Concurrent treatment method and system for multiple electric energy quality problems of low-voltage alternating current and direct current hybrid power distribution network

Abstract

The invention discloses a concurrent management method and system for multiple electric energy quality problems of a low-voltage AC / DC hybrid power distribution network. The method comprises the following steps: acquiring historical operation data of a power distribution area, wherein the historical operation data of the power distribution area at least comprises area voltage, power flow and output data of each device; carrying out load flow calculation according to the historical operation data of the power distribution area, and building a Markov decision process model for concurrent management of multiple power quality problems; and in combination with a reinforcement learning algorithm, solving the Markov decision process model for concurrent governance of multiple power quality problems to obtain an equipment scheduling decision. The invention provides a Markov decision process model for concurrent governance of multiple power quality problems, the model takes operation data such as transformer area voltage and power flow as a state space, builds a Markov low-voltage AC / DC hybrid power distribution network environment, solves the environment model, and realizes concurrent effective governance of the multiple power quality problems.

Description

Technical Field

[0001] The present invention relates to the technical field of concurrent management of multiple power quality problems, and in particular to a method and system for concurrent management of multiple power quality problems in a low-voltage AC / DC hybrid distribution network. Background Art

[0002] With the access of new sources and loads such as a high proportion of distributed photovoltaics, user-side energy storage devices, and electric vehicle charging piles to traditional low-voltage AC distribution networks, power quality problems have become frequent and complex, bringing new challenges to the safe, stable, and economical operation of traditional low-voltage AC distribution networks.

[0003] On the one hand, the output of new power sources such as distributed photovoltaics is random, volatile and unpredictable. Taking distributed photovoltaics as an example, during its peak power generation period, due to the increase in its output active power, the node voltage of the low-voltage AC distribution network will increase, and the voltage may even exceed the upper limit, resulting in high-voltage power quality problems. During the trough period of photovoltaic power generation, due to the decrease in the active power output of photovoltaic power generation, the user load at night begins to increase again, which will cause the overall voltage of the low-voltage AC distribution network to be low, and even cause low voltage problems. Due to the increase in the load rate of the distribution transformer, the problem of heavy overload also arises. On the other hand, the access of new loads such as electric vehicle charging piles, the volatility of their output and reactive power demand have changed the grid structure of the original low-voltage AC distribution network, making the load output curve change unpredictable, and the new loads difficult to absorb. The low-voltage AC distribution network is more prone to high voltage, low voltage, three-phase imbalance and other power quality problems.

[0004] In addition, the traditional low-voltage AC distribution network is affected by the "closed-loop design, open-loop operation" mode, and its power quality problem management equipment is limited and has poor flexibility. The traditional low-voltage AC distribution network can suppress the voltage limit at the head end of the substation by adjusting the on-load tap changer of the distribution transformer. However, with the access of a high proportion of new power sources and new loads, it is not enough to meet the regulation needs of the entire distribution substation by simply adjusting the on-load tap changer of the distribution transformer. Reactive compensation devices need to be introduced to participate in voltage control. Generally speaking, due to its limited economic conditions, the traditional low-voltage AC distribution substation has only one reactive compensation device as an additional means of regulation, which is also insufficient to meet the growing demand for new loads. At the power system level, the traditional low-voltage AC distribution network power quality problem management resources are "in short supply".

[0005] As the application scenarios of power electronics technology in new power systems continue to expand, various new rectifier inverters such as voltage source inverters are constantly being used, allowing low-voltage AC / DC hybrid distribution networks to replace traditional low-voltage AC distribution networks has become an industry development trend. Compared with traditional low-voltage AC distribution networks, low-voltage AC / DC hybrid distribution networks have the advantages of large distribution transformer capacity, low overall network loss, and easy access to new sources and loads. However, with the access of a high proportion of new power sources and new loads, the development of low-voltage AC / DC hybrid distribution networks is also facing new challenges.

[0006] First of all, the grid structure of the low-voltage AC / DC hybrid distribution network needs to be rebuilt. If the source-load characteristics of the interconnected low-voltage distribution substations are very different, it may also cause new power quality problems.

[0007] Secondly, new power electronic equipment such as traditional reactive compensation equipment, photovoltaic energy storage complexes, and flexible interconnection equipment have different access methods, control objectives, and scheduling characteristics. How to reasonably and quickly achieve coordinated management of traditional reactive compensation equipment, photovoltaic energy storage complexes, and flexible interconnection equipment is a nonlinear management problem with multiple objectives, multiple time scales, and multiple equipment coordination, and the problem is difficult to solve.

[0008] In recent years, with the continuous advancement of the integration of artificial intelligence technology and power systems, it has provided good technical support for data processing, data analysis, and dispatching strategy generation in low-voltage AC / DC hybrid distribution networks. After analyzing the historical dispatching data of equipment in low-voltage AC / DC hybrid distribution networks or the interaction between intelligent agents and the low-voltage AC / DC hybrid distribution network environment, a reasonable equipment dispatching strategy is generated based on artificial intelligence algorithms to more efficiently achieve multi-objective, multi-time scale, and multi-device collaborative low-voltage AC / DC hybrid distribution network. Concurrent management of multiple power quality problems.

[0009] In summary, how to develop and build a low-voltage AC / DC hybrid distribution network, form a new power system grid structure, replace the traditional low-voltage AC distribution network, and use deep reinforcement learning algorithms to achieve coordinated control of multiple devices in the low-voltage AC / DC hybrid distribution network is of great significance to improving the flexible regulation and control capabilities of the low-voltage distribution network, the ability to manage power quality issues, and ensuring the safe, stable and economical operation of the new power system. Summary of the invention

[0010] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0011] In view of the above existing problems, the present invention is proposed.

[0012] Therefore, the present invention provides a method and system for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network, which can solve the problems mentioned in the background technology.

[0013] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0014] In a first aspect, the present invention provides a method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network, comprising:

[0015] Acquire historical operation data of the power distribution area, wherein the historical operation data of the power distribution area at least includes the area voltage, power flow and output data of each device;

[0016] Perform power flow calculation based on the historical operation data of the distribution station area, and build a Markov decision process model for concurrent management of multiple power quality issues, wherein the Markov decision process model for concurrent management of multiple power quality issues includes a state space, an action space, and a reward space;

[0017] Combined with the reinforcement learning algorithm, the Markov decision process model for concurrent management of multiple power quality issues is solved to obtain equipment scheduling decisions.

[0018] As a preferred solution of the method for concurrently managing multiple power quality problems in the low-voltage AC / DC hybrid distribution network of the present invention, the power flow calculation based on the historical operation data of the distribution station area includes:

[0019] Establishing a mathematical model of a low-voltage AC / DC hybrid distribution network, wherein the mathematical model of the low-voltage AC / DC hybrid distribution network includes an objective function and constraint conditions;

[0020] The objective function is expressed as:

[0021]

[0022] in, Represents the total number of nodes with voltage exceeding the limit in the low voltage AC distribution area; Represents the total number of nodes where voltage exceeds the limit in the low voltage DC distribution area; It represents the total number of nodes with three-phase imbalance in the low-voltage AC distribution area, and m1, m2, and m3 are proportional coefficients;

[0023] If the power quality problem in the AC distribution station area needs to be addressed first, the values ​​of m1 and m3 are set higher than the first threshold.

[0024] As an optimal solution of the method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network described in the present invention, the constraints include flow constraints, node voltage amplitude constraints, three-phase imbalance constraints and equipment constraints.

[0025] As a preferred solution of the method for concurrently managing multiple power quality problems in the low-voltage AC / DC hybrid distribution network of the present invention, the three-phase unbalance constraint includes:

[0026]

[0027] in, It represents the three-phase unbalance rate of the phase i voltage at the node in the low-voltage AC distribution station area. As a maximum value constraint, It indicates the maximum value of the three-phase unbalance rate of the i-phase voltage at the node in the low-voltage AC distribution station area.

[0028] As a preferred solution of the method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network of the present invention, the Markov decision process model for concurrently managing multiple power quality problems includes:

[0029] In the state space of the Markov decision model, the state information includes the power flow distribution in a certain area of ​​the AC / DC hybrid distribution network, the voltage amplitude distribution in a certain area, the output of the SVC traditional reactive power compensation device in a certain area, the grid-connected and off-grid status of the distributed photovoltaic in a certain area, the grid-connected and off-grid status of the energy storage element in a certain area, and the power flow of the VSC flexible interconnection device. The expression is:

[0030] s={S q ,U q ,SVC q ,PV q ,ESS q ,VSC q}

[0031] Among them, S q represents the active power and reactive power matrix of nodes in a certain area q; U q Represents the node voltage amplitude matrix of a certain area q; SVC q Indicates the output of the traditional reactive power compensation device in a certain area q; PV q Indicates the on-grid and off-grid status of distributed photovoltaic in a certain area q; ESS q Indicates the charging and discharging status of the energy storage element in a certain area q; VSC q It indicates the power flow of a certain area q regulated by the flexible interconnection device.

[0032] As a preferred solution of the method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network of the present invention, the Markov decision process model for concurrently managing multiple power quality problems further includes:

[0033] In the action space of the Markov decision model, each action vector is represented as:

[0034] a={k1,k2,k3...,k n}

[0035] Among them, corresponding to each possible dispatching situation of treatment equipment; k n Indicates the apparent power injected into the substation by a certain device.

[0036] As a preferred solution of the method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network of the present invention, the Markov decision process model for concurrently managing multiple power quality problems further includes:

[0037] The immediate reward for executing an action in the Markov decision process model is:

[0038]

[0039] Among them, N PV Represents the total number of distributed photovoltaics removed from the station area, N ESS It indicates the total number of energy storage elements connected to the substation, and m1, m2, m3, m4, and m5 are proportional coefficients.

[0040] In a second aspect, the present invention provides a concurrent management system for multiple power quality issues in a low-voltage AC / DC hybrid distribution network, comprising:

[0041] A data acquisition module is used to acquire historical operation data of a power distribution area, wherein the historical operation data of the power distribution area at least includes the area voltage, power flow and output data of each device;

[0042] A model building module, used to perform power flow calculation based on the historical operation data of the distribution station area, and to build a Markov decision process model for concurrent management of multiple power quality issues, wherein the Markov decision process model for concurrent management of multiple power quality issues includes a state space, an action space, and a reward space;

[0043] The model solving module is used to solve the Markov decision process model of the concurrent management of multiple power quality problems in combination with the reinforcement learning algorithm to obtain the equipment scheduling decision.

[0044] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0046] Compared with the prior art, the present invention has the following beneficial effects: the present invention proposes a method and system for concurrent management of multiple power quality problems in a low-voltage AC / DC hybrid distribution network, which obtains historical operation data of a distribution station area, wherein the historical operation data of the distribution station area at least includes the voltage, flow and output data of each device in the area; the flow is calculated based on the historical operation data of the distribution station area, and a Markov decision process model for concurrent management of multiple power quality problems is constructed, wherein the Markov decision process model for concurrent management of multiple power quality problems includes a state space, an action space and a reward space; the Markov decision process model for concurrent management of multiple power quality problems is solved in combination with a reinforcement learning algorithm to obtain equipment scheduling decisions. The present invention proposes a Markov decision process model for concurrent management of multiple power quality problems. The model is based on the historical operation data of the substation combined with the Disflow flow calculation method, with the substation voltage, flow and other operation data as the state space, the output of each device as the action space, and the number of nodes with three-phase imbalance exceeding the limit and the number of nodes with voltage amplitude exceeding the limit as the penalty function. A Markov low-voltage AC / DC hybrid distribution network environment is built, and the environmental model is solved to obtain a method for concurrent management of multiple power quality problems in the low-voltage AC / DC hybrid distribution network. The present invention proposes a method for managing multiple power quality problems based on the "Deep Q-learning" reinforcement learning algorithm. The method is based on the Markov low-voltage AC / DC hybrid distribution network environment, trains the "Deep Q-learning" reinforcement learning agent, and can achieve effective management of multiple power quality problems concurrently. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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 labor. Among them:

[0048] Figure 1 A method flow chart of a method and system for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a Markov decision process model of a method and system for concurrently managing multiple power quality issues in a low-voltage AC / DC hybrid distribution network provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of a method and system for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network based on a "Deep Q-learning" algorithm for solving a Markov decision model according to an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of an agent training process based on a "Deep Q-learning" algorithm for a method and system for concurrently managing multiple power quality issues in a low-voltage AC / DC hybrid distribution network provided by an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the initial three-phase imbalance of a substation node in a method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network and a system provided by an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of initial three-phase voltage amplitudes of a substation node in a method and system for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network provided by an embodiment of the present invention;

[0054] Figure 7 A schematic diagram of a method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network and a system for managing three-phase imbalance in a backend area provided by an embodiment of the present invention;

[0055] Figure 8 A schematic diagram of three-phase voltage amplitudes in the background area of ​​a method and system for concurrently managing multiple power quality issues in a low-voltage AC / DC hybrid distribution network provided by an embodiment of the present invention;

[0056] Fig. 9 An internal structural diagram of a computer device of a method and system for concurrently managing multiple power quality issues in a low-voltage AC / DC hybrid distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] Example 1

[0059] Reference Figure 1-Figure 9 , which is the first embodiment of the present invention, and provides a method and system for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network, including:

[0060] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0061] Power quality: Power quality refers to the quality of the electric energy in the power system, which usually involves voltage deviation, frequency deviation, harmonic content, voltage fluctuation and flicker, three-phase imbalance, etc. Good power quality is a key factor in ensuring the normal operation of power users' equipment.

[0062] Low-voltage AC / DC hybrid distribution network: Low-voltage AC / DC hybrid distribution network is a new type of distribution network structure, which includes both AC and DC power transmission and distribution. This configuration can better integrate distributed energy (such as solar photovoltaic panels), energy storage systems and other DC loads, and improve the flexibility and efficiency of the system.

[0063] Concurrent multiple power quality issues: Concurrent multiple power quality issues refer to the situation where multiple power quality issues occur in the distribution network within the same time period, such as voltage exceeding the limit, frequency fluctuation, harmonic pollution, three-phase imbalance, etc. These problems may affect each other, making it difficult to solve any one of them alone.

[0064] Reinforcement learning: Reinforcement learning is a machine learning method that builds optimal strategies by letting an agent take actions in an environment and learn from environmental feedback. The goal of the agent is to maximize the cumulative reward through trial and error. Reinforcement learning is widely used in automatic control, robot navigation, games and other fields.

[0065] Markov decision process: Markov decision process is a mathematical framework used to describe the problem of intelligent agents making decisions in uncertain environments. MDP consists of state space, action space, transition probability and reward function. At each time step, the agent is in a certain state, takes an action, and then moves to the next state and receives a certain reward. MDP assumes that the future state depends only on the current state and the action taken, and not on the past history. This property is called Markov property.

[0066] In related technologies, new sources and loads such as high-proportion distributed photovoltaics, user-side energy storage devices, and electric vehicle charging piles are connected to traditional low-voltage AC distribution networks, resulting in frequent and complex power quality problems. In addition, traditional low-voltage AC distribution networks are affected by the "closed-loop design, open-loop operation" mode, and their power quality problem management equipment such as voltage control and reactive power compensation is limited, and the scheduling flexibility is poor. In contrast, low-voltage AC / DC hybrid distribution networks are convenient for the access of new sources and loads, and their power quality problem management methods are more diverse and flexible. However, with the access of a high proportion of new sources and loads, the real-time concurrent management of multiple power quality problems in low-voltage AC / DC hybrid distribution networks is still a huge challenge. In recent years, with the continuous development of deep reinforcement learning algorithms, its "offline training, online application" model has provided new ideas for the real-time management of multiple power quality problems. However, at this stage, most of the research on power quality problems in low-voltage distribution networks focuses on the improvement of power quality problem management equipment, and there is less research on the concurrent management of multiple power quality problems and real-time management of power quality problems. To this end, the present invention proposes a concurrent management method for multiple power quality problems in a low-voltage AC / DC hybrid distribution network based on a "Deep Q-learning" reinforcement learning algorithm.

[0067] The present application provides a method that can effectively solve the above-mentioned problems. Next, a method for concurrently managing multiple power quality problems of the low-voltage AC / DC hybrid distribution network will be described in detail in combination with multiple embodiments.

[0068] Figure 1 A method flow chart of a method and system for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network is shown, including:

[0069] S101, obtaining historical operation data of a power distribution area, where the historical operation data of a power distribution area at least includes area voltage, power flow, and output data of each device;

[0070] In an embodiment of the present application, historical operating data of the distribution station area is collected, and this data can be obtained through distribution automation systems, smart meters, sensors and other equipment.

[0071] Specifically, the data includes but is not limited to: Substation voltage: records the voltage level of each node, including AC and DC voltage. Power flow: records the flow of current and power in the power system, including the flow of active power and reactive power. Output data of each device: records the output of different devices in the distribution system, such as the output power of distributed power sources (such as solar panels, wind turbines), energy storage devices (such as battery packs), reactive compensation devices (such as static VAR generators SVC), etc.

[0072] In an optional embodiment, the acquired data may be preprocessed, the collected data may be cleaned, outliers and missing values ​​may be removed, and the quality of the data may be ensured. The data may be converted into a format suitable for analysis, such as standardization or normalization.

[0073] In an optional embodiment, historical data may also be analyzed to identify common power quality issues, such as voltage over-limit, three-phase imbalance, harmonic pollution, etc. The time range of the data is determined so as to cover different operating conditions, such as peak hours and off-peak hours.

[0074] In an optional embodiment, the data from the above different sources may also be integrated into a unified data set to facilitate subsequent analysis and modeling.

[0075] S102, perform power flow calculation based on historical operation data of the distribution station area, and build a Markov decision process model for concurrent management of multiple power quality issues. The Markov decision process model for concurrent management of multiple power quality issues includes a state space, an action space, and a reward space;

[0076] In the embodiment of the present application, the power flow calculation based on the historical operation data of the distribution station area includes:

[0077] Establish a mathematical model of a low-voltage AC / DC hybrid distribution network, which includes an objective function and constraint conditions;

[0078] Specifically, the objective function is expressed as:

[0079]

[0080] in, Represents the total number of nodes with voltage exceeding the limit in the low voltage AC distribution area; Represents the total number of nodes where voltage exceeds the limit in the low voltage DC distribution area; It represents the total number of nodes with three-phase imbalance in the low-voltage AC distribution area, and m1, m2, and m3 are proportional coefficients;

[0081] If the power quality problem in the AC distribution station area needs to be addressed first, the values ​​of m1 and m3 are set higher than the first threshold.

[0082] In the embodiment of the present application, the first threshold value may be a number greater than 0.3. By reasonably adjusting the proportional coefficient and specifically adjusting the management equipment of the low-voltage AC / DC hybrid distribution network, targeted management of the AC / DC hybrid distribution network may be achieved.

[0083] In the embodiment of the present application, the constraints include power flow constraints, node voltage amplitude constraints, three-phase imbalance constraints and equipment constraints. In the low-voltage AC / DC hybrid distribution network, the voltage phase deviation of the low-voltage AC distribution station is small. Since the present application uses the Distflow power flow model, when calculating the power flow of the low-voltage AC distribution station and the low-voltage DC distribution station, the voltage phase angle is not considered, only the voltage amplitude is considered. The power flow constraint formula is as follows:

[0084] For node j and branch ij in the low voltage AC distribution area:

[0085]

[0086] Wherein, the set g(j) represents the set of first nodes of all branches with node j as the last node in the low-voltage AC distribution area; the set h(j) represents the set of last nodes of all branches with node j as the first node in the low-voltage AC distribution area; Represents the active power flowing through branch ij; Represents the reactive power flowing through branch ij; represents the active power injected into node j; represents the reactive power injected into node j; and They represent the resistance and reactance of branch ij respectively; Represents the nodes i and j Phase voltage amplitude.

[0087] For node j and branch ij in the low voltage DC distribution area:

[0088]

[0089] in, Represents the active power flowing through branch ij; represents the active power injected into node j; and Respectively represent the current flowing through branch ij and the resistance of branch ij; V i DC , Represents the single-phase voltage amplitude at node i and node j.

[0090] In the embodiment of the present application, the supply voltage deviation above the upper limit indicates a high voltage power quality problem, and the supply voltage deviation below the lower limit indicates a low voltage power quality problem. In order to ensure that there is no power quality problem after the low-voltage AC / DC hybrid distribution station area is managed, the node voltage amplitude is constrained as follows:

[0091]

[0092] in, and They are the maximum and minimum values ​​of the voltage amplitude in the low voltage AC distribution station area respectively; and They are the maximum and minimum values ​​of the voltage amplitude of the low-voltage AC distribution station area, respectively. Here, the upper and lower limits of the voltage in the AC and DC stations are 1.07U respectively. N and 0.90U N ; U N is the voltage rating of each station area.

[0093] In the embodiment of the present application, the calculation formula of the three-phase unbalance of the PVUR voltage is as follows:

[0094]

[0095] In the embodiment of the present application, in order to ensure that there is no three-phase imbalance problem in the low-voltage AC / DC hybrid distribution station area, the three-phase imbalance is constrained. The three-phase imbalance constraint includes:

[0096]

[0097] in, It represents the three-phase unbalance rate of the phase i voltage at the node in the low-voltage AC distribution station area. As a maximum value constraint, It represents the maximum value of the three-phase unbalance rate of the phase voltage at the node i in the low-voltage AC distribution station area.

[0098] It should be noted that in order to achieve the management of low-voltage AC / DC hybrid distribution network, traditional reactive power compensation equipment, distributed photovoltaic, energy storage equipment, and flexible AC / DC interconnection equipment are introduced to participate in the regulation. The equipment constraints are as follows:

[0099]

[0100] S min_VSC ≤S VSC ≤S max_VSC

[0101] in, It represents the traditional reactive power compensation equipment SVC at the node i in the low voltage AC distribution area. The reactive power injected into the phase, and The maximum and minimum values ​​of reactive power injected into the SVC; They represent the distributed photovoltaic power generation in the AC and DC distribution area node i respectively. Active power injected into the phase; is the maximum value of injected active power; Represents the energy storage element ESS at node i in the AC and DC distribution area Active power injected into the phase; The maximum value of active power injected into ESS; S VSC Represents the apparent power passing through the flexible AC / DC interconnection device; S max_VSC , S min_VSC Indicates the maximum and minimum values ​​of the apparent power passing through the VSC flexible interconnection device.

[0102] Markov decision process is a mathematical framework in reinforcement learning. Figure 2 As shown in the figure, in the power system scenario, it is used to describe the process in which the core agent obtains rewards by taking a series of equipment scheduling actions to influence the environmental state of the distribution network in the distribution network environment. The Markov decision process is an extension of the Markov chain, which assumes that the probability of the current distribution network state is only related to the distribution network state at the previous moment, and has nothing to do with the earlier state. In the Markov decision process, the core agent needs to formulate an equipment scheduling strategy to decide which action should be taken in each state to maximize the cumulative reward. In order to realize the centralized scheduling of low-voltage AC / DC hybrid distribution network management equipment, the following Markov decision model is proposed.

[0103] In an embodiment of the present application, a Markov decision process model for concurrent management of multiple power quality issues includes: in the state space of the Markov decision model, the state information includes the power flow distribution of a certain area of ​​the AC / DC hybrid distribution network, the voltage amplitude distribution of a certain area, the output of the SVC traditional reactive power compensation device of a certain area, the distributed photovoltaic grid-connected and off-grid status of a certain area, the grid-connected and off-grid status of the energy storage element of a certain area, and the power flow of the VSC flexible interconnection device, and the expression is:

[0104] s={S q ,U q ,SVC q ,PV q ,ESS q ,VSC q}

[0105] Among them, S q represents the active power and reactive power matrix of nodes in a certain area q; U q Represents the node voltage amplitude matrix of a certain area q; SVC q Indicates the output of the traditional reactive power compensation device in a certain area q; PV q Indicates the on-grid and off-grid status of distributed photovoltaic in a certain area q; ESS q Indicates the charging and discharging status of the energy storage element in a certain area q; VSC q It indicates the power flow of a certain area q regulated by the flexible interconnection device.

[0106] In the embodiment of the present application, the Markov decision process model for concurrent management of multiple power quality issues also includes:

[0107] In the action space of the Markov decision model, there are discrete traditional reactive compensation device gear settings, distributed photovoltaic grid-connected and off-grid settings, energy storage element charging and discharging settings, and continuous flexible interconnection device power transmission control. In order to combine discrete governance equipment with continuous governance equipment, the continuous flexible interconnection device power transmission control is discretized. The specific granularity depends on the computing hardware conditions and the specific needs of the substation. Therefore, each action vector:

[0108] a={k1,k2,k3...,k n}

[0109] Among them, corresponding to each possible dispatching situation of treatment equipment; k n It indicates the apparent power injected into the substation by a certain device, which is reactive power for traditional reactive power compensation devices.

[0110] In the embodiment of the present application, the Markov decision process model for concurrent management of multiple power quality issues also includes:

[0111] The immediate reward for executing an action in the Markov decision process model is:

[0112]

[0113] Among them, N PV Represents the total number of distributed photovoltaics removed from the station area, N ESS It indicates the total number of energy storage elements connected to the substation, and m1, m2, m3, m4, and m5 are proportional coefficients.

[0114] In an optional embodiment, the application of this Markov decision process model in a low-voltage AC / DC hybrid distribution network is further expanded to further refine the design of the state space and action space, and introduce a more complex instantaneous reward function to better cope with the complexity and uncertainty in the actual power system.

[0115] For example, load forecast information is added as part of the state space (assuming that there is load forecast data for the next T time steps, which can be expressed as P load (t), t=1,2,…,T), adding weather information as part of the state space, such as solar radiation intensity and wind speed, adding network topology changes as part of the state space, such as switch status, considering the added state space, the expanded state space can be expressed as:

[0116] s={S q ,U q ,SVC q ,PVq ,ESS q ,VSC q , P load (t),I(t),W(t),K(t)}

[0117] Among them, P load (t) represents load forecast data, I(t) represents solar radiation intensity, W(t) represents wind speed, and K(t) represents switch status.

[0118] In an optional embodiment, the action space can be optimized and the action space granularity can be dynamically adjusted. The granularity of the power transmission control of the flexible interconnection device can be dynamically adjusted according to the current grid state and forecast information. For example, when the forecast load is high, a finer control granularity can be used to better regulate the power.

[0119] Exemplarily, each action vector in this application is represented as:

[0120] a={k1,k2,k3...,k n}

[0121] Among them, corresponding to each possible dispatching situation of treatment equipment; k n It indicates the apparent power injected into the substation by a certain device, which is reactive power for traditional reactive power compensation devices.

[0122] k i ={S pv ,S st ,S svc ,S vsc}

[0123] Among them, S pv Represents the output control of distributed photovoltaics, S st Represents the charge and discharge control of the energy storage element, S svc Represents the output control of the traditional reactive power compensation device, S vsc represents the power control of the flexible interconnection device, k i Belongs to the action vector set.

[0124] In an optional embodiment, the operating cost of the governance device is included in the immediate reward function to balance the governance effect and cost. run and maintenance cost C maint into the instant reward function. dev , harmonic distortion rate THD) into the immediate reward function. Introducing long-term reward items in the immediate reward function, such as potential benefits or risks evaluated based on load forecasts and weather information in the future, the immediate reward update for executing an action in the Markov decision process model is:

[0125]

[0126] Among them, m6, m7, m8, m9, m 10 is the coefficient of the corresponding reward item, R long-term Represents a long-term incentive item. N PV Represents the total number of distributed photovoltaics removed from the station area, N ESS It indicates the total number of energy storage elements connected to the substation, and m1, m2, m3, m4, and m5 are proportional coefficients.

[0127] It should be noted that by incorporating load forecast data, weather information (such as solar radiation intensity and wind speed), and network topology changes into the state space, the intelligent agent can comprehensively consider more real-time and predicted information when making equipment scheduling decisions. This comprehensive information input makes the decision-making process closer to the complexity and dynamics of the actual power system, thereby significantly improving the accuracy and effectiveness of the decision. The introduction of forward-looking information such as load forecasts and weather forecasts enables the system to make preventive adjustments before problems may arise in the future. For example, when an upcoming load peak is predicted, the intelligent agent can adjust the charging and discharging state of the energy storage element or adjust the power transmission of the flexible interconnection device in advance to smooth out load fluctuations and enhance the stability and robustness of the entire distribution system.

[0128] By dynamically adjusting the granularity of the action space, especially adopting a finer control granularity during high-load periods, this model can more effectively utilize and allocate limited governance equipment resources. This flexible resource scheduling strategy not only improves governance efficiency, but also reduces governance costs and achieves optimal resource allocation.

[0129] Since this model can more accurately predict and respond to various problems in the power system, such as voltage fluctuations and power imbalance, it can significantly improve users' power experience and satisfaction. By reducing the number of power outages and shortening the power outage time, this model provides power users with a more reliable and stable power supply.

[0130] The Markov decision process model proposed in the embodiment of this application provides strong support for the intelligent management of low-voltage AC / DC hybrid distribution networks. Through continuous learning and optimization, the intelligent agent can gradually master more complex and efficient management strategies, and promote the development of distribution networks in a more intelligent and automated direction.

[0131] To sum up, by refining the state space and action space design and introducing a more complex instant reward function, the embodiments of the present application have demonstrated significant advantages and benefits in the centralized dispatching of low-voltage AC / DC hybrid distribution network management equipment, providing a strong guarantee for the intelligent management of the power system.

[0132] S103, combined with the reinforcement learning algorithm, solves the Markov decision process model for concurrent management of multiple power quality issues to obtain equipment scheduling decisions.

[0133] The "Deep Q-learning" algorithm is a type of deep reinforcement learning. It is a Q-learning algorithm based on a neural network. It is mainly used to solve the problem that the state space in the reinforcement learning problem is too large, which makes it impossible to use the traditional Q-learning algorithm to solve it. The "Deep Q-learning" algorithm uses a deep neural network to approximate the Q function, so that it can handle reinforcement learning problems with high-dimensional state space. The "Deep Q-learning" algorithm can fit the action value function Q through a neural network. ε (s, a), learn the Q values ​​corresponding to all "state-action" in the interaction between a limited number of distribution network states and distribution network environment, and learn the optimal strategy ε according to the Bellman optimal equation * . Action value function Q ε (s,a), Bellman optimal equation, and optimization strategies for all discrete governance devices are shown below respectively.

[0134] Q ε (s,a)=E(r0+γr1+γ 2 r2+...+γ i r i |ε,s,a)

[0135]

[0136] As attached Figure 3 As shown in the figure, at a certain moment, the distribution network environment transmits information such as node voltage distribution, power flow distribution, network loss, etc. to the dispatch center. After being summarized and processed by the dispatch center, it is transmitted to the "Deep Q-learning" algorithm network. The "Deep Q-learning" algorithm network evaluates the current distribution network environment and gives the reward that can be obtained by executing the action instruction. The distribution network environment state, action instruction, and reward function are used to update the Q value network. After executing the device dispatch strategy, the "Deep Q-learning" algorithm network will manage the next action instruction of the device a i The data is transmitted to the dispatch center, which processes the data and issues the next action command. The distribution network environment performs the corresponding action according to the action command, and the node voltage distribution, power flow distribution, network loss and other environmental conditions of the distribution network also change accordingly. At the next moment, the dispatch center transmits the reward r to the Deep Q-learning algorithm network based on the node voltage distribution, power flow distribution, network loss and other environmental conditions of the distribution network that changed at the previous moment.i , and optimize the network parameters based on the reward at that moment and the experience pool of the currently executed action instructions.

[0137] Through the above process, the "Deep Q-learning" algorithm continuously optimizes its internal neural network parameters to more accurately predict the rewards that can be obtained by executing different equipment dispatch actions under different distribution network conditions. This iterative optimization process enables the algorithm to gradually learn the optimal dispatch strategy that adapts to the current distribution network environment.

[0138] In practical applications, as the scale of distribution networks continues to expand and the complexity increases, traditional dispatching methods are often unable to cope with various emergencies and complex problems. The use of the "Deep Q-learning" algorithm for equipment dispatching decisions can make full use of its powerful learning and optimization capabilities to achieve efficient management of distribution networks. The algorithm can comprehensively consider various real-time and predicted information of the distribution network, including load forecasts, weather information, network topology changes, etc., so as to make more accurate and effective dispatching decisions.

[0139] In addition, the "Deep Q-learning" algorithm also has strong generalization capabilities. During the training process, the algorithm will learn a large number of "state-action" pairs and their corresponding reward values. These experiences can be used to deal with new and unknown distribution network states. When the distribution network environment changes, the algorithm can quickly make adaptive scheduling decisions based on existing experience, thereby ensuring the stable operation of the distribution network.

[0140] In summary, the "Deep Q-learning" algorithm has shown significant advantages and potential in the centralized dispatching of low-voltage AC / DC hybrid distribution network management equipment. By continuously optimizing neural network parameters and learning adaptive dispatching strategies, the algorithm can provide strong support for the intelligent management of power systems and promote the development of distribution networks towards a more intelligent and automated direction.

[0141] Figure 4-8 The following are specific data charts generated during the practice of the method. It can be clearly seen that the power grid performance indicators before and after treatment have been significantly improved.

[0142] In summary, the present invention proposes a method for concurrent management of multiple power quality problems in a low-voltage AC / DC hybrid distribution network, which obtains historical operating data of a distribution station area, wherein the historical operating data of the distribution station area at least includes the station area voltage, flow and output data of each device; the flow is calculated based on the historical operating data of the distribution station area, and a Markov decision process model for concurrent management of multiple power quality problems is constructed, wherein the Markov decision process model for concurrent management of multiple power quality problems includes a state space, an action space and a reward space; in combination with a reinforcement learning algorithm, the Markov decision process model for concurrent management of multiple power quality problems is solved to obtain equipment scheduling decisions. The present invention proposes a Markov decision process model for concurrent management of multiple power quality problems. The model is based on the historical operation data of the substation combined with the Disflow flow calculation method, with the substation voltage, flow and other operation data as the state space, the output of each device as the action space, and the number of nodes with three-phase imbalance exceeding the limit and the number of nodes with voltage amplitude exceeding the limit as the penalty function. A Markov low-voltage AC / DC hybrid distribution network environment is built, and the environmental model is solved to obtain a method for concurrent management of multiple power quality problems in the low-voltage AC / DC hybrid distribution network. The present invention proposes a method for managing multiple power quality problems based on the "Deep Q-learning" reinforcement learning algorithm. The method is based on the Markov low-voltage AC / DC hybrid distribution network environment, trains the "Deep Q-learning" reinforcement learning agent, and can achieve effective management of multiple power quality problems concurrently.

[0143] This embodiment also provides a system for concurrently managing multiple power quality issues in a low-voltage AC / DC hybrid distribution network, including:

[0144] The data acquisition module is used to obtain the historical operation data of the power distribution area, which at least includes the area voltage, power flow and output data of each device;

[0145] The model building module is used to calculate the power flow based on the historical operation data of the distribution station area and build a Markov decision process model for the concurrent management of multiple power quality issues. The Markov decision process model for the concurrent management of multiple power quality issues includes state space, action space and reward space;

[0146] The model solving module is used to solve the Markov decision process model for concurrent management of multiple power quality issues in combination with the reinforcement learning algorithm to obtain equipment scheduling decisions.

[0147] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0148] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, 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. When the computer program is executed by the processor, a method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0149] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0150] Obtain the historical operation data of the power distribution area, which at least includes the area voltage, power flow and output data of each device;

[0151] The flow calculation is performed based on the historical operation data of the distribution station area, and a Markov decision process model for concurrent management of multiple power quality issues is built. The Markov decision process model for concurrent management of multiple power quality issues includes state space, action space, and reward space;

[0152] Combined with reinforcement learning algorithm, the Markov decision process model of concurrent management of multiple power quality issues is solved to obtain equipment scheduling decisions.

[0153] Example 2

[0154] Reference Figure 2-Figure 8 , is an embodiment of the present invention, which provides a method and system for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments. The experimental results and data in the process are shown in Table 1 and Figure 4-8 As shown;

[0155] Table 1: Performance comparison of concurrent management methods for multiple power quality issues in low-voltage AC / DC hybrid distribution networks.

[0156]

[0157] Among them, the average number of nodes with voltage amplitude exceeding the limit before governance: 20 nodes have voltage amplitude exceeding the limit. The average value after governance: Through governance, this problem has been reduced to 5 nodes. Improvement percentage: reduced by 75%.

[0158] Average value before the three-phase unbalance exceeded the limit: 12 nodes had the problem of three-phase unbalance exceeded the limit. Average value after the treatment: After the treatment, this problem was reduced to 2 nodes. Improvement percentage: reduced by 83.33%.

[0159] Average value of node voltage standard deviation before treatment: the standard deviation of node voltage was 0.07. Average value after treatment: the standard deviation was reduced to 0.03. Improvement percentage: reduced by 57.14%.

[0160] Average value of voltage fluctuations before treatment: 45 times. Average value after treatment: Through treatment, the number of voltage fluctuations was reduced to 10 times. Improvement percentage: reduced by 77.78%.

[0161] Average voltage deviation before treatment: average voltage deviation was 0.06. Average voltage deviation after treatment: average voltage deviation was reduced to 0.02. Improvement percentage: reduced by 66.67%.

[0162] Average value of power loss before treatment: 120kW. Average value after treatment: Through treatment, power loss was reduced to 80kW. Improvement percentage: 33.33% reduction.

[0163] From the above table, it can be seen that after adopting the proposed method for concurrently managing multiple power quality issues, the performance indicators of the low-voltage AC / DC hybrid distribution network have been significantly improved. This shows that this method can effectively solve power quality problems and improve the stability, reliability and efficiency of the system.

[0164] 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 may 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 claims of the present invention.

[0165] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0166] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0170] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for concurrently managing multiple power quality problems in a low-voltage AC / DC hybrid distribution network, characterized in that: include: Acquire historical operation data of the power distribution area, wherein the historical operation data of the power distribution area at least includes the area voltage, power flow and output data of each device; Perform power flow calculation based on the historical operation data of the distribution station area, and build a Markov decision process model for concurrent management of multiple power quality issues, wherein the Markov decision process model for concurrent management of multiple power quality issues includes a state space, an action space, and a reward space; Combined with the reinforcement learning algorithm, the Markov decision process model for concurrent management of multiple power quality issues is solved to obtain equipment scheduling decisions.

2. The method for concurrently managing multiple power quality problems in a low voltage AC / DC hybrid distribution network according to claim 1, characterized in that: The flow calculation according to the historical operation data of the distribution station area includes: Establishing a mathematical model of a low-voltage AC / DC hybrid distribution network, wherein the mathematical model of the low-voltage AC / DC hybrid distribution network includes an objective function and constraint conditions; The objective function is expressed as: in, Represents the total number of nodes with voltage exceeding the limit in the low voltage AC distribution area; Represents the total number of nodes where voltage exceeds the limit in the low voltage DC distribution area; It represents the total number of nodes with three-phase imbalance in the low-voltage AC distribution area, and m1, m2, and m3 are proportional coefficients; If the power quality problem in the AC distribution station area needs to be addressed first, the values ​​of m1 and m3 are set higher than the first threshold.

3. The method for concurrently managing multiple power quality problems in a low voltage AC / DC hybrid distribution network according to claim 2, characterized in that: The constraint conditions include power flow constraint, node voltage amplitude constraint, three-phase unbalance constraint and equipment constraint.

4. The method for concurrently managing multiple power quality problems in a low voltage AC / DC hybrid distribution network according to claim 3, characterized in that: The three-phase imbalance constraint includes: in, It represents the three-phase unbalance rate of the phase i voltage at the node in the low-voltage AC distribution station area. As a maximum value constraint, It indicates the maximum value of the three-phase unbalance rate of the i-phase voltage at the node in the low-voltage AC distribution station area.

5. The method for concurrently managing multiple power quality problems in a low voltage AC / DC hybrid distribution network according to claim 4, characterized in that: The Markov decision process model for concurrent management of multiple power quality issues includes: In the state space of the Markov decision model, the state information includes the power flow distribution in a certain area of ​​the AC / DC hybrid distribution network, the voltage amplitude distribution in a certain area, the output of the SVC traditional reactive power compensation device in a certain area, the grid-connected and off-grid status of the distributed photovoltaic in a certain area, the grid-connected and off-grid status of the energy storage element in a certain area, and the power flow of the VSC flexible interconnection device. The expression is: s={S q ,U q ,SVC q ,PV q ,ESS q ,VSC q } Among them, S q represents the active power and reactive power matrix of nodes in a certain area q; U q Represents the node voltage amplitude matrix of a certain area q; SVC q Indicates the output of the traditional reactive power compensation device in a certain area q; PV q Indicates the on-grid and off-grid status of distributed photovoltaic in a certain area q; ESS q Indicates the charging and discharging status of the energy storage element in a certain area q; VSC q It indicates the power flow of a certain area q regulated by the flexible interconnection device.

6. The method for concurrently managing multiple power quality problems in a low voltage AC / DC hybrid distribution network according to claim 5, characterized in that: The Markov decision process model for concurrent management of multiple power quality issues also includes: In the action space of the Markov decision model, each action vector is represented as: a={k1,k2,k3...,k n } Among them, corresponding to each possible dispatching situation of treatment equipment; k n Indicates the apparent power injected into the substation by a certain device.

7. The method for concurrently managing multiple power quality problems in a low voltage AC / DC hybrid distribution network according to claim 6, characterized in that: The Markov decision process model for concurrent management of multiple power quality issues also includes: The immediate reward for executing an action in the Markov decision process model is: Among them, N PV Represents the total number of distributed photovoltaics removed from the station area, N ESS It indicates the total number of energy storage elements connected to the substation, and m1, m2, m3, m4, and m5 are proportional coefficients.

8. A concurrent management system for multiple power quality problems in a low-voltage AC / DC hybrid distribution network, characterized in that: include: A data acquisition module is used to acquire historical operation data of a power distribution area, wherein the historical operation data of the power distribution area at least includes the area voltage, power flow and output data of each device; A model building module, used to perform power flow calculation based on the historical operation data of the distribution station area, and to build a Markov decision process model for concurrent management of multiple power quality issues, wherein the Markov decision process model for concurrent management of multiple power quality issues includes a state space, an action space, and a reward space; The model solving module is used to solve the Markov decision process model of the concurrent management of multiple power quality problems in combination with the reinforcement learning algorithm to obtain the equipment scheduling decision.

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 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 method according to any one of claims 1 to 7 are implemented.

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