Power system reactive power scheduling method and system based on PSOSHO algorithm

Abstract

The invention relates to the technical field of electric power system operation control, and provides an electric power system reactive power dispatching method and system based on a PSOSHO algorithm, and the method comprises the steps: constructing an electric vehicle charging load dynamic model; according to the dynamic model of the charging load of the electric vehicle, constructing a target function considering reactive power scheduling of the electric power system of the electric vehicle; constructing a power network constraint condition including the electric vehicle, and constructing a power system reactive power scheduling constraint condition according to the power network constraint condition; and according to the constraint condition of reactive power scheduling of the power system, solving a reactive power scheduling solution of the target function through a PSOSHO algorithm. According to the method, the algorithm can be effectively prevented from falling into a local optimal solution, the real-time scheduling requirement of the electric power system can be met, the negative influence of electric vehicle charging on the stability of the electric power system is reduced, it is ensured that the electric power system maintains voltage stability and power loss minimization in dynamic change, and therefore the stability and economical efficiency of the electric power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation control, and in particular to a power system reactive power dispatching method and system based on a PSOSHO algorithm. Background Art

[0002] For the reactive power dispatch problem of the power system, although the existing optimization algorithms can reduce the system power loss and voltage deviation to a certain extent, it is difficult for traditional optimization methods to effectively cope with this challenge due to the dynamic load fluctuations caused by the access of electric vehicles. The charging demand of electric vehicles varies greatly over time and has significant volatility, which increases the unpredictability of the load of the power system.

[0003] Although the reactive power dispatch optimization methods in the prior art have solved the power loss and voltage fluctuation problems in the power system to a certain extent, with the access of new loads such as electric vehicles, the existing optimization schemes seem to be unable to cope with the situation. First, the traditional methods have poor adaptability to dynamic load changes and cannot flexibly cope with the volatility of electric vehicle charging loads. Secondly, the global search and local optimization capabilities of the existing optimization algorithms are not sufficient to cope with the complex nonlinear constraints and multi-objective optimization problems of the power system. Therefore, when solving the reactive power dispatch problem in the power system, the prior art still has a certain amount of optimization space, especially in terms of the impact of load fluctuations on the system after the access of electric vehicles.

[0004] Many existing heuristic algorithms, such as genetic algorithms (GA) and particle swarm optimization (PSO), can explore a wider solution space during the optimization process, but due to the lack of sufficient global search capabilities, they are prone to fall into local optimal solutions and cannot find the global optimal solution. This problem is particularly prominent in complex power systems, especially in systems involving new loads such as electric vehicles (EVs). The local optimal solution problem of traditional algorithms may cause unstable operation of the power system or fail to achieve the expected optimization effect.

[0005] Traditional optimization methods usually face a variety of complex constraints when solving reactive power dispatch problems in power systems, including voltage constraints, power balance constraints, equipment capacity constraints, etc. Although some algorithms such as PSO have shown certain capabilities in dealing with constraints, under complex constraints, especially when it comes to multi-objective optimization, the flexibility and adaptability of existing methods are poor, and it is difficult to simultaneously meet multiple optimization objectives and complex constraints, resulting in optimization results that are not as expected. Summary of the invention

[0006] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a method and system for reactive power dispatching of a power system based on the PSOSHO algorithm. In a large-scale power system, the PSOSHO algorithm finds a better balance between operation time and accuracy, can effectively avoid falling into a local optimal solution, can meet the needs of real-time dispatching of the power system, reduce the negative impact of electric vehicle charging on the stability of the power system, ensure that the power system maintains voltage stability and minimizes power loss in dynamic changes, thereby improving the stability and economy of the power system.

[0007] The present invention provides a reactive power dispatching method for a power system based on a PSOSHO algorithm, comprising: S1: Construct a dynamic model of electric vehicle charging load; S2: Construct the objective function of reactive power dispatch of power system considering electric vehicles based on the dynamic model of electric vehicle charging load; S3: Construct power network constraints including electric vehicles, and construct power system reactive power dispatch constraints based on the power network constraints; S4: According to the constraints of reactive power dispatch in the power system, the reactive power dispatch solution of the objective function is solved by the PSOSHO algorithm; S5: Adjust the reactive power of the power system according to the reactive power dispatch solution to complete the reactive power dispatch of the power system.

[0008] Furthermore, a dynamic model of electric vehicle charging load is constructed according to the charging demand and charging time, and the calculation expression is: in, for The charging power of electric vehicles at all times, is a function of charging demand, is the maximum power of the charging station, The dynamic model of electric vehicle charging load is combined with the power balance equation of the power system to obtain the impact of electric vehicle charging on the system reactive power.

[0009] Furthermore, the power balance equation of the power system includes an active power balance equation and a reactive power balance equation. The active power balance equation is: in, For Node The active power generation of the bus, For Node The active power required by the bus, For Node The voltage amplitude, For Node The voltage amplitude, For the Branch admittance, For the Branch circuit susceptance, For the Branch phase angle difference, is the number of buses; The reactive power balance equation is: in, For Node The reactive power of the bus, For Node Reactive compensation power of the bus, For Node The reactive power required by the bus.

[0010] Furthermore, considering the impact of electric vehicle charging load on power loss and voltage fluctuation, the objective function of reactive power dispatch of the power system is constructed. The objective function of reactive power dispatch of the power system includes the objective function of minimizing power loss and the objective function of voltage deviation.

[0011] Furthermore, the objective function of minimizing power loss is for: in, For the Branch admittance, For Node The voltage amplitude, For Node The voltage amplitude, For the The phase angle difference of the branch, is the number of power system nodes; Voltage deviation objective function for: in, is the reference voltage, is the load quantity.

[0012] Furthermore, the constraints of the power network including electric vehicles are to ensure that the solution of the power system during the optimization process is physically feasible and meets the actual operation requirements of the power system; Power network constraints include equality constraints and inequality constraints; The equality constraint is used to balance all the generated, consumed and transmitted power in the system, and the equality constraint includes an active power balance equation and a reactive power balance equation; Inequality constraints are used to limit the voltage variables, power variables, and transformer setting variables in the system.

[0013] Furthermore, the constraints of reactive power dispatch in the power system include reactive power constraints, dispatch constraints of reactive compensation equipment, and voltage amplitude constraints; Reactive power constraint is to limit the reactive power output of each generator and reactive compensation device within the maximum and minimum value range; The scheduling constraint of the reactive power compensation equipment is that the charging load of electric vehicles needs to be adjusted within the output range of the reactive power compensation equipment; The voltage amplitude constraint is to limit the voltage amplitude of each bus to within the maximum and minimum values.

[0014] Furthermore, the PSOSHO algorithm collaboratively optimizes the dispatch of reactive power by alternately using the particle swarm optimization algorithm and the hippocampus optimization algorithm, including: S41: According to the fluctuation of charging load, the output of reactive generator and reactive compensation equipment is adjusted by particle swarm optimization algorithm to obtain reactive dispatch solution of objective function; S42: Optimizing the reactive power dispatch solution by using the Seahorse Optimization Algorithm (SHO) to obtain an optimized dispatch solution; S43: The particle swarm optimization algorithm and the hippocampus optimization algorithm are alternately used to fine-tune the optimization scheduling solution to obtain the optimal scheduling solution.

[0015] Furthermore, the particle swarm optimization algorithm performs global search optimization in the solution space, and the hippocampus optimization algorithm avoids the global search from falling into the local optimal solution through local search.

[0016] The present invention also provides a power system reactive power dispatching system based on the PSOSHO algorithm, which is used to execute the above-mentioned power system reactive power dispatching method based on the PSOSHO algorithm, comprising: A first building block, wherein the first building block builds a dynamic model of electric vehicle charging load; A second building module, wherein the second building module builds an objective function of reactive power dispatch of the power system considering electric vehicles according to a dynamic model of electric vehicle charging load; A third building module, wherein the third building module builds power network constraints including electric vehicles, and builds power system reactive power dispatch constraints based on the power network constraints; A solution module, wherein the solution module solves the reactive power dispatch solution of the objective function through the PSOSHO algorithm according to the constraints of the reactive power dispatch of the power system; The reactive power dispatching module adjusts the reactive power of the power system according to the reactive power dispatching solution to complete the reactive power dispatching of the power system.

[0017] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: In large-scale power systems, the PSOSHO algorithm finds a better balance between computing time and accuracy, can effectively avoid falling into local optimal solutions, ensure that the optimal solution is found, meet the needs of real-time scheduling of power systems, reduce the negative impact of electric vehicle charging on power system stability, and ensure that the power system maintains voltage stability and minimizes power losses during dynamic changes, thereby improving the stability and economy of the power system.

[0018] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of a reactive power dispatching method for a power system based on the PSOSHO algorithm provided by the present invention.

[0021] Figure 2 It is a structural schematic diagram of a power system reactive power dispatching system based on the PSOSHO algorithm provided by the present invention.

[0022] Reference numerals: 101. First building module; 102. Second building module; 103. Third building module; 104. Calculation module; 105. Reactive power dispatching module. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme in the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0024] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0025] Combine the following Figure 1 to Figure 2 The present invention describes a method and system for reactive power dispatching of a power system based on the PSOSHO algorithm.

[0026] like Figure 1 As shown, a reactive power dispatching method for a power system based on the PSOSHO algorithm comprises: S1: Construct a dynamic model of electric vehicle charging load; The power system consists of multiple nodes and lines connecting these nodes. The nodes include generator nodes, load nodes, and electric vehicle charging station nodes. The power balance equation of each node is used to describe the power input and output of the node, specifically: in, For Node The active power, For Node Reactive power; For Node The active power provided by the generator, For Node Reactive power provided by the generator; Is a node The load active power, For Node The reactive power of the load.

[0027] When considering the integration of electric vehicles (EVs), the impact of EV charging stations needs to be included in traditional power system models.

[0028] The dynamic model of electric vehicle charging load is constructed according to the charging demand and charging time. The calculation expression is: in, For at the moment The charging power of electric vehicles, is a function of charging demand, is the maximum power of the charging station; This model can be combined with the power balance equation of the power system to derive the impact of electric vehicle charging on the system reactive power.

[0029] The power balance equation of the power system includes the active power balance equation and the reactive power balance equation. The active power balance equation is: in, For Node The active power generation of the bus, For Node The active power required by the bus, For Node The voltage amplitude, For Node The voltage amplitude, For the Branch admittance, For the Branch circuit susceptance, For the first Branch phase angle difference, is the number of buses; The reactive power balance equation is: in, For Node The reactive power of the bus, For Node Reactive compensation power of the bus, For Node The reactive power required by the bus.

[0030] Substituting the electric vehicle charging power into the power balance equation, the reactive power of the power system when the electric vehicle is charging can be obtained.

[0031] In power systems, voltage regulation usually relies on the dispatch of reactive power, because changes in reactive power directly affect the voltage level of the system, and the node Voltage It is closely related to the generator, load and reactive power distribution to which it is connected. The voltage equation can be expressed as: in, is the reference voltage, For Node Voltage changes due to changes in reactive power dispatch.

[0032] S2: Construct the objective function of reactive power dispatch of power system considering electric vehicles based on the dynamic model of electric vehicle charging load; The goal of reactive power dispatch is to control the system voltage, reduce power loss, and ensure that the power system does not have excessively high or low voltage while meeting power balance by optimizing the power output of reactive generators and the dispatch of reactive compensation equipment. When electric vehicle charging stations are introduced into the system, their dynamic load demand will affect the reactive power demand and voltage distribution of the system, making reactive power dispatch more complicated. Specifically, the optimization tasks of reactive power dispatch include: Reactive power dispatch: Optimize the reactive power output of reactive generators to ensure that voltage level requirements are met while minimizing power losses.

[0033] Dispatch of reactive power compensation equipment: adjust the output of reactive power compensation equipment in the power system, reduce the reactive power flow of the line, and avoid voltage fluctuations caused by reactive power.

[0034] Consider the impact of electric vehicles: The charging demand of electric vehicles brings additional dynamic load to the system, and voltage fluctuations need to be balanced through reactive power scheduling. Especially when the charging load is large, the scheduling of reactive power can alleviate the impact of electric vehicle charging on voltage to a certain extent.

[0035] Considering the impact of electric vehicle charging load on power loss and voltage fluctuation, the objective function of reactive power dispatch of the power system is constructed. The objective function of reactive power dispatch of the power system includes the objective function of minimizing power loss and the objective function of voltage deviation.

[0036] The objective function of minimizing power loss is for: in, For the Branch admittance, For Node The voltage amplitude, For Node The voltage amplitude, For the The phase angle difference of the branch, is the number of power system nodes; The charging demand of electric vehicles is usually manifested as an increase in load, so the impact of charging load on power loss needs to be considered in the optimization process. The increased reactive power load when charging electric vehicles will affect the calculation of power loss.

[0037] Voltage deviation objective function for: in, is the reference voltage, is the load quantity.

[0038] The charging behavior of electric vehicles directly affects voltage fluctuations, as the charging process may generate large reactive power demands, which in turn affects the voltage level of the system.

[0039] S3: Construct power network constraints including electric vehicles, and construct power system reactive power dispatch constraints based on the power network constraints; The constraints of the power network are to ensure that the solution of the power system during the optimization process is physically feasible and meets the actual operation requirements of the system.

[0040] Power network constraints include equality constraints and inequality constraints. Among them, the equality constraints are used to balance all the generated, consumed and transmitted electricity in the system, and the inequality constraints are used to limit the voltage variables, power variables and transformer setting variables in the system.

[0041] The equality constraints include active power balance and reactive power balance. The active power balance is: in, For Node The active power generation of the bus, For Node The active power required by the bus, For Node The voltage amplitude, For Node The voltage amplitude, For the Branch admittance, For the Branch circuit susceptance, For the Branch phase angle difference, is the number of buses; The reactive power balance is: in, For Node The reactive power of the generator at For Node Reactive compensation power of reactive compensation equipment, For Node The reactive power required by the bus.

[0042] Inequality constraints are used to restrict the values ​​of system variables, such as voltage, power, and transformer settings, to ensure that these variables do not exceed physically feasible ranges.

[0043] Voltage amplitude limit: in, For Node The minimum value of voltage, For Node The maximum value of voltage, is the number of buses; Transformer tap setting limitations: in, For the The minimum value of the transformer tap setting, For the transformer tap settings, For the The maximum value of the transformer tap setting, is the number of transformer taps; Reactive power limitation of generator: in, For Node The minimum reactive power generation of the generator at For Node The maximum reactive power of the generator at is the number of generators; Branch power limitation: in, For the The power of each branch, For the The maximum power of each branch indicates that the branch power should be limited within the maximum carrying capacity. is the number of power system nodes; System voltage deviation (VD) limit: in, For Node The lower bound of the system voltage deviation, For Node The upper bound of the system voltage deviation, Due to the load quantity, the voltage deviation of the system should be kept within a certain range to avoid system instability.

[0044] Through these equality and inequality constraints, it can be ensured that the optimization solution not only meets the requirements of the objective function, but also conforms to the actual physical constraints of the power system, thereby ensuring the stable and safe operation of the power system.

[0045] Optimizing the charging load scheduling of electric vehicles has become a part of reactive power scheduling optimization. According to the constraints of the power network, the constraints of reactive power scheduling are constructed to ensure that reactive power scheduling is carried out within a reasonable range and to meet the additional load brought by electric vehicle charging, and to meet the active power balance and reactive power balance.

[0046] The constraints of reactive power dispatch include: Reactive power limitations: Each generator and reactive power compensation device has maximum and minimum limits on its reactive power output.

[0047] In some specific embodiments of the present invention, the generator is a photovoltaic bus (PV bus) and a reactive power compensation device, and the reactive power limit of the generator is: in, For Node The minimum reactive power generation of the generator at For Node The maximum reactive power of the generator at is the number of generators; The charging of electric vehicles increases the demand for reactive power, so the scheduling of reactive power must meet the new load requirements.

[0048] Reactive power compensation equipment scheduling restrictions: The charging load of electric vehicles needs to be adjusted within the output range of reactive power compensation equipment to avoid excessive voltage fluctuations: in, For Node The minimum reactive power of the reactive power compensation equipment, For Node The maximum reactive power of the reactive power compensation equipment at the For Node Reactive compensation power of reactive compensation equipment; The reactive output of these devices needs to be dynamically adjusted in the system, especially during periods of high charging load.

[0049] Voltage amplitude limitation: Charging of electric vehicles may cause local voltage fluctuations, so reactive power dispatch also needs to ensure that the voltage amplitude of each bus remains within the allowable range. Voltage amplitude limitation: in, For Node The minimum value of voltage, For Node The maximum value of voltage, is the number of buses; The charging behavior of electric vehicles makes the voltage amplitude change more drastically. Therefore, the scheduling of reactive power needs to alleviate the voltage fluctuations caused by electric vehicle charging while meeting the voltage amplitude limit.

[0050] S4: According to the constraints of reactive power dispatch in the power system, the reactive power dispatch solution of the objective function is solved by the PSOSHO algorithm.

[0051] The PSOSHO algorithm uses the particle swarm optimization algorithm and the hippocampus optimization algorithm alternately to coordinately optimize the dispatch of reactive power, including: S41: According to the fluctuation of charging load, the output of reactive generator and reactive compensation equipment is adjusted by particle swarm optimization algorithm to obtain reactive dispatch solution of objective function; The PSO algorithm performs local search optimization by simulating the flight and update of particle groups in the solution space. In reactive power scheduling optimization, PSO searches for reactive power scheduling solutions that adapt to the charging load of electric vehicles through the position and speed update rules of particles. Specifically, each position of a PSO particle represents a possible reactive power scheduling solution, while the speed represents the movement direction and step size of the particle in the search space.

[0052] When the electric vehicle load changes, PSO adjusts the output of reactive generators and reactive compensation equipment according to the fluctuation of charging load. The goal of PSO is to ensure system voltage stability and minimize power loss. In some specific embodiments of the present invention, the independent variables involved in the reactive power scheduling problem are three parameter values ​​of generator voltage, capacitor reactive power and transformer tap settings to provide the best system performance, which are expressed in the following form: in, For the Particle group The independent variable of a particle, is the first generator voltage, is the voltage of the Nth generator, is the reactive power of the first capacitor, is the reactive power of the Nth capacitor, is the tap setting for the first transformer, For the Nth transformer tap setting, The speed update formula is: in, for The speed of time, is the inertia weight, for The speed of time, is the first acceleration factor, is the historical optimal position of the particle, for Particles of time, is the second acceleration factor, is the historical optimal position of the group, The position update formula is: in, for Particles of time, , , are all artificially defined, i.e. Reactive power dispatch solution obtained by PSO.

[0053] S42: Optimizing the reactive power dispatching solution by using the Hippocampus optimization algorithm to obtain an optimized dispatching solution; SHO partially simulates the behavior of seahorses in nature, especially their exploration and predation behaviors, and avoids the global search from falling into the local optimal solution through local search. In reactive power scheduling optimization, SHO uses its exploratory behavior (such as Levy flight) to find better reactive power scheduling solutions in a wider search space, especially when the electric vehicle charging load has a significant impact on the system, SHO can provide new scheduling solutions through local search.

[0054] In the SHO algorithm, the position of the seahorse represents the reactive scheduling solution of the system. By simulating the movement and predation behavior of the seahorse, SHO can effectively explore and optimize the reactive scheduling, obtain the optimal scheduling solution, ensure that the system voltage fluctuation does not exceed the predetermined range, and reduce power loss.

[0055] The update formula of SHO performs exploration behavior by simulating Levy flight: in, for The new solution that the time algorithm tries to explore, for The moment Particles, is the random step length of the hippocampus, is the currently best known solution, is the horizontal position of the hippocampus, is the vertical position of the hippocampus, is the position of the hippocampus in the depth direction; The present invention uses the reactive power dispatch solution obtained by PSO as the currently known optimal solution.

[0056] Among them, Levy flight helps the seahorse to conduct extensive search in the solution space through random step size to avoid falling into local optimality.

[0057] SHO also further optimizes the search by simulating predation behavior. When the seahorse successfully preys, it moves closer to the optimal solution, thereby improving the search efficiency. The update process of the optimal solution is as follows: in, for New solution after the time algorithm update, As a reproductive factor, is the first random number, is the second random number, for The new solution that the time algorithm tries to explore, It is the optimal scheduling solution obtained by SHO algorithm.

[0058] S43: The particle swarm optimization algorithm and the hippocampus optimization algorithm are alternately used to fine-tune the optimization scheduling solution to obtain the optimal scheduling solution.

[0059] The particle swarm optimization algorithm performs global search optimization in the solution space, and the hippocampus optimization algorithm uses local search to avoid the global search from falling into the local optimal solution.

[0060] The PSOSHO algorithm balances global search and local optimization by alternating between PSO and SHO. The PSO part is responsible for global search and can quickly adjust the distribution of reactive power according to the changes in the charging load of electric vehicles, ensure the voltage stability of the power system, and find a better solution in a short time. The SHO part helps the algorithm avoid local optimal solutions through local optimization, ensuring that the optimal reactive power scheduling solution is found globally.

[0061] In each iteration, PSO and SHO are executed alternately, first PSO performs global search, then SHO performs local optimization, and finally PSO performs fine-tuning. In this way, PSO SHO can quickly adjust reactive power in the face of dynamic changes in electric vehicle charging loads to ensure system stability and efficiency.

[0062] S5: Adjust the reactive power of the power system according to the reactive power dispatch solution to complete the reactive power dispatch of the power system.

[0063] Through the optimization of the PSOSHO algorithm, the IEEE 30-bus and 57-bus systems were tested. The results show that PSOSHO performs well in reactive power scheduling optimization, and can minimize power loss and voltage deviation while considering the impact of electric vehicle charging and maintaining system voltage stability. Compared with other algorithms (such as PSO, GA, ABC, etc.), PSOSHO has obvious advantages in convergence speed and optimal solution quality, and can effectively schedule reactive power, reduce system losses, and alleviate voltage fluctuations caused by electric vehicle charging.

[0064] like Figure 2 As shown, a reactive power dispatching system of a power system based on the PSOSHO algorithm is used to execute the reactive power dispatching method of a power system based on the PSOSHO algorithm, including: The first building module 101 builds a dynamic model of electric vehicle charging load; The second construction module 102 constructs an objective function of reactive power dispatch of the power system considering electric vehicles according to a dynamic model of electric vehicle charging load; The third construction module 103 constructs power network constraints including electric vehicles, and constructs power system reactive power dispatch constraints based on the power network constraints; The solution module 104 solves the reactive power dispatch solution of the objective function through the PSOSHO algorithm according to the constraints of the reactive power dispatch of the power system; The reactive power dispatching module 105 adjusts the reactive power of the power system according to the reactive power dispatching solution to complete the reactive power dispatching of the power system.

[0065] Through the collaborative work of the above modules, efficient global and local optimization processes, shortened calculation time and improved algorithm efficiency, in large-scale power systems, the PSOSHO algorithm finds a better balance between computing time and accuracy, can effectively avoid falling into local optimal solutions, ensure that the optimal solution is found, can meet the needs of real-time scheduling of power systems, reduce the negative impact of electric vehicle charging on power system stability, ensure that the power system maintains voltage stability and minimizes power losses in dynamic changes, and meet all relevant constraints during the optimization process, thereby improving the stability and economy of the power system.

[0066] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 reactive power dispatching method for a power system based on the PSOSHO algorithm, characterized in that: include: S1: Construct a dynamic model of electric vehicle charging load; S2: Construct the objective function of reactive power dispatch of power system considering electric vehicles based on the dynamic model of electric vehicle charging load; S3: Construct power network constraints including electric vehicles, and construct power system reactive power dispatch constraints based on the power network constraints; S4: According to the constraints of reactive power dispatch in the power system, the reactive power dispatch solution of the objective function is solved by the PSOSHO algorithm; S5: Adjust the reactive power of the power system according to the reactive power dispatch solution to complete the reactive power dispatch of the power system.

2. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 1, characterized in that: The dynamic model of electric vehicle charging load is constructed according to the charging demand and charging time. The calculation expression is: in, for The charging power of electric vehicles at all times, is a function of charging demand, is the maximum power of the charging station, The dynamic model of electric vehicle charging load is combined with the power balance equation of the power system to obtain the impact of electric vehicle charging on the system reactive power.

3. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 2, characterized in that: The power balance equation of the power system includes the active power balance equation and the reactive power balance equation. The active power balance equation is: in, For Node The active power generation of the bus, For Node The active power required by the bus, For Node The voltage amplitude, For Node The voltage amplitude, For the Branch admittance, For the Branch circuit susceptance, For the Branch phase angle difference, is the number of buses; The reactive power balance equation is: in, For Node The reactive power of the bus, For Node Reactive compensation power of the bus, For Node The reactive power required by the bus.

4. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 1, characterized in that: Considering the impact of electric vehicle charging load on power loss and voltage fluctuation, the objective function of reactive power dispatch of the power system is constructed. The objective function of reactive power dispatch of the power system includes the objective function of minimizing power loss and the objective function of voltage deviation.

5. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 4 is characterized in that: The objective function of minimizing power loss is for: in, For the Branch admittance, For Node The voltage amplitude, For Node The voltage amplitude, For the The phase angle difference of the branch, is the number of power system nodes; Voltage deviation objective function for: in, is the reference voltage, is the load quantity.

6. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 3, characterized in that: The constraints of the power network including electric vehicles are to ensure that the solution of the power system in the optimization process is physically feasible and meets the actual operation requirements of the power system; Power network constraints include equality constraints and inequality constraints; The equality constraint is used to balance all the generated, consumed and transmitted power in the system, and the equality constraint includes an active power balance equation and a reactive power balance equation; Inequality constraints are used to limit the voltage variables, power variables, and transformer setting variables in the system.

7. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 1, characterized in that: The constraints of reactive power dispatch in power system include reactive power constraint, dispatch constraint of reactive compensation equipment and voltage amplitude constraint; Reactive power constraints are to limit the reactive power output of each generator and reactive compensation device within the maximum and minimum ranges; The scheduling constraint of the reactive power compensation equipment is that the charging load of electric vehicles needs to be adjusted within the output range of the reactive power compensation equipment; The voltage amplitude constraint is to limit the voltage amplitude of each bus to within the maximum and minimum values.

8. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 1, characterized in that: The PSOSHO algorithm uses the particle swarm optimization algorithm and the hippocampus optimization algorithm alternately to coordinately optimize the dispatch of reactive power, including: S41: According to the fluctuation of charging load, the output of reactive generator and reactive compensation equipment is adjusted by particle swarm optimization algorithm to obtain reactive dispatch solution of objective function; S42: Optimizing the reactive power dispatching solution by using the Hippocampus optimization algorithm to obtain an optimized dispatching solution; S43: The particle swarm optimization algorithm and the hippocampus optimization algorithm are alternately used to fine-tune the optimization scheduling solution to obtain the optimal scheduling solution.

9. The method for reactive power dispatching of a power system based on the PSOSHO algorithm according to claim 1, characterized in that: The particle swarm optimization algorithm performs global search optimization in the solution space, and the hippocampus optimization algorithm uses local search to avoid the global search from falling into the local optimal solution.

10. A reactive power dispatching system for a power system based on the PSOSHO algorithm, characterized in that: The method for executing a reactive power dispatching method of a power system based on a PSOSHO algorithm as claimed in any one of claims 1 to 9 comprises: A first building block, wherein the first building block builds a dynamic model of electric vehicle charging load; A second building module, wherein the second building module builds an objective function of reactive power dispatch of the power system considering electric vehicles according to a dynamic model of electric vehicle charging load; A third building module, wherein the third building module builds power network constraints including electric vehicles, and builds power system reactive power dispatch constraints based on the power network constraints; A solution module, wherein the solution module solves the reactive power dispatch solution of the objective function through the PSOSHO algorithm according to the constraints of the reactive power dispatch of the power system; The reactive power dispatching module adjusts the reactive power of the power system according to the reactive power dispatching solution to complete the reactive power dispatching of the power system.

Images