Photovoltaic energy storage system regulation and control method and system based on multiple time scales

Abstract

The invention relates to the technical field of new energy regulation and control, and provides a photovoltaic energy storage system regulation and control method and system based on multiple time scales. The method comprises the steps that in a short time scale, if power grid frequency fluctuation exceeds a set threshold value, a photovoltaic energy storage system conducts regulation and control so that the power grid frequency can be stable; based on the meteorological data and the illumination intensity of the medium time scale, adopting an illumination intensity prediction model to predict the illumination intensity of the medium time scale in the future; based on the meteorological data, the illumination intensity and the power grid power-on data, predicting the power grid demand of the time scale in the future by adopting a power grid demand prediction model; if it is judged that the illumination intensity fluctuation range of the time scale in the future exceeds the key threshold value, the charging and discharging states of the photovoltaic energy storage system are adjusted in advance; and acquiring long-time-scale meteorological data, power grid load change and electricity price trend, solving by taking profit maximization as an objective function and a constraint condition, and outputting a solution for regulating and controlling charging and discharging of the photovoltaic energy storage system.

Description

Technical Field

[0001] The present invention relates to the field of new energy regulation technology, and in particular to a photovoltaic energy storage system regulation method and system based on multiple time scales. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] At present, there are the following problems in the regulation and evaluation of photovoltaic power generation and its energy storage system:

[0004] 1. Distributed photovoltaic power generation systems are connected to the distribution network in large numbers, especially in rural power grids, where the power supply radius is long and the load is relatively stable, lacking peak-shifting controllable loads that can be controlled in real time.

[0005] 2. The randomness and intermittency of photovoltaic power generation make the voltage optimization control of the distribution network more complicated. The builders of distributed photovoltaic systems usually control the power factor of the inverter to 1.0, failing to fully utilize the reactive power regulation capability of the inverter. In the case of a high proportion of distributed photovoltaic access, this can easily lead to excessively high voltage levels at the grid connection point, resulting in overvoltage, which may damage the user's electrical equipment and increase power loss.

[0006] 3. The existing voltage regulation methods are relatively simple and cannot effectively deal with the voltage fluctuation problems caused by the access of a large number of distributed photovoltaics.

[0007] 4. The existing technology lacks an online evaluation method for the inertia of photovoltaic power generation units and energy storage systems and the primary frequency regulation support capacity, making it difficult to ensure the stability of the power system under various operating scenarios. Summary of the invention

[0008] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a photovoltaic energy storage system control method and system based on multiple time scales. Under the premise of not adding new reactive power sources to the existing distribution network, the present invention gives priority to ensuring the maximum absorption of distributed photovoltaics, while realizing layered and graded voltage optimization control, ensuring the economic optimization scheduling of the distribution network, and realizing efficient, safe and stable operation of the photovoltaic power generation system.

[0009] In order to achieve the above object, the present invention adopts the following technical solution:

[0010] The first aspect of the present invention provides a photovoltaic energy storage system control method based on multiple time scales.

[0011] A photovoltaic energy storage system control method based on multiple time scales, comprising:

[0012] Real-time acquisition of short-time-scale grid frequency and photovoltaic output. If the grid frequency fluctuation exceeds the set threshold within a short time scale, the photovoltaic energy storage system will be regulated to stabilize the grid frequency.

[0013] Obtain medium-time-scale meteorological data, light intensity, and grid power data. Based on the medium-time-scale meteorological data and light intensity, use a light intensity prediction model to predict the future medium-time-scale light intensity. Based on the meteorological data, light intensity, and grid power data, use a grid demand prediction model to predict the future medium-time-scale grid demand. If it is determined that the future medium-time-scale light intensity fluctuation range exceeds a critical threshold, adjust the charging and discharging state of the photovoltaic energy storage system in advance.

[0014] Obtain long-term meteorological data, grid load changes and electricity price trends, solve the objective function and constraints with maximum profit, and output the solution for regulating the charging and discharging of the photovoltaic energy storage system.

[0015] Furthermore, the objective function is:

[0016]

[0017] in, is the real-time buying and selling price of the electricity market at time t; E is the photovoltaic power transaction amount purchased and sold from the local power grid at time t; t is the power generation agreed upon the day before, V t represents the voltage of the node, C loss Represents the loss rate of energy storage equipment caused by charging and discharging behavior, R t Represents the subsidy provided by the local power grid during period t.

[0018] Furthermore, the constraint condition includes: a power balance constraint, which is expressed by the following formula:

[0019]

[0020] Among them, P t (I t ) is the photovoltaic power generation power at time t, I t is the light intensity at time t, It is the photovoltaic transaction electricity purchased and sold from the local power grid at time t.

[0021] Furthermore, the constraint condition also includes: energy storage charging and discharging power constraint, which is expressed by the following formula:

[0022]

[0023] in, is the photovoltaic power purchased and sold from the local power grid at time t, R m + , R m - is the maximum charging and discharging power of the energy storage device in the local power grid, u t is an integer variable.

[0024] Furthermore, the constraint condition also includes: energy storage capacity constraint, which is expressed by the following formula:

[0025] 0≤S t ≤S max

[0026]

[0027] Among them, S t is the stored energy, S max is the maximum value of energy storage, is the photovoltaic power purchased and sold from the local power grid at time t, η + and η - Indicates the energy storage charging and discharging efficiency.

[0028] Furthermore, the constraint condition also includes: voltage constraint, which is expressed by the following formula:

[0029]

[0030] Among them, V i (t) is the voltage of node i at time t, V min and V max are the voltage lower limit and upper limit respectively, N is the distribution network node set, and T is the time set.

[0031] Furthermore, the short time scale is from seconds to minutes, the medium time scale is from hours, and the long time scale is from days to weeks.

[0032] A second aspect of the present invention provides a photovoltaic energy storage system control system based on multiple time scales.

[0033] A photovoltaic energy storage system control system based on multiple time scales, comprising:

[0034] The short-time scale module is configured to: obtain the grid frequency and photovoltaic output in real time on a short-time scale. If the grid frequency fluctuation exceeds a set threshold within a short-time scale, the photovoltaic energy storage system is regulated to stabilize the grid frequency;

[0035] The medium time scale module is configured to: obtain medium time scale meteorological data, light intensity and power data on the grid, and based on the medium time scale meteorological data and light intensity, use a light intensity prediction model to predict the light intensity on the future medium time scale; based on the meteorological data, light intensity and power data on the grid, use a grid demand prediction model to predict the grid demand on the future medium time scale; if it is determined that the fluctuation range of the light intensity on the future medium time scale exceeds a critical threshold, adjust the charging and discharging state of the photovoltaic energy storage system in advance;

[0036] The long-time scale module is configured to obtain long-time scale meteorological data, grid load changes and electricity price trends, solve the objective function and constraints with maximum profit, and output the solution for regulating the charging and discharging of the photovoltaic energy storage system.

[0037] A third aspect of the present invention provides a computer-readable storage medium.

[0038] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the photovoltaic energy storage system control method based on multiple time scales as described in the first aspect above.

[0039] A fourth aspect of the present invention provides a computer device.

[0040] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the photovoltaic energy storage system control method based on multiple time scales as described in the first aspect above are implemented.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention can effectively regulate the photovoltaic energy storage system at multiple time scales, solve the problem of insufficient stability and reliability of the power system in various operating scenarios in the current state, realize online evaluation of the inertia of photovoltaic power generation units and energy storage systems, improve the primary frequency regulation support capability, and ensure that the power system has sufficient response capability when facing frequency fluctuations.

[0043] The present invention specifically solves the problems that the current multi-source data comprehensive analysis equipment parameters are diverse, the equipment status and photovoltaic grid-related operation status are difficult to judge, the operation adaptability evaluation indicators for the supporting energy storage equipment are vague, the hierarchical voltage optimization control cannot be effectively realized, the reactive power regulation capability of the inverter is not fully utilized, and the voltage of the distributed photovoltaic grid-connected point cannot be guaranteed to be within a safe range.

[0044] The existing voltage regulation means are relatively simple and cannot effectively deal with the voltage fluctuation problem caused by the access of a large number of distributed photovoltaics. Without adding new reactive power sources, the present invention completely solves the problems of being unable to regulate through multiple time scales and multiple scenarios, complex economic dispatch of distribution networks, insufficient absorption capacity of distributed photovoltaics, and high power loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0046] Figure 1 It is a diagram showing the multi-source data interaction structure of a photovoltaic power station shown in the present invention;

[0047] Figure 2 It is a multi-source data prediction control framework diagram shown in the present invention;

[0048] Figure 3 It is a flow chart of the photovoltaic energy storage system control method based on multiple time scales shown in the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0050] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0051] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0052] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0053] Embodiment 1

[0054] The present embodiment provides a photovoltaic energy storage system control method based on multiple time scales. The present embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In the present embodiment, the method includes the following steps:

[0055] Real-time acquisition of short-time-scale grid frequency and photovoltaic output. If the grid frequency fluctuation exceeds the set threshold within a short time scale, the photovoltaic energy storage system will be regulated to stabilize the grid frequency.

[0056] Obtain medium-time-scale meteorological data, light intensity, and grid power data. Based on the medium-time-scale meteorological data and light intensity, use a light intensity prediction model to predict the future medium-time-scale light intensity. Based on the meteorological data, light intensity, and grid power data, use a grid demand prediction model to predict the future medium-time-scale grid demand. If it is determined that the future medium-time-scale light intensity fluctuation range exceeds a critical threshold, adjust the charging and discharging state of the photovoltaic energy storage system in advance.

[0057] Obtain long-term meteorological data, grid load changes and electricity price trends, solve the objective function and constraints with maximum profit, and output the solution for regulating the charging and discharging of the photovoltaic energy storage system.

[0058] The present invention adopts a hierarchical voltage optimization control strategy, which significantly improves the voltage control problem of the distribution network without adding new reactive power sources, ensuring that the voltage of the distributed photovoltaic grid connection point is within a safe range. It solves the voltage control problem caused by photovoltaic access to the distribution network in the prior art, and improves the stability and economy of the power system through multi-time scale and multi-scenario control strategies.

[0059] This embodiment is described in detail with reference to the accompanying drawings:

[0060] A photovoltaic energy storage system control method based on multiple time scales, such as Figure 3 As shown:

[0061] Step 1: Hardware deployment

[0062] Deploy monitoring equipment including temperature, voltage, current and power sensors, and achieve wireless data communication with the central control system through technologies such as Bluetooth and Wi-Fi.

[0063] Step 2: Data collection and preprocessing

[0064] The equipment and related communication facilities deployed according to step 1 are shown in Figure 1 , after the data collection of the devices with the longest data collection lag, such as transformers and GIS sensors, is completed, the data such as photovoltaic power generation output, energy storage system status, and grid voltage are obtained. The data is transmitted to the central control system. Before processing the data such as photovoltaic power generation output, energy storage system status, and grid voltage, obvious errors or outliers are first eliminated through manual review and automated scripts, such as values ​​that exceed equipment specifications or discontinuous timestamps. Next, the data is formatted to meet the requirements of the central control system, unifying the timestamp format, standardizing the units of voltage readings, and normalizing all data to eliminate the impact of scale and unit differences. Finally, encryption protocols are used to ensure the security of data during network transmission. Step 2 helps ensure the consistency, accuracy, and security of the data, providing high-quality data input for subsequent analysis.

[0065] Step 3: Multi-time scale control and inertia frequency modulation

[0066] Step 3-1: Short time scale (seconds to minutes). For real-time primary frequency regulation technology, the grid frequency and photovoltaic output are automatically detected every 15 seconds. When the photovoltaic system's power generation suddenly drops due to cloud cover, the energy storage system will start discharging within 10 seconds to compensate for the gap and maintain power supply stability. In addition, the system will evaluate the overall demand and supply conditions of the grid every 30 seconds to fine-tune the response of the energy storage equipment. This rapid dynamic adjustment helps smooth photovoltaic fluctuations and maintain the stability of the grid frequency.

[0067] The present invention adopts online evaluation of the inertia and primary frequency regulation capability of the photovoltaic power generation unit and the energy storage system, and dynamically adjusts the output power of the energy storage system according to the evaluation results, provides effective inertia support, can effectively enhance the frequency regulation capability of the power system, and ensure the stability of the grid frequency.

[0068] Step 3-2: Medium time scale (hourly level). In medium-term scheduling, two long short-term memory (LSTM) models based on recurrent neural networks (RNN) are built to achieve the scheduled prediction of light intensity and grid demand in the next 6 hours. The construction of each LSTM model is divided into two stages: model training and model prediction. In the model training stage, for the light intensity prediction model, the meteorological data (including weather, wind speed, temperature, relative humidity, air pressure, etc.) and recorded light intensity data of the location of a certain energy storage system in the past three years were collected, and about 25,000 sample data were sorted out; for the grid demand prediction model, the meteorological data (including light intensity) and power data on the grid for nearly one and a half years were used to sort out about 12,000 sample data. Finally, based on the sample data of the two models, the training of the two LSTM models was completed. In the model prediction stage, the meteorological data, light intensity, and grid power of nearly 72 hours were used as the input of the two models to predict the light intensity and grid demand for the next 6 consecutive hours. When the system predicts that sunlight will drop below a critical threshold within the next hour, the energy storage equipment will be adjusted to a charging state 30 minutes in advance to ensure adequate power supply.

[0069] The present invention can effectively smooth the fluctuation of photovoltaic power generation, reduce the impact on the power grid, and improve the stability and reliability of the power system.

[0070] Step 3-3: Long time scale (day to week), such as Figure 2 As shown. For the long-term strategy, the system performs a comprehensive assessment based on the five-day weather forecast and electricity price trends once a day. On days when sufficient sunlight is expected, the system plans to charge aggressively during the day when electricity prices are low, and discharge at night or during peak hours to make a profit. Conversely, during the forecast of continuous rainy days, the system stores as much electricity as possible during the period when electricity prices are lowest in case of emergency. In addition, when electricity prices are predicted to rise, the system increases the amount of stored electricity to sell when prices are high, and delays discharge when electricity prices are predicted to fall to optimize cost-effectiveness.

[0071] The present invention combines electricity price information and load forecasting to optimize the charging and discharging plan of the energy storage system, thereby maximizing economic benefits while improving the distributed photovoltaic absorption capacity.

[0072] In summary, through step 3, in the short term, when the grid frequency fluctuates by more than 3%, the energy storage system will quickly adjust the output power, increase discharge to stabilize the frequency drop, and reduce discharge or increase charging to cope with the frequency increase. This rapid response mechanism ensures the stable operation of the grid. On a longer time scale (5 days), the system dynamically adjusts the operation plan according to the predicted grid load changes and weather conditions to optimize the charging and discharging cycle. In the future, when the weather is clear and the light is sufficient, photovoltaic power generation will be used first. The energy storage system is mainly used to adjust the fluctuation of photovoltaic power generation, charge and store excess electricity during the low electricity price period, and discharge during the peak electricity price period to supplement the insufficient photovoltaic power generation. When there are continuous rainy days in the future and insufficient light, the energy storage system will be used as the main source of power generation to supplement the insufficient photovoltaic power generation, and charge and discharge will be reasonably arranged according to the predicted load changes. In addition, according to the electricity price, the system chooses to charge during the low electricity price period and discharge during the peak electricity price period to further improve the economic benefits. Through the above strategies, the photovoltaic energy storage system can effectively provide the necessary frequency regulation support to ensure the stable operation of the power grid, while optimizing energy utilization, providing important support for the sustainable development of the modern power grid.

[0073] Step 4: Comprehensive analysis and optimal control

[0074] In the process of developing adaptability evaluation indicators for photovoltaic energy storage systems and optimizing operation strategies, a comprehensive methodology is adopted, including three key steps: model development, test verification, and strategy optimization.

[0075] Step 4-1: Quantify key adaptability evaluation indicators. Use a simple linear regression model to predict the system's reaction time, and estimate the response time based on the load change rate to assist in decision-making and guide the charging and discharging behavior of energy storage equipment. For equipment life, use Weibull distribution to analyze and predict the probability of equipment failure, and determine the parameters of the model based on historical failure data. At the same time, conduct a cost-benefit analysis and use the net present value (NPV) model to evaluate the economic benefits, taking into account the benefits and costs at different times, as well as the discount rate.

[0076] Step 4-2: Verify the accuracy of the model. The accuracy of the model is verified by setting up simulated environmental conditions in the laboratory and actual operating data on site. Laboratory tests mainly simulate the performance of equipment under extreme operating conditions, while field tests provide data on the equipment in actual operation, including response time and actual life of the equipment. These data help adjust and optimize model parameters to improve the accuracy and reliability of predictions.

[0077] Step 4-3: Develop and adjust the operation strategy of the energy storage system. This includes optimizing the charging and discharging time and margin according to changes in electricity prices, weather forecasts, and grid demand. The specific strategy is implemented by solving the method of maximizing total benefits, with the constraint that the hourly and daily charging and discharging amount does not exceed the equipment limit. This strategy not only optimizes energy utilization efficiency, but also ensures the system's stable contribution to the grid and efficient response.

[0078] The available income of energy storage equipment includes the following: the profit of photovoltaic users participating in the transaction; the profit of photovoltaic electricity sales; the consumption corresponding to photovoltaic electricity purchase in the day-ahead market; and the purchase and operation and maintenance expenses of the local power grid transaction market. Since the day-ahead transaction electricity price is generally lower than the normal transaction electricity price, the multi-time scale photovoltaic energy storage system control constraint equation should be as follows:

[0079]

[0080] Where: c t is the electricity price of photovoltaic equipment at time t; E t is the electricity generation agreed upon the previous day; is the photovoltaic transaction electricity purchased and sold from the local power grid at time t; is the real-time buying and selling price of the electricity market at time t; td is the contract electricity price of energy storage equipment in the power market at time t; The lowest and highest prices of the day within 24 hours. it represents the power consumption of the ith photovoltaic installation entity at time t; R m + , R m - is the maximum charging and discharging power of the energy storage equipment in the local power grid, the optimization constraint equation of the multi-scale photovoltaic energy storage system based on electricity price can be obtained as follows:

[0081]

[0082] In this equation, the integer variable u t Can only be 0 or 1, used to limit the energy storage device r t + and r t - The power consumption and discharge status of the energy storage device. Therefore, factors such as the capacity and efficiency of the energy storage device are taken into account in the formulation of charging strategies and electricity prices. By satisfying the above constraints, the charging and discharging behavior of the energy storage device can be effectively controlled. Therefore, the objective function can be set as:

[0083]

[0084] Among them, V t Represents the voltage of the node. C lossRepresents the loss rate of energy storage equipment caused by charging and discharging behavior, R t It is the subsidy provided by the local power grid during period t.

[0085] Constraints, including power balance constraints:

[0086]

[0087] Among them, P t (I t ) is the photovoltaic power generation power at time t, I t is the light intensity at time t.

[0088] Energy storage charging and discharging power constraints:

[0089]

[0090] Energy storage capacity constraints:

[0091] 0≤S t ≤S max (7)

[0092]

[0093] Among them, η + and η - Indicates the energy storage charging and discharging efficiency, S max is the maximum value of energy storage, S0 is the initial capacity. Voltage constraint (hierarchical):

[0094]

[0095] Among them, V i (t) is the voltage of node i at time t, V min and V max are the voltage lower limit and upper limit respectively, N is the set of distribution network nodes, and T is the time set. Therefore, the multi-time scale strategy can be considered from three levels: short time scale (seconds to minutes): by real-time monitoring of grid frequency and photovoltaic output power, using power balance constraints, that is, the equipment meets the load side power demand without exceeding the maximum allowable operating power, and quickly adjusts the energy storage charging and discharging power. Maintain the stability of the power grid frequency. Medium time scale (hour level): Based on the LSTM model, the light intensity (I t ) and grid demand (E t ), combined with the voltage constraint of the formula, adjust the energy storage charging and discharging strategy in advance to ensure power supply. Long time scale (daily to weekly): According to weather forecasts and electricity price trends, predict future electricity prices (π t + ,π t -) and photovoltaic power generation power (P t (I t )) and optimize the charging and discharging plan by using the objective function and constraints to maximize the economic benefits. Through the hierarchical voltage optimization control strategy, the electricity price constraint (Formula (2)) is incorporated into the objective function to achieve voltage stability control. loss (V t ) is the voltage deviation loss function, which is used to penalize the situation where the voltage deviates from the safe range.

[0096] The objective function and constraints are solved to obtain the long-term control strategy.

[0097] In this embodiment, a photovoltaic energy storage system is used to regulate the photovoltaic energy storage system at multiple time scales, effectively smoothing the fluctuation rate of photovoltaic power generation by about 15%, thereby improving the stability and reliability of the power system. By utilizing a hierarchical voltage optimization control strategy, without adding new reactive power sources, the voltage control problem of the distribution network is significantly improved, ensuring that the voltage of the distributed photovoltaic grid connection point is within a safe range and the reaction time is less than 0.1s. Combined with electricity price information and load forecasting, the charging and discharging plan of the energy storage system is optimized, which improves the distributed photovoltaic absorption capacity while increasing the economic benefits of the local power grid by more than 10 million yuan.

[0098] Embodiment 2

[0099] This embodiment provides a photovoltaic energy storage system control system based on multiple time scales.

[0100] A photovoltaic energy storage system control system based on multiple time scales, comprising:

[0101] The short-time scale module is configured to: obtain the grid frequency and photovoltaic output in real time on a short-time scale. If the grid frequency fluctuation exceeds a set threshold within a short-time scale, the photovoltaic energy storage system is regulated to stabilize the grid frequency;

[0102] The medium time scale module is configured to: obtain medium time scale meteorological data, light intensity and power data on the grid, and based on the medium time scale meteorological data and light intensity, use a light intensity prediction model to predict the light intensity on the future medium time scale; based on the meteorological data, light intensity and power data on the grid, use a grid demand prediction model to predict the grid demand on the future medium time scale; if it is determined that the fluctuation range of the light intensity on the future medium time scale exceeds a critical threshold, adjust the charging and discharging state of the photovoltaic energy storage system in advance;

[0103] The long-time scale module is configured to obtain long-time scale meteorological data, grid load changes and electricity price trends, solve the objective function and constraints with maximum profit, and output the solution for regulating the charging and discharging of the photovoltaic energy storage system.

[0104] The short-time scale module is specifically configured to monitor the photovoltaic power generation output and grid voltage in real time, adjust through the fast-response energy storage system, and combine the photovoltaic information and energy storage information. The charging and discharging function of the energy storage system is used to smooth the output when the photovoltaic power generation fluctuates to ensure the stability of the grid voltage. Multi-source data analysis (see Figure 1 ), collect and analyze multi-source data such as photovoltaic power generation, energy storage system, and distribution network operation status. Establish a data model to comprehensively evaluate equipment parameters, equipment status, and photovoltaic grid-related operation status. Formulate operation adaptability evaluation indicators for supporting energy storage equipment to ensure that the energy storage system can work effectively under various operating conditions. According to the evaluation results, dynamically adjust the operation strategy of the energy storage system to improve system adaptability.

[0105] The medium time scale module is specifically configured to: predict short-term changes in photovoltaic power generation, pre-schedule the charging and discharging plan of the energy storage system based on the prediction results, optimize the operating status of the energy storage system, and ensure sufficient support capacity during peak loads.

[0106] The long-term module is specifically configured to: analyze historical data and weather forecasts, and formulate long-term operation strategies for photovoltaic power generation and energy storage systems. Combine load forecasting and electricity price information to optimize the charging and discharging cycle of the energy storage system to maximize economic benefits. For details, see Figure 2 .

[0107] Based on the above, a hierarchical voltage optimization control strategy is formulated to ensure that the voltage at each level of the distribution network is within a safe range and to achieve hierarchical voltage optimization control. The energy storage system is optimized and dispatched to reduce the overvoltage problem caused by photovoltaic power generation. The economic dispatch strategy combines electricity price information and load forecasting to optimize the charging and discharging plan of the energy storage system to maximize economic benefits. Without adding new reactive power sources, the energy storage system can be adjusted to improve the absorption capacity of distributed photovoltaics and reduce power losses.

[0108] Embodiment 3

[0109] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the photovoltaic energy storage system control method based on multiple time scales as described in the first embodiment above are implemented.

[0110] Embodiment 4

[0111] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the photovoltaic energy storage system control method based on multiple time scales as described in the first embodiment above are implemented.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A photovoltaic energy storage system control method based on multiple time scales, characterized in that: include: Real-time acquisition of short-time-scale grid frequency and photovoltaic output. If the grid frequency fluctuation exceeds the set threshold within a short time scale, the photovoltaic energy storage system will be regulated to stabilize the grid frequency. Obtain medium-time-scale meteorological data, light intensity, and grid power data. Based on the medium-time-scale meteorological data and light intensity, use a light intensity prediction model to predict the future medium-time-scale light intensity. Based on the meteorological data, light intensity, and grid power data, use a grid demand prediction model to predict the future medium-time-scale grid demand. If it is determined that the future medium-time-scale light intensity fluctuation range exceeds a critical threshold, adjust the charging and discharging state of the photovoltaic energy storage system in advance. Obtain long-term meteorological data, grid load changes and electricity price trends, solve the objective function and constraints with maximum profit, and output the solution for regulating the charging and discharging of the photovoltaic energy storage system.

2. The photovoltaic energy storage system control method based on multiple time scales according to claim 1 is characterized in that: The objective function is: in, is the real-time buying and selling price of the electricity market at time t; E is the photovoltaic power transaction amount purchased and sold from the local power grid at time t; t is the power generation agreed upon the day before, V t represents the voltage of the node, C loss Represents the loss rate of energy storage equipment caused by charging and discharging behavior, R t Represents the subsidy provided by the local power grid during period t.

3. The photovoltaic energy storage system control method based on multiple time scales according to claim 1 is characterized in that: The constraints include: power balance constraints, which are expressed by the following formula: Among them, P t (I t ) is the photovoltaic power generation power at time t, I t is the light intensity at time t, It is the photovoltaic transaction electricity purchased and sold from the local power grid at time t.

4. The photovoltaic energy storage system control method based on multiple time scales according to claim 1 is characterized in that: The constraint conditions also include: energy storage charging and discharging power constraint, which is expressed by the following formula: in, is the photovoltaic power purchased and sold from the local power grid at time t, R m + , R m - is the maximum charging and discharging power of the energy storage device in the local power grid, u t is an integer variable.

5. The photovoltaic energy storage system control method based on multiple time scales according to claim 1 is characterized in that: The constraints also include: energy storage capacity constraints, which are expressed by the following formula: 0≤S t ≤S max Among them, S t is the stored energy, S max is the maximum value of energy storage, is the photovoltaic power purchased and sold from the local power grid at time t, η + and η - Indicates the energy storage charging and discharging efficiency.

6. The photovoltaic energy storage system control method based on multiple time scales according to claim 1 is characterized in that: The constraint conditions also include: voltage constraint, which is expressed by the following formula: Among them, V i (t) is the voltage of node i at time t, V min and V max are the voltage lower limit and upper limit respectively, N is the distribution network node set, and T is the time set.

7. The photovoltaic energy storage system control method based on multiple time scales according to claim 1 is characterized in that: The short time scale is from seconds to minutes, the medium time scale is from hours, and the long time scale is from days to weeks.

8. A photovoltaic energy storage system control system based on multiple time scales, characterized in that: include: The short-time scale module is configured to: obtain the grid frequency and photovoltaic output in real time on a short-time scale. If the grid frequency fluctuation exceeds a set threshold within a short-time scale, the photovoltaic energy storage system is regulated to stabilize the grid frequency; The medium time scale module is configured to: obtain medium time scale meteorological data, light intensity and power data on the grid, and based on the medium time scale meteorological data and light intensity, use a light intensity prediction model to predict the light intensity on the future medium time scale; based on the meteorological data, light intensity and power data on the grid, use a grid demand prediction model to predict the grid demand on the future medium time scale; if it is determined that the fluctuation range of the light intensity on the future medium time scale exceeds a critical threshold, adjust the charging and discharging state of the photovoltaic energy storage system in advance; The long-time scale module is configured to obtain long-time scale meteorological data, grid load changes and electricity price trends, solve the objective function and constraints with maximum profit, and output the solution for regulating the charging and discharging of the photovoltaic energy storage system.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the photovoltaic energy storage system control method based on multiple time scales as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the photovoltaic energy storage system control method based on multiple time scales as described in any one of claims 1-7 are implemented.

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