A microgrid decision control method based on behavior tree

Abstract

The application discloses a micro-grid decision control method based on a behavior tree, which comprises the following steps: Step 1, defining a behavior tree logic; the behavior tree logic comprises control nodes and execution nodes, the control nodes comprise fallback nodes and sequential nodes, and the execution nodes comprise conditional nodes and action nodes; Step 2, defining a micro-grid equipment model; Step 3, defining a micro-grid decision control model based on the behavior tree; and Step 4, establishing a micro-grid decision control architecture, and running the micro-grid decision control model based on the behavior tree in a micro-grid system. The application can intelligently respond to actual operation conditions under different working conditions, and the application of the behavior tree decision model greatly improves the intelligent level of the micro-grid on-site decision.

Description

TECHNICAL FIELD

[0001] The present application relates to a micro-grid decision control method based on behavior tree, belonging to the field of new energy access and micro-grid technology. BACKGROUND

[0002] The safety requirements for human and equipment in the operation process of new energy systems are gradually increasing, but in the prior art, with the improvement of performance, the control logic of the micro-grid energy storage system is complex, and the system operation economy is poor.

[0003] The prior art CN201910153508-a micro-grid energy storage optimization configuration method containing an electric vehicle, based on a multi-objective particle swarm algorithm as a method for multi-objective nonlinear optimization, converts the multi-objective function into a single objective function by linear weighting method and finally optimizes by particle swarm algorithm, but the logic readability is poor and the reuse is complex. SUMMARY

[0004] The present application aims to: in view of the shortcomings of the prior art, provide a micro-grid decision control method based on behavior tree, which can quickly respond to actual operating conditions intelligently under different operating conditions, and greatly improve the intelligence level of micro-grid on-site decision-making through the application of behavior tree decision-making model.

[0005] The specific technical scheme of the present application is:

[0006] A micro-grid decision control method based on behavior tree, comprising the following steps:

[0007] Step 1, define the behavior tree logic; the behavior tree logic includes control nodes and execution nodes, the control nodes include fallback (Fallback) nodes and sequence (Sequence) nodes, and the execution nodes include condition (Condition) nodes and action (Action) nodes;

[0008] Step 2, define the micro-grid device model; the micro-grid device model includes a photovoltaic model, an energy storage model, an electric vehicle charging and discharging model, and a grid-connected point model;

[0009] Step 3, define the micro-grid decision control model based on behavior tree:

[0010] The micro-grid decision control model based on behavior tree includes a total fallback node FB, an overload control subtree a reverse power control subtree and a normal operating condition interval control subtree

[0011] Step 4, establish a micro-grid decision control architecture, and run the micro-grid decision control model based on behavior tree in the micro-grid system.

[0012] A microgrid system includes energy storage, electric vehicles, photovoltaics, and loads. Loads include DC loads and AC loads. Energy storage is connected to the AC bus or DC bus through an energy storage converter. Photovoltaics are connected to the AC bus or DC bus through a photovoltaic optimizer. Electric vehicles are connected to the AC bus or DC bus through electric vehicle charging and discharging stations. DC loads are connected to the DC bus, and AC loads are connected to the AC load. The microgrid system is connected to the grid through a grid connection point.

[0013] The microgrid decision-making and control architecture is as follows:

[0014] (401) The microgrid controller collects the active power of the energy storage converter, photovoltaic optimizer, electric vehicle charging and discharging pile, grid connection point and load in the microgrid system through RS485 or CAN bus;

[0015] (402) The microgrid controller communicates with the microgrid energy cloud to obtain the target operating curve of energy storage and the target operating curve of electric vehicle charging and discharging;

[0016] (403) The microgrid controller determines the reference control commands for the current energy storage (energy storage system), photovoltaic (photovoltaic system) and electric vehicle based on the microgrid decision control model, and sends them to the corresponding energy storage converter, photovoltaic inverter and electric vehicle charging pile through the communication interface to complete the control process.

[0017] Overload control subtree The decision-making logic is as follows:

[0018] Overload control subtree It includes sequence node SQ1, sequence node SQ2, backtrack node FB1, condition node C1, condition node C2, action node A1, and action node A2;

[0019] When condition node C1 returns failure, sequence node SQ1 returns failure to the total backoff node FB; when condition node C1 returns success, the backoff node FB1 is checked. If backoff node FB1 returns success, sequence node SQ1 returns success to the total backoff node FB; otherwise, sequence node SQ1 returns failure to the total backoff node FB.

[0020] If the sequential node SQ2 returns a success, the fallback node FB1 returns a success to the sequential node SQ1; if the sequential node SQ2 returns a failure, the action node A1 is executed, and the fallback node FB1 returns a success to SQ1.

[0021] If condition node C2 returns failure, then sequence node SQ2 returns failure to backtrack node FB1. If condition node C2 returns success, then action node A2 is executed, and sequence node SQ2 returns success to backtrack node FB1.

[0022] The judgment logic of the condition node C1 is:

[0023] P pcc >P pcc-down (1)

[0024] When the grid-connected point power P pcc is greater than the maximum power P pcc-down allowed to be used by the grid-connected point, the condition node C1 returns success to the sequential node SQ1, otherwise the condition node C1 returns failure to the sequential node SQ1;

[0025] The judgment logic of the condition node C2 is:

[0026] P pcc -P pv -P es +P pv-up -P es-up <P pcc-down (2)

[0027] If the grid-connected point power P pcc - the photovoltaic power P pv - the energy storage output power P es + the photovoltaic power P pv-up - the energy storage power P es-up is less than the maximum power P pcc-down allowed to be used by the grid-connected point, the condition node C2 returns success to the sequential node SQ2, otherwise it returns failure to the sequential node SQ2;

[0028] The action node A1 implements the electric vehicle starting to limit charging, and the execution logic of the action node A1 is:

[0029] P es-set =P es-up (3)

[0030] P pv-set =P pv-up (4)

[0031] P ev-set =P pcc-down -P pcc +P ev (5)

[0032] The set power P es-set of the energy storage is the energy storage power P es-up , the set power P pv-set of the photovoltaic is the photovoltaic power P pv-up , and the set power P ev-set of the electric vehicle is: the maximum power P pcc-downP pcc P ev and return success to the backtracking node FB1.

[0033] Action node A2, which implements energy storage to reduce charging or increase discharging power, has the execution logic of

[0034] P es-set P pcc-down P pcc P es (6)

[0035] P pv-set P pv-up (7)

[0036] P ev-set P ev-up (8)

[0037] P es-set P pcc-down P pcc P es P pv-set P pv-up P ev-set P ev-up and return success to the backtracking node FB1.

[0038] Reverse power control subtree has the decision logic of:

[0039] Reverse power control subtree includes sequential node SQ3, sequential node SQ4, backtracking node FB2, backtracking node FB, conditional node C3, conditional node C4, conditional node C5, action node A3, action node A4, and action node A5:

[0040] When the conditional node C3 returns failure, the sequential node SQ3 returns failure to the total backtracking node FB. When the conditional node C3 returns success, the backtracking node FB2 is judged. If the backtracking node FB2 returns success, the sequential node SQ3 returns success to the total backtracking node FB. Otherwise, the sequential node SQ3 returns failure to the total backtracking node FB.

[0041] When the sequential node SQ4 returns success, the backtracking node FB2 returns success to the sequential node SQ3. When the sequential node SQ4 returns failure, the backtracking node FB3 is judged. If the backtracking node FB3 returns success to the sequential node SQ3, the backtracking node FB2 returns success. Otherwise, it returns failure to the sequential node SQ3.

[0042] When the condition node C4 returns failure, the sequential node SQ4 returns failure to the fallback node FB2, and when the condition node C4 returns success, the action node A3 is executed, and the sequential node SQ4 returns success to the fallback node FB2;

[0043] When the sequential node SQ5 returns success, the fallback node FB3 returns success; and when the sequential node SQ5 returns failure, the action node A4 is executed, and the fallback node FB3 returns success;

[0044] When the condition node C5 returns failure, the sequential node SQ5 returns failure; and when the condition node C5 returns success, the action node A5 is executed, and the sequential node SQ5 returns success.

[0045] The judgment logic of the condition node C3 is as follows:

[0046] P pcc <P pcc-up (9)

[0047] When the grid-connected point power P pcc exceeds the maximum power P pcc-up of power generation in the grid-connected point operation, the condition node C3 returns success to the sequential node SQ3, otherwise, the node returns failure to the sequential node SQ3;

[0048] The judgment logic of the condition node C4 is as follows:

[0049] P es-down -P es +P pcc >P pcc-up (10)

[0050] When the energy storage down-regulation power P es-down is greater than the energy storage output power P es + the grid-connected point power P pcc is greater than the maximum power P pcc-up of power generation in the grid-connected point operation, the condition node C4 returns success to the sequential node SQ4, otherwise, it returns failure to the sequential node SQ4;

[0051] The execution logic of the action node A3 is as follows:

[0052] P es-set =P pcc-up -P pcc +P es (12)

[0053] P pv-set =P pv-eco (13)

[0054] P ev-set =Pev-eco (14)

[0055] set power of the energy storage P es-set maximum generation power P for the grid-connected point pcc-up grid-connected point power P pcc output power P of the energy storage es set power of the photovoltaic P pv-set economic power P of the photovoltaic pv-eco set power of the electric vehicle P ev-set economic power P of the electric vehicle ev-eco and returns success to the sequential node SQ4;

[0056] The judgment logic of the conditional node C5 is:

[0057] P pcc -P es -P ev +P es-down +P ev-down >P pcc-up (11)

[0058] grid-connected point power P pcc output power P of the energy storage es output power P of the electric vehicle ev down-regulation power P of the energy storage es-down down-regulation power P of the electric vehicle ev-down greater than the maximum generation power P for the grid-connected point operation pcc-up , the node returns success to the sequential node SQ5, otherwise, returns failure to the sequential node SQ5;

[0059] The action node A4 is used to limit the photovoltaic generation power, and the execution logic of the action node A4 is:

[0060] P es-set =P es-down (15)

[0061] P pv-set =P pcc-up -P pcc -P ev-down +P ev +P pv (16)

[0062] P ev-set =P ev-down (17)

[0063] set power of the energy storage P es-set set down-regulation power P of the energy storage es-down set power of the photovoltaic P pv-set maximum generation power P for the grid-connected point operationpcc-up -Grid connection point power P pcc -Energy storage power reduction P ev-down +Electric vehicle output power P ev + Photovoltaic power generation capacity, economic power of photovoltaics P pv The power output P of electric vehicles ev-set For the reduced power P of electric vehicles ev-down And return a success message to the rollback node FB3;

[0064] Action node A5 enables electric vehicles to add charging. The execution logic of action node A5 is as follows:

[0065] P es-set =P es-down (18)

[0066] P pv-set =P pv-eco (19)

[0067] P ev-set =P pcc-up -P pcc +P ev (20)

[0068] Energy storage setting power P es-set The down-regulation power P for energy storage es-down Photovoltaic power output P pv-set The economic power P of photovoltaic pv-eco The power output P of electric vehicles ev-set The maximum generating capacity P at the grid connection point pcc-up -Grid connection point power P pcc +Electric vehicle output power P ev And return success to SQ5.

[0069] Normal operating condition interval control subtree The decision-making logic is as follows:

[0070] Normal operating condition interval control subtree This includes sequence nodes SQ6, SQ7, SQ8, SQ9, backtrack nodes FB4, FB5, FB6, FB7, condition nodes C6, C7, C8, C9, action nodes A6, A7, A8, A9, and A10.

[0071] When the sequential node SQ6 returns success, the fallback node FB4 returns success to the total fallback node FB, and when the sequential node SQ6 returns failure, the judgment fallback node FB6, if the fallback node FB6 returns success, the fallback node FB4 returns success to the total fallback node FB, otherwise the node FB4 returns failure to the total fallback node FB;

[0072] When the node condition node returns failure, the sequential node SQ6 returns failure to the fallback node FB4; when the condition node C6 returns success, the judgment fallback node FB5, if the fallback node FB5 returns success, the sequential node SQ6 returns success to the fallback node FB4, otherwise the sequential node SQ6 returns failure to FB4; when the sequential node SQ7 returns success, the fallback node FB5 returns success to the sequential node SQ6; when the sequential node SQ7 returns failure, the execution node A7, the fallback node FB5 returns success to the sequential node SQ6

[0073] When the condition node C7 returns failure, the sequential node SQ7 returns failure to the fallback node FB5; when the condition node C7 returns success, the execution node A6, the sequential node SQ7 returns success to the fallback node FB5;

[0074] When the node SQ8 returns success, the fallback node FB6 returns success to the total fallback node FB; when the node SQ8 returns failure, the execution node A10, the node FB6 returns success to the total fallback node FB;

[0075] When the condition node C8 returns failure, the sequential node SQ8 returns failure to the fallback node FB6; when the condition node C8 returns success, the judgment fallback node FB7, if the fallback node FB7 returns success, the sequential node SQ8 returns success to the fallback node FB6, otherwise returns failure to the fallback node FB6;

[0076] When the sequential node SQ9 returns success, the fallback node FB7 returns success to the sequential node SQ8; when the sequential node SQ9 returns failure, the execution node A9, the fallback node FB7 returns success to the sequential node SQ8;

[0077] When the condition node C9 returns failure, the sequential node SQ9 returns failure to the fallback node FB7; when the condition node C9 returns success, the execution node A8, the sequential node SQ9 returns success to the fallback node FB7.

[0078] The judgment logic of the condition node C6 is:

[0079] P pcc -P ev -P es +P ev-down +P es-down >P pcc-down(21)

[0080] When grid point power P pcc - the output power P ev - the output power P es + the output power P ev-down + the output power P es-down is greater than the maximum power P pcc-down , the condition node C6 returns success, and the fallback node FB5 is judged;

[0081] The judgment logic of the condition node C7 is:

[0082] P pcc +P ev-eco -P ev <P pcc-down (22)

[0083] When grid point power P pcc + the economic power P ev-eco - the output power P ev is less than the maximum power P pcc-down , the node returns success to SQ7, otherwise it returns failure to SQ7;

[0084] The execution logic of the action node A6 is:

[0085] P es-set =P pcc-down -P pcc +P es (25)

[0086] P pv-set =P pv-eco (26)

[0087] P ev-set =P ev-eco (27)

[0088] The set power P es-set of the energy storage is the maximum power P pcc-down - the grid point power P pcc + the output power P es , the set power P pv-set of the photovoltaic is the economic power P pv-eco of the photovoltaic, the set power P ev-set of the electric vehicle is the economic power P ev-eco of the electric vehicle, and success is returned to SQ7;

[0089] The execution logic of the action node A7 is:

[0090] Pes-set = P es-up (28)

[0091] P pv-set = P pv-eco (29)

[0092] P ev-set = P pcc-down - P pcc + P ev + P es-up - P es (30)

[0093] set power P of the energy storage es-set up power P of the energy storage es-up set power P of the photovoltaic pv-set economic power P of the photovoltaic pv-eco set power P of the electric vehicle ev-set maximum power P allowed by the grid point pcc-down grid point power P pcc output power P of the electric vehicle ev up power P of the energy storage es-up output power P of the energy storage es and returns success to the fallback node FB5;

[0094] The judgment logic of the condition node C8 is:

[0095] P pcc + P ev-eco + P es-eco - P ev - P es < P pcc-up (23)

[0096] If the grid point power P pcc economic power P of the electric vehicle ev-eco economic power P of the energy storage es-eco output power P of the electric vehicle ev output power P of the energy storage es is less than the maximum power P generated by the grid point in operation pcc-up , the node returns success to the sequential node SQ8, otherwise, it returns failure to the sequential node SQ8;

[0097] The judgment logic of the condition node C9 is:

[0098] P pcc + P ev-eco - P ev > P pcc-up (24)

[0099] If the grid point power Ppcc + economic power of electric vehicle P ev-eco - output power of electric vehicle P ev greater than maximum power generation of grid-connected point P pcc-up , the node returns success to the sequential node SQ9, otherwise, it returns failure to the sequential node SQ9;

[0100] The execution logic of the action node A8 is:

[0101] P es-set = P pcc-up -P pcc +P es (31)

[0102] P pv-set = P pv-eco (32)

[0103] P ev-set = P ev-eco (33)

[0104] set power of energy storage P es-set maximum power generation of grid-connected point P pcc-up - power of grid-connected point P pcc + output power of energy storage P es set power of photovoltaic P pv-set economic power of photovoltaic P pv-eco set power of electric vehicle P ev-set economic power of electric vehicle P ev-eco and returns success to the sequential node SQ9, the action node A9 is used for limiting the output power of the electric vehicle, and the execution logic of the action node A9 is:

[0105] P es-set = P es-down (34)

[0106] P pv-set = P pv-eco (35)

[0107] P ev-set = P pcc-up -P pcc +P es-down -P es +P ev (36)

[0108] set power of energy storage P es-set downward adjustment power of energy storage P es-down set power of photovoltaic P pv-set economic power of photovoltaic P pv-eco set power of electric vehicle P ev-setFor the maximum power P pcc-up - grid point power P pcc + energy storage down power P es-down - energy storage output power P es + electric vehicle output power P ev , and return success to the fallback node FB7;

[0109] The action node A10 is used for photovoltaic, energy storage and electric vehicle to run according to economic power, and the action node A10 executes the logic as follows:

[0110] P es-set = P es-eco (37)

[0111] P pv-set = P pv-eco (38)

[0112] P ev-set = P ev-eco (39)

[0113] The set power P es-set of the energy storage is the economic power P es-eco of the energy storage, the set power P pv-set of the photovoltaic is the economic power P pv-eco of the photovoltaic, and the set power P ev-set of the electric vehicle is the economic power P ev-eco of the electric vehicle, and return success to the fallback node FB6.

[0114] The working process of the control node and the execution node includes:

[0115] (101) The fallback node polls the child nodes from left to right until a child node returning "success" is found and returned to the parent node, and when all the child nodes return "failure", "failure" is returned to the parent node;

[0116] (102) The sequential node finds a node returning "failure" and returns it to the parent node, or when all the nodes are "success", "success" is returned to the parent node;

[0117] (103) The conditional node checks its own condition, and if the condition is "true", "success" is returned, otherwise "failure" is returned;

[0118] (104) The action node executes the command and returns "success".

[0119] The present application has the following beneficial effects:

[0120] This application discloses a microgrid decision-making and control method based on behavior trees, which solves the operation control problem of complex microgrids under different operating conditions and equipment states, ensuring that the system can operate safely, stably and economically under any state. The operating range of the grid-connected microgrid is decomposed into reverse power state, normal operation state and overload operation state, and a behavior tree decision model is given for each state. The behavior tree model has the characteristics of responsiveness and modularity. It has many advantages such as good logic readability, easy expansion, easy reuse, and easy monitoring of the operation process. It is very suitable for application scenarios with high logic complexity. This invention can quickly and intelligently respond according to the actual operating conditions under different operating conditions. The application of behavior tree decision model also greatly improves the intelligence level of microgrid on-site decision-making. Attached Figure Description

[0121] Figure 1 Microgrid decision-making and control architecture based on behavior tree;

[0122] Figure 2 Microgrid decision-making and control model based on behavior tree;

[0123] Figure 3 This embodiment uses the running data of the application case. Detailed Implementation

[0124] To make the objectives, technical solutions, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0125] The technical solutions in the embodiments of the present invention will be clearly and completely described and discussed below with reference to the accompanying drawings. Obviously, what is described here is only a part of the examples of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0126] A microgrid decision-making and control method based on behavior trees includes the following steps:

[0127] Step 1: Define the behavior tree logic;

[0128] The behavior tree logic includes control nodes and execution nodes. The control nodes include fallback nodes and sequence nodes, and the execution nodes include condition nodes and action nodes.

[0129] Behavior tree is a method of switching between different tasks in autonomous agents, which is easy to reuse in the context of another higher-level behavior without having to formulate their relationship with the subsequent behavior. The behavior tree contains two types of control nodes and two types of execution nodes, and the embodiment is improved according to the actual needs of micro-grid application. The use of node types includes two types of control nodes and two types of execution nodes, as shown in Table 1:

[0130] Table 1 Behavior tree node types

[0131] Node name Node type Sign Success Failure Fall-back node Control node FB One node success All nodes failure Sequential node Control node SQ All child nodes success One child node failure Conditional node Execution node Condition Condition is true Condition is false Action node Execution node Action Always true Never

[0132] The working process of control nodes and execution nodes includes:

[0133] (101) The fallback node polls the child nodes from left to right (the fallback node in the embodiment sends the clock from left to right to the child nodes), until the “success” returning node is found, which returns to the parent node, and when all child nodes return “failure”, “failure” is returned to the parent node, and the fallback node pseudo code is as follows:

[0134]

[0135] (102) The sequence node finds the node returning “failure” to the parent node, or all nodes are “success”, and returns “success” to the parent node, and the Sequence node pseudo code is as follows:

[0136]

[0137] (103) The condition node checks its own condition, and returns “success” if the condition is “true”, otherwise returns “failure”. The Condition node pseudo code is as follows:

[0138]

[0139] (104) The Action node executes the command and returns “success”, and the Action node pseudo code is as follows:

[0140]

[0141] Step 2, define the micro-grid device model; the micro-grid device model includes a photovoltaic model, an energy storage model, an electric vehicle charging and discharging model, and a grid connection point model;

[0142] The parameters of the photovoltaic model, the energy storage model, the electric vehicle charging and discharging model, and the grid connection point model in the embodiment are defined as follows:

[0143] Table 2 Parameters of photovoltaic model

[0144] P pv ]]> Photovoltaic generation power P pv-eco ]]> Photovoltaic economic power, i.e., the maximum available generation power of photovoltaic P pv-down ]]> Photovoltaic limitable power, i.e., the negation of generation power P pv-up ]]> Photovoltaic available increase power, i.e., the difference between the maximum available generation power of photovoltaic and the current power P pv-set ]]> Photovoltaic setting power

[0145] Table 3 Parameters of energy storage model

[0146] P es ]]> Energy storage output power P es-eco ]]> Energy storage economic power, i.e., the energy storage system planning curve power issued by the cloud P es-down ]]> Energy storage down-regulation power, i.e., the difference between the current allowed maximum charging power of energy storage and the current power P es-up ]]> Energy storage up-regulation power, i.e., the difference between the current allowed maximum generation power of energy storage and the current power P es-set ]]> Energy storage setting power

[0147] Table 4 Parameters of electric vehicle charging and discharging model

[0148]

[0149]

[0150] Table 5 Parameters of grid-connected point model

[0151] P pcc ]]> Grid-connected point power P pcc-down ]]> Grid-connected point allowed maximum power consumption, i.e., the load capacity limit of the upper distribution transformer P pcc-up ]]> Grid-connected point maximum generation power

[0152] Step 3, define the microgrid decision control model based on behavior tree:

[0153] The microgrid decision control model based on behavior tree is as shown in Figure 2 The microgrid decision control model described in the embodiment includes a total fallback node FB and three control sub-trees, which are respectively: an overload control sub-tree a reverse power control sub-tree and a normal operating interval control sub-tree

[0154] Step 4, establish a microgrid decision control architecture, and run the microgrid decision control model based on behavior tree in the microgrid system.

[0155] As shown in the accompanying Figure 1 The microgrid system includes energy storage, electric vehicles, photovoltaic and loads, the loads include DC loads and AC loads, the energy storage is connected to an AC bus or a DC bus through an energy storage converter, the photovoltaic is connected to an AC bus or a DC bus through a photovoltaic optimizer, the electric vehicles are connected to an AC bus or a DC bus through an electric vehicle charging and discharging pile, the DC loads are connected to a DC bus, the AC loads are connected to an AC load, and the microgrid is connected to a power grid through a grid-connected point.

[0156] The microgrid real-time decision control method is run in a microgrid controller, and the control architecture is:

[0157] (401) The microgrid controller collects active power of the energy storage converter, the photovoltaic optimizer, the electric vehicle charging and discharging pile, the grid-connected point and the loads in the microgrid through RS485 or CAN bus;

[0158] (402) The microgrid controller communicates with the microgrid energy cloud to obtain a target operating curve of the energy storage and a charging and discharging target operating curve of the electric vehicles;

[0159] (403)The micro-grid controller decides the reference control instructions of the current energy storage system, photovoltaic system and electric vehicle according to the micro-grid decision control model, and sends the reference control instructions to the corresponding energy storage converter, photovoltaic inverter and electric vehicle charging pile through a communication interface to complete the control process.

[0160] The control logic of each control subtree in the micro-grid decision control model of the behavior tree is as follows:

[0161] (301)Overload control subtree The decision logic is as follows:

[0162] The overload control subtree includes a sequence node SQ1, a sequence node SQ2, a fallback node FB1, a condition node C1, a condition node C2, an action node A1 and an action node A2.

[0163] Sequence node SQ1:

[0164] When the condition node C1 node returns failure, the sequence node SQ1 returns failure to the total fallback node FB; when the condition node C1 node returns success, the fallback node FB1 node is judged, if the fallback node FB1 returns success, the sequence node SQ1 returns success to the total fallback node FB, otherwise the sequence node SQ1 returns failure to the total fallback node FB.

[0165] Fallback node FB1:

[0166] When the sequence SQ2 node returns success, the fallback node FB1 returns success to the sequence SQ1. When the sequence node SQ2 node returns failure, the action node A1 is executed, and the fallback node FB1 returns success to SQ1.

[0167] Sequence node SQ2:

[0168] When the condition node C2 returns failure, the sequence node SQ2 returns failure to the fallback node FB1, and when the condition node C2 returns success, the action node A2 is executed, and the sequence node SQ2 returns success to the fallback node FB1.

[0169] The judgment logic of the condition node C1 is as follows:

[0170] P pcc >P pcc-down (1)

[0171] When the grid-connected point power P pcc is greater than the maximum power P pcc-down allowed by the grid-connected point, the condition node C1 returns success to the sequence node SQ1, otherwise the condition node C1 returns failure to the sequence node SQ1.

[0172] The condition node C2 is:

[0173] P pcc -P pv -P es +P pv-up -P es-up <P pcc-down (2)

[0174] If the grid-connected point power P pcc - photovoltaic power P pv - energy storage output power P es + photovoltaic power P pv-up - energy storage power P es-up is less than the maximum power P pcc-down allowed for use by the grid-connected point, then the condition node C2 returns success to the sequential node SQ2, otherwise it returns failure to the sequential node SQ2.

[0175] The action node A1 implements the electric vehicle starting to limit charging, and the execution logic of the action node A1 is:

[0176] P es-set = P es-up (3)

[0177] P pv-set = P pv-up (4)

[0178] P ev-set = P pcc-down -P pcc +P ev (5)

[0179] The set power P es-set of the energy storage is the up-regulation power P es-up of the energy storage, the set power P pv-set of the photovoltaic is the power P pv-up of the photovoltaic, and the set power P ev-set of the electric vehicle is: the maximum power P pcc-down allowed for use by the grid-connected point - the grid-connected point power P pcc + the output power P ev of the electric vehicle, and returns success to the backtracking node FB1;

[0180] The action node A2 implements the energy storage reducing charging or increasing discharging power, and the execution logic of the action node A2 is:

[0181] P es-set = P pcc-down -P pcc +P es (6)

[0182] P pv-set = Ppv-up (7)

[0183] P ev-set = P ev-up (8)

[0184] P es-set P pcc-down P pcc P es P pv-set P pv-up P ev-set P ev-up , and returns success to FB1;

[0185] (302) reverse power control subtree decision logic:

[0186] (2.1) reverse power control subtree comprises sequential node SQ3, sequential node SQ4, fallback node FB2, fallback node FB 、 conditional node C3, conditional node C4, conditional node C5, action node A3, action node A4, and action node A5;

[0187] sequential node SQ3:

[0188] When conditional node C3 returns failure, then sequential node SQ3 returns failure to total fallback node FB, and when conditional node C3 returns success, then judge fallback node FB2, if fallback node FB2 node returns success, then sequential node SQ3 returns success to total fallback node FB, otherwise sequential node SQ3 returns failure to total fallback node FB

[0189] conditional node C3:

[0190] P pcc P pcc-up (9)

[0191] When grid point power P pcc exceeds maximum power generation of grid point operation P pcc-up , conditional node C3 returns success to sequential node SQ3, otherwise the node returns failure to sequential node SQ3.

[0192] fallback node FB2:

[0193] When the sequential node SQ4 returns success, the fallback node FB2 returns success to the sequential node SQ3; when the sequential node SQ4 returns failure, the fallback node FB3 is judged, if the fallback node FB3 returns success to the sequential node SQ3, the fallback node FB2 returns success, otherwise returns failure to the sequential node SQ3.

[0194] Sequential node SQ4:

[0195] When the conditional node C4 returns failure, the sequential node SQ4 returns failure to the fallback node FB2, when the conditional node C4 returns success, the action node A3 is executed, and the sequential node SQ4 returns success to the fallback node FB2.

[0196] Fallback node FB3:

[0197] When the sequential node SQ5 returns success, the fallback node FB3 returns success. When the sequential node SQ5 returns failure, the action node A4 is executed, and the fallback node FB3 returns success.

[0198] Sequential node SQ5:

[0199] When the conditional node C5 returns failure, the sequential node SQ5 returns failure. When the conditional node C5 returns success, the action node A5 is executed, and the sequential node SQ5 returns success.

[0200] The judgment logic of the conditional node C4 is:

[0201] P es-down -P es +P pcc >P pcc-up (10)

[0202] Energy storage down-regulation power P es-down - Energy storage output power P es + Grid-connected point power P pcc Greater than the maximum power P of the grid-connected point operation pcc-up , the conditional node C4 returns success to the sequential node SQ4, otherwise returns failure to the sequential node SQ4.

[0203] The execution logic of the action node A3 is:

[0204] P es-set =P pcc-up -P pcc +P es (12)

[0205] P pv-set =P pv-eco (13)

[0206] P ev-set =Pev-eco (14)

[0207] set power of the energy storage P es-set maximum generation power P of the grid-connected point pcc-up grid-connected point power P pcc output power P of the energy storage es set power of the photovoltaic P pv-set economic power P of the photovoltaic pv-eco set power of the electric vehicle P ev-set economic power P of the electric vehicle ev-eco and returns success to the sequential node SQ4;

[0208] conditional node C5:

[0209] P pcc -P es -P ev +P es-down +P ev-down >P pcc-up (11)

[0210] grid-connected point power P pcc output power P of the energy storage es output power P of the electric vehicle ev down-regulation power P of the energy storage es-down down-regulation power P of the electric vehicle ev-down maximum generation power P of the grid-connected point operation pcc-up then the node returns success to the sequential node SQ5, otherwise returns failure to the sequential node SQ5;

[0211] action node A4 is used for limiting photovoltaic generation power, and the execution logic of the action node A4 is:

[0212] P es-set =P es-down (15)

[0213] P pv-set =P pcc-up -P pcc -P ev-down +P ev +P pv (16)

[0214] P ev-set =P ev-down (17)

[0215] set power of the energy storage P es-set down-regulation power P of the energy storage es-down set power of the photovoltaic P pv-set maximum generation power P of the grid-connected point operation pcc-upGrid point power P pcc Energy storage down-regulation power P ev-down Electric vehicle output power P ev Economic power of photovoltaic power generation P pv Set power of electric vehicle P ev-set Down-regulation power of electric vehicle P ev-down , and returns success to the fallback node FB3;

[0216] Action node A5, which implements electric vehicle charging increase, the execution logic of the action node A5 is:

[0217] P es-set =P es-down (18)

[0218] P pv-set =P pv-eco (19)

[0219] P ev-set =P pcc-up -P pcc +P ev (20)

[0220] Set power of energy storage P es-set Down-regulation power of energy storage P es-down Set power of photovoltaic P pv-set Economic power of photovoltaic P pv-eco Set power of electric vehicle P ev-set Maximum power generation of grid point operation P pcc-up Grid point power P pcc Electric vehicle output power P ev , and returns success to SQ5;

[0221] (303) Normal working condition interval control subtree The decision logic is:

[0222] The normal working condition interval control subtree includes sequential node SQ6, sequential node SQ7, sequential node SQ8, sequential node SQ9, fallback node FB4, fallback node FB5, fallback node FB6, fallback node FB7, conditional node C6, conditional node C7, conditional node C8, conditional node C9, action node A6, action node A7, action node A8, action node A9 and action node A10;

[0223] Fallback node FB4:

[0224] When the sequential node SQ6 returns success, the fallback node FB4 returns success to the total fallback node FB, and when the sequential node SQ6 returns failure, the judgment fallback node FB6, if the fallback node FB6 returns success, the fallback node FB4 returns success to the total fallback node FB, otherwise the node FB4 returns failure to the total fallback node FB.

[0225] The sequential node SQ6:

[0226] When the node condition node returns failure, the sequential node SQ6 returns failure to the fallback node FB4. When the condition node C6 returns success, the judgment fallback node FB5, if the fallback node FB5 returns success, the sequential node SQ6 returns success to the fallback node FB4, otherwise the sequential node SQ6 returns failure to the FB4 condition node C6:

[0227] P pcc -P ev -P es +P ev-down +P es-down >P pcc-down (21)

[0228] When the grid-connected point power P pcc - the output power P of the electric vehicle ev - the energy storage output power P es + the electric vehicle down-regulation power P ev-down + the energy storage down-regulation power P es-down is greater than the maximum power P allowed by the grid-connected point to consume pcc-down , the condition node C6 returns success, and the judgment fallback node FB5.

[0229] The fallback node FB5:

[0230] When the sequential node SQ7 returns success, the fallback node FB5 returns success to the sequential node SQ6; when the sequential node SQ7 returns failure, the execution node A7 is executed, and the fallback node FB5 returns success to the sequential node SQ6

[0231] The sequential node SQ7:

[0232] When the condition node C7 returns failure, the sequential node SQ7 returns failure to the fallback node FB5. When the condition node C7 returns success, the execution node A6 is executed, and the sequential node SQ7 returns success to the fallback node FB5 condition node C7:

[0233] P pcc +P ev-eco -P ev <P pcc-down (22)

[0234] When the grid-connected point power P pcc+ economic power of electric vehicle P ev-eco - output power of electric vehicle P ev less than the maximum power consumption allowed by the grid point P pcc-down , the node returns success to SQ7, otherwise returns failure to SQ7;

[0235] Action node A6:

[0236] P es-set = P pcc-down -P pcc + P es (25)

[0237] P pv-set = P pv-eco (26)

[0238] P ev-set = P ev-eco (27)

[0239] Set power of energy storage P es-set The maximum power consumption allowed by the grid point P pcc-down - grid point power P pcc + energy storage output power P es , the set power of photovoltaic P pv-set The economic power of photovoltaic P pv-eco , the set power of electric vehicle P ev-set The economic power of electric vehicle P ev-eco , and returns success to SQ7;

[0240] Action node A7:

[0241] P es-set = P es-up (28)

[0242] P pv-set = P pv-eco (29)

[0243] P ev-set = P pcc-down -P pcc + P ev + P es-up -P es (30)

[0244] Set power of energy storage P es-set The upward power of energy storage P es-up , the set power of photovoltaic P pv-set The economic power of photovoltaic P pv-eco , the set power of electric vehicle P ev-set The maximum power consumption allowed by the grid point P pcc-downGrid point power P pcc + Electric vehicle output power P ev + Energy storage up-regulation power P es-up - Energy storage output power P es and return success to the back-off node FB5;

[0245] Back-off node FB6:

[0246] When node SQ8 returns success, back-off node FB6 returns success to the total back-off node FB. When node SQ8 returns failure, execute node A10, node FB6 returns success to the total back-off node FB;

[0247] Sequential node SQ8:

[0248] When condition node C8 returns failure, sequential node SQ8 returns failure to back-off node FB6. When condition node C8 returns success, judge back-off node FB7, if back-off node FB7 returns success, then sequential node SQ8 returns success to back-off node FB6, otherwise returns failure to back-off node FB6;

[0249] Condition node C8:

[0250] P pcc + P ev-eco + P es-eco - P ev - P es < P pcc-up (23)

[0251] If grid point power P pcc + Electric vehicle economy power P ev-eco + Energy storage economy power P es-eco - Electric vehicle output power P ev - Energy storage output power P es is less than the maximum power generation power P pcc-up of the grid point operation, then the node returns success to the sequential node SQ8, otherwise returns failure to the sequential node SQ8;

[0252] Back-off node FB7:

[0253] When sequential node SQ9 returns success, back-off node FB7 returns success to the sequential node SQ8; when sequential node SQ9 returns failure, execute node A9, back-off node FB7 returns success to the sequential node SQ8;

[0254] Sequential node SQ9:

[0255] When the condition node C9 returns failure, the sequence node SQ9 returns failure to the fallback node FB7; when the condition node C9 returns success, the action node A8 is executed, and the sequence node SQ9 returns success to the fallback node FB7;

[0256] The condition node C9:

[0257] P pcc +P ev-eco -P ev >P pcc-up (24)

[0258] If the grid-connected point power P pcc + the economic power of the electric vehicle P ev-eco - the output power of the electric vehicle P ev is greater than the maximum power generation of the grid-connected point P pcc-up , the node returns success to the sequence node SQ9, otherwise returns failure to the sequence node SQ9;

[0259] The action node A8:

[0260] P es-set =P pcc-up -P pcc +P es (31)

[0261] P pv-set =P pv-eco (32)

[0262] P ev-set =P ev-eco (33)

[0263] The set power P es-set of the energy storage is the maximum power generation of the grid-connected point P pcc-up - the grid-connected point power P pcc + the output power of the energy storage P es , the set power P pv-set of the photovoltaic is the economic power of the photovoltaic P pv-eco , the set power P ev-set of the electric vehicle is the economic power of the electric vehicle P ev-eco , and returns success to the sequence node SQ9 action node A9 for limiting the output power of the electric vehicle, the action node A9 executes the logic including:

[0264] P es-set =P es-down (34)

[0265] P pv-set =P pv-eco (35)

[0266] Pev-set = P pcc-up - P pcc + P es-down - P es + P ev (36)

[0267] P es-set P es-down P pv-set P pv-eco P ev-set P pcc-up - P pcc + P es-down - P es + P ev , and returns success to the fallback node FB7.

[0268] The action node A10 is used for the photovoltaic, the energy storage and the electric vehicle to operate according to the economic power, and the action node A10 executes the logic as follows:

[0269] P es-set = P es-eco (37)

[0270] P pv-set = P pv-eco (38)

[0271] P ev-set = P ev-eco (39)

[0272] P es-set P es-eco P pv-set P pv-eco P ev-set P ev-eco , and returns success to the fallback node FB6.

[0273] The control effect of the method of the present application is described below by taking the actual control decision process of a whole day as an example. As shown in the following table. Figure 3

[0274] (1) At the beginning of the morning, the energy storage SOC value is 31, discharges to supply power to the load, and the operation node A8;

[0275] (2) At 8:42:59, the energy storage discharges to the minimum limit value, at this time, the energy storage has no discharging capacity, there is no electric vehicle, and the grid point has no reverse flow, and the operation node A11; ​

[0276] (3) 10:44:03, the electric vehicle discharges, instantaneously, the grid-connected point starts reverse power -5.4 kW, at this time the energy storage has 20 kW chargeable capacity, running node A6, starts charging the energy storage;

[0277] (4) 12:59:45, the electric vehicle stops discharging, the energy storage has discharge capacity, running node A8, supplies power to the load for discharging;

[0278] (5) 13:39:15, another electric vehicle discharges, the running condition is same as (3);

[0279] (6) 17:28:46, the energy storage gradually fills, the chargeable capacity gradually decreases, starts reverse flow, at this time runs to node A9, the electric vehicle discharge power starts to limit;

[0280] (7) 17:47:06, the electric vehicle stops discharging, the energy storage has discharge capacity, running node A8, supplies power to the load for discharging.

[0281] From the whole day's running condition analysis can be seen, the method described in the paper can completely automatically respond to different running conditions, equipment states, and realize all-day autonomous economic operation.

[0282] In the specification provided herein, a large number of specific details are described. However, it can be understood that the embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of the present description.

[0283] Similarly, it is to be understood that the embodiments of the application, while including particular embodiments, are not limited to the particular embodiments illustrated and described, and as such can vary. It is also to be understood that well-known methods, procedures and components have not been described in detail in order to not obscure the aspects of the present description. It will be apparent, however, to one of ordinary skill in the art having the benefit of this description that methods, procedures and components can be employed that vary from the specific embodiments described herein without departing from the spirit of the application. Like reference numerals designate like elements in the accompanying drawings.

[0284] It will be appreciated by those skilled in the art that the modules or units or groups of the devices in the examples disclosed herein can be arranged in a device as described in the examples, or alternatively can be located in one or more devices different from the device in the examples. The modules in the foregoing examples can be combined as a module or further divided into multiple sub-modules.

[0285] Those skilled in the art will appreciate that the modules in the apparatuses in the embodiments can be adapted and placed in one or more apparatuses other than the embodiments. The modules or units or inter-group combinations in the embodiments can be combined into one module or unit or inter-group, and further can be divided into a plurality of sub-modules or sub-units or sub-groups. Any combination of all the features disclosed in the specification (including the accompanying claims, abstract and drawings), and any method or apparatus of any combination of the features disclosed in the specification (including the accompanying claims, abstract and drawings) can be taken, except that at least some of such features and / or processes or units are mutually exclusive. Unless explicitly stated, each feature disclosed in the specification (including the accompanying claims, abstract and drawings) can be replaced by alternative features providing the same, equivalent or similar functionality.

[0286] Furthermore, those skilled in the art will appreciate that different embodiments of the application have different features, and thus that not all embodiments of the application will exhibit all of the features described in the specification (including the accompanying claims, abstract and drawings). Thus, features of the application are believed to be combinable in different embodiments, and in combinations which are not specifically set forth in the specification (including the accompanying claims, abstract and drawings).

[0287] Furthermore, some of the embodiments described herein are of a "method" or a "process" that can be embodied in computer executable code that can be accessed through one or more computer available media or memories, available via one or more of the computer's data storage devices or memories 1204. Accordingly, those skilled in the art will recognize that, where appropriate, one or more elements of the embodiments can be implemented using a computer program product. Such implementation can include other processes or methods according to the teachings of the specification.

[0288] The various techniques described herein can be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and apparatus of the application, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the application.

[0289] Where a program code is provided by a machine-readable storage medium, the computing device generally includes a processor that processes instructions and a memory that stores the instructions. The memory is generally a computer-readable medium, such as a floppy diskette, CD-ROM, hard disk drive, or other storage device. As used herein, the term "memory" is intended to encompass a combination of memory devices, such as cache and RAM. The memory is generally configured to store program code, which when executed by the processor, causes the machine to carry out the methods described herein.

[0290] In the interest of clarity, not all of the routine features of those implementations will be described. It will be appreciated that for software-implemented embodiments, that connection and physical components would be replaced with their software or virtual counterparts.

[0291] As used herein, unless otherwise indicated, the use of the ordinal adjectives "first", "second", "third", etc., merely to distinguish different instances of a same object, and are not intended to imply that a sequence or order to which the objects are described next, or otherwise.

[0292] While the application has been described in terms of several embodiments, those skilled in the art will recognize that the application can be practiced with modifications and alterations limited only by the spirit and scope of the claims. Additionally, although this description has focused on the use of the application in the context of a computer system, it will be appreciated that the application is applicable to other types of systems as well. Furthermore, those skilled in the art will recognize that references to the application as a process or method are merely for illustration and that the application is equally applicable as a data structure or data structure component, a computer program product, a system, or other means for accomplishing this process. Those skilled in the art will recognize or be able to ascertain using no more than routine skill and the preceding description the existence of such additional embodiments as can be submitted to the above indicated Patent Office. The disclosure is presented in the foregoing specification so as to enable other experts in the field of computer technology, including software engineers and computer programmers, to practice the many aspects of the application.

[0293] The foregoing specification is directed to preferred embodiments of the application and is not intended to limit the scope of the application. Many modifications and variations to the described embodiments of the application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims, the application can be practiced otherwise than as specifically described. For that reason, the following claims should be studied to determine the true scope and content of the application.

Claims

1. A method for microgrid decision control based on a behavior tree, characterized in that, The method comprises the following steps: Step 1, defining a behavior tree logic; the behavior tree logic comprises control nodes and execution nodes, the control nodes comprise fallback nodes and sequential nodes, and the execution nodes comprise conditional nodes and action nodes; Step 2, defining a micro-grid equipment model; the micro-grid equipment model comprises a photovoltaic model, an energy storage model, an electric vehicle charging and discharging model and a grid-connected point model; Step 3, defining a micro-grid decision control model based on the behavior tree: The microgrid decision control model based on the behavior tree includes a total fallback node FB, an overload control subtree An inverse power control subtree And a normal operating interval control subtree Step 4, establishing a micro-grid decision control architecture, and running the micro-grid decision control model based on the behavior tree in a micro-grid system; the working processes of the control nodes and the execution nodes comprise: (101) the fallback node polls the child nodes from left to right until a child node returning "success" is found and returned to the parent node, and when all the child nodes return "failure", "failure" is returned to the parent node; (102) the sequential node finds a node returning "failure" and returns it to the parent node, or when all the nodes are "success", "success" is returned to the parent node; (103) the conditional node checks its own condition, and if the condition is "true", "success" is returned, otherwise "failure" is returned; (104) the action node executes the command and returns "success".

2. The micro-grid decision control method based on the behavior tree according to claim 1, wherein the micro-grid system comprises energy storage, an electric vehicle, photovoltaic and a load, the load comprises a direct-current load and an alternating-current load, the energy storage is connected to an alternating-current bus or a direct-current bus through an energy storage converter, the photovoltaic is connected to the alternating-current bus or the direct-current bus through a photovoltaic optimizer, the electric vehicle is connected to the alternating-current bus or the direct-current bus through an electric vehicle charging and discharging pile, the direct-current load is connected to the direct-current bus, the alternating-current load is connected to the alternating-current load, and the micro-grid system is connected to a power grid through a grid-connected point.

3. The micro-grid decision control method based on the behavior tree according to claim 2, wherein the micro-grid decision control architecture comprises: (401) the micro-grid controller collects active power of the energy storage converter, the photovoltaic optimizer, the electric vehicle charging and discharging pile, the grid-connected point and the load in the micro-grid system through RS485 or CAN bus; (402) the micro-grid controller communicates with a micro-grid energy cloud to obtain a target operation curve of the energy storage and a charging and discharging target operation curve of the electric vehicle; (403) the micro-grid controller decides reference control instructions of the current energy storage, photovoltaic and electric vehicle according to the micro-grid decision control model, and sends the reference control instructions to corresponding energy storage converters, photovoltaic inverters and electric vehicle charging piles through a communication interface to complete the control process.

4. The micro-grid decision control method based on the behavior tree according to claim 1, wherein when the conditional node C1 returns failure, the sequential node SQ1 returns failure to the total fallback node FB; when the conditional node C1 returns success, the fallback node FB1 is judged, if the fallback node FB1 returns success, the sequential node SQ1 returns success to the total fallback node FB, otherwise the sequential node SQ1 returns failure to the total fallback node FB. ​ Overload control sub-tree The decision logic is: Overload control subtree comprises a sequential node SQ1, a sequential node SQ2, a fallback node FB1, a conditional node C1, a conditional node C2, an action node A1 and an action node A2; ​ ​ When the sequence node SQ2 returns success, the fallback node FB1 returns success to the sequence SQ1; when the sequence node SQ2 returns failure, the action node A1 is executed, and the fallback node FB1 returns success to SQ1; When the condition node C2 returns failure, the sequence node SQ2 returns failure to the fallback node FB1; when the condition node C2 returns success, the action node A2 is executed, and the sequence node SQ2 returns success to the fallback node FB1.

5. The microgrid decision control method based on the behavior tree according to claim 4, wherein, The judgment logic of the condition node C1 is: P pcc >P pcc-down (1) When the grid point power P pcc is greater than the maximum power P pcc-down allowed to be used by the grid point, the condition node C1 returns success to the sequential node SQ1, otherwise the condition node C1 returns failure to the sequential node SQ1; The judgment logic of the condition node C2 is: P pcc -P pv -P es +P pv-up -P es-up <P pcc-down (2) If the grid point power P pcc - the photovoltaic power P pv - the energy storage output power P es + the photovoltaic additional power P pv-up - the energy storage up-regulation power P es-up is less than the maximum power P pcc-down allowed for consumption at the grid point, then the condition node C2 returns success to the sequence node SQ2, otherwise it returns failure to the sequence node SQ2; The action node A1 implements the start of the electric vehicle to limit the charging, and the execution logic of the action node A1 is: P es-set = P es-up (3) P pv-set = P pv-up (4) P ev-set = P pcc-down - P pcc + P ev (5) P es-set P es-up P pv-set P pv-up P ev-set P pcc-down P pcc P ev P The action node A2 implements the energy storage to reduce the charging or increase the discharging power, and the execution logic of the action node A2 is P es-set = P pcc-down - P pcc + P es (6) P pv-set = P pv-up (7) P ev-set = P ev-up (8) Set power P of the energy storage es-set Maximum power P allowed to the grid point pcc-down Grid point power P pcc Output power P of the energy storage es Set power P of the photovoltaics pv-set Upward power P of the photovoltaics pv-up Set power P of the electric vehicle ev-set Upward power P of the electric vehicle ev-up and returns success to the fallback node FB1.

6. The microgrid decision control method based on the behavior tree according to claim 1, wherein, Inverse power control sub-tree The decision logic is: Inverse power control sub-tree comprises the sequential node SQ3, the sequential node SQ4, the sequential node SQ5, the fallback node FB2, the fallback node FB3, the conditional node C3, the conditional node C4, the conditional node C5, the action node A3, the action node A4 and the action node A5: When the condition node C3 returns failure, the sequence node SQ3 returns failure to the total fallback node FB; when the condition node C3 returns success, the judgment fallback node FB2 is determined, if the fallback node FB2 returns success, the sequence node SQ3 returns success to the total fallback node FB, otherwise, the sequence node SQ3 returns failure to the total fallback node FB; When the sequence node SQ4 returns success, the fallback node FB2 returns success to the sequence node SQ3; when the sequence node SQ4 returns failure, the judgment fallback node FB3 is determined, if the fallback node FB3 returns success to the sequence node SQ3, the fallback node FB2 returns success, otherwise, returns failure to the sequence node SQ3; When the condition node C4 returns failure, the sequence node SQ4 returns failure to the fallback node FB2; when the condition node C4 returns success, the action node A3 is executed, and the sequence node SQ4 returns success to the fallback node FB2; When the sequence node SQ5 returns success, the fallback node FB3 returns success; When the sequence node SQ5 returns failure, the action node A4 is executed, and the fallback node FB3 returns success; When the condition node C5 returns failure, the sequence node SQ5 returns failure; When the condition node C5 returns success, the action node A5 is executed, and the sequence node SQ5 returns success.

7. The microgrid decision control method based on the behavior tree according to claim 6, wherein, The judgment logic of the condition node C3 is: P pcc <P pcc-up (9) When the point of coupling power P pcc exceeds the maximum power generation P pcc-up at the point of coupling, the condition node C3 returns success to the sequence node SQ3, otherwise the node returns failure to the sequence node SQ3; The judgment logic of the condition node C4 is: P es-down -P es +P pcc >P pcc-up (10) When the energy storage down-regulates the power P es-down - the energy storage output power P es + the grid point power P pcc greater than the maximum power generation power P pcc-up then the condition node C4 returns success to the sequence node SQ4, otherwise it returns failure to the sequence node SQ4; The execution logic of the action node A3 is: P es-set = P pcc-up - P pcc + P es (12) P pv-set = P pv-eco (13) P ev-set = P ev-eco (14) set power P of the energy storage es-set set maximum power P of the energy storage pcc-up - grid point power Ppcc + output power P of the energy storage es set power P of the photovoltaics pv-set economic power P of the photovoltaics pv-eco set power P of the electric vehicle ev-set economic power P of the electric vehicle ev-eco and returns success to the sequence node SQ4; The judgment logic of the condition node C5 is: P pcc -P es -P ev +P es-down +P ev-down >P pcc-up (11) Grid point power P pcc - energy storage output power P es - electric vehicle output power P ev + energy storage down-regulation power P es-down + electric vehicle down-regulation power P ev-down greater than the maximum power generation power P of the grid point operation pcc-up then the node returns success to the sequential node SQ5, otherwise returns failure to the sequential node SQ5; The action node A4 is used to limit the photovoltaic power generation, and the execution logic of the action node A4 is: P es-set = P es-down (15) P pv-set = P pcc-up - P pcc - P ev-down + P ev + P pv (16) P ev-set = P ev-down (17) Set power P of the energy storage es-set Down-regulated power P of the energy storage es-down Set power P of the photovoltaic pv-set Maximum power P generated for grid point operation pcc-up Grid point power P pcc Down-regulated power P of the energy storage ev-down Output power P of the electric vehicle ev Power P generated by the photovoltaic pv Set power P of the electric vehicle ev-set Down-regulated power P of the electric vehicle ev-down , and returns success to the fallback node FB3; The action node A5 is used to increase the charging of the electric vehicle, and the execution logic of the action node A5 is: P es-set = P es-down (18) P pv-set = P pv-eco (19) P ev-set = P pcc-up - P pcc + P ev (20) Set power P for energy storage es-set Down-regulated power P for energy storage es-down Set power P for photovoltaics pv-set Economic power P for photovoltaics pv-eco Set power P for electric vehicles ev-set Maximum power generation P for grid point operation pcc-up Grid point power P pcc Output power P of electric vehicles ev , and returns success to SQ5.

8. The microgrid decision control method based on the behavior tree according to claim 1, wherein, Normal operating range control sub-tree The decision logic is as follows: Normal operating range interval control sub-tree comprises sequential node SQ6, sequential node SQ7, sequential node SQ8, sequential node SQ9, fallback node FB4, fallback node FB5, fallback node FB6, fallback node FB7, condition node C6, condition node C7, condition node C8, condition node C9, action node A6, action node A7, action node A8, action node A9, and action node A10; When the sequential node SQ6 returns success, the fallback node FB4 returns success to the total fallback node FB, and when the sequential node SQ6 returns failure, the judgment fallback node FB6 returns success, if the fallback node FB6 returns success, the fallback node FB4 returns success to the total fallback node FB, otherwise the node FB4 returns failure to the total fallback node FB; When the node condition node returns failure, the sequential node SQ6 returns failure to the fallback node FB4; when the condition node C6 returns success, the judgment fallback node FB5 returns success, if the fallback node FB5 returns success, the sequential node SQ6 returns success to the fallback node FB4, otherwise the sequential node SQ6 returns failure to the FB4 when the sequential node SQ7 returns success, the fallback node FB5 returns success to the sequential node SQ6; when the sequential node SQ7 returns failure, the execution node A7 is executed, and the fallback node FB5 returns success to the sequential node SQ6 When the condition node C7 returns failure, the sequential node SQ7 returns failure to the fallback node FB5; when the condition node C7 returns success, the execution node A6 is executed, and the sequential node SQ7 returns success to the fallback node FB5; When the node SQ8 returns success, the fallback node FB6 returns success to the total fallback node FB; When the node SQ8 returns failure, the execution node A10 is executed, and the node FB6 returns success to the total fallback node FB; When the condition node C8 returns failure, the sequential node SQ8 returns failure to the fallback node FB6; when the condition node C8 returns success, the judgment fallback node FB7 returns success, if the fallback node FB7 returns success, the sequential node SQ8 returns success to the fallback node FB6, otherwise returns failure to the fallback node FB6; When the sequential node SQ9 returns success, the fallback node FB7 returns success to the sequential node SQ8; when the sequential node SQ9 returns failure, the execution node A9 is executed, and the fallback node FB7 returns success to the sequential node SQ8; When the condition node C9 returns failure, the sequential node SQ9 returns failure to the fallback node FB7; when the condition node C9 returns success, the execution node A8 is executed, and the sequential node SQ9 returns success to the fallback node FB7.

9. The microgrid decision control method based on the behavior tree according to claim 8, characterized in that, the judgment logic of the condition node C6 is: P pcc -P ev -P es +P ev-down +P es-down >P pcc-down (21) Judgment and grid point power P pcc - the output power P of the electric vehicle ev - the output power P of the energy storage es + the down-regulation power P of the electric vehicle ev-down + the down-regulation power P of the energy storage es-down whether it is greater than the maximum power P allowed by the grid point pcc-down ; the judgment logic of the condition node C7 is: P pcc +P ev-eco -P ev <P pcc-down (22) When the grid-connected point power P pcc + the economic power P ev-eco - the output power P ev of the electric vehicle is less than the maximum power P pcc-down allowed for the grid-connected point, the node returns success to SQ7, otherwise, it returns failure to SQ7; the execution logic of the action node A6 is: P es-set = P pcc-down - P pcc + P es (25) P pv-set = P pv-eco (26) P ev-set = P ev-eco (27) P es-set P pcc-down - grid point power P pcc + storage output power P es P pv-set P pv-eco P ev-set P ev-eco , and returns success to SQ7; the execution logic of the action node A7 is: P es-set = P es-up (28) P pv-set = P pv-eco (29) P ev-set = P pcc-down - P pcc + P ev + P es-up - P es (30) Set power P of the energy storage es-set Up-regulated power P of the energy storage es-up Set power P of the photovoltaic pv-set Economic power P of the photovoltaic pv-eco Set power P of the electric vehicle ev-set Maximum power P allowed by the grid point pcc-down Grid point power P pcc Output power P of the electric vehicle ev Up-regulated power P of the energy storage es-up Output power P of the energy storage es And return success to the fallback node FB5; the judgment logic of the condition node C8 is: P pcc +P ev-eco +P es-eco -P ev -P es <P pcc-up (23) If the grid point power P pcc + the electric vehicle economic power P ev-eco + the energy storage economic power P es-eco - the electric vehicle output power P ev - the energy storage output power P es is less than the maximum power generation power P pcc-up of the grid point operation, the node returns success to the sequential node SQ8, otherwise returns failure to the sequential node SQ8; the judgment logic of the condition node C9 is: P pcc +P ev-eco -P ev >P pcc-up (24) If the grid point power P pcc + the electric vehicle economic power P ev-eco - the output power P ev of the electric vehicle is greater than the maximum power generation P pcc-up of the grid point operation, the node returns success to the sequential node SQ9, otherwise, it returns failure to the sequential node SQ9; the execution logic of the action node A8 is: P es-set = P pcc-up - P pcc + P es (31) P pv-set = P pv-eco (32) P ev-set = P ev-eco (33) Setting power P of the energy storage es-set Maximum generation power P for the point of connection pcc-up - Power P of the point of connection pcc + Output power P of the energy storage es Setting power P of the photovoltaic pv-set Economic power P for the photovoltaic pv-eco Setting power P of the electric vehicle ev-set Economic power P for the electric vehicle ev-eco And return success to the sequential node SQ9 action node A9 for limiting the output power of the electric vehicle, the action node A9 executes the logic: P es-set = P es-down (34) P pv-set = P pv-eco (35) P ev-set = P pcc-up - P pcc + P es-down - P es + P ev (36) Set power P of energy storage es-set Down-regulated power P of energy storage es-down Set power P of photovoltaic pv-set Economic power P of photovoltaic pv-eco Set power P of electric vehicle ev-set Maximum power P of grid-connected point pcc-up Power P of grid-connected point pcc Down-regulated power P of energy storage es-down Output power P of energy storage es Output power P of electric vehicle ev , and return success to fallback node FB7; the action node A10 is used for photovoltaic, energy storage and electric vehicle to run according to economic power, and the execution logic of the action node A10 is: P es-set = P es-eco (37) P pv-set = P pv-eco (38) P ev-set = P ev-eco (39) P es-set P es-eco P pv-set P pv-eco P ev-set P ev-eco and returns success to the fallback node FB6.

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