Adaptive light storage multi-source collaborative energy management method and computer device

Abstract

The application discloses a kind of self-adapting light storage multi-source collaborative energy management method and computer equipment, the method includes: collecting multi-source data and pre-processing the multi-source data, the multi-source data includes photovoltaic output power, load power, energy storage state of charge, battery temperature, power purchase price and power selling price;According to the multi-source data after pre-processing, calculate power gap difference and price difference;Based on preset device information, the multi-source data after pre-processing, the power gap difference and the price difference calculate target energy storage state of charge and energy storage power threshold;According to the power gap difference and preset dead zone value, select and output multi-source energy management scheme;Based on the evaluation of the multi-source energy management scheme, calculate optimal scheduling comprehensive index;According to the optimal scheduling comprehensive index, adjust the parameter of the multi-source management scheme and output updated multi-source management scheme;The updated multi-source management scheme is issued to execution layer;Using the application, it has the advantages of improving economic efficiency, prolonging the service life of multi-source management equipment.

Description

Technical Field

[0001] This invention relates to the field of distributed energy management, and in particular to an adaptive photovoltaic-storage multi-source collaborative energy management method and computer equipment. Background Technology

[0002] Currently available solutions for coordinated management of photovoltaic, energy storage, and grid power generation commonly employ approaches including: fixed priority switching, time-sharing control based on peak-valley tables, and the introduction of complex optimization methods such as fuzzy control, neural networks, and game theory. However, these solutions have revealed the following problems during engineering implementation: (1) The prediction model is too simple and cannot take into account the day-ahead, intraday and real-time fluctuations. The power gap prediction error is often ±20%, which leads to scheduling delay and frequent rollback.

[0003] (2) The SOC threshold and power limit of energy storage are mostly fixed and difficult to be adaptively adjusted according to the season, climate, electricity price and SOH changes. Common problems include premature derating, frequent triggering of overcurrent protection or overcharging and over-discharging risks.

[0004] (3) The control strategy is too complex (such as fuzzy + neural network + game theory multi-layer coupling), with high deployment and maintenance costs and poor interpretability.

[0005] (4) Safety protection and economic strategy are disconnected, and temperature, SOH and failure probability do not adequately constrain power command.

[0006] (5) Insufficient use of historical data, parameters are manually tuned, and there is a lack of online adaptive updates.

[0007] Therefore, a lightweight method that is not dependent on complex AI / game theory, is explainable, feasible, and adaptable is needed. Summary of the Invention

[0008] The problem to be solved by the present invention is to provide an adaptive photovoltaic-storage multi-source collaborative energy management method and computer equipment, which can optimize the power supply logic of photovoltaic-storage multi-source and improve power supply efficiency and economic benefits.

[0009] To address the aforementioned technical problems, this invention provides an adaptive photovoltaic-storage multi-source collaborative energy management method, comprising: collecting multi-source data and preprocessing the multi-source data, wherein the multi-source data includes photovoltaic output power, load power, energy storage state of charge, battery temperature, grid electricity purchase price, and grid electricity sales price; calculating a power gap difference and a price difference based on the preprocessed multi-source data; calculating a target energy storage state of charge and energy storage power threshold based on preset equipment information, the preprocessed multi-source data, the power gap difference, and the price difference; selecting and outputting a multi-source energy management scheme based on the power gap difference and a preset dead zone value; calculating an optimal scheduling comprehensive index based on the evaluation of the multi-source energy management scheme; adjusting the parameters of the multi-source management scheme based on the optimal scheduling comprehensive index and outputting an updated multi-source management scheme; and distributing the updated multi-source management scheme to the execution layer.

[0010] As an improvement to the above scheme, the method for calculating the power gap difference includes: defining an exponential moving average function and calculating a gap sequence based on the load power and photovoltaic output power; dividing the load power and photovoltaic output power into daily types and calculating the average power gap for the same daily type, wherein the daily type division includes weekdays, weekends and holidays; calculating the power residual based on the average power gap for the same daily type; and substituting the power residual and the gap sequence into the exponential moving average function to obtain the power gap difference.

[0011] As an improvement to the above scheme, the method for calculating the price difference includes: defining an exponential moving average function; calculating a price gap sequence based on the grid electricity purchase price and the grid electricity sales price; and substituting the price gap sequence into the exponential moving average function to obtain the price difference.

[0012] As an improvement to the above scheme, the calculation steps for the target energy storage state of charge and energy storage power threshold include: calculating the target energy storage state of charge based on the power gap difference, the price difference, and preset equipment information; and calculating the energy storage power threshold based on the preset equipment information, the target energy storage state of charge, the preprocessed multi-source data, the power gap difference, and the price difference, wherein the energy storage power threshold includes a soft power threshold and a hard power threshold.

[0013] As an improvement to the above scheme, the method for selecting and outputting a multi-source energy management scheme based on the power gap difference and the preset dead zone value includes: comparing the absolute value of the power gap difference with the preset dead zone value; when the absolute value of the power gap difference is greater than or equal to the preset dead zone value, selecting a smooth compensation operation strategy; when the absolute value of the power gap difference is less than the preset dead zone value, selecting a correction regression operation strategy.

[0014] As an improvement to the above scheme, the steps of the smooth compensation operation strategy include: assigning a compensation coefficient to the power gap difference and calculating its first ramp-up command through the ramp-up function; calculating the charging and discharging power of the energy storage device based on the first ramp-up command and the energy storage power threshold; and calculating the mains power based on the charging and discharging power of the energy storage device and the power gap difference.

[0015] As an improvement to the above scheme, the steps of the correction regression operation strategy include: calculating the energy storage charge error based on the target energy storage state of charge and the energy storage state of charge; calculating a correction command after assigning an error coefficient to the energy storage charge error; calculating a second climb limit command by performing a climb limit function on the correction command; and calculating the charging and discharging power of the energy storage device based on the second climb limit command and the soft power threshold.

[0016] As an improvement to the above scheme, the step of calculating the optimal scheduling comprehensive index based on the multi-source energy management scheme includes: calculating the economic score of the multi-source energy management scheme based on the preset benchmark economic impact and the multi-source data; calculating the safety score of the multi-source energy management scheme based on the preset over-temperature benchmark data and the multi-source data; calculating the smoothness score of the multi-source energy management scheme based on the multi-source data; calculating the periodic score vector by combining the economic score, safety score, and smoothness score; normalizing the periodic score vector to obtain a standardized vector; obtaining updated weights based on the standardized vector and the learning rate; and substituting the updated weights into the scheduling comprehensive index function to calculate the optimal scheduling comprehensive index.

[0017] As an improvement to the above scheme, the step of evaluating the safety of the multi-source energy management scheme based on over-temperature reference data and multi-source data includes calculating a threshold bandwidth coefficient based on preset over-temperature reference data, wherein the preset over-temperature reference data includes the original threshold bandwidth coefficient, temperature decay coefficient, battery reference temperature, and energy storage device health, and the threshold bandwidth coefficient includes a soft threshold bandwidth coefficient and a hard threshold bandwidth coefficient; calculating an energy storage charge threshold based on the threshold bandwidth coefficient and multi-source data, wherein the energy storage charge threshold includes a soft energy storage charge threshold and a hard energy storage charge threshold; and calculating a safety score based on the energy storage charge threshold.

[0018] As an improvement to the above scheme, it also includes: calculating a risk index based on the battery temperature and depth of discharge; when the risk index is greater than a preset risk index hard threshold, reducing the battery power until the risk index is lower than the preset safety level.

[0019] As an improvement to the above scheme, the formula for calculating the target energy storage state of charge is as follows:

[0020] in, Indicates the target energy storage state of charge. Indicates the reference energy storage state of charge. Indicates the power gap difference. Indicates the price difference. Indicates the minimum state of charge. Indicates the maximum state of charge of energy storage. Indicates the power factor. This represents the price coefficient.

[0021] As an improvement to the above scheme, the energy storage charge soft threshold includes an upper limit range and a lower limit range, and the energy storage charge hard threshold includes an upper limit range and a lower limit range. The calculation methods for the energy storage charge soft threshold and the energy storage charge hard threshold include: according to the formula... Calculate the upper limit range of the soft threshold for energy storage charge; according to the formula Calculate the lower limit range of the soft threshold for energy storage charge; according to the formula Calculate the upper limit range of the energy storage charge hard threshold; according to the formula Calculate the lower limit range of the energy storage charge hard threshold; where, This indicates the upper limit range of the soft threshold for energy storage charge. Indicates the target energy storage state of charge. Indicates the soft threshold bandwidth coefficient. This indicates the lower limit range of the soft threshold for energy storage charge. This indicates the upper limit range of the hard threshold for energy storage charge. This indicates the lower limit range of the hard threshold for energy storage charge. This represents the hard threshold bandwidth coefficient.

[0022] As an improvement to the above scheme, the method for calculating the energy storage power threshold includes: calculating according to the following formula:

[0023]

[0024]

[0025]

[0026] in, Indicates the soft power threshold. Indicates the hard power threshold. Temperature correction factor Indicates the health status of energy storage devices. Indicates the rated power of the energy storage converter. Indicates battery temperature. Indicates the temperature reference value. Indicates the power gap difference. Indicates the upper limit of device power. Indicates the power limit of the energy storage converter. Indicates the inverter power limit. Represents a dimensionless constant. This represents the temperature decay coefficient.

[0027] Accordingly, the present invention also provides a computer device, including a storage device and a processor, wherein the storage device stores a computer program, and the processor executes the computer program to implement the adaptive photovoltaic-storage multi-source collaborative energy management method described in any of the above claims.

[0028] Implementing this invention has the following beneficial effects: The adaptive photovoltaic-storage multi-source coordinated energy management method and computer equipment of the present invention comprehensively consider... Develop optimal charging and discharging strategies based on electricity prices, energy storage SOC status, load demand, and equipment operating status to achieve peak-valley electricity price arbitrage and avoid economic losses due to incomplete information. In addition, the present invention provides dual protection for the energy storage device through both hardware and software to prevent damage to the device caused by overcharging or over-discharging of the energy storage. Attached Figure Description

[0029] Figure 1 This is a flowchart of the first embodiment of the adaptive photovoltaic-storage multi-source coordinated energy management method of the present invention; Figure 2 This is a flowchart of the second embodiment of the adaptive photovoltaic-storage multi-source collaborative energy management method of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It is hereby declared that the directional terms such as up, down, left, right, front, back, inside, and outside used in this text are based solely on the accompanying drawings and are not intended to specifically limit the invention.

[0031] like Figure 1 As shown, Figure 1 The flowchart of the first embodiment of the adaptive photovoltaic-storage multi-source coordinated energy management method of the present invention is shown, which includes: S101. Collect multi-source data and preprocess the multi-source data; Specifically, the equipment collects multi-source data, including but not limited to photovoltaic power output, load power, energy storage state of charge, battery temperature, grid electricity purchase price and grid electricity sales price; Furthermore, the collected multi-source data has different data formats, so it is necessary to preprocess the multi-source data. In this invention, the preprocessing preferably adopts a normalization method. Specifically, the normalization method includes, but is not limited to, maximum value normalization, Z-score normalization, etc. The specific method can be reasonably selected according to the type of data, and no further restrictions are imposed here.

[0032] More preferably, the present invention adjusts the data acquisition period to 1s~60s, wherein the sampling period is dynamically adjusted according to the changing trend of the real-time operating status of the monitored object. When the system detects large fluctuations in the indicator data or an unstable state, it will automatically adopt a shorter acquisition period (e.g., close to 1 second) to capture rapid changes with high-frequency acquisition, ensuring the real-time nature of the data. Conversely, when the system is running smoothly, it will automatically extend the acquisition period (e.g., close to 60 seconds) to reduce resource consumption.

[0033] S102. Calculate the power gap difference and price difference based on the preprocessed multi-source data; The methods for calculating the power gap difference and price difference using the preprocessed multi-source data will be described in detail below: I. Power Gap Difference (1) Define the exponential moving average function and calculate the gap sequence based on the load power and photovoltaic output power; In the prediction and estimation of the power gap difference, the current power gap is defined as a gap sequence, and the calculation formula for the gap sequence is as follows:

[0034] in: Represents a gap sequence; Indicates load power; This indicates the photovoltaic power output.

[0035] Furthermore, the power gap needs to be estimated from the gap sequence. Based on the lightweight and efficient nature of the EMA algorithm, suitable for embedded systems or edge computing devices, this algorithm is chosen for estimation. No limitation is imposed here; a suitable algorithm can be selected according to the actual situation. The exponential moving average (EMA) algorithm in this embodiment is shown below:

[0036] in: Indicates the exponential sliding value. You can take the average of the first window or ; Represents a gap sequence; Represents the smoothing factor. Pick or , This refers to the window size.

[0037] (2) The load power and photovoltaic output power are divided according to the daily type and the average power gap of the same daily type is calculated. The daily type division includes weekdays, weekends and holidays; Specifically, to improve accuracy, the daily patterns are categorized into weekdays, weekends, and holidays. The predicted value is corrected by taking the average of historical data from the same period. The formula for taking the average of historical data from the same period is as follows:

[0038] in: Indicates the day type; Indicates the Japanese type Below, every day The average power deficit at any given time (historical baseline). Indicates the number of days used to calculate the baseline historical data; This indicates the number before today. Heaven, The actual observed value at that moment.

[0039] (3) Calculate the power residual based on the average power deficit of the same type of day; The formula for calculating the power residual based on the average power deficit of the same type of day is as follows:

[0040] in: Indicates the Japanese type Below, every day Residual power at time step; express The gap sequence at time points; Indicates the Japanese type Below, every day Historical baseline value at any given time.

[0041] (4) Substitute the power residual and the gap sequence into the exponential moving average function to obtain the power gap difference.

[0042] The formula for calculating the power gap difference using the exponential sliding function is shown below:

[0043] in: Indicates the power gap difference; Indicates the Japanese type Below, every day The residual power at any given time.

[0044] Furthermore, the method for calculating the power gap difference also includes directly processing the gap sequence. Pick ; More preferably, to ensure data uniformity, the power gap difference can be adjusted when necessary. Amplitude clipping is performed, and the formula for amplitude clipping is:

[0045] in: This represents the power gap interpolation after amplitude clipping; Indicates the power gap difference; This indicates the maximum power gap.

[0046] II. Price Difference The steps for calculating the price difference are as follows:

[0047]

[0048] in: Indicates a price gap sequence; express Real-time electricity purchase price; express Real-time electricity sales price; This indicates the price difference.

[0049] S103. Calculate the target energy storage state of charge and energy storage power threshold based on the health status of the energy storage device, preset device information, the power gap difference, and the price difference; (1) Calculate the target energy storage state of charge based on the power gap difference, the price difference, and the preset equipment information; The preset equipment information includes minimum energy storage state of charge, maximum energy storage state of charge, reference energy storage state of charge, rated power, battery reference temperature, temperature decay coefficient, energy storage equipment health, equipment power limit, energy storage converter power limit and inverter power limit. In this invention, the ideal charge value of the energy storage device is determined by comprehensively considering factors such as economic benefits, energy storage power, and battery losses. The calculation formula for the target energy storage state of charge of the energy storage device is as follows:

[0050] in: Indicates the target energy storage state of charge; Indicates the reference energy storage state of charge; Indicates the power gap difference; Indicates the price difference; Indicates the minimum state of charge of stored energy; Indicates the maximum state of charge of energy storage; Indicates the power factor; This represents the price coefficient.

[0051] It should be noted that the mathematical expression for the clip function is:

[0052] Based on the power gap and the price difference gap, the target energy storage state of charge is calculated using the clip function, the minimum system energy storage state of charge and the maximum system state of charge. (2) The energy storage power threshold is calculated based on the preset equipment information, the target energy storage state of charge, the preprocessed multi-source data, the power gap difference and the price difference; Among them, the energy storage power threshold includes the soft power threshold and the hard power threshold.

[0053] Specifically, the method for calculating the energy storage power threshold includes: Calculate using the following formula:

[0054]

[0055] in: Indicates the soft power threshold; This represents the temperature correction factor; Indicates the health status of energy storage devices; Indicates rated power; Indicates the temperature decay coefficient; Indicates battery temperature; This indicates the battery reference temperature.

[0056]

[0057]

[0058] in: Indicates the hard power threshold; Indicates the power gap difference; Indicates the upper limit of the device's power; Indicates the power limit of the energy storage converter; Indicates the inverter power limit; This represents a dimensionless constant.

[0059] It should be noted that the rated power of the energy storage converter is determined by the specific energy storage device model, and there are no further restrictions here. Suitable energy storage devices can be selected according to actual needs. The value of the temperature correction coefficient is determined by the current energy storage device model and the current operating temperature of the energy storage device.

[0060] S104. Select and output a multi-source energy management scheme based on the power gap difference and the preset dead zone value; The dead zone determination mechanism is a key design in the "two-stage scheduling" stage of the system. Its main purpose is to prevent the control system from generating frequent and minor mode switching or command oscillations near the power balance point, thereby improving the stability of the system and the life of the equipment. It determines whether the system is in the dead zone based on the absolute value of the power gap difference and the preset dead zone value. Specifically, the method for selecting and outputting a multi-source energy management scheme based on the power gap difference and a preset dead zone value includes: (1) Compare the absolute value of the power gap difference with the preset dead zone value; (2) When the absolute value of the power gap difference is greater than or equal to the preset dead zone value, a smooth compensation operation strategy is selected; (3) When the absolute value of the power gap difference is less than the preset dead zone value, the correction regression operation strategy is selected.

[0061] The implementation methods of the smoothing compensation strategy and the deviation correction regression strategy are described below: I. Smooth Compensation Operation Strategy (1) Assign a compensation coefficient to the power gap difference and calculate its first ramp limit command through the ramp limit function; (2) The charging and discharging power of the energy storage device is calculated based on the first climb limit command and the energy storage power threshold. (3) Calculate the mains power based on the charging and discharging power of the energy storage device and the power gap difference.

[0062] Furthermore, the formula for the smooth compensation operation strategy is expressed as follows:

[0063]

[0064]

[0065]

[0066] in: Indicates the power gap difference; Indicates the compensation coefficient; Indicates the initial instruction; This indicates the power of the energy storage device at the previous moment; Indicates the maximum power ramp-up rate; Indicates the first climb limit command; Indicates the soft power threshold; Indicates the time between the two scheduling events; Indicates the charging and discharging power of the energy storage device; This indicates the mains power.

[0067] II. Corrective Regression Operation Strategy (1) Calculate the energy storage charge error based on the target energy storage state of charge and the energy storage state of charge; (2) After assigning an error coefficient to the energy storage charge error, a correction command is calculated; (3) Calculate the second climb limit command by performing a climb limit function on the correction command; (4) The charging and discharging power of the energy storage device is calculated based on the second ramp limit command and the soft power threshold.

[0068] Furthermore, the formula for the corrective regression operation strategy is expressed as follows:

[0069]

[0070]

[0071]

[0072]

[0073] in: Indicates the error of the energy storage device; Indicates the target energy storage state of charge; Indicates the current state of charge of the energy storage; Indicates the amount of corrected power; This represents the charge regression coefficient of energy storage devices; This indicates the power of the energy storage device at the previous moment; Indicates the maximum power ramp-up rate; Indicates the second climb limit command; Indicates the soft power threshold; Indicates the time between the two scheduling events; Indicates the charging and discharging power of the energy storage device; This indicates the mains power.

[0074] S105. Calculate the optimal scheduling comprehensive index based on the score of the multi-source energy management scheme; The multi-source energy output is optimized according to the selected scheme, and three-dimensional indicators (i.e., economy, safety and smoothness) are defined. The multi-source energy management scheme is then comprehensively updated and optimized through the three-dimensional indicators. Specifically, the multi-source energy management solution is comprehensively updated and optimized through the following steps: (1) The economic performance of the multi-source energy management scheme is scored based on the preset benchmark economic impact and the multi-source data; The steps for calculating the economic score are as follows: (1.1) Calculate the net economic impact using the following formula:

[0075] in: The period is represented as Net economic impact within; The period is represented as The average value of the mains power, a positive value indicates that electricity is purchased from the grid, and a negative value indicates that electricity is sold to the grid; Indicates the duration of the period; Indicates the effective electricity price, when Electricity purchase price ,when Electricity purchase and sale price .

[0076] (1.2) To convert the economic impact into a uniform score, the following normalization method is used to calculate the economic score.

[0077] Define a benchmark economic impact, which can be the absolute value of typical daily average periodic revenue / cost, or a fixed empirical value.

[0078] in: Indicates the economic score; The period is represented as Net economic impact within; This indicates the baseline economic impact.

[0079] (2) Assess the safety of the multi-source energy management scheme based on the over-temperature reference data and the multi-source data; It should be noted that the security score includes the SOC security score. and temperature safety rating .

[0080] Specifically, the security score is:

[0081] in: Indicates the security score; , These are the weighting coefficients; Indicates the SOC security score; This indicates the temperature safety score.

[0082] The following sections provide detailed descriptions of the SOC safety score and temperature safety score: I. SOC Security Rating SOC security score The SOC safety score is used to assess the safety of energy storage state of charge. It is calculated using a piecewise function or a distance function based on the relationship between the current cycle average SOC value and the energy storage charge threshold. Specifically, the steps for calculating the energy storage charge threshold are as follows, where the energy storage charge threshold includes a hard threshold and a soft threshold: K1 is the threshold bandwidth coefficient calculated based on the preset over-temperature reference data. The preset over-temperature reference data includes the original threshold bandwidth coefficient, temperature decay coefficient, battery reference temperature and energy storage device health status, and the threshold bandwidth coefficient includes the soft threshold bandwidth coefficient and the hard threshold bandwidth coefficient. The soft threshold bandwidth coefficient is calculated as follows:

[0083]

[0084]

[0085] in: Indicates the soft threshold bandwidth coefficient; This represents the original soft threshold bandwidth coefficient; Indicates the temperature scaling factor; This represents the health scaling factor; Indicates the temperature decay coefficient; Indicates battery temperature; Indicates the battery reference temperature; This indicates the health status of energy storage devices.

[0086] The hard threshold bandwidth coefficient is calculated as follows:

[0087] in: Indicates the hard threshold bandwidth coefficient; This represents the original hard threshold bandwidth coefficient; Indicates the temperature scaling factor; This represents the health scaling factor.

[0088] K2, the energy storage charge threshold is calculated based on the threshold bandwidth coefficient and multi-source data, wherein the energy storage charge threshold includes a soft energy storage charge threshold and a hard energy storage charge threshold. Specifically, the method for calculating the soft threshold of energy storage charge is as follows:

[0089]

[0090] in: Indicates the upper limit range of the soft threshold for energy storage charge; Indicates the lower limit range of the soft threshold for energy storage charge; Indicates the target energy storage state of charge; This represents the soft threshold bandwidth coefficient.

[0091] Similarly, the method for calculating the hard threshold range of energy storage based on the preset hard threshold parameters and the target energy storage state of charge is as follows:

[0092]

[0093] in: Indicates the target energy storage state of charge; Indicates the upper limit range of the hard threshold for energy storage charge; Indicates the lower limit range of the energy storage charge hard threshold; This represents the hard threshold bandwidth coefficient.

[0094] K3, the steps for calculating the safety score based on the energy storage charge threshold are as follows: K31, when The score is 1, which falls within the ideal range. K32, when When the score is between the soft and hard thresholds, the score decreases linearly with increasing distance from the soft threshold boundary. hour,

[0095] K33, when When the hard threshold is exceeded, the score is set to 0 or negative, indicating a serious lack of security.

[0096] II. Temperature Safety Rating Temperature safety rating This is used to assess the battery's temperature state, and the temperature safety score is based on the deviation between the current battery temperature and a reference temperature.

[0097] in: Indicates temperature safety score; Indicates the temperature penalty coefficient; Indicates battery temperature; This indicates the battery reference temperature.

[0098] (3) Calculate the smoothness score of the multi-source energy management scheme based on the multi-source data; The calculation steps for smoothness evaluation are as follows:

[0099]

[0100] in: Indicates smoothness score; Indicates the smoothness sensitivity coefficient; Indicates the change in power; The period is represented as The average value of the mains power within the unit; The period is represented as The charging and discharging power of the internal energy storage devices.

[0101] (4) Calculate the cycle score vector by combining the scores of economy, safety and smoothness;

[0102] in: Represents the periodic score vector; Indicates the economic score; Indicates the security score; This indicates the smoothness score.

[0103] (5) Normalize the periodic score vector to obtain a standardized vector; To ensure consistency of the score values, the periodic vector is normalized to obtain a uniform periodic score. .

[0104] (6) Obtain the updated weights based on the standardized vectors and learning rate; The score weights are updated exponentially weighted according to the following formula:

[0105]

[0106] in: Indicates the weight of the economic score; Indicates the weight of the security score; Indicates the smoothness score weights; Represents the new weight vector; Represents the normalization function; The learning rate is usually expressed as a percentage of the total number of days. ; Represents the old weight vector; This represents the score for a uniform period.

[0107] (7) Substitute the updated weights into the scheduling comprehensive index function to calculate the optimal scheduling comprehensive index.

[0108]

[0109] in: Indicates the weight of the economic score; Indicates the weight of the security score; Indicates the smoothness score weights; This represents the optimal scheduling comprehensive index.

[0110] S106. Based on the optimal scheduling comprehensive index, adjust the parameters of the multi-source management scheme and output the updated multi-source management scheme; Calculate the overall scheduling objective To obtain the optimal strategy, based on the scores of the new weight vector and the actual three-dimensional indicators, strategies with lower scores are adjusted. The adjustment directions include, but are not limited to, the climb limit function, etc., in order to achieve optimal implementation conditions in terms of safety, economy and smoothness.

[0111] S107. The updated multi-source management scheme is sent to the execution layer.

[0112] During operation, this adaptive photovoltaic-storage multi-source collaborative energy management method first collects and preprocesses multi-source data such as photovoltaic output, load power, energy storage SOC, battery temperature, and grid electricity price, and then calculates the power gap difference and the electricity price difference. Based on these two differences, it dynamically constructs soft and hard thresholds for the energy storage SOC and power thresholds, and decides the operation strategy based on whether the power gap difference enters the dead zone: if it does not enter the dead zone, it performs smoothing compensation to fill the power gap with energy storage power; if it enters the dead zone, it performs corrective regression to adjust the energy storage SOC to the target value. Subsequently, it evaluates the economy, safety, and smoothness of the strategy, updates the weights, and calculates the optimal scheduling comprehensive index to adjust the scheme parameters. Finally, it sends the updated multi-source energy management scheme to the execution layer.

[0113] In summary, the difference between this invention and the prior art is that this invention achieves economic, safe, and smooth coordinated control of photovoltaic, energy storage, and grid power without using fuzzy control, deep learning, or game theory, through moving average prediction, linear dynamic threshold, two-stage scheduling, and weight updates, thereby reducing implementation complexity and operation and maintenance costs.

[0114] like Figure 2 As shown, Figure 2 The flowchart of a second embodiment of the adaptive photovoltaic-storage multi-source coordinated energy management method of the present invention is shown, which includes: S201. Collect multi-source data and preprocess the multi-source data, wherein the multi-source data includes photovoltaic output power, load power, energy storage state of charge, battery temperature, grid electricity purchase price and grid electricity sales price. S202. Calculate the power gap difference and price difference based on the preprocessed multi-source data; S203. Calculate the target energy storage state of charge and energy storage power threshold based on preset equipment information, preprocessed multi-source data, power gap difference, and price difference; S204. Select and output a multi-source energy management scheme based on the power gap difference and the preset dead zone value; S205. Calculate the optimal scheduling comprehensive index based on the evaluation of the multi-source energy management scheme; S206. Based on the optimal scheduling comprehensive index, adjust the parameters of the multi-source management scheme and output an updated multi-source management scheme; S207. Calculate the risk index based on the battery temperature and depth of discharge. Specifically, the formula for calculating the risk index is as follows:

[0115] in: Indicates a risk index; Indicates battery temperature; Indicates the battery reference temperature; Indicates the recent depth of discharge; Indicates the amount of voltage change; , , This represents the weighting coefficient.

[0116] S208. Determine and adjust the charging and discharging power of the energy storage device based on the risk index and the preset risk index hard threshold. When the risk index When the risk level exceeds a preset hard threshold, the charging and discharging power of the energy storage device will be limited to [a certain value]. Until the risk index decreases to within the preset risk index hard threshold.

[0117] S209. The updated multi-source management scheme is sent to the execution layer.

[0118] Unlike the first embodiment, the second embodiment introduces steps S207 and S208 to further manage the risks of energy storage devices, thereby improving the durability and service life of energy storage devices.

[0119] Accordingly, the present invention also provides a computer device, including a storage device and a processor, wherein the storage device stores a computer program, and the processor executes the computer program to implement the adaptive photovoltaic-storage multi-source collaborative energy management method described in any one of the above-mentioned methods.

[0120] In summary, this invention provides an adaptive photovoltaic-storage multi-source collaborative energy management method and computer device. By processing and analyzing information from photovoltaic-storage multi-source devices, a power allocation strategy is obtained. Furthermore, by integrating the optimal operating conditions of the energy storage devices and the dead-zone operation mechanism, the power allocation strategy is optimized, ensuring a balance between economy, safety, and smoothness, and maximizing benefits. In addition, this invention further subdivides the risks of energy storage devices, quantifying thermal risk, cyclic aging risk, and electrical failure risk through risk indices, and adjusting the power of the energy storage devices according to the risk indices, ensuring the long-term stable operation of the energy storage devices.

[0121] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. An adaptive photovoltaic-storage multi-source coordinated energy management method, characterized in that, include: Collect multi-source data and preprocess the multi-source data, which includes photovoltaic power output, load power, energy storage state of charge, battery temperature, grid electricity purchase price and grid electricity sales price; Based on the preprocessed multi-source data, calculate the power gap difference and price difference; The target energy storage state of charge and energy storage power threshold are calculated based on the preset equipment information, the preprocessed multi-source data, the power gap difference, and the price difference. Based on the power gap difference and the preset dead zone value, a multi-source energy management scheme is selected and output; The optimal scheduling comprehensive index is calculated based on the score of the multi-source energy management scheme. Based on the optimal scheduling comprehensive index, the parameters of the multi-source management scheme are adjusted and an updated multi-source management scheme is output. The updated multi-source management scheme is then distributed to the execution layer.

2. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1, characterized in that, The method for calculating the power gap difference includes: Define an exponential moving average function and calculate the gap sequence based on the load power and photovoltaic output power; The load power and photovoltaic output power are divided according to the daily type, and the average power gap of the same daily type is calculated. The daily type division includes weekdays, weekends and holidays. Calculate the power residual based on the average power deficit of the same type of day; The power residual and the gap sequence are substituted into the exponential moving average function to obtain the power gap difference.

3. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1, characterized in that, The method for calculating the price difference includes: Define the exponential moving average function; Calculate the price gap sequence based on the mains electricity purchase price and the mains electricity sales price; The price gap sequence is substituted into the exponential moving average function to obtain the price difference.

4. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1, characterized in that, The calculation steps for the target energy storage state of charge and energy storage power threshold include: The target energy storage state of charge is calculated based on the power gap difference, the price difference, and the preset equipment information; Based on preset equipment information, the target energy storage state of charge, preprocessed multi-source data, power gap difference, and price difference, the energy storage power threshold is calculated, wherein the energy storage power threshold includes a soft power threshold and a hard power threshold.

5. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1, characterized in that, The method for selecting and outputting a multi-source energy management scheme based on the power gap difference and a preset dead zone value includes: The absolute value of the power gap difference is compared with a preset dead zone value; When the absolute value of the power gap difference is greater than or equal to the preset dead zone value, a smooth compensation operation strategy is selected. When the absolute value of the power gap difference is less than the preset dead zone value, the correction regression operation strategy is selected.

6. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 5, characterized in that, The steps of the smooth compensation operation strategy include: A compensation coefficient is assigned to the power gap difference, and its first ramp limit command is calculated through the ramp limit function; The charging and discharging power of the energy storage device is calculated based on the first climb limit command and the energy storage power threshold. The mains power is calculated based on the charging and discharging power of the energy storage device and the power gap difference.

7. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 5, characterized in that, The steps of the corrective regression operation strategy include: Calculate the energy storage charge error based on the target energy storage state of charge and the energy storage state of charge. After assigning an error coefficient to the energy storage charge error, a correction command is calculated. The second climb limit command is obtained by calculating the climb limit function on the correction command; The charging and discharging power of the energy storage device is calculated based on the second ramp-up command and the soft power threshold.

8. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1, characterized in that, The steps for calculating the optimal scheduling comprehensive index based on the score of the multi-source energy management scheme include: The economic score of the multi-source energy management scheme is calculated based on the preset benchmark economic impact and the multi-source data. The safety score of the multi-source energy management scheme is calculated based on the preset over-temperature reference data and the multi-source data. Calculate the smoothness score of the multi-source energy management scheme based on the multi-source data; The cycle score vector is calculated by combining the economic score, safety score, and smoothness score. The periodic score vector is normalized to obtain a standardized vector; The updated weights are obtained based on the standardized vectors and the learning rate; The updated weights are substituted into the scheduling comprehensive index function to calculate the optimal scheduling comprehensive index.

9. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 8, characterized in that, The step of calculating the safety score of the multi-source energy management scheme based on preset over-temperature reference data and multi-source data includes: The threshold bandwidth coefficient is calculated based on preset over-temperature reference data, wherein the preset over-temperature reference data includes the original threshold bandwidth coefficient, temperature decay coefficient, battery reference temperature and energy storage device health, and the threshold bandwidth coefficient includes soft threshold bandwidth coefficient and hard threshold bandwidth coefficient. The energy storage charge threshold is calculated based on the threshold bandwidth coefficient and multi-source data, wherein the energy storage charge threshold includes a soft energy storage charge threshold and a hard energy storage charge threshold. A safety score is calculated based on the energy storage charge threshold.

10. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1, characterized in that, Also includes: The risk index is calculated based on the battery temperature and depth of discharge. When the risk index is greater than a preset risk index hard threshold, the battery power is reduced until the risk index is lower than the preset safety level.

11. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1 or 4, characterized in that, The formula for calculating the target energy storage state of charge is: in, Indicates the target energy storage state of charge. Indicates the reference energy storage state of charge. Indicates the power gap difference. Indicates the price difference. Indicates the minimum state of charge. Indicates the maximum state of charge of energy storage. Indicates the power factor. This represents the price coefficient.

12. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 9, characterized in that, The energy storage charge soft threshold includes an upper limit range and a lower limit range, and the energy storage charge hard threshold includes an upper limit range and a lower limit range. The calculation methods for the energy storage charge soft threshold and the energy storage charge hard threshold include: According to the formula Calculate the upper limit range of the soft threshold for energy storage charge; According to the formula Calculate the lower limit range of the soft threshold for energy storage charge; According to the formula Calculate the upper limit range of the hard threshold for energy storage charge; According to the formula Calculate the lower limit range of the energy storage charge hard threshold; in, This indicates the upper limit range of the soft threshold for energy storage charge. Indicates the target energy storage state of charge. Indicates the soft threshold bandwidth coefficient. This indicates the lower limit range of the soft threshold for energy storage charge. This indicates the upper limit range of the hard threshold for energy storage charge. This indicates the lower limit range of the hard threshold for energy storage charge. This represents the hard threshold bandwidth coefficient.

13. The adaptive photovoltaic-storage multi-source coordinated energy management method as described in claim 1 or 4, characterized in that, The calculation method for the energy storage power threshold includes: Calculate using the following formula: in, Indicates the soft power threshold. Indicates the hard power threshold. Temperature correction factor Indicates the health status of energy storage devices. Indicates the rated power of the energy storage converter. Indicates battery temperature. Indicates the temperature reference value. Indicates the power gap difference. Indicates the upper limit of device power. Indicates the power limit of the energy storage converter. Indicates the inverter power limit. Represents a dimensionless constant. This represents the temperature decay coefficient.

14. A computer device comprising a storage unit and a processor, wherein the storage unit stores a computer program, characterized in that, When the processor executes the computer program, it implements the adaptive photovoltaic-storage multi-source collaborative energy management method according to any one of claims 1 to 13.

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