, the power grid adopts the "successive approximation" load reduction scheme as the mainstream, and there are successively semi-adaptive methods that use the frequency change rate and low-frequency over-limit start-up and adaptive methods that use frequency response models. The existing research introduces the unit load factor and proposes a comprehensive cost optimal load shedding model. Through the optimization allocation of different load shedding points, the loads that are severely disturbed and have low load rates are preferentially shelved; a load shedding optimization setting algorithm based on adaptive inertia weighted chaotic particle swarm is proposed, with the minimum load shedding as the goal and the addition of frequency constraints, to obtain the optimized load shedding amount of each node when single and multiple machines fail.

[0004] The frequency characteristics of the power grid after the access of new energy are different from those of the traditional power grid. The traditional low-frequency load reduction scheme may no longer be applicable in the power grid with a high proportion of new energy. The adaptability of the low-frequency load reduction strategy using comprehensive coordinated control remains to be verified; further, considering the impact of new energy access on system inertia, a real-time system inertia evaluation method based on load-side response information is proposed, but the traditional low-frequency load reduction strategy is still implemented and the strategy itself is not optimized.

[0005] In summary, with the continuous advancement of new energy construction, the power system's ability to cope with extreme faults faces challenges. It is necessary to propose new low-frequency load reduction solutions to adapt to the large-scale grid connection of new energy and improve the safety and stability of the power grid. Summary of the invention

[0006] The present invention provides a method, system, device and medium for low-frequency load shedding of a power grid containing new energy, which are used for low-frequency load shedding adapted to the situation of large-scale grid connection of new energy.

[0007] In view of this, the first aspect of the present application provides a method for low-frequency load reduction in a power grid containing new energy, the method comprising:

[0008] Constructing evaluation indicators of the low-frequency load shedding method, wherein the evaluation indicators include: execution load shedding qualification rate, control load shedding deviation rate, and load shedding control stability;

[0009] Taking the minimum total load shedding as the optimization goal and combining the evaluation index, a low-frequency load shedding setting model is constructed;

[0010] The improved Hippo optimization algorithm is used to solve the low-frequency load shedding setting model to obtain the load shedding amount of the low-frequency load shedding, wherein the improved method of the improved Hippo optimization algorithm includes: introducing Gaussian chaos mapping and adaptive weights to optimize the Hippo optimization algorithm.

[0011] Optionally, the expression of the load shedding qualification rate is:

[0012] ;

[0013] In the formula, For round exist The control load evaluation results at the time point, For round At the time point The load control rate, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

[0014] Optionally, the expression for controlling the load shedding deviation rate is:

[0015] ;

[0016] In the formula, To control the load shedding deviation rate, For rounds exist Real-time control of load shedding ratio at any time point, Request rounds for load shedding scheme The lower limit of the load shedding ratio controlled, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

[0017] Optionally, the expression of load shedding control stability is:

[0018] ;

[0019] In the formula, To control the stability of load shedding, , Round Daily maximum load control rate, minimum load control rate, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

[0020] Optionally, the optimization objective of minimizing the total amount of load shedding and combining the evaluation index to construct a low-frequency load shedding setting model includes:

[0021] Taking the minimum total load shedding amount and the minimum evaluation index as the objective function, a low-frequency load shedding setting model is constructed in combination with emergency control constraints;

[0022] Among them, the emergency control constraints include: static safety constraints that the system should be within the acceptable domain of the system's stable operating point when it reaches a new equilibrium point after emergency control, and transient stability constraints, and the transient stability constraints include: power angle stability, voltage stability, and frequency stability.

[0023] Optionally, the improved Hippo optimization algorithm is used to solve the low-frequency load shedding setting model to obtain the load shedding amount of the low-frequency load shedding, including:

[0024] S31, input the parameters of the low-frequency load reduction setting model and the parameters of the Hippo optimization algorithm;

[0025] S32, setting initial parameters of the hippo population, the initial parameters including: population size, maximum number of iterations;

[0026] S33, introducing Gaussian chaos mapping to update the hippopotamus position, using the position of the hippopotamus in the river or pond during the exploration phase to add an adaptive weight strategy to update the hippopotamus position;

[0027] S34, update the position of the hippo population using the update formulas for hippopotamus defense against predators in the exploration phase and hippopotamus escape from predators in the exploitation phase;

[0028] S35, calculating the fitness value of the hippo population, evaluating the population to determine whether the maximum number of iterations has been reached, if not, returning to step S33, if yes, outputting the load shedding amount of the low-frequency load shedding.

[0029] Optionally, the expression of the adaptive weight is:

[0030] ;

[0031] in, ;

[0032] In the formula, For the position of male hippos, is the weight factor; is the maximum number of iterations, t is the current number of iterations, Indicates the position of the dominant male hippopotamus, is a random number in the interval [0,1]; is a random number in the interval [1,2], is the position of the i-th candidate solution.

[0033] A second aspect of the present application provides a low-frequency load reduction system for a new energy grid, the system comprising:

[0034] A first construction unit is used to construct evaluation indicators of the low-frequency load shedding method, wherein the evaluation indicators include: a qualified rate of load shedding execution, a control load shedding deviation rate, and a load shedding control stability;

[0035] The second construction unit constructs a low-frequency load shedding setting model by taking the minimum total load shedding amount as the optimization goal and combining the evaluation index;

[0036] A solving unit is used to solve the low-frequency load shedding setting model by using an improved Hippo optimization algorithm to obtain the load shedding amount of the low-frequency load shedding, wherein the improved method of the improved Hippo optimization algorithm comprises: introducing Gaussian chaos mapping and adaptive weights to optimize the Hippo optimization algorithm.

[0037] A third aspect of the present invention provides a low-frequency load reduction device for a new energy grid, the device comprising a processor and a memory:

[0038] The memory is used to store program code and transmit the program code to the processor;

[0039] The processor is used to execute the steps of the low-frequency load reduction method for a new energy power grid as described in the first aspect according to the instructions in the program code.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the low-frequency load reduction method for a new energy power grid described in the first aspect above.

[0041] It can be seen from the above technical solutions that the present invention has the following advantages:

[0042] A low-frequency load shedding method for a power grid containing new energy is provided in an embodiment of the present invention. With the goal of minimizing the total amount of load shedding and minimizing the low-frequency load shedding evaluation index, a low-frequency load shedding setting model for a power grid containing new energy is constructed. Gaussian chaos mapping and adaptive weight factor optimization of the Hippo optimization algorithm are used to make the initial population distribution range more uniform. In the algorithm position update phase, a detailed search is performed around the optimal solution to accelerate the convergence speed and enhance the algorithm's ability to jump out of the local optimum. The low-frequency load shedding method for a power grid containing new energy provided by the present invention has a relatively smaller load shedding amount and dynamic frequency offset, and a stronger frequency recovery and stability capability, thereby improving the safe and stable operation level after a power grid failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative labor.

[0044] Figure 1 A schematic flow chart of a method for reducing load in a low-frequency power grid containing new energy provided by an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a flow chart for solving a low-frequency load reduction setting model provided by an embodiment of the present invention;

[0046] Figure 3 A schematic structural diagram of a low-frequency load reduction system for a power grid containing new energy sources provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions 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 embodiments described below 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 are within the scope of protection of the present invention.

[0048] See also Figure 1 , a method for reducing load on a power grid containing new energy sources at low frequency is provided in an embodiment of the present invention, comprising:

[0049] Step 101: construct evaluation indicators for the low-frequency load shedding method, where the evaluation indicators include: load shedding execution qualification rate, load shedding control deviation rate, and load shedding control stability.

[0050] Step 102: Taking the minimum total load shedding amount as the optimization goal and combining the evaluation indexes, a low-frequency load shedding setting model is constructed.

[0051] Step 103: using an improved Hippo optimization algorithm to solve the low-frequency load shedding setting model to obtain the load shedding amount of the low-frequency load shedding, wherein the improved method of the improved Hippo optimization algorithm includes: introducing Gaussian chaos mapping and adaptive weights to optimize the Hippo optimization algorithm.

[0052] In one embodiment, in step 101:

[0053] The expression for the load shedding qualification rate is:

[0054] ;

[0055] In the formula, For round exist The control load evaluation results at the time point, For round At the time point The load control rate, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

[0056] The expression for controlling the load shedding deviation rate is:

[0057] ;

[0058] In the formula, To control the load shedding deviation rate, For rounds exist Real-time control of load shedding ratio at any time point, Request rounds for load shedding scheme The lower limit of the load shedding ratio controlled, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

[0059] The expression of load shedding control stability is:

[0060] ;

[0061] In the formula, To control the stability of load shedding, , Round Daily maximum load control rate, minimum load control rate, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

[0062] Regarding step 101, it should be noted that:

[0063] 1) Qualification rate of load shedding:

[0064] The low-frequency load shedding scheme is divided into multiple basic rounds and special rounds according to the set action frequency and delay. The load that can be cut in each round at each point is calculated in real time in the load curve dimension at 96 o'clock every day, and the proportion of the controlled load at each point in each round relative to the load of the entire city network is obtained. According to the control ratio and allowable deviation stipulated in the provincial load shedding scheme, the qualified rate of the load shedding execution scheme round is calculated.

[0065] Control load shedding ratio calculation:

[0066] ;

[0067] In the formula, Indicates round exist Real-time control of load shedding ratio at any time point; express Total network load at a given time point; Indicates round At the time point The controlled load shedding amount is calculated as follows.

[0068] ;

[0069] In the formula, Indicates round The mth line in the The load

[0070] Determination of qualified rate of load shedding control:

[0071] The normalized value of the actual controlled load shedding ratio is obtained based on the control lower limit ratio in each round as the load control rate.

[0072] ;

[0073] In the formula, Indicates round At the time point Load control rate; It indicates the lower limit of load shedding ratio required by the load shedding scheme for round j control. As round exist The basis for controlling whether the load is qualified at the time point is The multiple corresponding to the upper limit of the control load shedding ratio from 1 to the upper limit of the control load shedding ratio When the load reduction at this point is between , the load reduction at this point is qualified. Characterization rounds exist The control load evaluation results at each time point.

[0074] ;

[0075] 2) Control load shedding deviation rate:

[0076] Due to the lack of fast and effective analysis methods for massive data, the traditional method uses the single statistical load of the entire network and the line as the benchmark to realize the line configuration of each round. However, the line load changes in real time and has different characteristics. After the execution plan configured according to the load shedding amount of each round under a single load level is put into the field, the load shedding amount of each subsequent round changes in real time, making it difficult to ensure the qualified rate of load shedding in real time in the curve dimension. In order to reflect the deviation degree of the load shedding control rate of each round and allow a certain margin, the load shedding deviation rate is used. Make an evaluation.

[0077] ;

[0078] In the formula, The smaller it is, the closer the controlled load shedding ratio is to the middle value of the upper and lower limits of the ratio required by the load reduction plan, allowing a larger upper and lower deviation margin for the controlled load, and its ability to resist load fluctuations is stronger.

[0079] 3) Load shedding control stability:

[0080] The load shedding control stability is evaluated based on the ratio of the daily load control rate fluctuation range to the required load control rate upper and lower limit fluctuation range interval length. Quantitative calculation.

[0081] ;

[0082] In the formula, , Respectively represent rounds Daily maximum and minimum load control rates; The smaller it is, the more stable the solution is.

[0083] In one embodiment, step 102 includes:

[0084] Taking the minimum total load shedding and the minimum evaluation index as the objective function, a low-frequency load reduction setting model is constructed in combination with emergency control constraints. Among them, the emergency control constraints include: static safety constraints that the system should be within the acceptable domain of the stable operating point when it reaches a new equilibrium point after emergency control, and transient stability constraints. The transient stability constraints include: power angle stability, voltage stability, and frequency stability.

[0085] What needs to be explained about step 102 is:

[0086] The load shedding strategy is considered, the minimum total load shedding amount is taken as the optimization goal, the evaluation index of the low-frequency load shedding method is considered, and the low-frequency load shedding setting model is constructed.

[0087] Objective function:

[0088] ;

[0089] ;

[0090] In the formula, is the number of nodes that can cut load; For the The load shedding cost of each load node is set to 1; For the Load shedding amount of each load node; is the control variable.

[0091] Constraints:

[0092] The premise of load shedding is that all variables in the system are within a reasonable constraint range. Emergency control constraints are divided into two categories: one is static safety constraints, which means that when the system reaches a new equilibrium point after emergency control, it should be within the acceptable domain of the system's stable operating point; the other is transient stability constraints, which usually include power angle stability, voltage stability, frequency stability, etc.

[0093] Static safety constraints are divided into equality constraints and inequality constraints. Equality constraints are power flow constraints and should satisfy power conservation:

[0094] ;

[0095] In the formula, , The active and reactive power generated by the generator; , is the active and reactive power consumed by the load; , is the amplitude and phase angle difference of the node voltage; , are the conductance and susceptance of the line.

[0096] Inequality constraints are constraints on state variables and control variables. State variables should meet the voltage, power, and frequency requirements of the power grid during steady-state operation, which are determined by the power grid operating conditions. Control variables should meet the equipment's own and emergency control requirements.

[0097] ;

[0098] In the formula, , The upper and lower limits of the generator active power; , The upper and lower limits of the reactive power of the generator; , The upper and lower limits of the bus voltage; , The upper and lower limits of the frequency.

[0099] Transient stability constraints are used to determine the stability level of the system after the generator is cut off and the load is shed, which is the basic condition for the safe and stable operation of the power system. A reasonable load shedding strategy should make the unstable system maintain transient stability, and the stability margin should be greater than a certain threshold.

[0100] ;

[0101] In the formula, is the transient stability rule expression after load shedding; It is the system operation status after load shedding; is the transient stability threshold.

[0102] In one embodiment, in step 103, the improved Hippo optimization algorithm is used to solve the low-frequency load shedding setting model to obtain the load shedding amount of the low-frequency load shedding, including:

[0103] S31, input the parameters of the low-frequency load reduction setting model and the parameters of the Hippo optimization algorithm;

[0104] S32, setting initial parameters of the hippo population, the initial parameters including: population size, maximum number of iterations;

[0105] S33, introducing Gaussian chaos mapping to update the hippopotamus position, using the position of the hippopotamus in the river or pond during the exploration phase to add an adaptive weight strategy to update the hippopotamus position;

[0106] S34, update the position of the hippo population using the update formulas for hippopotamus defense against predators in the exploration phase and hippopotamus escape from predators in the exploitation phase;

[0107] S35, calculating the fitness value of the hippo population, evaluating the population to determine whether the maximum number of iterations has been reached, if not, returning to step S33, if yes, outputting the load shedding amount of the low-frequency load shedding.

[0108] In one embodiment, the expression of the adaptive weight in step 103 is:

[0109] ;

[0110] in, ;

[0111] In the formula, For the position of male hippos, is the weight factor; is the maximum number of iterations, t is the current number of iterations, Indicates the position of the dominant male hippopotamus, is a random number in the interval [0,1]; is a random number in the interval [1,2], is the position of the i-th candidate solution.

[0112] What needs to be explained about step 103 is:

[0113] 1031) Hippo optimization algorithm description:

[0114] The hippopotamus are candidate solutions to the optimization problem, which means that the position of each hippopotamus in the search space is updated to represent the value of the decision variable. The vector formula for generating the decision variable is:

[0115] ;

[0116] In the formula, represents the position of the i-th candidate solution; Represents a random number in the interval [0, 1]; , denote the lower and upper limits of the ith decision variable respectively.

[0117] The exploration is divided into two phases. In the first phase of the exploration, the position of hippos in the river or pond is updated: hippos tend to gather close to each other, and the dominant hippo is determined iteratively based on the objective function value. The position formula of male hippos in the group is:

[0118] ;

[0119] In the formula, Indicates the position of the male hippopotamus; Indicates the position of the dominant male hippopotamus; is a random number in the interval [0,1]; Represents a random number in the interval [1,2].

[0120] When the juvenile hippopotamus is far away from the mother hippopotamus, the position update formula of male and female or juvenile hippopotamus in the group is:

[0121] ;

[0122] In the formula, Indicates the position of female or juvenile hippos in the group; represents a random vector; represents the average of a random selection of hippos; represents the probability of selection; Represents a random number in the interval [1, 2].

[0123] Exploration Stage 2 Hippopotamus Defending against Predators: When a hippopotamus is attacked by a predator or other creature intruding into its territory, it triggers a defensive response, using their fearsome jaws to make sounds to deter and repel the attacker. The location of the predator or other creature intruding into the hippopotamus' territory is:

[0124] ;

[0125] In the formula, Indicates the location of predators or other creatures invading the hippopotamus’ territory; represents a random vector with Levy distribution, which is used to describe the sudden change of the predator's position when attacking the hippopotamus; represents the position of the predator in the search space; represents the distance from the i-th hippopotamus to the predator; b, c, d, and g all represent uniformly distributed random numbers, but their value intervals are different. The value interval of b is [2,4], the value interval of c is [1,1.5], the value interval of d is [2,3], and the value interval of g is [2,4]; is a random variable in the interval [-1,1]; factors that contribute to hippos' defensive behaviors to protect themselves from predators; is the objective function value.

[0126] Hippopotamus fleeing from predators: Warnings are ineffective. Lone, sick, and immature hippos are vulnerable to predators. Hippos seek to stay away from predators. The safe locations that hippos search for the nearest safe place are:

[0127] ;

[0128] In the formula, It means searching for the location of the hippopotamus in order to find the nearest safe place; Represents a random number in the interval [0,1]; Represents a random number that conforms to the normal distribution; t is the current iteration number.

[0129] 1032) Improvements to the Hippo optimization algorithm (HO algorithm):

[0130] In the initialization stage of the HO algorithm, Gaussian chaotic mapping is introduced to enable the population initialization process to traverse the entire solution space. The boundary of the Gaussian mapping is [0,1], which can positively change the movement on the global optimal point, making the particle movement more flexible and improving the efficiency of the algorithm.

[0131] ;

[0132] ;

[0133] Where mod represents the remainder function and [·] represents rounding.

[0134] When the weight is relatively large at the beginning of the algorithm, the algorithm has a strong global search capability. As the number of iterations increases, the weight coefficient gradually decreases, and the algorithm can search finely around the optimal solution to accelerate the convergence speed. The adaptive weight formula is:

[0135] ;

[0136] ;

[0137] In the formula, is the weight factor; is the maximum number of iterations.

[0138] 1033) Solution of low-frequency load reduction setting model based on improved Hippo optimization algorithm:

[0139] See also Figure 2 , the model solving process steps based on the improved HO algorithm are as follows:

[0140] Step 1: Input and low-frequency load reduction setting model related parameters and HO algorithm parameters;

[0141] Step 2: Set the initial parameters of the hippo population, including population size, maximum number of iterations, etc.

[0142] Step 3: Introduce Gaussian chaos mapping to update the hippopotamus position, and use the position of the hippopotamus in the river or pond during the exploration phase to add an adaptive weight strategy to update the hippopotamus position;

[0143] Step 4: Update the position of the hippo population using the update formulas for hippo defense against predators in the exploration phase and hippo escape from predators in the exploitation phase;

[0144] Step 5: Calculate the fitness value of the hippo population, evaluate the population to determine whether the maximum number of iterations has been reached, if not, go to step 3 and recalculate, if so, output the load shedding amount for low-frequency load shedding.

[0145] In summary, the present invention provides a method for low-frequency load shedding of a power grid containing new energy sources, with the goal of minimizing the total amount of load shedding and minimizing the low-frequency load shedding evaluation index, constructing a low-frequency load shedding setting model for a power grid containing new energy sources, and using Gaussian chaos mapping and adaptive weight factor optimization of the Hippo optimization algorithm to make the initial population distribution range more uniform, and to perform a detailed search around the optimal solution in the algorithm position update phase, accelerate the convergence speed, and enhance the algorithm's ability to jump out of the local optimum. The load shedding amount and dynamic frequency offset of the low-frequency load shedding method for a power grid containing new energy sources proposed by the present invention are relatively smaller, and the frequency recovery and stability capability is stronger, which improves the safe and stable operation level after a power grid failure.

[0146] The above is a low-frequency load shedding method for a power grid containing new energy provided in an embodiment of the present invention, and the following is a low-frequency load shedding system for a power grid containing new energy provided in an embodiment of the present invention.

[0147] See also Figure 3 , a low-frequency load reduction system for a power grid containing new energy is provided in an embodiment of the present invention, comprising:

[0148] The first constructing unit 201 is used to construct evaluation indicators of the low-frequency load shedding method, where the evaluation indicators include: load shedding execution qualification rate, load shedding control deviation rate, and load shedding control stability.

[0149] The second construction unit 202 constructs a low-frequency load shedding setting model by taking the minimum total load shedding amount as the optimization goal and combining the evaluation index.

[0150] The solving unit 203 is used to solve the low-frequency load shedding setting model by using the improved Hippo optimization algorithm to obtain the load shedding amount of the low-frequency load shedding, wherein the improved method of the improved Hippo optimization algorithm includes: introducing Gaussian chaos mapping and adaptive weights to optimize the Hippo optimization algorithm.

[0151] Furthermore, an embodiment of the present invention also provides a low-frequency load reduction device for a new energy grid, the device including a processor and a memory:

[0152] The memory is used to store program code and transmit the program code to the processor;

[0153] The processor is used to execute the steps of the low-frequency load reduction method for a new energy power grid as described in the above method embodiment according to the instructions in the program code.

[0154] Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program codes, and the program codes are used to execute the method described in the above method embodiment.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0156] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0160] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reducing load on a low-frequency power grid containing new energy, characterized in that: include: Constructing evaluation indicators of the low-frequency load shedding method, wherein the evaluation indicators include: execution load shedding qualification rate, control load shedding deviation rate, and load shedding control stability; Taking the minimum total load shedding as the optimization goal and combining the evaluation index, a low-frequency load shedding setting model is constructed; The improved Hippo optimization algorithm is used to solve the low-frequency load shedding setting model to obtain the load shedding amount of the low-frequency load shedding, wherein the improved method of the improved Hippo optimization algorithm includes: introducing Gaussian chaos mapping and adaptive weights to optimize the Hippo optimization algorithm.

2. The method for reducing load in a low-frequency power grid containing new energy sources according to claim 1, characterized in that: The expression of the load shedding pass rate is: ； In the formula, For round exist The evaluation results of the control load at the time point, For round At the time point The load control rate, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

3. The method for reducing load in a low-frequency power grid containing new energy sources according to claim 1, characterized in that: The expression for controlling the load shedding deviation rate is: ； In the formula, To control the load shedding deviation rate, For rounds exist Real-time control of load shedding ratio at any time point, Request rounds for load shedding scheme The lower limit of the load shedding ratio controlled, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

4. The method for reducing load in a low-frequency power grid containing new energy sources according to claim 1, characterized in that: The expression of load shedding control stability is: ； In the formula, To control the stability of load shedding, , Round Daily maximum load control rate, minimum load control rate, It is the multiple corresponding to the upper limit of the allowed control load shedding ratio.

5. The method for reducing load in a low-frequency power grid containing new energy sources according to claim 1, characterized in that: The optimization goal is to minimize the total amount of load shedding and, in combination with the evaluation index, to construct a low-frequency load shedding setting model, including: Taking the minimum total load shedding amount and the minimum evaluation index as the objective function, a low-frequency load shedding setting model is constructed in combination with emergency control constraints; Among them, the emergency control constraints include: static safety constraints that the system should be within the acceptable domain of the system's stable operating point when it reaches a new equilibrium point after emergency control, and transient stability constraints, and the transient stability constraints include: power angle stability, voltage stability, and frequency stability.

6. The method for reducing load in a low-frequency power grid containing new energy sources according to claim 1, characterized in that: The improved Hippo optimization algorithm is used to solve the low-frequency load shedding setting model to obtain the load shedding amount of the low-frequency load shedding, including: S31, input the parameters of the low-frequency load reduction setting model and the parameters of the Hippo optimization algorithm; S32, setting initial parameters of the hippo population, the initial parameters including: population size, maximum number of iterations; S33, introducing Gaussian chaos mapping to update the hippopotamus position, using the position of the hippopotamus in the river or pond during the exploration phase to add an adaptive weight strategy to update the hippopotamus position; S34, update the position of the hippo population using the update formulas for hippopotamus defense against predators in the exploration phase and hippopotamus escape from predators in the exploitation phase; S35, calculating the fitness value of the hippo population, evaluating the population to determine whether the maximum number of iterations has been reached, if not, returning to step S33, if yes, outputting the load shedding amount of the low-frequency load shedding.

7. The method for reducing load in a low-frequency power grid containing new energy sources according to claim 1, characterized in that: The expression of the adaptive weight is: ； in, ; In the formula, For the position of male hippos, is the weight factor; is the maximum number of iterations, t is the current number of iterations, Indicates the position of the dominant male hippopotamus, is a random number in the interval [0,1]; is a random number in the interval [1,2], is the position of the i-th candidate solution.

8. A low-frequency load reduction system for a power grid containing new energy, characterized in that: include: A first construction unit is used to construct evaluation indicators of the low-frequency load shedding method, wherein the evaluation indicators include: a qualified rate of load shedding execution, a control load shedding deviation rate, and a load shedding control stability; The second construction unit constructs a low-frequency load shedding setting model by taking the minimum total load shedding amount as the optimization goal and combining the evaluation index; A solving unit is used to solve the low-frequency load shedding setting model by using an improved Hippo optimization algorithm to obtain the load shedding amount of the low-frequency load shedding, wherein the improved method of the improved Hippo optimization algorithm comprises: introducing Gaussian chaos mapping and adaptive weights to optimize the Hippo optimization algorithm.

9. A low-frequency load reduction device for a new energy grid, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the low-frequency load reduction method for a new energy power grid according to any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the low-frequency load reduction method for a new energy power grid according to any one of claims 1 to 7.

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