Reactive differential game distribution method and system for power distribution network

Abstract

The invention discloses a power distribution network reactive power differential game distribution method and system, and relates to the technical field of power system optimization scheduling, and the method comprises the following steps: constructing a power distribution network differential game voltage deviation optimization model, and calculating to obtain a sensitivity matrix; based on a reactive power distribution mechanism module, priority and nearby adjustment is realized; constructing a full-period multi-target model, and solving to obtain equipment output and voltage change information in a full period; and feeding back to the hour-level voltage deviation optimization model to obtain an optimal reactive power distribution scheme and a control strategy. According to the method, the generalization capability of increasing a population through chaotic mapping, the local optimization capability of an adaptive weight factor enhancement algorithm and the global search capability of a reverse learning and Cauchy variation improvement algorithm are considered, so that the algorithm jumps out of local optimization, and the optimization result is updated through non-dominated sorting screening so as to obtain a Pareto optimal solution.

Description

Technical Field

[0001] The invention relates to the technical field of power system optimization dispatching, and in particular to a distribution network reactive power differential game allocation method and system. Background Art

[0002] Reactive voltage and line loss levels are key indicators of the operation of the power grid in the new power system. The access of a large number of reactive regulation devices will significantly affect the voltage stability and economic operation of the distribution network. At present, relying solely on reactive devices in the station for graded compensation is not effective and has failed to achieve coordinated interaction at the station level. Distributed photovoltaics and adjustable resources on the substation side have flexible reactive regulation capabilities and a fast response speed. How to coordinate and optimize the output of different reactive devices in the power grid and realize refined reactive compensation is of great significance for stabilizing the feeder voltage of the distribution network, reducing the line loss level of the power grid, and improving the reactive support capacity of the distribution network.

[0003] Power reactive power optimization mainly includes two aspects: optimization model and optimization algorithm. In terms of optimization model, unlike the decoupling control of active power, reactive power and voltage in the transmission network, it is difficult to simply decouple active power, reactive power and voltage in the distribution network due to its large impedance. The interactive influence of active power and reactive power on node voltage in the distribution network makes voltage stability optimization control difficult and the voltage is difficult to maintain within a reasonable range. At present, reactive power and voltage optimization mainly adopts decentralized control, that is, various reactive voltage regulation equipment is regulated and controlled based on decentralized controllers. At this time, voltage optimization control can be transformed into a multi-agent decision-making optimization problem. Game theory, as an important means to solve multi-agent decision-making problems, has been applied in power system dispatching and control, and can effectively solve many problems in the power system. In terms of optimization algorithm, considering that power reactive power optimization is a complex mixed nonlinear programming problem with multiple variables and multiple constraints, traditional optimization methods such as differential algorithms and genetic algorithms have certain limitations in dealing with complex reactive power optimization problems, and are prone to local optimality, slow convergence speed, low accuracy, and unstable reactive power regulation. Summary of the invention

[0004] In view of the problems existing in the existing reactive power differential game allocation and system of distribution network, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that it is impossible to solve the problem of refined reactive power compensation in a distribution network containing multiple types of regulating equipment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for allocating reactive power in a distribution network using differential game theory, which comprises the following steps:

[0008] Construct a differential game voltage deviation optimization model for distribution network and calculate the sensitivity matrix;

[0009] Based on the reactive power distribution mechanism module, priority and proximity adjustment are realized;

[0010] Construct a full-cycle multi-objective model and solve it to obtain the equipment output and voltage change information within the full cycle;

[0011] Feedback is provided to the hourly voltage deviation optimization model to obtain the optimal reactive power distribution plan and control strategy.

[0012] As a preferred solution of the reactive power differential game allocation method for distribution network of the present invention, wherein: the differential game voltage deviation optimization model of distribution network includes an hourly voltage deviation optimization module, a long time scale reactive power optimization allocation module, and an optimization algorithm calculation module;

[0013] The hourly voltage deviation optimization module includes a voltage deviation optimization model, a system network loss model, and a sensitivity matrix calculation module;

[0014] The long-term reactive power optimization allocation module includes a reactive power compensation cost model, a voltage fluctuation model and a multi-agent reactive power allocation mechanism module;

[0015] The optimization algorithm calculation module takes the improved Osprey optimization algorithm as the core and calculates the reactive power optimization allocation scheme of each regulating resource at each moment.

[0016] As a preferred solution of the reactive differential game allocation method for the distribution network of the present invention, the hourly voltage deviation optimization module aims to minimize network loss and minimize voltage deviation, wherein the voltage deviation optimization model is expressed as:

[0017]

[0018]

[0019] Where ΔV is the voltage deviation column vector of the node where the device is located in the system; Δu is the voltage regulation amount of the tie line node; H is the diagonal weighted matrix of the device node voltage in the system; G is the diagonal weighted matrix of the PCC point voltage; the diagonal elements of H reflect the importance of the device node voltage; the diagonal elements of G represent the unit control cost of the voltage control amount; For f The terminal revenue function of the i-th device at time t f Line loss level at the moment;

[0020] In the voltage deviation objective function, the voltage regulation deviation is expressed as:

[0021]

[0022] The system network loss model is expressed as:

[0023]

[0024]

[0025]

[0026] Where C3 is the system network loss at time t; g ij is the conductance of line ij; U i , U j is the voltage value of nodes i and j; θ ij is the power factor angle of line ij; U i lim is the voltage constraint value of node i; U imax , U imin is the voltage upper and lower limits of node i; Q j lim is the reactive power constraint value of node j; Q jmax , Q jmin is the upper and lower limits of reactive power of node j; NDG is the number of distributed power sources; NPV is the number of distributed photovoltaics;

[0027] The active-reactive voltage comprehensive sensitivity index is used to measure the correlation and priority ranking between and within the subjects. The active voltage sensitivity and reactive voltage sensitivity are calculated based on the Jacobian matrix obtained by the power flow calculation. The active voltage sensitivity and reactive voltage sensitivity are comprehensively considered to form a comprehensive sensitivity influencing factor, and the equipment that can output power is prioritized. The specific expression is as follows:

[0028]

[0029]

[0030] ΔV=S PV ΔP+S QV ΔQ

[0031]

[0032] As a preferred solution of the reactive differential game allocation method for distribution network of the present invention, the reactive power optimization allocation module of the long time scale is used to construct a reactive power optimization model of distribution network with the goal of minimizing the feeder voltage fluctuation, reactive power compensation cost and active power loss within the cycle, wherein the reactive power optimization model of distribution network is expressed as:

[0033]

[0034]

[0035] Where, C1 is the feeder voltage fluctuation value; U t is the actual value of feeder voltage at time t; is the mean feeder voltage; U max , U min is the upper and lower limits of the feeder voltage; T in is the cycle; C2 is the reactive power compensation cost; Q pv,n,t is the reactive power compensation power of photovoltaic; ρ pv,n,t is the unit cost of photovoltaic reactive power compensation; Q MT,n,t is the reactive compensation power of gas turbine; ρ MT,n,t is the unit cost of reactive power compensation for gas turbine; Q DG,n,t is the reactive power compensation power of distributed generation; ρ DG,n,t is the unit cost of reactive power compensation for distributed generation; Q SVG,n,t is the SVG reactive compensation power; ρ SVG,n,t N is the unit cost of SVG reactive power compensation; i,CB,t is the number of capacitor banks put into use; ρ CB,i,t Unit compensation cost invested in capacitor banks;

[0036] The reactive power optimization allocation mechanism in the long-term reactive power optimization allocation module includes a reactive power allocation mechanism for the substation area. By designing the reactive power allocation mechanism for the substation area, the reactive power regulation potential calculation of the resources on the substation side and the reactive power refined compensation correction are realized.

[0037] The reactive power distribution mechanism of the substation area includes the modification of reactive power voltage sensitivity matrix on the substation side and the design of substation area voltage control logic;

[0038] The reactive voltage sensitivity matrix correction on the substation side first determines the active output plan participating in the active power optimization, and brings the result of the active output plan into the voltage regulation deviation formula, thereby establishing a satisfactory mapping relationship between the voltage and the injected active and reactive power, and determining the unit reactive compensation amount as:

[0039] ΔV′ Q =ΔV-ΔV P =S QV ΔQ

[0040] S QV =ΔV′ Q ΔQ -1 ;

[0041]

[0042] Where ΔV′ Q To determine the reactive voltage change after active power planning; ΔV is the voltage change; ΔV P is the active voltage change; S QVis the reactive power-voltage sensitivity; ΔQ is the reactive power change; Z S,j,t is the reactive voltage sensitivity of node j to the whole system; V i,ref is the reference voltage of node i; V i,t is the actual voltage of node i;

[0043] The voltage control logic design of the station area, wherein the calculation formula for setting the voltage value of the compensation point is:

[0044]

[0045] Where V t,dim is the voltage threshold value; a1 and a2 are the preset value and actual value of the voltage respectively; ΔU is the voltage change value;

[0046] The formula for inverse time voltage operation and reactive action is:

[0047] T U =-k U U+d U ;

[0048] T Q =-k Q Q+d Q ;

[0049] Where, T U , T Q is the voltage and reactive power action value; k U , k Q is the slope of the curve; d U d Q is the time constant;

[0050] In addition, the inverse time voltage reactive power control strategy couples and integrates the single-dimensional voltage and reactive power to form a voltage-reactive reactive inverse time action criterion, which is expressed by the formula:

[0051] T=-k Q |ΔQ|-k U |ΔU|+d;

[0052]

[0053] Where ΔQ and ΔU are the distances from reactive power and voltage to the corresponding reactive power and voltage constant boundaries respectively; A is the integral constant; t Q is the starting time of reactive power exceeding the limit; t U It is the starting time of voltage over-limit.

[0054] As a preferred solution of the reactive differential game allocation method of the distribution network of the present invention, the reactive power allocation mechanism of the photovoltaic power station includes determining the upper and lower limits of the reactive power of the photovoltaic power station, setting the current voltage boundary as U0, and the current voltage value as U t , then the voltage change is expressed as:

[0055]

[0056]

[0057] The reactive power regulation range in different scenarios of photovoltaic power stations includes scenarios where the voltage exceeds the upper limit, scenarios where the voltage exceeds the lower limit, and scenarios where the voltage does not exceed the limit;

[0058] When the voltage exceeds the upper limit, the upper and lower limits of reactive power regulation are expressed as follows:

[0059]

[0060] When the voltage exceeds the lower limit, the upper and lower limits of reactive power regulation are expressed as follows:

[0061]

[0062] In the scenario where the voltage does not exceed the limit, the upper and lower limits of reactive power regulation are expressed as:

[0063]

[0064] As a preferred solution of the reactive power differential game allocation method for distribution network of the present invention, the specific steps of the optimization algorithm calculation module include:

[0065] Initialization phase: put all the liberations into the set to be sorted;

[0066] Comparison phase: Compare each pair of solutions. If one solution is not inferior to the other solution in all objective functions, the latter is marked as dominated.

[0067] Hierarchical division: The solutions are divided into different levels according to the dominance relationship. The first level contains all non-dominated solutions, and the subsequent levels contain suboptimal solutions;

[0068] Iterative update: After the comparison is completed within each level, the dominated solutions are removed and a new round of non-dominated sorting is performed on the remaining solutions until all solutions are correctly classified into their respective levels.

[0069] As a preferred scheme of the reactive differential game allocation method for the distribution network described in the present invention, after obtaining the optimal reactive power allocation scheme and control strategy, the performance of the reactive power allocation scheme and control strategy is regularly evaluated, including voltage stability, reactive power compensation cost and system reliability.

[0070] In a second aspect, an embodiment of the present invention provides a reactive differential game allocation system for a distribution network, which includes: a construction module, an analysis module, and an output module;

[0071] The construction module is used to construct a differential game voltage deviation optimization model for the distribution network;

[0072] The analysis module is used to realize priority and proximity adjustment based on the reactive power distribution mechanism module;

[0073] The output module is used to obtain the optimal reactive power distribution scheme and control strategy.

[0074] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned distribution network reactive differential game allocation method.

[0075] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned distribution network reactive differential game allocation method is implemented.

[0076] The beneficial effects of the present invention are as follows: a differential game model for reactive power optimization at the cycle-hour level of the distribution network is constructed to realize the Nash equilibrium of reactive power distribution of each device; the reactive power optimization allocation mechanism of the substation and the photovoltaic power station is improved to realize the priority division and regulation capacity calculation of different reactive power voltage regulation resources; an improved multi-objective Osprey optimization algorithm is designed, chaotic mapping is considered to increase the generalization ability of the population, an adaptive weight factor is used to enhance the local optimization ability of the algorithm, reverse learning and Cauchy mutation are used to enhance the global search ability of the algorithm, so that the algorithm jumps out of the local optimum, and non-dominated sorting is used to screen and update the optimization result to obtain the Pareto optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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. Among them:

[0078] Figure 1 Flow chart of the reactive power differential game allocation method for distribution network.

[0079] Figure 2 This is the structural diagram of the reactive power differential game allocation method for distribution network.

[0080] Figure 3This is a schematic diagram of the nine-zone voltage control of the improved reactive differential game allocation method for the distribution network.

[0081] Figure 4 It is the trend table of the inverse time voltage reactive power control strategy of the reactive power differential game allocation method for the distribution network. DETAILED DESCRIPTION

[0082] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0083] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0084] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0085] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0086] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0087] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0088] Example 1

[0089] Reference Figure 1 to Figure 4 , which is the first embodiment of the present invention, and provides a method for allocating reactive power in a distribution network by differential game, comprising the following steps:

[0090] S1. Construct a differential game voltage deviation optimization model for the distribution network and calculate the sensitivity matrix.

[0091] The distribution network differential game voltage deviation optimization model includes an hourly voltage deviation optimization module, a long-time scale reactive power optimization allocation module, and an optimization algorithm calculation module;

[0092] The hourly voltage deviation optimization module includes a voltage deviation optimization model, a system network loss model, and a sensitivity matrix calculation module;

[0093] The long-term reactive power optimization allocation module includes a reactive power compensation cost model, a voltage fluctuation model and a multi-agent reactive power allocation mechanism module;

[0094] The optimization algorithm calculation module takes the improved Osprey optimization algorithm as the core and calculates the reactive power optimization allocation scheme of each regulating resource at each moment.

[0095] The hourly voltage deviation optimization module aims to minimize network loss and voltage deviation, and constructs the network loss optimization objective function in the form of penalty function by constraining the node voltage. Based on the non-cooperative game goal, each subject takes maximizing its own interests as the strategic goal. When there is a shortage or surplus, the nearest adjustment is given priority to achieve that the node voltage deviation and line loss level are both less than the voltage deviation value and line loss level under direct action, and the Nash equilibrium state is achieved. The voltage deviation optimization model is expressed as:

[0096]

[0097]

[0098] Where ΔV is the voltage deviation column vector of the node where the device is located in the system; Δu is the voltage regulation amount of the tie line node; H is the diagonal weighted matrix of the device node voltage in the system; G is the diagonal weighted matrix of the PCC point voltage; the diagonal elements of H reflect the importance of the device node voltage; the diagonal elements of G represent the unit control cost of the voltage control amount; For f The terminal revenue function of the i-th device at time t f Line loss level at the moment;

[0099] The voltage deviation objective function reflects the relationship between the regional voltage and the active and reactive power at the current moment. The voltage regulation deviation is expressed as:

[0100]

[0101] The system network loss model is expressed as:

[0102]

[0103]

[0104]

[0105] Where C3 is the system network loss at time t; g ij is the conductance of line ij; U i , U j is the voltage value of nodes i and j; θ ij is the power factor angle of line ij; U i lim is the voltage constraint value of node i; U imax , U imin is the voltage upper and lower limits of node i; Q j lim is the reactive power constraint value of node j; Q jmax , Q jmin is the upper and lower limits of reactive power of node j; NDG is the number of distributed power sources; NPV is the number of distributed photovoltaics;

[0106] The active-reactive voltage comprehensive sensitivity index is used to measure the correlation and priority ranking between and within the subjects. The active voltage sensitivity and reactive voltage sensitivity are calculated based on the Jacobian matrix obtained by the power flow calculation. The active voltage sensitivity and reactive voltage sensitivity are comprehensively considered to form a comprehensive sensitivity influencing factor, and the equipment that can output power is prioritized. The specific expression is as follows:

[0107]

[0108]

[0109] ΔV=S PV ΔP+S QV ΔQ

[0110]

[0111] In the long-term reactive power optimization allocation module, the reactive power optimization model of the distribution network is constructed with the goals of minimizing the feeder voltage fluctuation, reactive power compensation cost and active power loss within the cycle. The reactive power optimization model of the distribution network is expressed as:

[0112]

[0113]

[0114] Where, C1 is the feeder voltage fluctuation value; U t is the actual value of feeder voltage at time t; is the mean feeder voltage; U max , U min is the upper and lower limits of the feeder voltage; T in is the cycle; C2 is the reactive power compensation cost; Q pv,n,t is the reactive power compensation power of photovoltaic; ρ pv,n,t is the unit cost of photovoltaic reactive power compensation; Q MT,n,t is the reactive compensation power of gas turbine; ρ MT,n,t is the unit cost of reactive power compensation for gas turbine; Q DG,n,t is the reactive power compensation power of distributed generation; ρ DG,n,t is the unit cost of reactive power compensation for distributed generation; Q SVG,n,t is the SVG reactive compensation power; ρ SVG,n,t N is the unit cost of SVG reactive power compensation; i,CB,t is the number of capacitor banks put into use; ρ CB,i,t Unit compensation cost invested in capacitor banks;

[0115] The reactive power optimization allocation mechanism in the long-term reactive power optimization allocation module includes a reactive power allocation mechanism for the substation area. By designing the reactive power allocation mechanism for the substation area, the reactive power regulation potential calculation of the resources on the substation side and the reactive power refined compensation correction are realized.

[0116] The reactive power distribution mechanism of the substation area includes the modification of reactive power voltage sensitivity matrix on the substation side and the design of substation area voltage control logic;

[0117] The reactive voltage sensitivity matrix correction on the substation side first determines the active output plan participating in the active power optimization, and brings the result of the active output plan into the voltage regulation deviation formula to obtain the node voltage amplitude change ΔUP when the DG active output is determined, thereby establishing a satisfactory mapping relationship between the voltage and the injected active and reactive power, and determining the unit reactive compensation amount as:

[0118] ΔV′ Q =ΔV-ΔV P =S QV ΔQ

[0119] S QV =ΔV′ Q ΔQ -1 ;

[0120]

[0121] Where ΔV′ Q To determine the reactive voltage change after active power planning; ΔV is the voltage change; ΔV P is the active voltage change; S QV is the reactive power-voltage sensitivity; ΔQ is the reactive power change; Z S,j,t is the reactive voltage sensitivity of node j to the whole system; V i,ref is the reference voltage of node i; V i,t is the actual voltage of node i;

[0122] The voltage control logic design of the substation area is designed according to the standard. The voltage deviation of the low-voltage distribution network node is not higher than 7% of the nominal voltage and not lower than 10% of the nominal voltage. According to the voltage state of the substation area, the reactive power regulation margin of the substation area is calculated. Combined with the inverse time voltage reactive power control strategy, the nine-area diagram method is improved to form an adaptive voltage / reactive power boundary calculation to realize dynamic automatic voltage reactive power control. The improved substation voltage control nine-area Figure 2 As shown, the calculation formula for the compensation point setting voltage value is:

[0123]

[0124] Where V t,dim is the voltage threshold value; a1 and a2 are the preset value and actual value of the voltage respectively; ΔU is the voltage change value;

[0125] Inverse time voltage reactive power control strategy Figure 3 As shown, the inverse time voltage / reactive power is simplified, and the inverse time voltage working and reactive power action formulas are:

[0126] T U =-k U U+d U ;

[0127] T Q =-k Q Q+d Q ;

[0128] Where, T U 、T Q is the voltage and reactive power action value; k U , k Q is the slope of the curve; d U d Q is the time constant;

[0129] In addition, the inverse time voltage reactive power control strategy couples and integrates the single-dimensional voltage and reactive power to form a voltage-reactive reactive inverse time action criterion, which is expressed by the formula:

[0130] T=-k Q |ΔQ|-k U |ΔU|+d;

[0131]

[0132] Where ΔQ and ΔU are the distances from reactive power and voltage to the corresponding reactive power and voltage constant boundaries respectively; A is the integral constant; t Q is the starting time of reactive power exceeding the limit; t U It is the starting time of voltage over-limit.

[0133] Add judgment criteria to clarify the reactive power voltage regulation output of the substation area,

[0134] Criterion 1: This indicates that reactive power accounts for most of the reasons leading to over-limit, so the action is to switch on and off the capacitor;

[0135] Criterion 2: This indicates that voltage factors account for most of the reasons leading to the over-limit, so the action is to adjust the reactive power output DG of distributed generation.

[0136] S2. Based on the reactive power distribution mechanism module, priority and proximity adjustment are realized.

[0137] S3. Construct a full-cycle multi-objective model and solve it to obtain the equipment output and voltage change information within the full cycle.

[0138] The reactive power allocation mechanism of the photovoltaic power station includes determining the upper and lower limits of the reactive power of the photovoltaic power station. Considering that the voltage cannot exceed the limit, adjusting the power factor alone may aggravate the voltage exceeding the limit. Therefore, for the voltage exceeding the limit, the voltage change after adjusting the reactive power is within the feasible range. The current voltage boundary is set to U0, and the current voltage value is U t , then the voltage change is expressed as:

[0139]

[0140]

[0141] The reactive power regulation range in different scenarios of photovoltaic power stations includes scenarios where the voltage exceeds the upper limit, scenarios where the voltage exceeds the lower limit, and scenarios where the voltage does not exceed the limit;

[0142] In the voltage over-limit scenario, the PV power station voltage exceeds the upper limit range and needs to absorb reactive power to reduce the voltage. At this time, the PV power station can only absorb reactive power. The upper and lower limits of reactive power regulation are expressed as:

[0143]

[0144] In the voltage lower limit scenario, the voltage of the photovoltaic power station is lower than the lower limit range, and reactive power needs to be generated to increase the voltage. At this time, the photovoltaic power station can only generate reactive power. The upper and lower limits of reactive power regulation are expressed as:

[0145]

[0146] In the scenario where the voltage does not exceed the limit, the upper and lower limits of reactive power regulation are expressed as:

[0147]

[0148] Assume that the feeder voltage of the photovoltaic power source at the current moment is U t , the reactive power of the photovoltaic power source at the current moment before participating in the regulation is Q t , the reactive power regulation potential of the photovoltaic power station under different scenarios is calculated as shown in the following formula.

[0149]

[0150] In the reactive power distribution mechanism of the photovoltaic power station, the reactive power regulation capability of the photovoltaic power source is jointly affected by the current active power and the power factor angle. Therefore, different from the comprehensive sensitivity calculation method of the substation, photovoltaic power needs to consider the sensitivity calculation of active and reactive power to voltage at the same time; according to the calculated photovoltaic sensitivity matrix, it is combined with the reactive power regulation margin to calculate the regulation priority of distributed photovoltaic power.

[0151]

[0152]

[0153] In the formula, sep PV,i,t is the comprehensive active power-voltage sensitivity priority of photovoltaic; seq PV,i,t Comprehensive photovoltaic The margin and Ω are the photovoltaic collection.

[0154] S4. Feedback to the hourly voltage deviation optimization model to obtain the optimal reactive power distribution plan and control strategy.

[0155] The optimization algorithm calculation module takes the improved multi-objective osprey optimization algorithm as the core, solves the optimization model, and obtains the reactive power optimization allocation plan of each device at each time. The algorithm includes the positioning and fishing stages as well as the hunting and feeding stages; in the initialization stage, in order to ensure that the population has good diversity, the ICMIC chaotic map is applied to the initialization of the population, and the superior value within the population is selected through reverse population calculation to update the population to obtain a better population sample; secondly, in the positioning and fishing stages, the adaptive adjustment rate is considered, and the iteration cycle of appropriate length is selected to enhance the local search capability; finally, in the local development stage, the combination of reverse learning and Cauchy mutation is used to make the algorithm jump out of the limitation of local optimality, and at the same time feedback is given to the population initialization stage to enhance the coverage of the population.

[0156] The positioning and fishing stage includes an initialization stage and a positioning and fishing stage. In the initialization stage, ICMIC chaotic mapping is selected as an improved scheme for initializing the population. ICMIC mapping is a chaotic model with an infinite number of mapping folds. Compared with the common Logistic mapping and Tent mapping, the mapping has the advantages of uniform traversal and faster convergence, and is expressed as:

[0157] r i+1,j = sin(a / r i,j );

[0158] x i,j =lb j +r·(ub j -lb j );

[0159] In the positioning and fishing stage, the “underwater fish school” is expressed by the formula: FP i ={x k |k∈{1,2,...,N}^F k <F i}∪{x best In the process of updating the osprey's position, an adaptive weight that changes with the number of iterations is introduced to control the direction and distance of the osprey's fishing, thereby improving the algorithm's optimization accuracy and convergence speed;

[0160]

[0161]

[0162]

[0163]

[0164]

[0165] In the formula, ω(t) is the adaptive weight factor; item is the current iteration number; item max is the maximum number of iterations; r is the action coefficient; X i is the current position vector.

[0166] During the hunting and feeding phases, reverse learning and Cauchy mutation are used to optimize the local development phase of OOA, enhance the global search capability of OOA, and prevent it from falling into the local optimal situation.

[0167]

[0168]

[0169]

[0170] The random number distribution range generated by the Cauchy distribution is relatively wide, which can effectively improve the global search ability of the algorithm and escape the local optimum. The Cauchy distribution function is expressed as:

[0171]

[0172] Incorporating Cauchy mutation into the algorithm, it can be expressed as:

[0173]

[0174] After calculating the fitness function, the elite selection strategy is used to retain the best individuals and store them in the initialization population. The internal and external populations jointly participate in the selection operation. The reverse learning and Cauchy mutation are integrated and applied to the elite selection strategy. In the selection stage, the optimal number of individuals retained by the algorithm is divided by reverse learning, and Cauchy mutation is used to jump out of the local optimum. The two are determined by the selection probability P. The calculation formula is expressed as:

[0175]

[0176] The improved elite selection strategy, based on the new optimal value obtained by integrating reverse learning and Cauchy mutation strategy, is expressed as:

[0177]

[0178] In the local development stage, the latest optimization value calculation is expressed as:

[0179]

[0180]

[0181]

[0182] The specific steps of the optimization algorithm calculation module include:

[0183] Initialization phase: put all the liberations into the set to be sorted;

[0184] Comparison phase: Compare each pair of solutions. If one solution is not inferior to the other solution in all objective functions, the latter is marked as dominated.

[0185] Hierarchical division: The solutions are divided into different levels according to the dominance relationship. The first level contains all non-dominated solutions, and the subsequent levels contain suboptimal solutions;

[0186] Iterative update: After the comparison is completed within each level, the dominated solutions are removed and a new round of non-dominated sorting is performed on the remaining solutions until all solutions are correctly classified into their respective levels.

[0187] After obtaining the optimal reactive power allocation scheme and control strategy, the performance of the reactive power allocation scheme and control strategy is regularly evaluated, including voltage stability, reactive power compensation cost, and system reliability.

[0188] In summary, a differential game model for reactive power optimization at the cycle-hour level of the distribution network was constructed to achieve the Nash equilibrium of reactive power distribution of each device; the reactive power optimization allocation mechanism of the substation and photovoltaic power station was improved to realize the priority division and regulation capacity calculation of different reactive voltage regulation resources; an improved multi-objective Osprey optimization algorithm was designed, which considered chaotic mapping to increase the generalization ability of the population, and adaptive weight factors to enhance the local optimization ability of the algorithm. The reverse learning and Cauchy mutation were used to improve the global search ability of the algorithm, so that the algorithm jumped out of the local optimum, and the non-dominated sorting was used to screen and update the optimization results to obtain the Pareto optimal solution.

[0189] Example 2

[0190] On the basis of the first embodiment, this embodiment further provides a reactive power differential game allocation system for a distribution network, comprising a construction module, an analysis module, and an output module;

[0191] The construction module is used to construct a differential game voltage deviation optimization model for the distribution network;

[0192] The analysis module is used to realize priority and proximity adjustment based on the reactive power distribution mechanism module;

[0193] The output module is used to obtain the optimal reactive power distribution scheme and control strategy.

[0194] This embodiment also provides a computer device, which is applicable to the case of a reactive differential game allocation method for a distribution network, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the reactive differential game allocation method for a distribution network as proposed in the above embodiment.

[0195] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0196] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing reactive power differential game allocation in a distribution network as proposed in the above embodiment is implemented.

[0197] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0198] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the present invention.

Claims

1. A reactive power differential game allocation method for a distribution network, characterized by: The following steps are included: Construct a differential game voltage deviation optimization model for distribution network and calculate the sensitivity matrix; Based on the reactive power distribution mechanism module, priority and proximity adjustment are realized; Construct a full-cycle multi-objective model and solve it to obtain the equipment output and voltage change information within the full cycle; Feedback is provided to the hourly voltage deviation optimization model to obtain the optimal reactive power distribution plan and control strategy.

2. The reactive power differential game allocation method for distribution network according to claim 1, characterized in that: The distribution network differential game voltage deviation optimization model includes an hourly voltage deviation optimization module, a long-time scale reactive power optimization allocation module, and an optimization algorithm calculation module; The hourly voltage deviation optimization module includes a voltage deviation optimization model, a system network loss model, and a sensitivity matrix calculation module; The long-term reactive power optimization allocation module includes a reactive power compensation cost model, a voltage fluctuation model and a multi-agent reactive power allocation mechanism module; The optimization algorithm calculation module takes the improved Osprey optimization algorithm as the core and calculates the reactive power optimization allocation scheme of each regulating resource at each moment.

3. The reactive power differential game allocation method for distribution network according to claim 2, characterized in that: The hourly voltage deviation optimization module aims to minimize network loss and voltage deviation, wherein the voltage deviation optimization model is expressed as: Where ΔV is the voltage deviation column vector of the node where the device is located in the system; Δu is the voltage regulation amount of the tie line node; H is the diagonal weighted matrix of the device node voltage in the system; G is the diagonal weighted matrix of the PCC point voltage; the diagonal elements of H reflect the importance of the device node voltage; the diagonal elements of G represent the unit control cost of the voltage control amount; For f The terminal revenue function of the i-th device at time t f Line loss level at the moment; In the voltage deviation objective function, the voltage regulation deviation is expressed as: The system network loss model is expressed as: Where, C3 is the system network loss at time t; g ij is the conductance of line ij; U i , U j is the voltage value of nodes i and j; θ ij is the power factor angle of line ij; U ilim is the voltage constraint value of node i; U imax , U imin is the voltage upper and lower limits of node i; Q jlim is the reactive power constraint value of node j; Q jmax , Q jmin is the upper and lower limits of reactive power of node j; NDG is the number of distributed power sources; NPV is the number of distributed photovoltaics; The active-reactive voltage comprehensive sensitivity index is used to measure the correlation and priority ranking between and within the subjects. The active voltage sensitivity and reactive voltage sensitivity are calculated based on the Jacobian matrix obtained by the power flow calculation. The active voltage sensitivity and reactive voltage sensitivity are comprehensively considered to form a comprehensive sensitivity influencing factor, and the equipment that can output power is prioritized. The specific expression is as follows: ΔV=S PV ·ΔP+S QV ·ΔQ; 4. The reactive power differential game allocation method for distribution network according to claim 3, characterized in that: In the long-term reactive power optimization allocation module, a reactive power optimization model of the distribution network is constructed with the goals of minimizing the feeder voltage fluctuation, reactive power compensation cost and active power loss within the cycle, wherein the reactive power optimization model of the distribution network is expressed as: Where, C1 is the feeder voltage fluctuation value; U t is the actual value of feeder voltage at time t; is the mean feeder voltage; U max , U min is the upper and lower limits of the feeder voltage; T in is the cycle; C2 is the reactive power compensation cost; Q pv,n,t is the reactive power compensation power of photovoltaic; ρ pv,n,t is the unit cost of photovoltaic reactive power compensation; Q MT,n,t is the reactive compensation power of gas turbine; ρ MT,n,t is the unit cost of reactive power compensation for gas turbine; Q DG,n,t is the reactive power compensation power of distributed generation; ρ DG,n,t is the unit cost of reactive power compensation for distributed generation; Q SVG,n,t is the SVG reactive compensation power; ρ SVG,n,t N is the unit cost of SVG reactive power compensation; i,CB,t is the number of capacitor banks put into use; ρ CB,i,t Unit compensation cost invested in capacitor banks; The reactive power optimization allocation mechanism in the long-term reactive power optimization allocation module includes a reactive power allocation mechanism for the substation area. By designing the reactive power allocation mechanism for the substation area, the reactive power regulation potential calculation of the resources on the substation side and the reactive power refined compensation correction are realized. The reactive power distribution mechanism of the substation area includes the modification of reactive power voltage sensitivity matrix on the substation side and the design of substation area voltage control logic; The reactive voltage sensitivity matrix correction on the substation side first determines the active output plan participating in the active power optimization, and brings the result of the active output plan into the voltage regulation deviation formula, thereby establishing a satisfactory mapping relationship between the voltage and the injected active and reactive power, and determining the unit reactive compensation amount as: ΔV′ Q =ΔV-ΔV P =S QV ·ΔQ; S QV =ΔV′ Q ΔQ -1 ; Where ΔV′ Q To determine the reactive voltage change after active power planning; ΔV is the voltage change; ΔV P is the active voltage change; S QV is the reactive power-voltage sensitivity; ΔQ is the reactive power change; Z S,j,t is the reactive voltage sensitivity of node j to the whole system; V i,ref is the reference voltage of node i; V i,t is the actual voltage of node i; The voltage control logic design of the station area, wherein the calculation formula for setting the voltage value of the compensation point is: Where V t,dim is the voltage threshold value; a1 and a2 are the preset value and actual value of the voltage respectively; ΔU is the voltage change value; The formula for inverse time voltage operation and reactive action is: T U =-k U U+d U ; T Q =-k Q Q+d Q ; Where, T U , T Q is the voltage and reactive power action value; k U , k Q is the slope of the curve; d U d Q is the time constant; In addition, the inverse time voltage reactive power control strategy couples and integrates the single-dimensional voltage and reactive power to form a voltage-reactive reactive inverse time action criterion, which is expressed by the formula: T=-k Q |ΔQ|-k U |ΔU|+d; Where ΔQ and ΔU are the distances from reactive power and voltage to the corresponding reactive power and voltage constant boundaries respectively; A is the integral constant; t Q is the starting time of reactive power exceeding the limit; t U It is the starting time of voltage over-limit.

5. The reactive power differential game allocation method for distribution network according to claim 4, characterized in that: The reactive power distribution mechanism of the photovoltaic power station includes determining the upper and lower limits of the reactive power of the photovoltaic power station, setting the current voltage boundary as U0, and the current voltage value as U t , then the voltage change is expressed as: The reactive power regulation range in different scenarios of photovoltaic power stations includes scenarios where the voltage exceeds the upper limit, scenarios where the voltage exceeds the lower limit, and scenarios where the voltage does not exceed the limit; When the voltage exceeds the upper limit, the upper and lower limits of reactive power regulation are expressed as follows: When the voltage exceeds the lower limit, the upper and lower limits of reactive power regulation are expressed as follows: In the scenario where the voltage does not exceed the limit, the upper and lower limits of reactive power regulation are expressed as:

6. The reactive power differential game allocation method for distribution network according to claim 5, characterized in that: The specific steps of the optimization algorithm calculation module include: Initialization phase: put all the liberations into the set to be sorted; Comparison phase: Compare each pair of solutions. If one solution is not inferior to the other solution in all objective functions, the latter is marked as dominated. Hierarchical division: The solutions are divided into different levels according to the dominance relationship. The first level contains all non-dominated solutions, and the subsequent levels contain suboptimal solutions; Iterative update: After the comparison is completed within each level, the dominated solutions are removed and a new round of non-dominated sorting is performed on the remaining solutions until all solutions are correctly classified into their respective levels.

7. The reactive power differential game allocation method for distribution network according to claim 6, characterized in that: After obtaining the optimal reactive power allocation scheme and control strategy, the performance of the reactive power allocation scheme and control strategy is regularly evaluated, including voltage stability, reactive power compensation cost, and system reliability.

8. A reactive power differential game allocation system for a distribution network, based on the reactive power differential game allocation method for a distribution network according to any one of claims 1 to 7, characterized in that: Includes construction module, analysis module, and output module; The construction module is used to construct a differential game voltage deviation optimization model for the distribution network; The analysis module is used to realize priority and proximity adjustment based on the reactive power distribution mechanism module; The output module is used to obtain the optimal reactive power distribution scheme and control strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distribution network reactive power differential game allocation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the reactive power differential game allocation method for a distribution network according to any one of claims 1 to 7 are implemented.

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