Cooperative control method for optical storage and charging in transformer area

Abstract

The invention, which relates to the technical field of optical storage and charging, discloses an optical storage and charging cooperative control method for a transformer area, and the method comprises the steps: carrying out the intelligent and multivariate collection and prediction through intelligent equipment, constructing a function through collected data, solving an optimal solution, selecting an optimal optical storage and charging cooperative control strategy from the optimal solution, generating a corresponding control instruction, and carrying out the transmission of the corresponding control instruction. Cooperative regulation and control of a photovoltaic power generation system, an energy storage system and a charging pile system are achieved, a feedback and correction mechanism is further established, and it is ensured that the photovoltaic storage and charging system stably operates according to a preset strategy. According to the transformer area light storage and charging cooperative control method, a light storage and charging cooperative control mechanism is established by comprehensively considering photovoltaic power generation, an energy storage system and a charging pile system, power resource distribution in a transformer area is optimized, maximum consumption of photovoltaic power generation is achieved, the light abandoning phenomenon is reduced, meanwhile, unnecessary charging and discharging loss of the energy storage system is reduced, and the power utilization rate of the transformer area is improved. And the impact of disordered charging of the charging piles on a power grid system is relieved, so that the overall operation performance of a transformer area power system is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of light storage and charging, in particular to a transformer area light storage and charging collaborative control method. BACKGROUND

[0002] Light storage and charging refers to photovoltaic, energy storage and charging pile, through the technical integration of photovoltaic power generation, energy storage system and charging pile system, the efficient production, storage and utilization of energy are realized, especially in the background of popularization of new energy vehicles and development of distributed energy, it becomes an important mode to optimize energy structure and improve stability of power system, therefore, more and more enterprises begin to pay attention to the integrated construction of light storage and charging.

[0003] However, the existing light storage and charging system still has some deficiencies in actual application, for example, most of the light storage and charging systems in the transformer area run independently, lack effective collaborative control mechanism, the excess power generated by photovoltaic power generation in peak period cannot be reasonably stored or consumed in time, causing light abandonment phenomenon, the charging and discharging strategy of the energy storage system is unreasonable, and the regulating effect cannot be fully played, and the disordered charging of the charging pile aggravates the voltage fluctuation and three-phase imbalance problem of the transformer area, the above problems lead to the instability of the transformer area power system operation.

[0004] In view of the above problems, it is urgent to make innovative design on the basis of the original, to realize the coordinated operation of each device, improve the energy utilization efficiency, and ensure the stable and reliable operation of the transformer area power grid system, therefore, a transformer area light storage and charging collaborative control method is proposed to solve the above problems. SUMMARY

[0005] The purpose of the application is to provide a transformer area light storage and charging collaborative control method to solve the problems that most of the light storage and charging systems in the transformer area run independently, lack effective collaborative control mechanism, the excess power generated by photovoltaic power generation in peak period cannot be reasonably stored or consumed in time, causing light abandonment phenomenon, the charging and discharging strategy of the energy storage system is unreasonable, and the regulating effect cannot be fully played, and the disordered charging of the charging pile aggravates the voltage fluctuation and three-phase imbalance problem of the transformer area, and then leads to the instability of the transformer area power system operation.

[0006] In order to achieve the above purpose, the application provides the following technical scheme: a transformer area light storage and charging collaborative control method.

[0007] S1, intelligent multi-element collection and prediction: During the operation of the transformer area, the intelligent device is used to collect the operation data of the transformer area in real time, such as photovoltaic power generation power, energy storage system state of charge, charging pile charging power demand, transformer area node voltage and power grid price and other data, and the photovoltaic power generation power prediction and charging pile charging load prediction are carried out.

[0008] S2, the optimal solution is obtained by constructing a function: The controller is given a governance task for intelligent operation, and the minimum operation cost of the transformer area, the maximum photovoltaic power consumption rate, and the minimum voltage deviation are taken as the optimization objectives to construct a multi-objective optimization function. Through iterative calculation, a set of optimal solutions is obtained. According to the actual demand, the optimal photovoltaic storage and charging collaborative control strategy is selected to determine the photovoltaic power distribution, the energy storage system charging and discharging power, and the charging power of the charging pile at each time.

[0009] S3, precise collaborative regulation of multiple parties: According to the optimal control scheme obtained by the collaborative control strategy formulation module, the corresponding control instructions are generated, and the operation data instructions are sent to the transformer area energy storage through the energy storage protocol converter: for the photovoltaic power generation system, the output power instruction of the photovoltaic inverter is adjusted to control the photovoltaic power generation; for the energy storage system, the charge and discharge control instructions are sent to the energy storage converter to adjust the charge and discharge power and state of the energy storage system; for the charging pile facility, the communication technology (such as WiFi, Bluetooth, power line carrier communication, etc.) is used to send the charging power adjustment instructions to the charging pile control system to realize the orderly control of the charging process of the charging pile.

[0010] S4, feedback and correction mechanism is established: During the execution of the control instructions, the running state and actual power output of each device are monitored in real time, and compared with the control instructions. If there is a deviation, such as the actual power and the instruction power are inconsistent due to device failure or external interference, feedback and correction are performed in time; when the actual running data deviates from the predicted value by more than the preset threshold, S2 and S3 are re-implemented to correct the control instructions to realize closed-loop control, ensuring that the photovoltaic storage and charging system operates stably according to the predetermined collaborative control strategy.

[0011] Further, the collaborative control method needs to construct a hierarchical collaborative control architecture including a device layer, a regional coordination layer, and a cloud optimization layer to correspond to the cooperation of data collection, instruction sending, cloud control, and other processes.

[0012] Further, the device layer includes photovoltaic inverters, energy storage converters, charging piles, and smart meters; the regional coordination layer includes a coordination controller; the cloud optimization layer includes a cloud platform server, wherein each photovoltaic inverter, energy storage converter, and charging pile is equipped with an edge computing module, so that these devices have certain local data processing and decision-making capabilities, and are connected with the regional coordination layer through power line carrier communication (PLC), without the need for additional communication lines, reducing the construction cost of the system, and having strong anti-interference ability. The smart meter collects the total voltage and current data of the transformer area in real time.

[0013] Further, the photovoltaic power generation power prediction can adopt a time series prediction model based on historical data and meteorological information, such as a long short-term memory (LSTM) model, by inputting historical photovoltaic power generation power data, light intensity, temperature, humidity and other meteorological data, the photovoltaic power generation power in the next few hours can be predicted, and the accuracy can be as high as 95%.

[0014] Further, the charging pile charging load prediction is achieved by collecting charging pile user charging behavior data, including charging start time, charging duration, charging power and the like, and constructing a probability prediction model based on user behavior patterns, specifically, the user charging behavior can be divided into different types by clustering analysis, a charging load prediction curve is established for each type of user, and the charging pile charging power demand in the next few hours is predicted in combination with the real-time monitored charging pile access quantity and state.

[0015] Further, in the model training process, the model prediction control algorithm performs normalization processing on multiple targets by introducing adaptive weight coefficients, the weight coefficients are dynamically adjusted according to the real-time running state, and the model parameters are optimized through root mean square error (RMSE) and other indicators, to further improve the accuracy of photovoltaic power generation power prediction and charging pile charging load prediction.

[0016] Further, the multi-objective optimization function is solved by using an improved non-dominated sorting genetic algorithm (NSGA-II), which has the advantages of high calculation efficiency, good convergence and distribution of solution set, and no need to manually set shared parameters, significantly improving the solution performance of multi-objective optimization problems.

[0017] Further, the specific operation of the control instruction regulating the energy storage system is that when the photovoltaic power generation output is maximum, the energy storage in the transformer area is charged to promote the maximum consumption of photovoltaic power and reduce the burden of the power grid; if it is in the peak period of electricity consumption, the energy storage in the transformer area is automatically switched to the discharging mode to release the stored electric energy and meet the electricity demand in the peak period. This intelligent scheduling can successfully solve the problems of transformer area overload and low voltage, and provides reliable support for transformer area power supply.

[0018] Further, the photovoltaic storage charging collaborative control method specifically integrates a smart transformer area acquisition system, a controller, an energy storage system, an energy storage protocol converter and a 4G / 5G communication module, and constructs a dynamic collaborative control system of "light-storage-use", realizes the observation, measurement, adjustment, control of transformer area energy, and thus systematically improves the overall operation performance of the transformer area power system.

[0019] Compared with the prior art, the beneficial effects of the present application are: a transformer area photovoltaic storage charging collaborative control method.

[0020] 1. Improve energy utilization: Through accurate photovoltaic power prediction and charging pile charging load prediction, combined with optimized energy storage system charging and discharging strategy, realize the maximum consumption of photovoltaic power generation, reduce the phenomenon of light abandonment, and improve the utilization efficiency of clean energy in the transformer area. 2. Increase the stability of the power grid: Through coordinated control, reduce the peak-valley difference of the transformer area load, reduce the voltage fluctuation and three-phase imbalance degree, and the energy storage system discharges at peak load and charges photovoltaic at valley load, which fully plays the role of stabilizing voltage and balancing power, thereby enhancing the stability and reliability of the transformer area power grid and improving the power supply quality. 3. Prolong the service life of the equipment: The optimized energy storage system charging and discharging strategy avoids frequent charging and discharging and overcharging and overdischarging, reduces the loss of energy storage equipment, prolongs its service life, and reasonably controls the charging power and time of the charging pile, effectively alleviating the impact of disordered charging of the charging pile on the power grid, prolonging the service life of the charging equipment, and reducing the equipment maintenance cost. 4. Reduce operating costs: By reasonably arranging the charging time and power of the charging pile, photovoltaic power generation and low-valley electricity price period electricity can be fully utilized, thereby reducing the cost of purchasing electricity from the power grid and improving the overall economic efficiency of energy utilization. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The figure is a schematic diagram of the operation process of the light storage and charging coordination system of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] Embodiment one: please refer to Figure 1 The present application provides the following technical solutions: a transformer area light storage and charging coordination control method, the steps are as follows: S1, intelligent multi-element collection and prediction: During the operation of the transformer area, real-time collection of operation data of the transformer area is performed by using intelligent equipment, such as photovoltaic power generation power, state of charge of energy storage system, charging power demand of charging pile, node voltage of transformer area, and power grid price, etc. data, and photovoltaic power generation power prediction and charging pile charging load prediction are performed.

[0024] S2, construct function to obtain optimal solution: The controller is instructed to perform governance tasks for intelligent operation. Specifically, the minimum operation cost of the transformer area, the maximum photovoltaic power consumption rate, and the minimum voltage deviation are the optimization objectives. A multi-objective optimization function is constructed. Through iterative calculation, a set of optimal solutions is obtained. According to the actual demand, the optimal photovoltaic storage and charging collaborative control strategy is selected from the optimal solutions to determine the photovoltaic power distribution, the storage system charging and discharging power, and the charging power of the charging pile at each time.

[0025] S3, precise coordination of multiple parties: According to the optimal control scheme obtained by the collaborative control strategy formulation module, the corresponding control instructions are generated, and the operation data instructions are sent to the transformer area storage energy through the storage energy protocol converter: for the photovoltaic power generation system, the output power instruction of the photovoltaic inverter is adjusted to control the photovoltaic power generation; for the storage energy system, the charge-discharge control instruction is sent to the storage energy converter to adjust the charge-discharge power and state of the storage energy system; for the charging pile facility, the communication technology (such as WiFi, Bluetooth, power line carrier communication, etc.) is used to send the charging power adjustment instruction to the charging pile control system to realize the orderly control of the charging process of the charging pile.

[0026] S4, establish a feedback and correction mechanism: During the execution of the control instructions, the running state and actual power output of each device are monitored in real time, and compared with the control instructions. If there is a deviation, such as the actual power and the instruction power are inconsistent due to device failure or external interference, feedback and correction are performed in time; when the actual running data and the predicted value deviate more than the preset threshold, S2 and S3 are re-implemented to correct the control instructions to realize closed-loop control, ensuring that the photovoltaic storage and charging system operates stably according to the predetermined collaborative control strategy. Embodiment two:

[0027] On the basis of embodiment one, this collaborative control method needs to construct a hierarchical collaborative control architecture including device layer, regional coordination layer and cloud optimization layer in advance to realize the cooperation of data collection, instruction sending, cloud control and other processes. Specifically as follows: Device layer: The device layer is the data acquisition and execution terminal of the entire system, including photovoltaic inverters, energy storage converters, charging piles, and smart meters. Specifically, when put into application, photovoltaic inverters, as the core equipment for converting direct current generated by solar photovoltaic panels into alternating current, directly affect the utilization efficiency of photovoltaic energy. Different models of photovoltaic inverters, such as Solid Power SDT G4 series and Ates CSI-15-25kW series, can be used according to actual needs in terms of power range, efficiency, and adaptability. Energy storage converters are responsible for energy conversion between the energy storage system and the power grid. They can realize the bidirectional flow of electric energy between the energy storage device and the power grid according to system requirements, ensuring efficient charging and discharging of the energy storage system. Charging piles are the key interface connecting new energy vehicles and the power grid, responsible for providing charging services for electric vehicles. Their power adjustment capability is of great significance for balancing the load of the power grid. Smart meters are used to collect real-time total voltage and current data in the area, providing basic data support for the optimization control of the system.

[0028] Regional coordination layer: It plays a key role in the entire system and its core is the coordination controller layer. In specific operation, the regional coordination layer performs the first optimization at 0 o'clock based on the prediction results (which include solar radiation, area power load demand, electric vehicle charging demand, and other key factors for the next 24 hours) to obtain the energy storage charging and discharging plan and charging pile power adjustment instructions for the next 24 hours. Then it performs rolling optimization every 15 minutes to correct the control instructions based on real-time data to prevent deviations in photovoltaic power generation from predicted values due to sudden weather changes or fluctuations in area load due to sudden power surges. This rolling optimization approach can respond to various emergencies in a timely manner, ensuring the timeliness and accuracy of control instructions and keeping the system in optimal operating condition.

[0029] Among them, photovoltaic inverters, energy storage converters, and charging piles are equipped with edge computing modules, which enable these devices to have certain local data processing and decision-making capabilities, quickly respond to some simple control instructions, and reduce the delay of data transmission to the upper layer. The above-mentioned devices are connected to the regional coordination layer through power line carrier communication (PLC), which eliminates the need for additional communication lines, reduces the construction cost of the system, and has strong anti-interference ability, ensuring the stability and reliability of data transmission.

[0030] Cloud optimization layer: The cloud optimization layer takes the cloud platform server as the core, responsible for macro-control and data analysis of the entire system. It can receive various data uploaded by the regional coordination layer through the 4G / 5G communication module, including prediction data, real-time running data, control instruction execution, etc., and conduct in-depth mining and analysis on these data. Through big data analysis and artificial intelligence algorithms, the cloud optimization layer can provide more accurate prediction models and optimization strategies for the optimization decision of the regional coordination layer, and also can monitor and evaluate the overall operation of the power system in the entire area, providing strong support for long-term planning and upgrading of the system.

[0031] In summary, this light storage charging collaborative control method specifically integrates a smart transformer area acquisition system, a controller, an energy storage system, an energy storage protocol converter, and a 4G / 5G communication module, and builds a dynamic collaborative control system of "light-storage-use", realizing the observability, measurability, adjustability, and controllability of transformer area energy. Not only does it improve energy utilization efficiency and reduce power loss, but also enhances the stability and reliability of the power grid, thereby systematically improving the overall operation performance of the transformer area power system. Example three:

[0032] Based on example two, first, the prediction of photovoltaic power generation can use a time series prediction model based on historical data and meteorological information, such as a long short-term memory (LSTM) model. By inputting historical photovoltaic power generation data, light intensity, temperature, humidity, and other meteorological data, the photovoltaic power generation in the next few hours can be predicted, with an accuracy rate of up to 95%.

[0033] At the same time, the prediction of charging load of charging piles is achieved by collecting charging behavior data of charging pile users, including charging start time, charging duration, charging power, etc., and building a probabilistic prediction model based on user behavior patterns. Specifically, clustering analysis can be used to classify user charging behavior into different types, and a charging load prediction curve can be established for each type of user. Combined with the real-time monitoring of the number and status of charging piles, the charging power demand of charging piles in the next few hours can be predicted, with a charging demand prediction error of less than 8%.

[0034] In the model training process, the model prediction control algorithm normalizes multiple objectives by introducing adaptive weight coefficients, which are dynamically adjusted according to real-time operating conditions. At the same time, model parameters are optimized through indicators such as root mean square error (RMSE) to further improve the accuracy of photovoltaic power generation prediction and charging load prediction of charging piles. By reasonably controlling charging power and time of charging piles, the impact of disordered charging on the power grid is effectively alleviated, the service life of charging equipment is extended, and the maintenance cost is reduced. In addition, photovoltaic power generation and off-peak electricity can be fully utilized to reduce the cost of purchasing electricity from the grid, and the overall economic efficiency of energy utilization is improved.

[0035] Secondly, the control instruction regulates the specific operation of the energy storage system: when the photovoltaic power generation output is maximum, the energy storage of the transformer area charges, promotes the maximum consumption of photovoltaic, and reduces the burden of the power grid; if it is the peak period of electricity consumption, the energy storage of the transformer area automatically switches to the discharging mode, releases the stored electric energy, and meets the electricity demand in the peak period. This intelligent scheduling can successfully solve the problems of heavy overload and low voltage of the transformer area, and provides reliable support for the power supply of the transformer area. The optimized charging and discharging strategy of the energy storage system avoids frequent charging and discharging and overcharging and overdischarging, reduces the loss of the energy storage equipment, and prolongs its service life.

[0036] In addition, the multi-objective optimization function is solved by using an improved non-dominated sorting genetic algorithm (NSGA-II), which has the advantages of high calculation efficiency, good convergence and distribution of solution set, and no need to manually set shared parameters, which significantly improves the solution performance of multi-objective optimization problems.

[0037] In summary, through accurate photovoltaic power prediction and charging pile charging load prediction, combined with the optimized charging and discharging strategy of the energy storage system, the maximum degree of photovoltaic power consumption (which can be increased by more than 15%) is realized, the phenomenon of light abandonment is reduced, the intelligent and orderly power supply and emergency power supply of the transformer area are realized, and the effective consumption of distributed photovoltaic energy is supported, and the clean energy utilization efficiency in the transformer area is improved. Through collaborative control, the peak-valley difference of the transformer area load is reduced (by more than 20%), the voltage fluctuation and three-phase imbalance degree are reduced, the energy storage system discharges at peak load and charges at valley load, plays a role in stabilizing voltage and balancing power, enhances the stability and reliability of the transformer area power grid, and improves the power supply quality.

[0038] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.

[0039] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for coordinated control of photovoltaic, energy storage, and charging in a distribution network, characterized in that, include: S1, Intelligent and Diverse Data Acquisition and Prediction: The system utilizes smart devices to collect real-time operational data from the distribution area, such as photovoltaic power generation, energy storage system state of charge, charging power demand, distribution area node voltage, and grid electricity price, and then forecasts photovoltaic power generation and charging pile charging load. S2. Construct a function to obtain the optimal solution: After receiving the instruction, the controller performs intelligent calculations. Specifically, it constructs a multi-objective optimization function with the optimization objectives of minimizing the operating cost of the transformer area, maximizing the photovoltaic power generation absorption rate, and minimizing the voltage deviation. It then selects the optimal photovoltaic-storage-charging coordinated control strategy from these solutions to determine the photovoltaic power generation allocation, the energy storage system charging and discharging power, and the charging pile charging power at each time. S3. Precise and coordinated multi-party regulation: Based on the collaborative control strategy, corresponding control commands are generated and operation data commands are sent to the three systems respectively: for the photovoltaic power generation system, the output power command of the photovoltaic inverter is adjusted to control the photovoltaic power generation; for the energy storage system, the charging and discharging control command is sent to the energy storage converter to adjust the charging and discharging power of the energy storage system; for the charging pile system, the charging power adjustment command is sent to the charging pile system to achieve orderly control of the charging process. S4. Establish a feedback and correction mechanism: During the execution of control commands, the operating status and actual power output of each device are monitored in real time and compared with the control commands. If a deviation occurs, feedback and correction are made in a timely manner. When the deviation exceeds the preset threshold, S2 and S3 are reimplemented to realize closed-loop control and ensure that the photovoltaic energy storage and charging system operates stably according to the predetermined collaborative control strategy.

2. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 1, characterized in that: The aforementioned collaborative control method requires the prior construction of a layered collaborative control architecture, including a device layer, a regional coordination layer, and a cloud optimization layer, to enable the coordinated operation of processes such as data acquisition, command transmission, and cloud control.

3. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 2, characterized in that: The equipment layer includes photovoltaic inverters, energy storage converters, charging piles, and smart meters; the regional coordination layer includes a coordination controller; and the cloud optimization layer includes a cloud platform server.

4. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 3, characterized in that: The photovoltaic power generation forecast can be made using a time series forecasting model based on historical data and meteorological information, such as a Long Short-Term Memory (LSTM) network model. By inputting historical photovoltaic power generation data, light intensity, temperature, humidity and other meteorological data, the photovoltaic power generation in the next few hours can be predicted.

5. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 4, characterized in that: The charging load prediction of the charging pile is achieved by collecting charging behavior data of charging pile users, including charging start time, charging duration, and charging power, and constructing a probabilistic prediction model based on user behavior patterns to predict the charging power demand of the charging pile in the next few hours.

6. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 5, characterized in that: During the model training process, the photovoltaic power generation prediction and charging pile charging load prediction model prediction algorithm normalizes multiple objectives by introducing adaptive weight coefficients. The weight coefficients are dynamically adjusted according to the real-time operating status, and the model parameters are optimized by indicators such as root mean square error (RMSE).

7. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 6, characterized in that: The multi-objective optimization function is solved using an improved non-dominated sorting genetic algorithm (NSGA-II), which has advantages such as high computational efficiency, good solution set convergence and distribution.

8. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 7, characterized in that: The specific operation of the control command regulating the energy storage system is as follows: when the photovoltaic power generation output is at its maximum, the energy storage in the distribution area is charged; when it reaches the peak electricity consumption period, the energy storage in the distribution area automatically switches to the discharge mode to release the stored electrical energy.

9. The method for coordinated control of photovoltaic, energy storage, and charging in a distribution area according to claim 8, characterized in that: The aforementioned photovoltaic-storage-charging coordinated control method specifically integrates a smart distribution area acquisition system, a controller, an energy storage system, an energy storage protocol converter, and a 4G / 5G communication module, constructing a dynamic coordinated control system for "photovoltaics-storage-use".

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