Novel power system source network load storage cooperative control method and device

Abstract

The embodiment of the invention provides a novel power system source network load storage cooperative control method and device, is applied to an intelligent terminal, and comprises the following steps: receiving operation data of operation equipment; classifying the operation data according to the type of the regulation and control scene to obtain different types of operation data; according to each type of operation data, determining a load prediction result of the corresponding type by using the load prediction model of the corresponding type; generating a reasonable load regulation and control strategy according to each type of load prediction result; sending the various types of load regulation and control strategies to a master station to enable the master station to evaluate the reported load regulation and control strategies, and if the reported load regulation and control strategies are feasible, determining a regulation and control instruction of the operation equipment and issuing the regulation and control instruction to the intelligent terminal; and the intelligent terminal receives the regulation and control instruction issued by the master station and controls the operation equipment according to the regulation and control instruction. According to the invention, by using the intelligent terminal at the edge side, the optimized cooperative control of the source network load storage can be realized.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of power grid technology, and in particular to a novel method and device for coordinated control of source, grid, load and storage in a power system. Background Art

[0002] The new power system is a power system supported by the interaction of source, grid, load and storage and the complementarity of multiple energies. Source, grid, load and storage integrate power sources, grids, loads and energy storage to form a collaborative system, aiming to improve the flexibility, reliability and sustainability of the power system and solve the problem of the mismatch between the volatility of renewable energy and the load demand of the power system. At present, the collaborative control methods of source, grid, load and storage are mainly divided into centralized and distributed. The centralized control method is that the central control system collects equipment operation data uniformly and coordinates and controls various systems, which puts great pressure on data processing; the distributed control method is that multiple intelligent terminals collect equipment operation data within a certain range respectively, and analyze the data transmitted by other terminals to determine the control instructions of the equipment. Due to the large number of intelligent terminals, the maintenance difficulty is also relatively large. Summary of the invention

[0003] In view of this, the purpose of the embodiments of the present application is to propose a novel method and device for coordinated control of source, grid, load and storage in a power system.

[0004] Based on the above purpose, the embodiment of the present application provides a novel power system source-grid-load-storage coordinated control method, which is applied to a smart terminal, including:

[0005] Receive operation data of the operation equipment;

[0006] Classifying the operation data according to the control scenario type to obtain different types of operation data;

[0007] According to various types of operating data, corresponding types of load forecasting results are determined using corresponding types of load forecasting models;

[0008] Generate corresponding load control strategies based on various types of load forecast results;

[0009] Sending various types of load control strategies to the master station so that the master station determines the control instructions for the operating equipment according to the various types of load control strategies;

[0010] Receive the control instruction sent by the master station, and control the running device according to the control instruction.

[0011] Optionally, the operation data is classified according to the control scenario type to obtain different types of operation data, including:

[0012] The operation data is clustered according to the control scenario type to obtain the operation data corresponding to each type of control scenario.

[0013] Optionally, according to various types of operating data, corresponding types of load forecasting results are determined using corresponding types of load forecasting models, including:

[0014] For each type of operating data, current operating data is filtered out;

[0015] The current operating data and the saved historical operating data are input into a load forecasting model corresponding to the type, and the model outputs a corresponding load forecasting result.

[0016] Optionally, the load forecast results include medium- and long-term load forecast results, short-term load forecast results, and ultra-short-term load forecast results; and according to each type of load forecast result, a corresponding load control strategy is generated, including:

[0017] According to the medium- and long-term load forecast results, short-term load forecast results, and ultra-short-term load forecast results, corresponding medium- and long-term load control strategies, short-term load control strategies, and ultra-short-term load control strategies are generated.

[0018] Optionally, the method further includes:

[0019] Receive load forecast results from other intelligent terminals;

[0020] The generation of corresponding load control strategies according to the load forecast results of various types includes:

[0021] Generate corresponding load control strategies based on various types of load forecast results and load forecast results of other intelligent terminals.

[0022] Optionally, receiving a control instruction issued by the master station, and controlling the operating device according to the control instruction includes:

[0023] Parsing the control instruction to determine the target control scenario type corresponding to the control instruction;

[0024] According to the target regulation scenario type, calling a control script pre-built for the target regulation scenario type;

[0025] Parsing the control instruction using the control script to obtain multiple instruction parameters;

[0026] According to the preset verification rules, each instruction parameter is verified to obtain the verification result;

[0027] If the verification result shows that the control instruction is correct, the control instruction is sent to the corresponding operating device so that the operating device performs the corresponding action according to the control instruction.

[0028] Optionally, the instruction parameters include a target operating device identifier and a target state of the target operating device; and the step of verifying each instruction parameter according to a preset verification rule to obtain a verification result includes:

[0029] Determine whether the target operating device corresponding to the target operating device identifier exists. If so, determine whether the current state of the target operating device is consistent with the target state. If not, determine that the verification result is correct.

[0030] Optionally, the method further includes:

[0031] Generate local control instructions and control the running equipment according to the local control instructions.

[0032] Optionally, generating a local control instruction and controlling the running device according to the local control instruction includes:

[0033] For each type of running equipment, when the preset equipment monitoring time is reached, if no control instruction issued by the master station is received, a local control instruction for the type of running equipment is generated, and the type of running equipment is controlled according to the local control instruction.

[0034] The embodiment of the present application also provides a novel power system source-grid-load-storage coordinated control device, including:

[0035] A receiving module, used to receive the operation data of the operation equipment; and receive the control instruction issued by the master station, and control the operation equipment according to the control instruction;

[0036] A classification module, used to classify the operation data according to the control scenario type to obtain different types of operation data;

[0037] A prediction module is used to determine a corresponding type of load prediction result using a corresponding type of load prediction model according to various types of operating data;

[0038] A generation module is used to generate corresponding load control strategies according to various types of load forecast results;

[0039] The sending module is used to send various types of load control strategies to the master station, so that the master station can determine the control instructions of the operating equipment according to the various types of load control strategies.

[0040] From the above, it can be seen that the new power system source-grid-load-storage collaborative control method and device provided by the embodiment of the present application, the intelligent terminal receives the operating data of the operating equipment; classifies the operating data according to the type of control scenario to obtain different types of operating data; determines the corresponding type of load forecasting results according to each type of operating data using the corresponding type of load forecasting model; generates corresponding load control strategies according to each type of load forecasting results; sends each type of load control strategy to the master station, so that the master station determines the control instructions of the operating equipment according to each type of load control strategy; receives the control instructions issued by the master station, and controls the operating equipment according to the control instructions. The present application uses the intelligent terminal on the edge side to achieve optimized collaborative control of the source-grid-load-storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A schematic diagram of a method flow of an embodiment of the present application;

[0043] Figure 2 A schematic diagram of the system architecture of an embodiment of the present application;

[0044] Figure 3 This is a source-grid-load-storage collaborative control framework diagram of an embodiment of the present application;

[0045] Figure 4 This is a demand response function framework diagram of an embodiment of the present application;

[0046] Figure 5 A training flow chart of a load forecasting model according to an embodiment of the present application;

[0047] Figure 6 This is a functional framework diagram of the demand response control strategy of an embodiment of the present application;

[0048] Figure 7 A flowchart of executing control instructions according to an embodiment of the present application;

[0049] Figure 8 A flowchart of executing control instructions according to another embodiment of the present application;

[0050] Fig. 9 A principle block diagram of the rolling execution control strategy of an embodiment of the present application;

[0051] Fig.10This is a hardware structure block diagram of the intelligent terminal according to an embodiment of the present application;

[0052] Fig.11 This is a principle block diagram of an AC sampling unit according to an embodiment of the present application;

[0053] Fig.12 This is a principle block diagram of a power module according to an embodiment of the present application;

[0054] Fig.13 This is the overall technical framework diagram of the embodiment of the present application;

[0055] Fig.14 A schematic diagram of the functional architecture of an application program according to an embodiment of the present application;

[0056] Fig.15 This is a block diagram of the device structure of an embodiment of the present application;

[0057] Fig.16 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0059] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] like Figure 1-3 As shown, the embodiment of the present application provides a novel power system source-grid-load-storage coordinated control method, which is applied to a smart terminal, and the method includes:

[0061] S101: receiving operation data of an operating device;

[0062] Combination Figure 2As shown, in this embodiment, the smart terminal is deployed on the edge side and the main station is deployed on the cloud. On the one hand, the smart terminal on the edge side collects the operating data of various operating equipment in the new power system, and obtains the load forecast result after processing the operating data. At the same time, it generates a load control strategy based on the load forecast result, and sends the load control strategy to the main station. The main station determines the control instruction based on the power supply situation of the power grid, application scenarios, specific needs and other factors, and sends the control instruction to the smart terminal; on the other hand, the smart terminal receives the control instruction, parses the control instruction, and sends it to the corresponding operating equipment to control the operating equipment.

[0063] In some embodiments, the operating equipment includes various types of source, grid, load and storage equipment such as distribution network side equipment, microgrid side equipment, and power consumption side equipment, such as sensors, smart meters, charging piles, distributed photovoltaics, flexible loads, distribution transformers, etc.; the operating data of the operating equipment includes electrical data such as voltage, current, power, electricity, equipment status, and non-electrical data such as temperature and humidity, sunshine intensity, and environmental parameters. The operating equipment and corresponding operating data in the new power system are not specifically listed and explained.

[0064] S102: Classifying the operation data according to the control scenario type to obtain different types of operation data;

[0065] In this embodiment, in the power system, there are different control scenarios such as virtual power plant resource control, low-voltage distributed photovoltaic control, air conditioning load control, vehicle-grid interactive control, customer-side energy storage control, integrated photovoltaic storage and charging control, and microgrid interactive management. In different control scenarios, the operating equipment is different, and the control methods for each operating equipment are different. For example, in the air conditioning load control scenario, different energy users such as industrial users, commercial users, and residential users have different electricity priorities. For different energy users, resource optimization scheduling of various energy users is achieved through collaborative control. For another example, in the photovoltaic control scenario, the control priorities of photovoltaic entities such as household photovoltaics, park / enterprise distributed photovoltaics, energy storage stations, and local power plants are different. For different photovoltaic entities, photovoltaic resource optimization scheduling is achieved through collaborative control.

[0066] In order to achieve coordinated control of different control scenarios, the collected operation data is classified according to the type of control scenario to obtain different types of operation data, that is, the operation data is divided into corresponding types of operation data according to the type of control scenario. For example, the operation data related to the air-conditioning load control scenario is divided into one category, and the operation data related to the photovoltaic control scenario is divided into another category; further, in the same control scenario, the operation data can be further classified according to the control requirements. For example, the operation data related to air-conditioning equipment in the air-conditioning load control scenario can be divided into operation data related to industrial users, operation data related to commercial users, etc. according to the type of energy users.

[0067] In some ways, clustering methods can be used to classify operating data to obtain operating data related to various control scenarios. For example, using the K-center point clustering method, first establish an optimization mathematical model, and then use the gradient algorithm to find its center point on the basis of relaxing the range of its random variable values, and decompose the optimization model, and use the feature point vector to find the direction of the gradient vector. For continuously accumulated time series data, the center point can be continuously corrected as the amount of data grows. The multi-data fusion K-center point clustering method is actually a combinatorial optimization algorithm, which can be described as the following mathematical model, and the objective function is:

[0068]

[0069] Ω 1 ∪Ω 2 ∪…∪ Ω k =Ω

[0070] Ω 1 ∩Ω 2 ∩…∩Ω k =i (1)

[0071] In order to reduce the amount of calculation of the traditional multi-data fusion K-center point clustering method, a multi-source data fusion gradient algorithm is used based on the above mathematical model. Since the newly added data set does not intersect with the original data set, for the determined center point, the original data set and the newly added data set can be clustered according to the distance, and the state variables when the optimal solution is obtained are X(t) and X', respectively. In actual engineering calculations, the center point can be corrected using the following formula:

[0072] X(t+1)=αX(t)+α′X′

[0073] α=s(t) / s(t+1), α′=s′ / s(t+1) (2)

[0074] S103: Determine a corresponding type of load forecast result using a corresponding type of load forecast model according to each type of operation data;

[0075] In this embodiment, after the operating data is divided into a plurality of types of operating data corresponding to the control scenarios, a corresponding type of load forecasting result is predicted using a corresponding type of load forecasting model according to each type of operating data.

[0076] In some embodiments, according to each type of operating data, a corresponding type of load forecasting result is determined using a corresponding type of load forecasting model, including:

[0077] For each type of operating data, current operating data is filtered out;

[0078] The current operating data and the saved historical operating data are input into the load forecasting model corresponding to the type, and the model outputs the corresponding load forecasting results.

[0079] In this embodiment, for each type of operation data, the current operation data of this type of control scenario is screened out, combined with the saved historical operation data of this type of control scenario, the current operation data and the historical operation data are input into the load forecasting model corresponding to this type of control scenario, and the load forecasting model outputs the predicted load forecasting result. For example, in the air conditioning load control scenario, the current operation data and the historical operation data mainly include the electricity data and non-electricity data of energy users, such as the current and historical electricity consumption of industrial users, the real-time electric energy information such as power, voltage, current, frequency, etc. of the power grid line, the historical electricity consumption includes 15-minute electricity consumption, daily electricity consumption, 7-day electricity consumption, monthly electricity consumption, etc., and non-electricity data such as ambient temperature and humidity, etc. In the photovoltaic control scenario, the current operation data and historical operation data mainly include sunshine intensity, photovoltaic module temperature, photovoltaic module DC side voltage, photovoltaic module DC side current, photovoltaic module DC side power, inverter voltage, inverter current, inverter frequency, inverter power, current active power, current reactive power, daily power generation, total power generation, single-cell battery voltage, single-cell battery current, single-cell battery temperature, single-cell battery internal resistance, single-cell battery capacity, remaining capacity (SOC), battery pack total current, battery pack total voltage, inverter operation status, inverter voltage alarm, inverter current alarm, energy storage control switch status, generator control switch status, power limit switch status, reactive power regulation switch status and other electrical data and non-electrical data.

[0080] In some methods, the load forecasting model is built based on LSTM neural network, which has a memory structure and can well reflect the temporal relationship of operating data. Figure 5 As shown in the figure, the training process of the load forecasting model includes collecting historical operation data, preprocessing the historical operation data to obtain a training sample set, dividing the training sample set into a training set, a validation set and a test set, and using the training samples of the training set to train the LSTM neural network model. Iterative training is performed until the network converges to meet the set error conditions, and the training is stopped to obtain the trained load forecasting model.

[0081] In some implementations, for different control scenarios, the LSTM neural network model is trained according to the corresponding historical operation data and the preset load configuration file, and a load forecasting model suitable for different control scenarios is obtained after training. Among them, the load configuration file is used to provide relevant configuration parameters for load data collection, control, and processing in the control scenario, and the specific configuration parameters are related to the application scenario and the equipment manufacturer.

[0082] For example, in the air conditioning load control scenario, the load monitor uses the DLT645 communication protocol, and the load configuration file includes the forward active energy, active power, power factor, A phase voltage, B phase voltage, C phase voltage, A phase current, B phase current, C phase current and other collection quantities, as well as 485 communication baud rate 2400, no parity, 8 data bits, 1 stop bit and other communication parameters; VRV air conditioning system uses serial port modbus communication protocol, and the load configuration file includes switch status, temperature setting value, mode setting status, wind speed setting, room temperature, fault code, switch command, temperature setting value command, mode setting command, wind speed setting command and other collection control point parameters, as well as 485 communication baud rate 9600, even parity, 8 data bits, 1 stop bit and other communication parameters. In the photovoltaic control scenario, the load configuration file includes the collection control quantities and communication parameters of photovoltaic strings, inverters, combiner boxes, energy storage, and grid connection, and the specific configuration parameters need to be configured according to the communication point table provided by the equipment manufacturer.

[0083] In some implementations, when the energy consumption of future users is predicted by the load forecasting algorithm and the control instructions for the operating equipment are issued, the optimal control method is given by the model predictive control algorithm. The model predictive control algorithm includes: establishing an internal model, determining a reference trajectory, designing a control algorithm, and implementing online optimization. The three key parts are the prediction model, rolling optimization, and feedback correction. The prediction model is used to run the output for a period of time in the future, the rolling optimization is used to perform rolling online optimization in a limited time domain, and the feedback correction is used to correct the prediction model and improve the prediction accuracy through prediction error feedback. The rolling optimization and feedback correction make up for the shortcomings of the low model accuracy, suppress disturbances, and improve robustness.

[0084] S104: Generate corresponding load control strategies according to the load forecast results of each type;

[0085] In this embodiment, after using various types of load forecasting models to predict the load forecast results of various control scenarios, the intelligent terminal can generate corresponding load adjustment strategies based on the load forecast results. In some embodiments, the load forecast results output by the load forecasting model include medium- and long-term load forecast results, short-term load forecast results, and ultra-short-term load forecast results, that is, it can predict the power consumption in the medium- and long-term, short-term, and ultra-short-term in a specific control scenario. The intelligent terminal can determine the load control strategy suitable for medium- and long-term control, the load control strategy suitable for short-term control, and the load control strategy suitable for ultra-short-term control based on the medium- and long-term load forecast results, the short-term load forecast results, and the ultra-short-term load forecast results.

[0086] In some embodiments, corresponding load control strategies are generated based on various types of load forecast results, including: generating corresponding medium- and long-term load control strategies, short-term load control strategies and ultra-short-term load control strategies based on medium- and long-term load forecast results, short-term load forecast results and ultra-short-term load forecast results.

[0087] In some implementations, the load control strategy generated by the intelligent terminal includes control strategies for different levels, control strategies for different regions, and control strategies for different time periods. That is, according to the division of various types of terminals at different levels in coordinated control, the load control strategy includes control strategies for the master station, load aggregator, edge intelligent agent, terminal load, etc., and each level of terminal formulates a corresponding load control strategy. According to the regional scope, the load control strategy includes load control strategies corresponding to different regional scopes, for example, load control strategies for provincial, municipal, and county regions. According to the control time interval, the load control strategy includes control strategies for different time periods, for example, load control strategies for load peaks and valleys. It can also be a load control strategy formulated at a comprehensive level and regional scope, for example, regional aggregation of scattered loads is achieved through load aggregation, and regionalized control is achieved based on load aggregators and edge intelligent agents.

[0088] For example, when the load control strategy is a local balancing strategy, the main goal is to maximize the local consumption of electricity. Based on multi-dimensional related data such as the operating status of interactive resources, customer electricity consumption behavior profiles, geographical location, and meteorological conditions, priority is given to achieving real-time dynamic balance of electricity in the current area of ​​aggregated resources, reducing short-term, high-cost electricity or load return, improving the utilization efficiency of distribution microgrids, increasing the proportion of clean energy consumption, and enhancing the economy of power transmission.

[0089] When the load control strategy is a peak-shaving and frequency regulation support strategy, in response to the power grid's stable operation needs such as peak shaving and valley filling and electric energy backup, the dynamic real-time balance of the total power generated on the source side and the total power consumed on the load side and the stability of the system frequency are achieved during the uninterrupted operation of the power system. At the same time, the advantages and endowments of classified and aggregated interactive resources are combined to realize the integrated reporting of peak-shaving business capabilities based on different response time requirements.

[0090] When the load control strategy is the load transfer strategy, when the power system encounters an emergency, the execution plan formulated by the business system is followed to achieve the emergency, safe and stable grid connection or off-grid function of source and load resources. The specific control process is: through the effective aggregation of interactive resources with fast response rate, stable regulation and control ability, and large adjustable space, the corresponding control instructions are determined, and the grid connection or off-grid is completed by executing the control instructions.

[0091] S105: Sending various types of load control strategies to the master station, so that the master station determines the control instructions of the operating equipment according to the load control strategy;

[0092] In this embodiment, after the smart terminal determines the load forecast results and corresponding load control strategies for various control scenarios based on the collected operating data, the load control strategy is sent to the main station on the cloud. The main station evaluates the reported load control strategies based on the reported load control strategies of different control scenarios, combined with the load, user conditions, application scenarios, etc. of the entire distribution network, determines the final control instructions, and sends the control instructions to the smart terminal.

[0093] S106: Receive the control instruction issued by the master station, and control the running equipment according to the control instruction.

[0094] In this embodiment, after the intelligent terminal receives the control instructions issued by the master station, it controls the operating equipment according to the control instructions, thereby realizing a collaborative control process through the interaction between the cloud master station, the edge-side intelligent terminal, and the source, network, load and storage operating equipment.

[0095] In some embodiments, the intelligent terminal receives the control instruction and controls the running device according to the control instruction, including:

[0096] Parse the control instruction to determine the target control scenario type corresponding to the control instruction;

[0097] According to the target control scenario type, call the control script pre-built for the target control scenario type.

[0098] Use the control script to parse the control instructions and obtain multiple instruction parameters;

[0099] According to the preset verification rules, each instruction parameter is verified to obtain the verification result;

[0100] If the verification result shows that the control instruction is correct, the control instruction will be sent to the corresponding operating equipment so that the operating equipment will perform the corresponding action according to the control instruction.

[0101] In this embodiment, after receiving the control instruction, the intelligent terminal verifies the correctness of the instruction by verifying the adjustment instruction to ensure the accuracy of the control. The intelligent terminal pre-configures the control scripts corresponding to various control scenarios. After receiving the control instruction, the intelligent terminal determines the target control scenario corresponding to the control instruction by parsing, determines the control script corresponding to the target control scenario, uses the control instruction as the execution parameter of the control script, and parses the control instruction by calling the control script to obtain multiple instruction parameters. Each instruction parameter is verified according to the set verification rules to determine whether the instruction parameters in the control instruction are correct. If they are all correct, it is determined that the control instruction is correct, and the intelligent terminal sends the control instruction to the corresponding operating device, so that the operating device performs the corresponding action according to the control instruction; if the control instruction is incorrect, the intelligent terminal does not send the control instruction, and sends the wrong control instruction and the cause of the error to the main station, and the main station readjusts the control instruction.

[0102] In some embodiments, the instruction parameters include a target operating device identifier and a target state of the target operating device; and each instruction parameter is verified according to a preset verification rule to obtain a verification result, including:

[0103] Determine whether the target operating device corresponding to the target operating device identifier exists. If so, determine whether the current state of the target operating device is consistent with the target state. If not, determine that the verification result is correct.

[0104] This embodiment provides a verification method for control instructions. The control script parses the target operating device identifier and the target state of the target operating device from the control instruction. It first determines whether the target operating device corresponding to the target operating device identifier exists, that is, whether it is within the controllable range of the smart terminal. If it exists, it determines whether the current state of the target operating device is consistent with the target state that needs to be controlled. If they are inconsistent, it is determined that the control instruction for the target operating device is correct.

[0105] For example, the control instruction includes circuit breaker A and the target state is open. During the verification process of the control script, it is determined that circuit breaker A exists and the current state of circuit breaker A is closed, then the control instruction is determined to be correct, and the control instruction is sent to circuit breaker A, and circuit breaker A is adjusted to the open state according to the control instruction. The control instruction includes air-conditioning equipment B and the target state is to adjust the temperature to 26 degrees. During the verification process of the control script, it is determined that air-conditioning equipment B exists and the current temperature of air-conditioning equipment B is not 26 degrees, then the control instruction is determined to be correct, and the control instruction is sent to air-conditioning equipment B to adjust the temperature to 26 degrees. The control instruction includes energy storage unit C and the target state is charging. During the verification process of the control script, it is determined that energy storage unit C exists and the current state of energy storage unit C is discharging, then the control instruction is determined to be correct, and the control instruction is sent to energy storage unit C, and energy storage unit C is adjusted to the charging state according to the control instruction. The control instruction is a return-to-school type remote control, including three processes: remote control selection, remote control verification and return, and remote control execution. The master station sends a remote control selection instruction. After receiving the remote control selection instruction, the intelligent terminal needs to verify the remote control target device identification, remote control point, and whether the current state of the remote control target device is repeated with the target state. If the verification passes, it returns the verification to the master station. If the verification fails, the master station cancels the remote control instruction. When the verification passes, the master station sends a remote control execution command. After receiving the remote control execution instruction, the intelligent terminal processes it and sends it to the target device for execution.

[0106] Combination Figure 4 As shown, in some embodiments, the intelligent terminal and the main station communicate with each other through the MQTT communication module according to the MQTT protocol. The intelligent terminal sends the load control strategy to the main station in the cloud. After the main station determines the control instruction based on the specific situation, the control instruction is sent to the intelligent terminal. After the intelligent terminal parses and verifies the control instruction, it sends the correct instruction to the corresponding operating equipment. After the operating equipment executes according to the control instruction, the execution result is sent to the intelligent terminal. The intelligent terminal evaluates the control effect according to the execution result, and sends the execution result and evaluation result of the operating equipment to the main station.

[0107] like Figure 6 , 7 As shown, in some implementations, the intelligent terminal receives the control instruction issued by the main station, starts the control strategy operation management application, the application selects the control script corresponding to the control instruction, calls the JS engine, uses the JS engine to parse the control script, obtains multiple JS events corresponding to the instruction parameters, verifies the JS events according to preset rules, obtains the verification result, and returns the verification result to the control strategy operation management application. If the verification result is correct, the control strategy operation management application executes the control strategy normally, otherwise it does not execute.

[0108] In some embodiments, the method further includes: generating a local control instruction, and controlling the operating device according to the local control instruction. That is, in the new power system, the intelligent terminal can not only control the operating device according to the control instruction issued by the master station, but also generate a local control instruction to control the operating device.

[0109] The generating of local control instructions and controlling the running equipment according to the local control instructions include:

[0110] For each type of running equipment, when the preset equipment monitoring time is reached, if no control instruction issued by the master station is received, a local control instruction for the type of running equipment is generated, and the type of running equipment is controlled according to the local control instruction.

[0111] Combination Figure 8 As shown, in this embodiment, the smart terminal subscribes to the control strategy topic published in the demand response through the MQTT protocol. When the control instruction issued by the main station is received from the control strategy subject, the control strategy operation management application is started to parse and verify the control instruction to obtain the verification result; when no control instruction is received and the device monitoring time of the target operating device is reached, the smart terminal generates a local control instruction for the target operating device, and uses the control strategy operation management application to parse and verify the local control instruction to obtain the verification result. If the instruction is correct, it is sent to the target operating device to control the target operating device to perform the action.

[0112] In some embodiments, the method also includes: receiving load forecast results from other smart terminals; then, generating corresponding load regulation strategies based on various types of load forecast results, including: generating corresponding load regulation strategies based on various types of load forecast results and load forecast results from other smart terminals.

[0113] like Fig. 9 As shown in the figure, in the collaborative control process between the intelligent terminal and the master station, the intelligent terminal interacts with other intelligent terminals, optimizes the load control strategy through continuous feedback correction and rolling optimization process, and realizes the precise control of the source, grid, load and storage of the new power system. The intelligent terminal receives the load forecast results sent by other intelligent terminal devices based on the collected operation data, and uses the load forecast model to predict the load forecast results. Combined with the pre-established regional coordinated control optimization objectives and the load forecast results of other intelligent terminals, a reasonable load control strategy is determined, and the optimized load control strategy is sent to the master station. The master station formulates the optimal control instruction based on the optimized load control strategy and sends it to the operating equipment. After the operating equipment executes the action according to the instruction, it feeds back the execution result to the intelligent terminal. The intelligent terminal updates the optimized load control strategy according to the execution result, the operation data collected after the execution, and the load forecast results of other intelligent terminals.

[0114] like Fig.10As shown, in this embodiment, the intelligent terminal includes an AC sampling unit, a combined current transformer module, a loop inspection unit, a main control storage unit, a communication module, an AC power supply, a backup power supply, a power management module, an external interface, etc., which is based on the integrated terminal of the substation, and the AC sampling circuit, the backup power supply and the power management circuit, the expansion module, the operating system kernel layer and the system service layer are optimized and designed. Among them, the external interface includes a serial interface, a Bluetooth module, an Ethernet module, etc., which are used to establish a communication connection with various operating equipment and a master station, and to interact with data, including obtaining operating data, receiving control instructions, issuing control instructions, etc. The intelligent terminal has the ability to continuously sample the voltage and current of the substation at a high speed, meets the steady-state data detection requirements and transient data detection requirements of the power quality, and maintains the original electricity metering function and accuracy of the integrated terminal, and optimizes the design of the signal conditioning circuit, the sampling chip selection, the topology of the sampling circuit, and the sampling auxiliary circuit.

[0115] like Fig.11 As shown, in some embodiments, the AC sampling unit includes a PT acquisition unit, a CT acquisition unit, an MCU unit, etc. The PT acquisition unit is used to collect the three-phase voltage instantaneous values ​​UAN, UBN and UCN passing through the three-phase four-wire voltage protection circuit, and the CT acquisition unit is used to collect the three-phase four-wire current instantaneous values ​​IA+, IA-, IB+, IB-, IC+, IC-. The voltage differential signal and the current differential signal are input into the electric energy metering chip (for example, ADE9000 device), which can achieve 640 points sampling per cycle, and output AC current effective value, active power, reactive power, apparent power, power factor, four-quadrant accumulated electricity, phase angle and other metering data at a conversion rate of 8kSPS. The data output by the electric energy metering chip is connected to the MCU unit through the SPI interface for data interaction.

[0116] The MCU unit can realize 10-cycle 4096-point sampling, which is used to process data such as power, harmonic voltage content, harmonic current effective value, voltage total harmonic distortion rate, voltage deviation, frequency deviation and three-phase imbalance, and transmits metering data and power quality data (3-second value, minute value, transient data half-cycle value) to the main control board of the intelligent terminal through the SPI interface; the storage units SRAM and FRAM are connected to the MCU unit, SRAM is used to store 1-minute continuous half-cycle effective value and several recording files, FRAM is used to store the calibration coefficient of the electric energy metering chip, and the clock module RTC is used to provide a real-time clock for the AC sampling unit; the main control board transmits the 4-way remote signal input to the MCU unit, and the MCU unit adds a timestamp to the signal and then returns it to the main control board; the MCU unit transmits the active power and reactive power to the pulse output circuit through the GPIO interface.

[0117] The pulse output circuit drives the active / reactive LED of the main control board to output optical pulse signals through the MOS tube, and outputs active / reactive electrical pulse signals and second pulse signals to the heavy-duty connector through the isolation optical coupler. The pulse width is 80ms±20ms, and the frequency reflects the number of equivalent electrical energy consumption.

[0118] In some embodiments, the intelligent terminal is equipped with a supercapacitor backup power supply and meets the specification requirement of full-function operation for 3 minutes after power failure, so as to ensure the reliable operation of the terminal when power quality events such as voltage sag, voltage swell, and voltage interruption occur, while achieving low power consumption. Fig.12 As shown in the figure, in order to meet the communication requirements of the HPLC module, the power module needs to first transmit the three-phase four-wire voltage signal connected to the heavy-duty connector to the three-phase AC signal connector on the main control board, and then convert the AC voltage into 12V and 3.3V DC voltages to power the main control board and the AC sampling unit related circuits respectively. A 12V DC power supply is used to charge the supercapacitor backup power circuit, and the charging time is required to be no more than 1 hour; when the power supply is disconnected, the supercapacitor discharge circuit is automatically and seamlessly put into use, and the normal working time of the intelligent terminal is not less than 3 minutes. After removing the supercapacitor module, the intelligent terminal still meets the voltage sag and short-term interruption requirements. In order to meet the technical requirements of reporting power outages immediately after power outages, the power module includes a power-off monitoring circuit, which notifies the main control board of the power outage through the weak current signal interface between the AC sampling unit and the main control board. In the case of a power grounding fault, the voltage between the two phases reaches 1.9 times the nominal voltage and is maintained for 4 hours. The intelligent terminal should not be damaged. The three-phase four-wire voltage connected to the heavy-duty connector is designed as 1.9×AC 220V=AC 418V. The voltage protection circuit protects the three-phase four-wire voltage connected to the heavy-duty connector through safety devices to prevent the terminal from being damaged by the surge short-circuit current, surge and electrical fast transient pulse group's level 4 interference voltage.

[0119] Optionally, the intelligent terminal is based on the low-power ARM Contex F4 series chip, which can realize functional expansion in various scenarios, ensure the power supply reliability, communication stability, interface interchangeability, hardware protection security and other basic performance of the functional expansion circuit, and support the expansion module customization function in different scenarios. At the same time, by optimizing the operating system kernel layer and the system service layer, the system performance can be improved.

[0120] like Fig.13 , 14As shown, in this embodiment, multiple applications are installed on the intelligent terminal, including basic information application, data collection application, data processing application, northbound interface management application, southbound interface management application, control strategy operation management application, distribution network three-level coordination application, etc. Among them, the basic information application is used to manage the basic information and status information of various types of operating equipment and energy users, perform full life cycle management and equipment information model management on various types of operating equipment, and report various information to the master station, including collection parameters, control parameters, alarm parameters, indicator parameters, security policy configuration, etc. of various types of operating equipment. The data collection application is used to collect power parameters, status parameters, environmental parameters, event information, etc. of various types of operating equipment. The data processing application is used to pre-process, store, display, statistically analyze, abnormal alarm, data encryption and decryption, etc. of the collected parameters. The northbound interface management application is used to manage the connection and data interaction with the master station, including parameter maintenance, data reporting, receiving control instructions, etc. The southbound interface management application is used to manage the connection and data interaction with various types of operating equipment, including channel management, protocol management, etc.

[0121] The three-level coordination application of the distribution network includes the autonomous regional coordination control application, the feeder-level coordination control application, and the prediction and analysis application. Among them, the autonomous regional coordination control application is used to realize the coordinated control of the substation, including the local voltage-reactive power control of the photovoltaic inverter, the flexible control of energy storage and adjustable loads, and the coordinated optimization of the distribution substation. The local voltage-reactive power control of the photovoltaic inverter is realized by the voltage-power sensitivity analysis of the low-voltage network, and the local voltage-reactive power control of the photovoltaic inverter is realized, including the control scenarios such as overvoltage, undervoltage, and power factor optimization. The energy storage charging and discharging control takes into account the uncertainty of the energy storage charging and discharging on the demand side, based on the model predictive control operation decision method, and combines the voltage, demand response and other scenarios to realize the charging and discharging control of the energy storage. The flexible control of adjustable load is based on the response capability of different users and different types of loads, monitors the real-time operation status of the load, considers the control target demand, control strategy, control time, user control reputation and other factors, takes the optimal complementarity and reliability as the goal, and distributes the load shortage data according to the principle of "meeting the overall control target and selecting users according to user control reputation".

[0122] The feeder-level coordinated control application is used to implement multi-feeder coordinated control, including multi-level coordinated voltage control, controllable feeder-substation power fluctuations, and multi-feeder coordinated load peak-to-valley difference control.

[0123] The prediction and analysis application is used to support the distributed collaborative interactive control of multi-scenario source-grid-load-storage operation, and realize the regional power generation and consumption feature analysis, adjustable margin prediction and sharing functions. The power generation and consumption feature analysis uses methods such as correlation analysis, feature extraction, and cluster identification to establish a correlation relationship model between the node dispatchable capacity and influencing factors, and realize the feature extraction of power generation and consumption characteristics. The influencing factors include various data such as historical weather data, historical production capacity data, historical energy consumption data, and historical scheduling execution data. The adjustable margin prediction is to build a node dispatchable capacity and cost relationship model by analyzing the relationship between unit capacity and cost, environmental protection and other indicators, and the relationship between dispatchable capacity and cost of different nodes under the same working conditions and the same node under different working conditions, so as to quickly predict the actual dispatchable capacity of the node in actual scheduling. Adjustable margin information sharing is to realize the information sharing of the dispatchable capacity of each node through the M2M communication mechanism and consistency information model, and support the realization of collaborative interactive control.

[0124] The new power system source-grid-load-storage collaborative control method provided in the embodiment of the present application utilizes the intelligent terminal deployed on the edge side to collect the operating data of various operating equipment of the source-grid-load-storage, perform load forecasting based on the operating data, and generate corresponding load control strategies, and send the load control strategies to the main station in the cloud. The main station determines the control instructions based on the whole network situation, and sends them to the intelligent terminal. The intelligent terminal parses and verifies the control instructions and controls the corresponding operating equipment to perform actions; at the same time, the intelligent terminal can autonomously control various operating equipment, and continuously optimize the load control strategy through feedback of execution results, rolling optimization of control strategies, and load forecasting results of other intelligent terminals, so as to realize precise control of each device in the system. In addition, the intelligent terminal has achieved optimization from hardware configuration to software processing, with powerful functions and strong scalability, and can realize optimized control from the main station to the terminal and from the terminal to the operating equipment.

[0125] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0126] It should be noted that the above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] like Fig.15 As shown, the embodiment of the present application provides a novel power system source-grid-load-storage coordinated control device, including:

[0128] A receiving module, used to receive the operation data of the operation equipment; and receive the control instructions issued by the master station, and control the operation equipment according to the control instructions;

[0129] The classification module is used to classify the operation data according to the control scenario type to obtain different types of operation data;

[0130] A prediction module is used to determine a corresponding type of load prediction result using a corresponding type of load prediction model according to various types of operating data;

[0131] A generation module is used to generate corresponding load control strategies according to various types of load forecast results;

[0132] The sending module is used to send various types of load control strategies to the master station, so that the master station can determine the control instructions of the operating equipment according to the load control strategy.

[0133] For the convenience of description, the above devices are described in terms of functions divided into various modules. Of course, when implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0134] The apparatus of the above-mentioned embodiment is used to implement the corresponding method in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0135] Fig.16 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0136] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0137] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0138] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0139] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0140] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0141] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0142] The electronic device of the above embodiment is used to implement the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0143] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0144] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present disclosure, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0145] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0146] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present disclosure.

Claims

1. A new power system source-grid-load-storage coordinated control method, applied to smart terminals, It is characterized in that include: Receive operation data of the operation equipment; Classifying the operation data according to the control scenario type to obtain different types of operation data; According to various types of operating data, corresponding types of load forecasting results are determined using corresponding types of load forecasting models; Generate corresponding load control strategies based on various types of load forecast results; Sending various types of load control strategies to the master station so that the master station determines the control instructions for the operating equipment according to the various types of load control strategies; Receive the control instruction sent by the master station, and control the running device according to the control instruction.

2. The method according to claim 1, It is characterized in that According to the type of control scenario, the operation data is classified to obtain different types of operation data, including: The operation data is clustered according to the control scenario type to obtain the operation data corresponding to each type of control scenario.

3. The method according to claim 2, It is characterized in that According to various types of operating data, the corresponding types of load forecasting models are used to determine the corresponding types of load forecasting results, including: For each type of operating data, current operating data is filtered out; The current operating data and the saved historical operating data are input into a load forecasting model corresponding to the type, and the model outputs a corresponding load forecasting result.

4. The method according to claim 3, It is characterized in that The load forecast results include medium- and long-term load forecast results, short-term load forecast results, and ultra-short-term load forecast results. According to each type of load forecast result, a corresponding load control strategy is generated, including: According to the medium- and long-term load forecast results, short-term load forecast results, and ultra-short-term load forecast results, corresponding medium- and long-term load control strategies, short-term load control strategies, and ultra-short-term load control strategies are generated.

5. The method according to claim 1, It is characterized in that Also includes: Receive load forecast results from other intelligent terminals; The generation of corresponding load control strategies according to the load forecast results of various types includes: Generate corresponding load control strategies based on various types of load forecast results and load forecast results of other intelligent terminals.

6. The method according to claim 1, It is characterized in that Receiving the control instruction issued by the master station, and controlling the running device according to the control instruction, including: Parsing the control instruction to determine the target control scenario type corresponding to the control instruction; According to the target regulation scenario type, calling a control script pre-built for the target regulation scenario type; Parsing the control instruction using the control script to obtain multiple instruction parameters; According to the preset verification rules, each instruction parameter is verified to obtain the verification result; If the verification result shows that the control instruction is correct, the control instruction is sent to the corresponding operating device so that the operating device performs the corresponding action according to the control instruction.

7. The method according to claim 6, It is characterized in that The instruction parameters include the target operating device identification and the target state of the target operating device; The verification of each instruction parameter according to the preset verification rules to obtain the verification result includes: Determine whether the target operating device corresponding to the target operating device identifier exists. If so, determine whether the current state of the target operating device is consistent with the target state. If not, determine that the verification result is correct.

8. The method according to claim 1, It is characterized in that Also includes: Generate local control instructions and control the running equipment according to the local control instructions.

9. The method according to claim 8, It is characterized in that Generate local control instructions and control the running equipment according to the local control instructions, including: For each type of running equipment, when the preset equipment monitoring time is reached, if no control instruction issued by the master station is received, a local control instruction for the type of running equipment is generated, and the type of running equipment is controlled according to the local control instruction.

10. A new type of power system source-grid-load-storage coordinated control device, It is characterized in that include: A receiving module, used for receiving operation data of the operation equipment; and receiving a control instruction issued by the master station, and controlling the operating device according to the control instruction; A classification module, used to classify the operation data according to the control scenario type to obtain different types of operation data; A prediction module is used to determine a corresponding type of load prediction result using a corresponding type of load prediction model according to various types of operating data; A generation module is used to generate corresponding load control strategies according to various types of load forecast results; The sending module is used to send various types of load control strategies to the master station, so that the master station can determine the control instructions of the operating equipment according to the various types of load control strategies.

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