Equivalent modeling control method and device for microgrid

Abstract

The invention discloses an equivalent modeling control method and device for a micro-grid. The method comprises the following steps: acquiring electrical data and non-electrical data of each boundary node of the micro-grid; carrying out mean value calculation on the solar irradiance of all boundary nodes to obtain a solar irradiance mean value, and identifying the solar irradiance mean value; if the illumination intensity is smaller than an illumination intensity threshold value, constructing an equivalent load model based on the electrical data, and monitoring boundary nodes of which the node types are loads in the microgrid through the equivalent load model; if the illumination intensity is greater than or equal to the illumination intensity threshold, constructing an equivalent load model and an equivalent photovoltaic unit model based on the electrical data and the non-electrical data, and monitoring boundary nodes in the microgrid through the equivalent load model and the equivalent photovoltaic unit model; wherein the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated through a Markov algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of equivalent modeling of distribution networks, and in particular to an equivalent modeling control method and device for a microgrid. Background Art

[0002] For microgrids that are connected to a large amount of renewable energy, establishing an equivalent model can simplify the topology of the original distribution network, reduce granularity, and reduce the complexity of power system analysis. An accurate load equivalent model can play an important role in power system analysis and control, power flow analysis, and optimal power flow calculation. However, the impact of environmental factors on the equivalent model is not considered in the modeling process of microgrids, resulting in low monitoring accuracy in the existing microgrid equivalent model.

[0003] Therefore, there is an urgent need for an equivalent modeling control strategy for microgrids to solve the problem of low monitoring accuracy of microgrid equivalent models. Summary of the invention

[0004] The embodiments of the present invention provide a microgrid equivalent modeling control method and device to solve the problem of low monitoring accuracy of the microgrid equivalent model.

[0005] In order to solve the above problems, an embodiment of the present invention provides an equivalent modeling control method for a microgrid, comprising:

[0006] Acquire electrical data and non-electrical data of each boundary node of the microgrid; wherein the non-electrical data includes: solar irradiance;

[0007] Calculating the average of the solar irradiance of all boundary nodes to obtain an average solar irradiance value, and identifying the average solar irradiance value;

[0008] If the light intensity is less than the light intensity threshold, an equivalent load model is constructed for the boundary nodes whose node type is load based on the electrical data, and the boundary nodes whose node type is load in the microgrid are monitored by the equivalent load model;

[0009] If the light intensity is greater than or equal to the light intensity threshold, based on the electrical data and the non-electrical data, an equivalent load model is constructed for the boundary nodes whose node type is load, and an equivalent photovoltaic unit model is constructed for the boundary nodes whose node type is photovoltaic unit. The boundary nodes whose node type is load in the microgrid are monitored by the equivalent load model, and the boundary nodes whose node type is photovoltaic unit in the microgrid are monitored by the equivalent photovoltaic unit model; wherein, the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by the Markov algorithm.

[0010] As an improvement of the above solution, the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by using a Markov algorithm, including:

[0011] Collecting operation data of the microgrid; wherein the operation data includes: one or more of the actual total active power of nodes corresponding to all equivalent load models, the actual total reactive power of nodes corresponding to all equivalent load models, the actual total active output of nodes corresponding to all equivalent photovoltaic unit models, and the actual total reactive output of nodes corresponding to all equivalent photovoltaic unit models;

[0012] Determine the initial model parameters of each current boundary node; wherein the initial model parameters include: one of the initial load model parameters of the equivalent load model or the initial photovoltaic unit model parameters of the equivalent photovoltaic unit model;

[0013] The running data and initial model parameters of each boundary node are used as input data, and the Markov algorithm is repeatedly executed based on the input data until the reward function value is equal to the reward function threshold, then the Markov algorithm is stopped and the updated initial model parameters are output;

[0014] The updated initial load model parameters are used as the load model parameters of the equivalent load model, and the updated initial photovoltaic unit model parameters are used as the photovoltaic unit model parameters of the equivalent photovoltaic unit model.

[0015] As an improvement of the above scheme, the Markov algorithm includes:

[0016] Under the initial model parameters in the current input data of each boundary node, obtain simulation data; wherein the simulation data includes: the simulated total active power of all equivalent load models under the current initial model parameters, the simulated total reactive power of all equivalent load models under the current initial model parameters, the simulated total active output of all equivalent photovoltaic unit models under the current initial model parameters, and the simulated total reactive output of all equivalent photovoltaic unit models under the current initial model parameters;

[0017] The initial load model parameters and the initial photovoltaic unit model parameters in the current input data are used as the action space, and a set of simulated total active power, simulated total reactive power, simulated total active output, and simulated total reactive output are randomly selected from the simulation data as the state space;

[0018] Calculate the error function of the operating data and the state space to obtain the error function value;

[0019] Determine the value of the error function;

[0020] If the error function value is greater than or equal to the error function threshold, the action space is updated according to the preset algorithm, and the initial load model parameters and initial photovoltaic unit model parameters in the updated action space are used as inputs of the next Markov algorithm;

[0021] If the error function value is less than the error function threshold, the current reward function value is increased by one; wherein, after the current reward function value is increased by one, if the current reward function value is less than the reward function threshold, the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are used as the input of the next Markov algorithm; when the current reward function value is equal to the reward function threshold, the Markov algorithm is stopped, and the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are output.

[0022] As an improvement of the above solution, the state space satisfies the following conditions:

[0023]

[0024] In the formula, S state,i is the state space, To simulate the total active power, To simulate the total active power, To simulate the total active power output, To simulate the total reactive power output.

[0025] The action space satisfies the following conditions:

[0026] A actioni =[η i , K 1i , K Vi , a Pi1 , a Pi2 , a Pi3 , a Qi1 , a Qi2 , a Qi3 ]

[0027] In the formula, A actioni is the action space, K 1i , K Vi is the initial PV unit model parameter, a Pi1 、a Pi2 、a Pi3 、a Qi1 , a Qi2 , a Qi3 are the initial load model parameters.

[0028] As an improvement of the above solution, the error function satisfies the following conditions:

[0029]

[0030] In the formula, REW t is the error function value, P de-ZIP is the actual total active power, Q de-ZIP is the actual total reactive power, P de-pho is the actual total active contribution, Q de-pho For the actual total reactive power contribution.

[0031] Accordingly, an embodiment of the present invention further provides an equivalent modeling control device for a microgrid, comprising: a data acquisition module, a data judgment module, a first modeling module and a second modeling module;

[0032] The data acquisition module is used to acquire electrical data and non-electrical data of each boundary node of the microgrid; wherein the non-electrical data includes: solar irradiance;

[0033] The data judgment module is used to calculate the average value of the solar irradiance of all boundary nodes, obtain the average value of the solar irradiance, and identify the average value of the solar irradiance;

[0034] The first modeling module is used to construct an equivalent load model for the boundary nodes whose node type is load based on the electrical data if the light intensity is less than the light intensity threshold, and monitor the boundary nodes whose node type is load in the microgrid through the equivalent load model;

[0035] The second modeling module is used to construct an equivalent load model for the boundary nodes whose node type is load, and to construct an equivalent photovoltaic unit model for the boundary nodes whose node type is photovoltaic unit, if the light intensity is greater than or equal to the light intensity threshold, based on the electrical data and the non-electrical data, and to monitor the boundary nodes whose node type is load in the microgrid through the equivalent load model, and to monitor the boundary nodes whose node type is photovoltaic unit in the microgrid through the equivalent photovoltaic unit model; wherein the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated through the Markov algorithm.

[0036] As an improvement of the above solution, the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by using a Markov algorithm, including:

[0037] Collecting operation data of the microgrid; wherein the operation data includes: one or more of the actual total active power of nodes corresponding to all equivalent load models, the actual total reactive power of nodes corresponding to all equivalent load models, the actual total active output of nodes corresponding to all equivalent photovoltaic unit models, and the actual total reactive output of nodes corresponding to all equivalent photovoltaic unit models;

[0038] Determine the initial model parameters of each current boundary node; wherein the initial model parameters include: one of the initial load model parameters of the equivalent load model or the initial photovoltaic unit model parameters of the equivalent photovoltaic unit model;

[0039] The running data and initial model parameters of each boundary node are used as input data, and the Markov algorithm is repeatedly executed based on the input data until the reward function value is equal to the reward function threshold, then the Markov algorithm is stopped and the updated initial model parameters are output;

[0040] The updated initial load model parameters are used as the load model parameters of the equivalent load model, and the updated initial photovoltaic unit model parameters are used as the photovoltaic unit model parameters of the equivalent photovoltaic unit model.

[0041] As an improvement of the above scheme, the Markov algorithm includes:

[0042] Under the initial model parameters in the current input data of each boundary node, obtain simulation data; wherein the simulation data includes: the simulated total active power of all equivalent load models under the current initial model parameters, the simulated total reactive power of all equivalent load models under the current initial model parameters, the simulated total active output of all equivalent photovoltaic unit models under the current initial model parameters, and the simulated total reactive output of all equivalent photovoltaic unit models under the current initial model parameters;

[0043] The initial load model parameters and the initial photovoltaic unit model parameters in the current input data are used as the action space, and a set of simulated total active power, simulated total reactive power, simulated total active output, and simulated total reactive output are randomly selected from the simulation data as the state space;

[0044] Calculate the error function of the operating data and the state space to obtain the error function value;

[0045] Determine the value of the error function;

[0046] If the error function value is greater than or equal to the error function threshold, the action space is updated according to the preset algorithm, and the initial load model parameters and initial photovoltaic unit model parameters in the updated action space are used as inputs of the next Markov algorithm;

[0047] If the error function value is less than the error function threshold, the current reward function value is increased by one; wherein, after the current reward function value is increased by one, if the current reward function value is less than the reward function threshold, the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are used as the input of the next Markov algorithm; when the current reward function value is equal to the reward function threshold, the Markov algorithm is stopped, and the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are output.

[0048] As an improvement of the above solution, the state space satisfies the following conditions:

[0049]

[0050] In the formula, S state,i is the state space, To simulate the total active power, To simulate the total active power, To simulate the total active power output, To simulate the total reactive power output.

[0051] The action space satisfies the following conditions:

[0052] A actioni =[η i , K 1i , K Vi , a Pi1 , a Pi2 , a Pi3 , a Qi1 , a Qi2 , a Qi3 ]

[0053] In the formula, A actioni is the action space, K 1i , K Vi is the initial PV unit model parameter, a Pi1 、a Pi2 、a Pi3 、a Qi1 , a Qi2 , a Qi3 is the initial load model parameter d.

[0054] As an improvement of the above solution, the error function satisfies the following conditions:

[0055]

[0056] In the formula, REW t is the error function value, P de-ZIP is the actual total active power, Q de-ZIP is the actual total reactive power, P de-pho is the actual total active contribution, Q de-pho For the actual total reactive power contribution.

[0057] As can be seen from the above, the present invention has the following beneficial effects:

[0058] The present invention provides an equivalent modeling control method for a microgrid, which obtains electrical data and non-electrical data of each boundary node of the microgrid; wherein the non-electrical data includes: solar irradiance; average calculation of the solar irradiance of all boundary nodes is performed to obtain the average solar irradiance value, and the average solar irradiance value is identified; if the light intensity is less than a light intensity threshold, an equivalent load model is constructed based on the electrical data, and the boundary nodes of the microgrid whose node type is load are monitored through the equivalent load model; if the light intensity is greater than or equal to the light intensity threshold, an equivalent load model and an equivalent photovoltaic unit model are constructed based on the electrical data and the non-electrical data, and the boundary nodes of the microgrid whose node type is load are monitored through the equivalent load model, and the boundary nodes of the microgrid whose node type is photovoltaic unit are monitored through the equivalent photovoltaic unit model; wherein the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated through a Markov algorithm. The present invention takes into account the situation where the photovoltaic unit does not operate due to weak solar irradiance. When the solar irradiance is strong, the microgrid is monitored by an equivalent load model and an equivalent photovoltaic unit model. When the solar irradiance is weak, the microgrid is monitored by an equivalent load model. This can more accurately simulate the microgrid in combination with environmental influences, which is beneficial to improving the monitoring accuracy of the microgrid equivalent model.

[0059] Furthermore, the present invention solves the model parameters of the equivalent load model and the equivalent photovoltaic unit model through the Markov algorithm, thereby improving the accuracy of the equivalent load model and the equivalent photovoltaic unit model, and further improving the monitoring accuracy of the microgrid equivalent model. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of an equivalent modeling control method for a microgrid provided by an embodiment of the present invention;

[0061] Figure 2 It is a structural schematic diagram of an equivalent modeling control device for a microgrid provided by an embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram of the structure of a terminal device provided by an embodiment of the present invention;

[0063] Figure 4 This is a topological diagram of an IEEE33 node provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Embodiment 1

[0066] See also Figure 1 , Figure 1 is a flow chart of an equivalent modeling control method for a microgrid provided by an embodiment of the present invention, such as Figure 1 As shown, this embodiment includes steps 101 to 104, and each step is specifically as follows:

[0067] Step 101: Acquire electrical data and non-electrical data of each boundary node of a microgrid; wherein the non-electrical data includes: solar irradiance.

[0068] In this embodiment, the boundary nodes are internal and external connection nodes.

[0069] In a specific embodiment, the electrical data includes node voltage, and the non-electrical data also includes temperature. Figure 4 , the generator nodes are marked in blue, and the rest are load nodes. Each generator node can be connected to several distributed photovoltaic generators, and each load node can be connected to several loads. For these loads, Figure 4 Each load node in can be defined as a boundary node. Similarly, each generator node can also be considered as a boundary node of the generator.

[0070] Step 102: Calculate the average of the solar irradiance of all boundary nodes to obtain an average solar irradiance value, and identify the average solar irradiance value.

[0071] In this embodiment, by calculating the solar irradiance, it is possible to determine the disturbance caused by the environment in which the microgrid is located to the microgrid, and the disturbance is mainly for equivalent photovoltaics. For example, a cloud floats over, or the photovoltaic panel is covered by foreign objects, resulting in a decrease in power generation. This is a disturbance, but it does not affect the equivalent model structure including the equivalent photovoltaic model. However, if the solar irradiance is not enough to support the photovoltaic generator to generate electricity, then we believe that this is a larger disturbance, because this disturbance is enough to change the composition structure of the equivalent model. The main purpose of this step is indeed to determine the impact of the disturbance on the equivalent model structure.

[0072] Step 103: If the light intensity is less than the light intensity threshold, an equivalent load model is constructed for the boundary nodes whose node type is load based on the electrical data, and the boundary nodes whose node type is load in the microgrid are monitored by the equivalent load model.

[0073] In a specific embodiment, the equivalent load model is obtained by:

[0074] First, determine the single load ZIP equivalent model:

[0075]

[0076] Finally, the equivalent load model is determined as:

[0077]

[0078] Among them, the load model parameters of the equivalent load model are: a Pi1 , a Pi2 , a Pi3 , a Qi1 , a Qi2 , a Qi3 .

[0079] Step 104: If the light intensity is greater than or equal to the light intensity threshold, based on the electrical data and the non-electrical data, an equivalent load model is constructed for the boundary nodes whose node type is load, and an equivalent photovoltaic unit model is constructed for the boundary nodes whose node type is photovoltaic unit, and the boundary nodes whose node type is load in the microgrid are monitored by the equivalent load model, and the boundary nodes whose node type is photovoltaic unit in the microgrid are monitored by the equivalent photovoltaic unit model; wherein, the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by the Markov algorithm.

[0080] In a specific embodiment, the equivalent photovoltaic group model is obtained by:

[0081] First, the active output of a single photovoltaic:

[0082]

[0083] In the formula, is the maximum power of a single photovoltaic unit, η i is the conversion efficiency;

[0084]

[0085] In the formula, is the voltage corresponding to the maximum power of a single photovoltaic unit, is the current corresponding to the maximum power of a single photovoltaic unit;

[0086]

[0087] In the formula, I max-pho,st is the current corresponding to the maximum power of a single photovoltaic unit under standard conditions, K 1i is the first coefficient, T st is the temperature corresponding to the maximum power of a single photovoltaic unit under standard conditions, G st It is the solar irradiance corresponding to the maximum power of a single photovoltaic unit under standard conditions.

[0088]

[0089] Where V the K is the thermal voltage of the light-emitting diode Vi is the second parameter;

[0090] V the =kT / q

[0091] Where k is the Boltzman constant and q is the electron charge.

[0092] Therefore, the total active output of the equivalent photovoltaic unit model can be described as:

[0093]

[0094] Where n is the total number of photovoltaic units.

[0095] At the same time, the reactive power of a single equivalent photovoltaic unit can be expressed as:

[0096]

[0097] In the formula, is the deterministic component of reactive power.

[0098] Therefore, the total reactive power of the equivalent photovoltaic unit model can be expressed as:

[0099]

[0100] Where n is the total number of photovoltaic units.

[0101] Among them, the photovoltaic unit model parameter of the equivalent photovoltaic unit model is η i , K 1i , K Vi .

[0102] In this embodiment, the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by using a Markov algorithm, including:

[0103] Collecting operation data of the microgrid; wherein the operation data includes: one or more of the actual total active power of nodes corresponding to all equivalent load models, the actual total reactive power of nodes corresponding to all equivalent load models, the actual total active output of nodes corresponding to all equivalent photovoltaic unit models, and the actual total reactive output of nodes corresponding to all equivalent photovoltaic unit models;

[0104] Determine the initial model parameters of each current boundary node; wherein the initial model parameters include: one of the initial load model parameters of the equivalent load model or the initial photovoltaic unit model parameters of the equivalent photovoltaic unit model;

[0105] The running data and initial model parameters of each boundary node are used as input data, and the Markov algorithm is repeatedly executed based on the input data until the reward function value is equal to the reward function threshold, then the Markov algorithm is stopped and the updated initial model parameters are output;

[0106] The updated initial load model parameters are used as the load model parameters of the equivalent load model, and the updated initial photovoltaic unit model parameters are used as the photovoltaic unit model parameters of the equivalent photovoltaic unit model.

[0107] In this embodiment, the Markov algorithm includes:

[0108] Under the initial model parameters in the current input data of each boundary node, obtain simulation data; wherein the simulation data includes: the simulated total active power of all equivalent load models under the current initial model parameters, the simulated total reactive power of all equivalent load models under the current initial model parameters, the simulated total active output of all equivalent photovoltaic unit models under the current initial model parameters, and the simulated total reactive output of all equivalent photovoltaic unit models under the current initial model parameters;

[0109] The initial load model parameters and the initial photovoltaic unit model parameters in the current input data are used as the action space, and a set of simulated total active power, simulated total reactive power, simulated total active output, and simulated total reactive output are randomly selected from the simulation data as the state space;

[0110] Calculate the error function of the operating data and the state space to obtain the error function value;

[0111] Determine the value of the error function;

[0112] If the error function value is greater than or equal to the error function threshold, the action space is updated according to the preset algorithm, and the initial load model parameters and initial photovoltaic unit model parameters in the updated action space are used as inputs of the next Markov algorithm;

[0113] If the error function value is less than the error function threshold, the current reward function value is increased by one; wherein, after the current reward function value is increased by one, if the current reward function value is less than the reward function threshold, the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are used as the input of the next Markov algorithm; when the current reward function value is equal to the reward function threshold, the Markov algorithm is stopped, and the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are output.

[0114] In a specific embodiment, the reward function value is obtained as follows:

[0115]

[0116] In the formula, R rewardt is the single reward function value, REW t is the error function value, β is the error function threshold, is the reward function value.

[0117] It can be understood that the larger the reward function value is, the more stable the equivalent model corresponding to the initial load model parameters and the initial photovoltaic unit model parameters in the current action space is.

[0118] In this embodiment, the state space satisfies the following conditions:

[0119]

[0120] In the formula, S state,i is the state space, To simulate the total active power, To simulate the total active power, To simulate the total active power output, To simulate the total reactive power output.

[0121] The action space satisfies the following conditions:

[0122] A actioni =[η i , K 1i , K Vi , a Pi1 , a Pi2 , a Pi3 , a Qi1 , a Qi2 , a Qi3 ]

[0123] In the formula, A actioni is the action space, K 1i , K Vi is the initial PV unit model parameter, a Pi1 、a Pi2 、aPi3 、a Qi1 , a Qi2 , a Qi3 are the initial load model parameters.

[0124] In this embodiment, the error function satisfies the following conditions:

[0125]

[0126] In the formula, REW t is the error function value, P de-ZIP is the actual total active power, Q de-ZIP is the actual total reactive power, P de-pho is the actual total active contribution, Q de-pho For the actual total reactive power contribution.

[0127] To better illustrate, Figure 4 As shown, this embodiment uses the IEEE33 node system as the microgrid model, the first node is connected to the transmission network through a bus, photovoltaic generators are connected to the 4th, 13th, 16th, 20th, 30th, and 33rd nodes, thermometers and solar irradiance measuring devices are added to the photovoltaic generator nodes, and PMU measuring equipment is added to each node.

[0128] Will be like Figure 4 The system shown is determined as the area to be modeled. The light intensity and temperature of the photovoltaic generator node are collected, and the node voltage of the key node is collected to determine whether the photovoltaic node has sufficient conditions for power generation. If it is in normal working conditions, the integrated photovoltaic generator and the equivalent ZIP load are connected in parallel as the components of the equivalent model. If all photovoltaic units are in a condition where they cannot generate power normally, the equivalent ZIP load is used as the structure of the equivalent model. The composition structure of the equivalent model is determined according to the disturbance type, and the set of parameters to be identified is determined as follows: The load model parameter update process of the equivalent load model is converted into a Markov decision process, and the key parameters are solved by a reinforcement learning algorithm. The values ​​of each element in the action space are continuously adjusted, and the error function and reward function are calculated until the error function value is less than 5% (that is, the error function threshold of the present invention, which can be adaptively modified by the user) and the reward function value is greater than 10 (that is, the reward function threshold of the present invention, which can be adaptively modified by the user), which means that the equivalent model accuracy of the initial load model parameters and the initial photovoltaic unit model parameters in the current action space is within 5% and no longer changes.

[0129] See also Figure 2 , Figure 2It is a structural schematic diagram of an equivalent modeling control device for a microgrid provided by an embodiment of the present invention, comprising: a data acquisition module 201, a data judgment module 202, a first modeling module 203 and a second modeling module 204;

[0130] The data acquisition module is used to acquire electrical data and non-electrical data of each boundary node of the microgrid; wherein the non-electrical data includes: solar irradiance;

[0131] The data judgment module is used to calculate the average value of the solar irradiance of all boundary nodes, obtain the average value of the solar irradiance, and identify the average value of the solar irradiance;

[0132] The first modeling module is used to construct an equivalent load model for the boundary nodes whose node type is load based on the electrical data if the light intensity is less than the light intensity threshold, and monitor the boundary nodes whose node type is load in the microgrid through the equivalent load model;

[0133] The second modeling module is used to construct an equivalent load model for the boundary nodes whose node type is load, and to construct an equivalent photovoltaic unit model for the boundary nodes whose node type is photovoltaic unit, if the light intensity is greater than or equal to the light intensity threshold, based on the electrical data and the non-electrical data, and to monitor the boundary nodes whose node type is load in the microgrid through the equivalent load model, and to monitor the boundary nodes whose node type is photovoltaic unit in the microgrid through the equivalent photovoltaic unit model; wherein the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated through the Markov algorithm.

[0134] As an improvement of the above solution, the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by using a Markov algorithm, including:

[0135] Collecting operation data of the microgrid; wherein the operation data includes: one or more of the actual total active power of nodes corresponding to all equivalent load models, the actual total reactive power of nodes corresponding to all equivalent load models, the actual total active output of nodes corresponding to all equivalent photovoltaic unit models, and the actual total reactive output of nodes corresponding to all equivalent photovoltaic unit models;

[0136] Determine the initial model parameters of each current boundary node; wherein the initial model parameters include: one of the initial load model parameters of the equivalent load model or the initial photovoltaic unit model parameters of the equivalent photovoltaic unit model;

[0137] The running data and initial model parameters of each boundary node are used as input data, and the Markov algorithm is repeatedly executed based on the input data until the reward function value is equal to the reward function threshold, then the Markov algorithm is stopped and the updated initial model parameters are output;

[0138] The updated initial load model parameters are used as the load model parameters of the equivalent load model, and the updated initial photovoltaic unit model parameters are used as the photovoltaic unit model parameters of the equivalent photovoltaic unit model.

[0139] As an improvement of the above scheme, the Markov algorithm includes:

[0140] Under the initial model parameters in the current input data of each boundary node, obtain simulation data; wherein the simulation data includes: the simulated total active power of all equivalent load models under the current initial model parameters, the simulated total reactive power of all equivalent load models under the current initial model parameters, the simulated total active output of all equivalent photovoltaic unit models under the current initial model parameters, and the simulated total reactive output of all equivalent photovoltaic unit models under the current initial model parameters;

[0141] The initial load model parameters and the initial photovoltaic unit model parameters in the current input data are used as the action space, and a set of simulated total active power, simulated total reactive power, simulated total active output, and simulated total reactive output are randomly selected from the simulation data as the state space;

[0142] Calculate the error function of the operating data and the state space to obtain the error function value;

[0143] Determine the value of the error function;

[0144] If the error function value is greater than or equal to the error function threshold, the action space is updated according to the preset algorithm, and the initial load model parameters and initial photovoltaic unit model parameters in the updated action space are used as inputs of the next Markov algorithm;

[0145] If the error function value is less than the error function threshold, the current reward function value is increased by one; wherein, after the current reward function value is increased by one, if the current reward function value is less than the reward function threshold, the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are used as the input of the next Markov algorithm; when the current reward function value is equal to the reward function threshold, the Markov algorithm is stopped, and the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are output.

[0146] As an improvement of the above solution, the state space satisfies the following conditions:

[0147]

[0148] In the formula, S state,i is the state space, To simulate the total active power, To simulate the total active power, To simulate the total active power output, To simulate the total reactive power output.

[0149] The action space satisfies the following conditions:

[0150] A actioni =[η i , K 1i , K Vi , a Pi1 , a Pi2 , a Pi3 , a Qi1 , a Qi2 , a Qi3 ]

[0151] In the formula, A actioni is the action space, K 1i , K Vi is the initial PV unit model parameter, a Pi1 、a Pi2 、a Pi3 、a Qi1 , a Qi2 , a Qi3 are the initial load model parameters.

[0152] As an improvement of the above solution, the error function satisfies the following conditions:

[0153]

[0154] In the formula, REW t is the error function value, P de-ZIP is the actual total active power, Q de-ZIP is the actual total reactive power, P de-pho is the actual total active contribution, Q de-pho For the actual total reactive power contribution.

[0155] This embodiment obtains electrical data and non-electrical data of each boundary node of the microgrid; wherein the non-electrical data includes: solar irradiance; averages the solar irradiance of all boundary nodes to obtain the average solar irradiance, and identifies the average solar irradiance; if the light intensity is less than the light intensity threshold, an equivalent load model is constructed based on the electrical data, and the boundary nodes of the microgrid whose node type is load are monitored through the equivalent load model; if the light intensity is greater than or equal to the light intensity threshold, an equivalent load model and an equivalent photovoltaic unit model are constructed based on the electrical data and the non-electrical data, and the boundary nodes of the microgrid whose node type is load are monitored through the equivalent load model, and the boundary nodes of the microgrid whose node type is photovoltaic unit are monitored through the equivalent photovoltaic unit model; wherein the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated through the Markov algorithm. The present invention takes into account the situation where the photovoltaic unit does not operate due to weak solar irradiance. When the solar irradiance is strong, the microgrid is monitored by an equivalent load model and an equivalent photovoltaic unit model. When the solar irradiance is weak, the microgrid is monitored by an equivalent load model. This can more accurately simulate the microgrid in combination with environmental influences, which is beneficial to improving the monitoring accuracy of the microgrid equivalent model.

[0156] Embodiment 2

[0157] See also Figure 3 , Figure 3 It is a schematic diagram of the structure of a terminal device provided in one embodiment of the present invention.

[0158] A terminal device of this embodiment includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the steps of the equivalent modeling control method of each microgrid described above in the embodiment are implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module in the above-mentioned device embodiments are realized, for example: Figure 2 Equivalent modeling of the microgrid with all modules of the control device shown.

[0159] In addition, an embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the equivalent modeling control method of the microgrid as described in any of the above embodiments.

[0160] Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0161] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 301 is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0162] The memory 302 can be used to store the computer program and / or module. The processor 301 implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0163] Wherein, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0164] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0165] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A microgrid equivalent modeling control method, characterized in that: include: Acquire electrical data and non-electrical data of each boundary node of the microgrid; wherein the non-electrical data includes: solar irradiance; Calculating the average of the solar irradiance of all boundary nodes to obtain an average solar irradiance value, and identifying the average solar irradiance value; If the light intensity is less than the light intensity threshold, an equivalent load model is constructed for the boundary nodes whose node type is load based on the electrical data, and the boundary nodes whose node type is load in the microgrid are monitored by the equivalent load model; If the light intensity is greater than or equal to the light intensity threshold, based on the electrical data and the non-electrical data, an equivalent load model is constructed for the boundary nodes whose node type is load, and an equivalent photovoltaic unit model is constructed for the boundary nodes whose node type is photovoltaic unit. The boundary nodes whose node type is load in the microgrid are monitored by the equivalent load model, and the boundary nodes whose node type is photovoltaic unit in the microgrid are monitored by the equivalent photovoltaic unit model; wherein, the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by the Markov algorithm.

2. The equivalent modeling control method of a microgrid according to claim 1, characterized in that: The load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by using a Markov algorithm, including: Collecting operation data of the microgrid; wherein the operation data includes: one or more of the actual total active power of nodes corresponding to all equivalent load models, the actual total reactive power of nodes corresponding to all equivalent load models, the actual total active output of nodes corresponding to all equivalent photovoltaic unit models, and the actual total reactive output of nodes corresponding to all equivalent photovoltaic unit models; Determine the initial model parameters of each current boundary node; wherein the initial model parameters include: one of the initial load model parameters of the equivalent load model or the initial photovoltaic unit model parameters of the equivalent photovoltaic unit model; The running data and initial model parameters of each boundary node are used as input data, and the Markov algorithm is repeatedly executed based on the input data until the reward function value is equal to the reward function threshold, then the Markov algorithm is stopped and the updated initial model parameters are output; The updated initial load model parameters are used as the load model parameters of the equivalent load model, and the updated initial photovoltaic unit model parameters are used as the photovoltaic unit model parameters of the equivalent photovoltaic unit model.

3. The equivalent modeling control method of the microgrid according to claim 2 is characterized in that: The Markov algorithm comprises: Under the initial model parameters in the current input data of each boundary node, obtain simulation data; wherein the simulation data includes: the simulated total active power of all equivalent load models under the current initial model parameters, the simulated total reactive power of all equivalent load models under the current initial model parameters, the simulated total active output of all equivalent photovoltaic unit models under the current initial model parameters, and the simulated total reactive output of all equivalent photovoltaic unit models under the current initial model parameters; The initial load model parameters and the initial photovoltaic unit model parameters in the current input data are used as the action space, and a set of simulated total active power, simulated total reactive power, simulated total active output, and simulated total reactive output are randomly selected from the simulation data as the state space; Calculate the error function of the operating data and the state space to obtain the error function value; Determine the error function value; If the error function value is greater than or equal to the error function threshold, the action space is updated according to the preset algorithm, and the initial load model parameters and initial photovoltaic unit model parameters in the updated action space are used as inputs of the next Markov algorithm; If the error function value is less than the error function threshold, the current reward function value is increased by one; wherein, after the current reward function value is increased by one, if the current reward function value is less than the reward function threshold, the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are used as the input of the next Markov algorithm; when the current reward function value is equal to the reward function threshold, the Markov algorithm is stopped, and the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are output.

4. The equivalent modeling control method of the microgrid according to claim 3 is characterized in that: The state space satisfies the following conditions: In the formula, S state,i is the state space, To simulate the total active power, To simulate the total active power, To simulate the total active power output, To simulate the total reactive power output. The action space satisfies the following conditions: A actioni =[η i ,K 1i ,K Vi ,a Pi1 ,a Pi2 ,a Pi3 ,a Qi1 ,a Qi2 ,a Qi3 ] In the formula, A actioni is the action space, K 1i , K Vi is the initial PV unit model parameter, a Pi1 、a Pi2 、a Pi3 、a Qi1 , a Qi2 , a Qi3 are the initial load model parameters.

5. The equivalent modeling control method of the microgrid according to claim 4 is characterized in that: The error function satisfies the following conditions: In the formula, REW t is the error function value, P de-ZIP is the actual total active power, Q de-ZIP is the actual total reactive power, P de-pho is the actual total active contribution, Q de-pho For the actual total reactive power contribution.

6. An equivalent modeling control device for a microgrid, characterized in that: include: A data acquisition module, a data judgment module, a first modeling module and a second modeling module; The data acquisition module is used to acquire electrical data and non-electrical data of each boundary node of the microgrid; wherein the non-electrical data includes: solar irradiance; The data judgment module is used to calculate the average value of the solar irradiance of all boundary nodes, obtain the average value of the solar irradiance, and identify the average value of the solar irradiance; The first modeling module is used to construct an equivalent load model for the boundary nodes whose node type is load based on the electrical data if the light intensity is less than the light intensity threshold, and monitor the boundary nodes whose node type is load in the microgrid through the equivalent load model; The second modeling module is used to construct an equivalent load model for the boundary nodes whose node type is load, and to construct an equivalent photovoltaic unit model for the boundary nodes whose node type is photovoltaic unit, if the light intensity is greater than or equal to the light intensity threshold, based on the electrical data and the non-electrical data, and to monitor the boundary nodes whose node type is load in the microgrid through the equivalent load model, and to monitor the boundary nodes whose node type is photovoltaic unit in the microgrid through the equivalent photovoltaic unit model; wherein the load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated through the Markov algorithm.

7. The equivalent modeling control device for a microgrid according to claim 6, characterized in that: The load model parameters of the equivalent load model and the photovoltaic unit model parameters of the equivalent photovoltaic unit model are updated by using a Markov algorithm, including: Collecting operation data of the microgrid; wherein the operation data includes: one or more of the actual total active power of nodes corresponding to all equivalent load models, the actual total reactive power of nodes corresponding to all equivalent load models, the actual total active output of nodes corresponding to all equivalent photovoltaic unit models, and the actual total reactive output of nodes corresponding to all equivalent photovoltaic unit models; Determine the initial model parameters of each current boundary node; wherein the initial model parameters include: one of the initial load model parameters of the equivalent load model or the initial photovoltaic unit model parameters of the equivalent photovoltaic unit model; The running data and initial model parameters of each boundary node are used as input data, and the Markov algorithm is repeatedly executed based on the input data until the reward function value is equal to the reward function threshold, then the Markov algorithm is stopped and the updated initial model parameters are output; The updated initial load model parameters are used as the load model parameters of the equivalent load model, and the updated initial photovoltaic unit model parameters are used as the photovoltaic unit model parameters of the equivalent photovoltaic unit model.

8. The equivalent modeling control device for a microgrid according to claim 7, characterized in that: The Markov algorithm comprises: Under the initial model parameters in the current input data of each boundary node, obtain simulation data; wherein the simulation data includes: the simulated total active power of all equivalent load models under the current initial model parameters, the simulated total reactive power of all equivalent load models under the current initial model parameters, the simulated total active output of all equivalent photovoltaic unit models under the current initial model parameters, and the simulated total reactive output of all equivalent photovoltaic unit models under the current initial model parameters; The initial load model parameters and the initial photovoltaic unit model parameters in the current input data are used as the action space, and a set of simulated total active power, simulated total reactive power, simulated total active output, and simulated total reactive output are randomly selected from the simulation data as the state space; Calculate the error function of the operating data and the state space to obtain the error function value; Determine the error function value; If the error function value is greater than or equal to the error function threshold, the action space is updated according to the preset algorithm, and the initial load model parameters and initial photovoltaic unit model parameters in the updated action space are used as inputs of the next Markov algorithm; If the error function value is less than the error function threshold, the current reward function value is increased by one; wherein, after the current reward function value is increased by one, if the current reward function value is less than the reward function threshold, the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are used as the input of the next Markov algorithm; when the current reward function value is equal to the reward function threshold, the Markov algorithm is stopped, and the initial load model parameters and the initial photovoltaic unit model parameters in the current action space are output.

9. The equivalent modeling control device for a microgrid according to claim 8, characterized in that: The state space satisfies the following conditions: In the formula, S state,i is the state space, To simulate the total active power, To simulate the total active power, To simulate the total active power output, To simulate the total reactive power output. The action space satisfies the following conditions: The action space satisfies the following conditions: A actioni =[η i ,K 1i ,K Vi ,a Pi1 ,a Pi2 ,a Pi3 ,a Qi1 ,a Qi2 ,a Qi3 ] In the formula, A actioni is the action space, K 1i , K Vi is the initial PV unit model parameter, a Pi1 、a Pi2 、a Pi3 、a Qi1 , a Qi2 , a Qi3 are the initial load model parameters.

10. The equivalent modeling control device for a microgrid according to claim 9, characterized in that: The error function satisfies the following conditions: In the formula, REW t is the error function value, P de-ZIP is the actual total active power, Q de-ZIP is the actual total reactive power, P de-pho is the actual total active contribution, Q de-pho For the actual total reactive power contribution.

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