Two-layer multi-target electric power rapid allocation method and system based on adjustable priority

Abstract

The invention discloses a two-layer multi-target electric power rapid allocation method and system based on adjustable priorities. The method comprises the following steps: collecting related data of electric power, and establishing a parameter model; optimizing a power grid distribution power partition control objective function; designing a priority mechanism; a priority mechanism is combined, a two-layer allocation mechanism is utilized, an allocation strategy is dynamically adjusted, and optimal allocation of resources is achieved. A simulation experiment result verifies that the method has obvious excellent performance in the aspect of improving the power grid voltage distribution partition control capability. According to the power distribution optimization scheme, the operation efficiency and the resource utilization rate of the power grid are remarkably improved by establishing the parameter model and the optimization control objective function.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent electricity consumption, and in particular relates to a two-layer multi-objective rapid electricity allocation method and system based on adjustable priorities. Background Art

[0002] In China, with the improvement of the national economic level, various production equipment and household appliances have higher and higher requirements for the reliability of power supply, and the use of uninterruptible power supply in the actual operation of the power grid is becoming more and more widespread. With the continuous improvement of demand-side management technology, the user's demand-side response behavior has been reasonably changed, which makes some loads relatively controllable. Therefore, reasonable planning and scheduling of grid voltage controllable resources will help to achieve differentiated services for voltage distribution data, at the expense of the performance of low-priority data, to ensure the performance of high-priority data and absorb renewable energy, reduce environmental pollution, and alleviate the increasingly serious energy crisis. Summary of the invention

[0003] In order to improve the control capability of power grid distribution power partition, the object of the present invention is to provide a two-layer multi-objective power rapid allocation method and system based on adjustable priority.

[0004] To achieve the purpose of the present invention, the technical solution provided by the present invention is as follows:

[0005] First aspect

[0006] The present invention provides a two-layer multi-objective rapid power allocation method based on adjustable priorities, comprising the following steps:

[0007] Step S1: Collect relevant data of electricity and establish a parameter model;

[0008] Step S2: Optimizing the power distribution partition control objective function of the power grid;

[0009] Step S3: Design a priority mechanism;

[0010] Step S4: Combine the priority mechanism and utilize the two-layer allocation mechanism to dynamically adjust the allocation strategy to achieve optimal resource configuration.

[0011] Wherein, the step S1 comprises the following steps:

[0012] Step S11: taking the static limit current and the static limit power angle parameters as the control independent variables, designing the control constraint parameter optimization scheduling, adopting the grid-connected converter single machine control method, constructing the steady-state tracking control parameter model, and forming the coupling model of the power distribution partition control of the power grid;

[0013] Step S12: In the static distribution mode of the power system, the grid voltage distribution is dispatched by partition to obtain the static voltage stability parameter distribution of the system;

[0014] Step S13: Based on the generalized characteristic analysis method defined by static characteristics, the partition control state parameters of the grid voltage distribution are analyzed to obtain the closed-loop control characteristics of the output current; combined with the morphological parameter distribution of the power load, the partition distribution of the grid voltage is completed, and the historical maximum load of the distribution network is analyzed to determine whether the current flowing through the tie line satisfies the mapping relationship; on this basis, an optimal solution is found that satisfies the dispatching model of active grid partition control, and finally the adaptive optimization function shown in the formula is obtained;

[0015] Step S14: construct a topological structure model, and obtain the spatial distribution of historical data of distribution network voltage according to the principal component;

[0016] Step S15: Based on the above analysis, a fuzzy state distribution set of parameters for grid voltage distribution zoning control is established to obtain steady-state characteristic quantities for distribution network power control. Fuzzy parameter constraint control is performed based on the steady-state characteristic parameters for distribution network power zoning control constructed above.

[0017] Among them, the priority mechanism combines the power load rating and the priority of the message; based on the QoS metric, the QoS level can be further refined according to environmental requirements, so that it is more suitable for the WMSN priority mechanism in the smart grid environment; the QoS level is determined by subjective and objective weights.

[0018] Among them, in power distribution, QoS priority is divided into two parts: subjective QoS measurement and objective QoS measurement; subjective QoS measurement reflects user needs and usage experience preferences, while objective QoS measurement OQ is based on the stability and performance goals of power grid system resources; the balance between subjective and objective components is adjusted by parameter α to adapt to distribution needs in different scenarios.

[0019] Second aspect

[0020] The present invention provides a two-layer multi-objective power rapid dispatching system based on adjustable priorities, comprising the following units: a data acquisition unit, a function optimization unit, a priority mechanism design unit, and a dispatching unit;

[0021] The data acquisition unit is used to collect relevant data of electricity and establish a parameter model;

[0022] The function optimization unit is used to optimize the power grid distribution power partition control objective function;

[0023] The priority mechanism design unit is used to design a priority mechanism;

[0024] The allocation unit is used to utilize a two-layer allocation mechanism to dynamically adjust the allocation strategy to achieve optimal configuration of resources.

[0025] The data acquisition unit is specifically used to perform the following steps:

[0026] Step S11: taking the static limit current and the static limit power angle parameters as the control independent variables, designing the control constraint parameter optimization scheduling, adopting the grid-connected converter single machine control method, constructing the steady-state tracking control parameter model, and forming the coupling model of the power distribution partition control of the power grid;

[0027] Step S12: In the static distribution mode of the power system, the grid voltage distribution is dispatched by partition to obtain the static voltage stability parameter distribution of the system;

[0028] Step S13: Based on the generalized characteristic analysis method defined by static characteristics, the partition control state parameters of the grid voltage distribution are analyzed to obtain the closed-loop control characteristics of the output current; combined with the morphological parameter distribution of the power load, the partition distribution of the grid voltage is completed, and the historical maximum load of the distribution network is analyzed to determine whether the current flowing through the tie line satisfies the mapping relationship; on this basis, an optimal solution is found that satisfies the dispatching model of active grid partition control, and finally the adaptive optimization function shown in the formula is obtained;

[0029] Step S14: construct a topological structure model, and obtain the spatial distribution of historical data of distribution network voltage according to the principal component;

[0030] Step S15: Based on the above analysis, a fuzzy state distribution set of parameters for grid voltage distribution zoning control is established to obtain steady-state characteristic quantities for distribution network power control. Fuzzy parameter constraint control is performed based on the steady-state characteristic parameters for distribution network power zoning control constructed above.

[0031] Among them, the priority mechanism combines the power load rating with the priority of the message; based on the QoS measurement, the QoS level can be further refined according to the environmental requirements, making it more suitable for the WMSN priority mechanism in the smart grid environment; the QoS level is determined by subjective and objective weights.

[0032] Among them, in power distribution, QoS priority is divided into two parts: subjective QoS measurement and objective QoS measurement; subjective QoS measurement reflects user needs and usage experience preferences, while objective QoS measurement OQ is based on the stability and performance goals of power grid system resources; the balance between subjective and objective components is adjusted by parameter α to adapt to distribution needs in different scenarios.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The simulation experimental results verify that the proposed method has obvious superior performance in improving the control capability of power grid voltage distribution partition.

[0035] The distribution ratio factor of the power grid is 0.3, the active load is 180MW, the peak load is 280MW, and the active frequency is 30KHz. According to the above parameter settings, the power grid distribution area control is carried out to obtain the voltage waveform and current waveform of the input end as shown in the figure. Figure 1 As shown. According to the data, the voltage and current input of the power grid distribution are used to control the power grid distribution area. The patent method is used to control the power grid distribution area. The parameters are well integrated, the static stability parameters of the power grid are more integrated, and the robustness of the power grid distribution area control is improved.

[0036] This power distribution optimization scheme significantly improves the operation efficiency and resource utilization of the power grid by establishing a parameter model and optimizing the control objective function. Dividing resource allocation into two layers can more finely manage and allocate power resources to ensure that the needs of various regions and users are met, thereby improving the overall resource utilization efficiency. At the same time, the adjustable priority strategy can flexibly adjust the priority of resource allocation according to real-time demand and environmental changes, and improve the response speed and adaptability of the power system. This mechanism ensures that important users or critical loads can get priority power supply under high demand conditions, making the power network more stable and reliable when dealing with emergencies. Overall, this method has laid a solid foundation for the intelligent and modern transformation of the power industry and promoted more efficient and flexible power management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following is a clear and complete description of the technical solutions in the embodiments of the present invention 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 are within the scope of protection of the present invention.

[0039] In order to improve the power distribution zoning control capability of the power grid, a two-layer multi-objective fast matching method based on adjustable priority is designed. The power zoning control parameter constraint model is constructed, and the static limit current, static limit power angle and other parameters are used as control independent variables. Through the distribution of static power state stability characteristics of the power grid, the impedance of the power line of the control grid distribution and the static parameters of the system are fitted to complete the grid voltage distribution zoning scheduling and priority control, and realize the optimization design of the grid voltage distribution zoning control.

[0040] This patent proposes a priority mechanism that takes into account the heterogeneous characteristics of power grid traffic and proposes a two-layer multi-objective fast matching method based on adjustable priorities, called the preemptive tidal flow queuing model. This scheme implements differentiated services for diversified communication data when the wireless multimedia sensor network is connected to the core power grid, and guarantees the performance of high-priority data at the expense of the performance of low-priority data. On this basis, a two-layer allocation mechanism is proposed to achieve optimal resource allocation by dynamically adjusting the allocation strategy, thereby improving the overall operation efficiency and load response capability of the power grid. Simulation analysis shows that the performance of high-priority messages can be reliably guaranteed, and the preemptive tidal flow queuing model meets the requirements for operation in a power grid distribution environment.

[0041] like Figure 1 As shown, this embodiment provides a two-layer multi-objective power rapid allocation method based on adjustable priorities, including the following steps:

[0042] Step S1: Collect relevant data of electricity and establish a parameter model; specifically including the following:

[0043] Step S11: The model uses parameters such as static limit current and static limit power angle as control independent variables, designs control constraint parameter optimization scheduling, adopts grid-connected converter single-machine control method, constructs steady-state tracking control parameter model, and forms a coupling model for power grid distribution power partition control:

[0044]

[0045] Where: m is the voltage signal component of different frequencies; c is the parameter of grid voltage distribution partition control; b i is the static limiting current parameter; i is the number of transmission lines; s(e) is the static limiting power angle parameter.

[0046] Step S12: In the static distribution mode of the power system, the grid voltage distribution is dispatched by partition, and the static voltage stability parameter distribution of the system is obtained as shown in the formula:

[0047] k t =m+(b i +s(e))

[0048] Step S13: Based on the generalized characteristic analysis method defined by static characteristics, the partition control state parameters of the grid voltage distribution are analyzed to obtain the closed-loop control characteristics of the output current. Combined with the morphological parameter distribution of the power load, the partition distribution of the grid voltage is completed, and the historical maximum load of the distribution network is analyzed to determine whether the current flowing through the tie line satisfies the mapping relationship Φ. On this basis, an optimal solution is found that satisfies the dispatch model of active grid partition control, and finally the adaptive optimization function shown in the formula is obtained.

[0049]

[0050] Step S14: construct a topological structure model, and obtain the spatial distribution of historical data of distribution network voltage according to the principal component:

[0051] j=∑r(w)+Φ+c i

[0052] Under the constraints of sampling frequency and control period, the fuzzy characteristic quantity c of grid voltage distribution partition control is obtained. i (i=1,2,……,n);

[0053] Step S15: Based on the above analysis, a parameter fuzzy state distribution set of the grid voltage distribution partition control is established to obtain the steady-state characteristic quantity of the distribution network power distribution control:

[0054] t=m+∑j+c i

[0055] Where m = ∑c i =1p i m i The voltage signal components of different frequencies are obtained. According to the steady-state characteristic parameters of the power distribution partition control of the power grid constructed above, fuzzy parameter constraint control is performed to improve the stability of the power distribution partition control of the power grid.

[0056] Step S2: Optimize the grid distribution power partition control objective function

[0057] Step S21: Taking the static limit current, static limit power angle and other parameters as the control independent variables, based on the stability characteristic distribution of the static power grid distribution, the detection objective function of the voltage fluctuation in the voltage partition is defined as:

[0058]

[0059] Where: ε is a constant; t is the steady-state characteristic quantity of power distribution control in the power grid.

[0060] Step S22: Select the transfer branch as a voltage signal, and output the amplitude of the injected signal f u (x) is defined as:

[0061]

[0062] Step S23: According to the voltage generated by the converter, the vector state equation of the grid voltage distribution partition control is obtained, where σ is the adaptive compensation coefficient of the grid voltage distribution partition control. The vector state equation of the grid power distribution partition control is obtained:

[0063]

[0064] Where: g Na is the control period and protection factor; a m The sampling frequency for active power distribution network allocation control; g L To control the cycle.

[0065] Step S24: Combine the parameter fusion results of the control cycle and the protection sampling cycle to perform optimal design in the process of active distribution network power distribution control, obtain the grid distribution voltage partition state, and use the differences in voltage waveforms to obtain the margin and detection statistical characteristics of distribution network power distribution control.

[0066]

[0067] Step S25: construct a parameter configuration model for grid voltage distribution zoning control, and complete grid distribution power parameter fusion and optimization adjustment by combining the low voltage criterion setting adjustment method.

[0068] Step S3: A priority mechanism is proposed, and an innovative allocation method and flow scheduling scheme is demonstrated based on the objective function of optimizing the power grid distribution power partition control. The model dynamically allocates grid resources and schedules flow to ensure high performance under high demand conditions.

[0069] Step S31: Partition control of grid voltage distribution Partition control based on adjustable priority belongs to the research field of highly intelligent systems. A special smart grid WMSN priority mechanism is urgently needed to meet specific application requirements and provide support for priority queuing models. This application proposes an innovative priority sorting mechanism that combines power load ratings with message priorities. Based on QoS metrics, the QoS level can be further refined according to environmental requirements, making it more suitable for the WMSN priority mechanism in the smart grid environment. Among them, the QoS level is determined by subjective and objective weights.

[0070] This method can automatically adapt to user preferences through subjective weight calculation, while accurately evaluating service performance requirements through objective weight calculation. After applying this method to messages and final priorities, a more reasonable prioritization mechanism is achieved, thereby achieving a balance between power load and message demand. OWDM aims to correct the one-sidedness of prioritization based only on user preferences to achieve optimal performance results. It can also be applied to our priority mechanism, using SWDM to calculate the weight of power load and using OWDM to calculate the weight of a series of message attributes.

[0071] I=(WS,A)

[0072] Step S32: Further, the subjective QoS metric can be derived as:

[0073] SQ(ws)=∑ p∈P nv ws (p)*sw(p)=∑ p∈P nv ws (p) = nv ws (p0)

[0074] Step S33: Consider an information system Ix = (U, A, V, f) where U is a non-empty set of finite objects; A is a non-empty set of finite attributes: V = Ua∈AVa such that a∈A,V, is the attribute a, and can take f:UxA→V to be an information function, and is defined such that for any a∈A and u∈U we have f(u,a)∈V, f(u,a) can be abbreviated to a(u) and I x It can be abbreviated as I=(U,A).

[0075] Step S34: Given an information system I = (U, A), for any X∈A, the indiscernibility relation I x Defined as:

[0076]

[0077] If (x,y)∈I x , then x and y are in X, which means they have the same attribute value. x It can also be called an equivalence relation or knowledge relation of association. In addition, X can be expressed as U / I x , or simply U / X, which means divided by I x .

[0078] Step S35: Given an information system I = (U, A) with X∈A and knowledge relationship granularity dimension I x Defined as:

[0079] GD(I x )=|I x | / |U 2 |=|I x | / |U| 2

[0080] |E| represents the number of elements in the set E and GD(I x ) can be abbreviated as GD(x); in general, 1 / |U|<GD(x)<1. When GD(x) is small, its discrimination ability is strong, and I x Can be used to convert division U to more equivalence classes.

[0081] Step S36: Given an information system I = (U, A) if K∈A, K∈A and I kis an indiscernibility relation K in , then K with respect to K is defined as:

[0082]

[0083] This means that when k is added to K, the attribute set K can be used to distinguish more equivalence classes U. In general, 0≤SIGK(x)<1-|I{k} / |U|2; k becomes more important K such as SIG k (k) increases. SIG(k) represents the number of objects U in the Discourse domain that are distinguished by κ.

[0084] Step S37: In the service set The reference level REF(p) can be defined as:

[0085] REF(p)=SIG0(P)=1-|I {P} | / |WS| 2

[0086] This reflects that p is oriented toward the service WS in the set, and in the same set S, the target weight ow(p) for attribute p(∈A) can be defined as:

[0087]

[0088] Step S38: The QoS metric used in this application combines the subjective QoS metric SQ(ws) and the objective QoS metric OQ(ws), and the balance between the subjective component and the objective component is determined by the parameter α where α∈(0,1]; this parameter is called Preference intensity, which indicates the degree of dependence on user preferences. The QoS metric is calculated using the following QoS utility function:

[0089] Q(ws)=α·SQ(ws)+(1-α)·OQ(ws);

[0090] Among them, α is defined by the user and can be adjusted to achieve a balance between power load and message demand. If α increases, the impact of power load becomes greater. Based on Q(ws), QoS metrics for various services can be calculated, providing a quantitative basis for classifying different WMSN communication services with different power loads in various application environments into different QoS levels. The density of QoS levels can be determined by the user based on the smoothness requirements of message classification. The source node needs to use this method to calculate the priority of the data packet before sending, and this calculation can be a preprocessing for our QoS-aware queue model.

[0091] Step S4: Power distribution hierarchy

[0092] In the power distribution system, in order to improve the effective allocation of power resources, the QoS priority is divided into two parts: subjective QoS metric (SQ) and objective QoS metric (OQ). The subjective QoS metric SQ (ws) reflects the user's needs and usage experience preferences, while the objective QoS metric OQ (ws) is based on the stability and performance goals of the power grid system resources. The balance between the subjective and objective components is adjusted by the parameter α to adapt to the allocation needs in different scenarios.

[0093] S41: First layer: Regional power distribution

[0094] In the first level of regional power distribution, the total power resource P total Distribute to each region, taking into account the needs of each region D i The QoS priority of each area is expressed as the sum of subjective and objective components:

[0095] Q(ws)=α·SQ(ws)+(1-α)·OQ(ws);

[0096] Among them: SQ i represents the subjective QoS metric for the i-th region, based on user demand and experience feedback; OQ i represents the objective QoS metric for the ith region, based on grid stability and power resource availability.

[0097] The objective function of regional power distribution is defined as follows, and the resource allocation of different regions is adjusted by QoS priority weights:

[0098]

[0099] Among them, P i is the power resources allocated to the i-th domain; D i is the expected electricity demand in the i-th region; w i and weights and tuning parameters assigned to regions; is the historical average electricity demand.

[0100] Constraints:

[0101] 1. Total power resource constraints:

[0102]

[0103] 2. Regional priority constraints: Based on the QoS requirements of each region, ensure that the needs of high-priority regions are met first.

[0104] P i ≥D i QoS i ;

[0105] S42, Second layer: User power distribution

[0106] In the second layer, the power resources P in the region i It will be further distributed to each household to meet the electricity demand of each household. The QoS priority of each household is also composed of subjective and objective components:

[0107] QoS j =α·SQ j +(1-α)·OQ j ;

[0108] SQ j It is the user's subjective QoS measurement, reflecting the user's preference requirements; OQ j Objective QoS measurement for users, based on the stability requirements of the power system.

[0109] The objective function of user power allocation is as follows, taking into account the user's QoS priority and historical demand to achieve accurate resource allocation:

[0110]

[0111] p j is the power resources allocated to the jth household; d j is the demand of the jth household; j and γ j Weights and tuning parameters assigned to users; Represents the expected value of historical demand of the jth household.

[0112] Constraints:

[0113] 1. Regional resource total amount constraints:

[0114]

[0115] 2. User QoS priority constraints: Based on the QoS requirements of each household, ensure that the needs of high-priority users are met first.

[0116] p j ≥d j QoS j .

[0117] Finally, it should be noted that the above embodiments are only used to illustrate and explain the present invention, and are not intended to limit the present invention to the scope of the described embodiments. In addition, those skilled in the art can understand that the present invention is not limited to the above embodiments, and more variations and modifications can be made according to the teachings of the present invention, and these variations and modifications all fall within the scope of the protection claimed by the present invention.

Claims

1. A two-layer multi-objective rapid power dispatching method based on adjustable priorities, characterized in that: The steps include: Step S1: Collect relevant data of electricity and establish a parameter model; Step S2: Optimizing the power distribution partition control objective function of the power grid; Step S3: Design a priority mechanism; Step S4: Combine the priority mechanism and utilize the two-layer allocation mechanism to dynamically adjust the allocation strategy to achieve optimal resource configuration.

2. According to claim 1, a two-layer multi-objective rapid power allocation method based on adjustable priorities is characterized in that: The step S1 comprises the following steps: Step S11: taking the static limit current and the static limit power angle parameters as the control independent variables, designing the control constraint parameter optimization scheduling, adopting the grid-connected converter single machine control method, constructing the steady-state tracking control parameter model, and forming the coupling model of the power distribution partition control of the power grid; Step S12: In the static distribution mode of the power system, the grid voltage distribution is dispatched by partition to obtain the static voltage stability parameter distribution of the system; Step S13: Based on the generalized characteristic analysis method defined by static characteristics, the partition control state parameters of the grid voltage distribution are analyzed to obtain the closed-loop control characteristics of the output current; combined with the morphological parameter distribution of the power load, the partition distribution of the grid voltage is completed, and the historical maximum load of the distribution network is analyzed to determine whether the current flowing through the tie line satisfies the mapping relationship; On this basis, an optimal solution is found, which satisfies the dispatching model of active power grid partition control, and finally the adaptive optimization function is obtained as shown in the formula; Step S14: construct a topological structure model, and obtain the spatial distribution of historical data of distribution network voltage according to the principal component; Step S15: Based on the above analysis, a fuzzy state distribution set of parameters for grid voltage distribution zoning control is established to obtain steady-state characteristic quantities for distribution network power control. Fuzzy parameter constraint control is performed based on the steady-state characteristic parameters for distribution network power zoning control constructed above.

3. According to claim 2, a two-layer multi-objective rapid power allocation method based on adjustable priorities is characterized in that: The priority mechanism is a combination of power load rating and message priority; Based on QoS metrics, the QoS level can be further refined according to environmental requirements, making it more adaptable to the WMSN priority mechanism in the smart grid environment; the QoS level is determined by subjective and objective weights.

4. The method for rapid power allocation based on two layers and multiple objectives with adjustable priorities according to claim 3 is characterized in that: In power distribution, QoS priority is divided into two parts: subjective QoS metric and objective QoS metric. The subjective QoS metric reflects the user's needs and usage experience preferences, while the objective QoS metric OQ is based on the stability and performance goals of the power grid system resources. The balance between subjective and objective components is adjusted by parameter α to adapt to the distribution needs in different scenarios.

5. A two-layer multi-objective rapid power dispatch system based on adjustable priorities, characterized in that: It includes the following units: data collection unit, function optimization unit, priority mechanism design unit, and allocation unit; The data acquisition unit is used to collect relevant data of electricity and establish a parameter model; The function optimization unit is used to optimize the power grid distribution power partition control objective function; The priority mechanism design unit is used to design a priority mechanism; The allocation unit is used to utilize a two-layer allocation mechanism to dynamically adjust the allocation strategy to achieve optimal configuration of resources.

6. The two-layer multi-objective rapid power dispatching system based on adjustable priorities according to claim 5 is characterized in that: The data acquisition unit is specifically used to perform the following steps: Step S11: taking the static limit current and the static limit power angle parameters as the control independent variables, designing the control constraint parameter optimization scheduling, adopting the grid-connected converter single machine control method, constructing the steady-state tracking control parameter model, and forming the coupling model of the power distribution partition control of the power grid; Step S12: In the static distribution mode of the power system, the grid voltage distribution is dispatched by partition to obtain the static voltage stability parameter distribution of the system; Step S13: Based on the generalized characteristic analysis method defined by static characteristics, the partition control state parameters of the grid voltage distribution are analyzed to obtain the closed-loop control characteristics of the output current; combined with the morphological parameter distribution of the power load, the partition distribution of the grid voltage is completed, and the historical maximum load of the distribution network is analyzed to determine whether the current flowing through the tie line satisfies the mapping relationship; On this basis, an optimal solution is found, which satisfies the dispatching model of active power grid partition control, and finally the adaptive optimization function is obtained as shown in the formula; Step S14: construct a topological structure model, and obtain the spatial distribution of historical data of distribution network voltage according to the principal component; Step S15: Based on the above analysis, a fuzzy state distribution set of parameters for grid voltage distribution zoning control is established to obtain steady-state characteristic quantities for distribution network power control. Fuzzy parameter constraint control is performed based on the steady-state characteristic parameters for distribution network power zoning control constructed above.

7. The two-layer multi-objective rapid power dispatching system based on adjustable priorities according to claim 6 is characterized in that: The priority mechanism combines the power load rating with the priority of the message; Based on QoS metrics, the QoS level can be further refined according to environmental requirements, making it more adaptable to the WMSN priority mechanism in the smart grid environment; the QoS level is determined by subjective and objective weights.

8. The two-layer multi-objective rapid power dispatching system based on adjustable priorities according to claim 7 is characterized in that: In power distribution, QoS priority is divided into two parts: subjective QoS metric and objective QoS metric. The subjective QoS metric reflects the user's needs and usage experience preferences, while the objective QoS metric OQ is based on the stability and performance goals of the power grid system resources. The balance between subjective and objective components is adjusted by parameter α to adapt to the distribution needs in different scenarios.

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