Energy storage control and adjustment method and system for distributed photovoltaic absorption

Abstract

The invention discloses an energy storage control and adjustment method and system for distributed photovoltaic consumption, and relates to the technical field of renewable energy sources, and the method comprises the steps: obtaining illumination intensity, temperature data and historical load data, and predicting the future photovoltaic power generation amount and load demand changes; analyzing the voltage fluctuation of the power grid and the charging demand of the energy storage system based on the prediction results of the photovoltaic generating capacity and the load demand; according to the health state of the energy storage battery, optimizing the charging and discharging depth, and outputting an energy storage adjustment plan; dynamically optimizing the charging and discharging plan and reactive power regulation of the energy storage system by using a multi-objective optimization algorithm, and generating an optimal reactive power regulation instruction; according to the reactive power adjustment instruction, performing transformer area self-adaptive cooperative control to obtain a cooperative control result; according to the invention, through an area adaptive cooperative control mechanism, the reactive power regulation capability of the adjacent areas can be automatically coordinated when the regulation capability of the local energy storage system is insufficient, and the stability of the power grid and the utilization efficiency of the energy storage system are further enhanced.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy technology, and in particular to a distributed photovoltaic energy storage control and regulation method and system. Background Art

[0002] As the global demand for renewable energy increases, distributed photovoltaic power generation systems have been widely used. However, the volatility and instability of photovoltaic power generation, especially under the influence of environmental factors (such as light intensity, temperature, etc.), pose challenges to the voltage stability and load balance of the power grid. In order to improve the photovoltaic absorption rate, energy storage systems are widely used, which can store energy when photovoltaic power generation is in excess and release energy during peak loads. However, existing energy storage control strategies are often relatively simple and cannot fully consider the volatility of photovoltaic power generation, the dynamic changes in load demand, and the health status of energy storage batteries, resulting in the failure to achieve optimal efficiency of energy storage systems and stability of the power grid.

[0003] Existing technologies have certain limitations in the control strategies of energy storage systems. For example, traditional control methods are mostly static strategies that lack dynamic response to real-time voltage fluctuations and load changes, and cannot fully optimize the charging and discharging plans of energy storage systems. In addition, in terms of multi-objective optimization, existing technologies fail to fully consider the balance of multiple objectives such as voltage stability, photovoltaic absorption rate, energy storage system life, and economy, so the operating efficiency of the energy storage system and the overall performance of the power grid have not been maximized. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a distributed photovoltaic energy storage control and regulation method to solve the problems of grid voltage instability and low efficiency of energy storage system caused by the volatility of photovoltaic power generation in distributed photovoltaic power generation systems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for controlling and regulating energy storage for distributed photovoltaic consumption, which comprises:

[0008] Obtain light intensity, temperature data and historical load data to predict future changes in photovoltaic power generation and load demand;

[0009] Analyze grid voltage fluctuations and charging requirements of energy storage systems based on the forecast results of photovoltaic power generation and load demand;

[0010] Based on the analysis results, formulate a charging and discharging plan for the energy storage system;

[0011] According to the health status of the energy storage battery, optimize the charge and discharge depth and output the energy storage adjustment plan;

[0012] Use multi-objective optimization algorithms to dynamically optimize the charging and discharging plan and reactive power regulation of the energy storage system and generate optimal reactive power regulation instructions;

[0013] According to the reactive power regulation instruction, the substation adaptive coordinated control is executed to obtain the coordinated control result.

[0014] As a preferred solution of the energy storage control and regulation method for distributed photovoltaic consumption described in the present invention, obtaining light intensity, temperature data and historical load data, and predicting future photovoltaic power generation and load demand changes include the following steps:

[0015] Predict future photovoltaic power generation based on the acquired light intensity and temperature data;

[0016] Process the acquired historical load data in sections;

[0017] Build a long short-term memory network model and train it using segmented data to learn the daily, weekly, and seasonal fluctuations of the load;

[0018] Based on the load data, light intensity and temperature data at the current time point, the load demand in the short term is predicted.

[0019] As a preferred solution of the energy storage control and regulation method for distributed photovoltaic consumption described in the present invention, the following steps are included in the analysis of grid voltage fluctuations and charging requirements of the energy storage system based on the prediction results of photovoltaic power generation and load demand:

[0020] Combine the photovoltaic power generation and load forecast results to calculate the power difference in each future period;

[0021] Evaluate the feasibility of charging and discharging based on the current state of the energy storage battery.

[0022] As a preferred solution of the energy storage control and regulation method for distributed photovoltaic consumption described in the present invention, the following steps are included in formulating a charge and discharge plan for the energy storage system based on the analysis results:

[0023] Set the photovoltaic power generation peak threshold. When the photovoltaic power generation power exceeds the photovoltaic power generation peak threshold, the period is defined as the photovoltaic peak period.

[0024] Set the load peak threshold. When the load power exceeds the load peak threshold, the period is defined as the load peak period.

[0025] Formulate a charging and discharging plan for the energy storage system based on the power difference and the feasibility assessment results of charging and discharging;

[0026] When P sur When (t)>0, it means that the photovoltaic power generation is greater than the load demand, there is a power surplus in the power grid at the current moment, and the energy storage system is charged;

[0027] When P sur When (t)<0, it means that the load demand is greater than the photovoltaic power generation, there is a power gap in the power grid at the current moment, and the energy storage system discharges.

[0028] As a preferred solution of the energy storage control and regulation method for distributed photovoltaic consumption described in the present invention, wherein: according to the health status of the energy storage battery, the charge and discharge depth is optimized, and the output energy storage regulation plan includes the following steps:

[0029] Set dynamic upper and lower limits of charging and discharging according to the health status of the battery;

[0030] During the peak period of photovoltaic power generation, the energy storage system is scheduled to charge, and charging is stopped when the battery capacity limit is met:

[0031] During peak load periods, the energy storage system is scheduled to discharge and stop discharging when the battery reaches its minimum capacity;

[0032] Dynamically adjust the charging power and discharging power as well as the depth of charging and discharging in each period to generate an energy storage adjustment plan.

[0033] As a preferred solution of the energy storage control and regulation method for distributed photovoltaic consumption described in the present invention, wherein: using a multi-objective optimization algorithm to dynamically optimize the charging and discharging plan and reactive power regulation of the energy storage system, generating the optimal reactive power regulation instruction includes the following steps:

[0034] Determine the optimization objectives of minimizing voltage deviation, maximizing PV absorption rate, maximizing energy storage system life, and minimizing electricity purchase cost;

[0035] Setting constraints for voltage constraints, energy storage system power limits, and energy storage capacity constraints;

[0036] Generate an initial population through genetic algorithm, each individual represents a possible energy storage regulation strategy;

[0037] Evaluate the fitness of each individual based on the optimization goal, select individuals with high fitness for mating based on the fitness evaluation results, and generate new individuals through crossover and mutation operations;

[0038] The process of fitness evaluation, selection, crossover and mutation is repeated until the optimization convergence condition is met and the optimal reactive power regulation instruction is output.

[0039] As a preferred solution of the energy storage control and regulation method for distributed photovoltaic consumption described in the present invention, wherein: according to the reactive power regulation instruction, performing the adaptive coordinated control of the substation area and obtaining the coordinated control result include the following steps:

[0040] Obtain the real-time voltage of each substation, compare it with the reference voltage of the substation, and calculate the voltage deviation;

[0041] Dynamically adjust the reactive power output of the energy storage system according to the voltage deviation of each substation;

[0042] When the reactive power output of the energy storage system in a certain substation has reached its limit and the voltage deviation in the substation has not yet returned to the normal range, the substation adaptive collaborative control is performed according to the topology of the power network, and the reactive power regulation capability of the neighboring substation is sought to help the substation regulate the voltage and obtain a collaborative control result.

[0043] In a second aspect, the present invention provides a distributed photovoltaic energy storage control and regulation system, comprising:

[0044] Prediction and analysis module, which obtains light intensity, temperature data and historical load data to predict future changes in photovoltaic power generation and load demand;

[0045] The grid status analysis module analyzes grid voltage fluctuations and charging requirements of the energy storage system based on the forecast results of photovoltaic power generation and load demand;

[0046] The energy storage adjustment plan module formulates the charging and discharging plan of the energy storage system based on the analysis results;

[0047] The battery management module optimizes the charge and discharge depth and outputs the energy storage adjustment plan according to the health status of the energy storage battery;

[0048] The multi-objective optimization module uses a multi-objective optimization algorithm to dynamically optimize the charging and discharging plan and reactive power regulation of the energy storage system and generate the optimal reactive power regulation instructions;

[0049] The coordinated control module executes the adaptive coordinated control of the substation according to the reactive power regulation instruction and obtains the coordinated control result.

[0050] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the energy storage control and regulation method for distributed photovoltaic absorption as described in the first aspect of the present invention is implemented.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the energy storage control and regulation method for distributed photovoltaic absorption as described in the first aspect of the present invention is implemented.

[0052] The beneficial effects of the present invention are as follows: by combining light intensity, temperature data and historical load data, the long short-term memory network is used to predict future photovoltaic power generation and load demand, so that the charging and discharging plan of the energy storage system can be formulated more accurately under uncertain environments; based on the prediction results, by analyzing the voltage fluctuations of the power grid, the charging and discharging strategy of the energy storage system is flexibly adjusted, which not only effectively balances the power difference between photovoltaic power generation and load demand, but also reduces the operating risk caused by the voltage fluctuations of the power grid; by using a multi-objective optimization algorithm, the voltage stability, photovoltaic absorption, life of the energy storage system and economic factors are comprehensively considered, and the charging and discharging plan and reactive power regulation of the energy storage system are dynamically optimized to ensure the optimal performance of the system under different operating conditions; through the adaptive collaborative control mechanism of the substation, when the regulation capacity of the local energy storage system is insufficient, the reactive regulation capacity of the adjacent substation is automatically coordinated to further enhance the stability of the power grid and the utilization efficiency of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0054] Figure 1 This is a flow chart of the energy storage control and adjustment method for distributed photovoltaic consumption in Example 1.

[0055] Figure 2 This is a schematic diagram of the energy storage control and regulation system for distributed photovoltaic consumption in Example 1. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0057] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for controlling and regulating energy storage for distributed photovoltaic consumption, comprising the following steps:

[0058] S1. Obtaining light intensity, temperature data and historical load data, and predicting future changes in photovoltaic power generation and load demand include the following steps:

[0059] Based on the acquired light intensity and temperature data, the future photovoltaic power generation is predicted, and the expression is:

[0060] P pv (t) = A × η × I solar (t)×(1-γ(T(t)-T ref ));

[0061] Among them, P pv (t) is the photovoltaic power generation at time t, A is the effective area of ​​the photovoltaic panel, η is the reference efficiency of the photovoltaic panel, I solar (t) is the solar radiation intensity at time t, γ is the temperature coefficient, T(t) is the ambient temperature at time t, T ref is the reference temperature of the PV module;

[0062] The acquired historical load data is processed in segments; a long short-term memory network model is constructed, and the model is trained using the segmented data to learn the daily, weekly and seasonal fluctuation patterns of the load; based on the load data, light intensity and temperature data at the current time point, the load demand in the short term is predicted.

[0063] Specifically, light intensity and temperature are key environmental factors that affect photovoltaic power generation. The power generation efficiency of photovoltaic modules not only depends on the intensity of solar radiation, but is also affected by the ambient temperature. Generally, the power generation efficiency of photovoltaic modules decreases with increasing temperature. Therefore, the temperature coefficient is a parameter used to describe the effect of temperature on photovoltaic power generation efficiency.

[0064] Load data usually has periodicity and volatility. Typical fluctuation patterns include:

[0065] Daily load fluctuations (e.g., differences between day and night);

[0066] Weekly load fluctuations (e.g., differences between weekdays and weekends);

[0067] Seasonal load fluctuations (such as peak power consumption for air conditioning in summer, etc.).

[0068] It should be noted that by combining the real-time collected light intensity and temperature data, the system can accurately calculate the current photovoltaic power generation in a short time and quickly predict the changes in power demand. This real-time response capability has great advantages in dealing with the volatility of photovoltaic power generation and the dynamic changes in grid load. It can identify the imbalance point of power supply and demand in advance, adjust the charging and discharging strategy of the energy storage system in time, and avoid unnecessary grid fluctuations.

[0069] S2. Based on the forecast results of photovoltaic power generation and load demand, the analysis of grid voltage fluctuations and charging requirements of the energy storage system includes the following steps:

[0070] The expression of grid voltage fluctuation is:

[0071]

[0072] Where V(t) is the voltage at time t, V base is the reference voltage of the power grid, k is the sensitivity coefficient of voltage to power change, S base is the reference apparent power, P load (t) is the load power at time t;

[0073] Combining the photovoltaic power generation and load forecast results, the power difference in each future period is calculated, and the expression is:

[0074] P sur (t) = P pv (t)-P load (t);

[0075] Among them, P sur (t) is the power difference, which reflects the difference between photovoltaic power generation and load demand, indicating whether the power generation and load demand are balanced in the current period;

[0076] The feasibility of charging and discharging is evaluated according to the current state of the energy storage battery. The expression is:

[0077]

[0078]

[0079] Among them, P charge (t) is the charging power of the energy storage system at time t, P dis (t) is the discharge power of the energy storage system at time t, SOC max is the maximum allowable state of charge of the energy storage battery, SOC(t) is the percentage of the remaining capacity of the energy storage battery at time t, C bat is the total capacity of the energy storage battery, η c is the charging efficiency of the energy storage system, η d is the discharge efficiency of the energy storage system.

[0080] It should be noted that by evaluating the current state of the energy storage battery to reasonably control the charging and discharging operations, the problem of overcharging or discharging the battery that may occur in the traditional method is effectively avoided. By introducing the charging and discharging efficiency parameters, the charging and discharging efficiency of the energy storage system can be maximized and the battery life can be extended while ensuring the health of the battery. This dynamic management strategy centered on battery health significantly improves the operating efficiency and economy of the energy storage system and avoids excessive battery loss.

[0081] S3. Based on the analysis results, the charging and discharging plan of the energy storage system is formulated, including the following steps:

[0082] Set the photovoltaic power generation peak threshold. When the photovoltaic power generation exceeds the photovoltaic power generation peak threshold, define this period as the photovoltaic peak period; set the load peak threshold. When the load power exceeds the load peak threshold, define this period as the load peak period; formulate the charging and discharging plan of the energy storage system based on the power difference and the feasibility assessment results of charging and discharging; when P sur When (t)>0, it means that the photovoltaic power generation is greater than the load demand, there is a power surplus in the power grid at the current moment, and the energy storage system is charged; when P sur When (t)<0, it means that the load demand is greater than the photovoltaic power generation, there is a power gap in the power grid at the current moment, and the energy storage system discharges.

[0083] It should be noted that by rationally utilizing the surplus electricity during the peak period of photovoltaic power generation, the reliance on external power purchases can be reduced during peak load periods, thus reducing the operating costs of the power grid. In addition, the energy storage system is charged during periods of low electricity prices and discharged during periods of high electricity prices, which not only balances electricity supply and demand, but also achieves greater economic benefits through the price difference.

[0084] S4. According to the health status of the energy storage battery, optimize the charge and discharge depth, and the output energy storage adjustment plan includes the following steps:

[0085] According to the health status of the battery, set dynamic upper and lower limits for charging and discharging. During the peak period of photovoltaic power generation, arrange the energy storage system to charge, and stop charging when the battery capacity upper limit is met. During the peak load period, arrange the energy storage system to discharge, and stop discharging when the battery minimum capacity is reached. Dynamically adjust the charging power and discharging power of each time period as well as the depth of charging and discharging to generate an energy storage regulation plan.

[0086] Specifically, dynamic charging power adjustment: during the photovoltaic peak period, the charging power is dynamically adjusted according to the current power difference and the remaining capacity of the battery; when the power difference is large, the charging power can be increased; when the battery is close to the charging upper limit, the charging power is gradually reduced.

[0087] Dynamic discharge power adjustment: During peak load periods, the discharge power is dynamically adjusted based on the current power gap and the remaining capacity of the battery. When the grid power gap is large, the discharge power can be increased. When the battery capacity approaches the lower limit of discharge, the discharge power gradually decreases.

[0088] Charge and discharge depth control: By dynamically setting the upper and lower limits of charge and discharge, the charge and discharge depth of each period is controlled to ensure that the battery will not be overcharged or over-discharged, thereby extending the battery life.

[0089] It should be noted that by dynamically adjusting the charging and discharging power in each period, the charging and discharging efficiency of the energy storage system can be maximized while meeting the power demand of the power grid. Especially during the peak period of photovoltaic power generation, the excess power can be fully utilized for efficient charging; during the peak load period, the system can flexibly discharge according to the power grid demand to avoid power waste.

[0090] S5. Dynamically optimizing the charging and discharging plan and reactive power regulation of the energy storage system using a multi-objective optimization algorithm to generate the optimal reactive power regulation instruction includes the following steps:

[0091] Determine the optimization goals of minimizing voltage deviation, maximizing photovoltaic absorption rate, maximizing energy storage system life and minimizing electricity purchase cost; set the constraints of voltage constraint, energy storage system power limit and energy storage capacity constraint; generate an initial population through genetic algorithm, each individual represents a possible energy storage regulation strategy; evaluate the fitness of each individual based on the optimization goal, and select individuals with high fitness for mating according to the fitness evaluation results, and generate new individuals through crossover and mutation operations; repeat the process of fitness evaluation, selection, crossover and mutation until the optimization convergence conditions are met and the optimal reactive power regulation instructions are output.

[0092] Specifically, maximize the photovoltaic absorption rate: the absorption rate of photovoltaic power generation represents the degree of utilization of clean energy; maximize the life of the energy storage system: the charging and discharging depth and frequency of the energy storage system affect the battery life. By optimizing the charging and discharging strategy, the overcharging and over-discharging of the battery can be reduced, and the service life of the battery can be extended; minimize the cost of purchasing electricity: the energy storage system can be charged during periods of lower electricity prices and discharged during periods of higher electricity prices, thereby reducing the cost of purchasing electricity from the power grid.

[0093] It should be noted that through the multi-objective optimization method, a balance is achieved between minimizing voltage deviation, maximizing photovoltaic absorption rate, extending the life of the energy storage system and reducing the cost of electricity purchase. This multi-objective optimization model significantly improves the comprehensive performance of the energy storage system and ensures the optimal operating state in many aspects.

[0094] S6. According to the reactive power regulation instruction, the adaptive coordinated control of the substation is performed to obtain the coordinated control result, which includes the following steps:

[0095] The real-time voltage of each substation is obtained and compared with the reference voltage of the substation to calculate the voltage deviation. The reactive power output of the energy storage system is dynamically adjusted according to the voltage deviation of each substation. When the reactive power output of the energy storage system in a certain substation has reached its limit and the voltage deviation of the substation has not yet returned to the normal range, adaptive coordinated control of the substation is performed according to the topological structure of the power network, and the reactive regulation capability of the adjacent substation is sought to help the substation adjust the voltage and obtain the coordinated control result.

[0096] Specifically, when the voltage is low: the energy storage system in the adjacent substation increases reactive power output to help improve the voltage in the current substation; when the voltage is high: the energy storage system in the adjacent substation reduces reactive power output (or even absorbs reactive power) to help lower the voltage in the current substation.

[0097] It should be noted that through dynamic adjustment and coordinated regulation strategies, the system can make full use of the reactive regulation capabilities of the energy storage systems in each substation, which not only avoids waste of resources but also improves the overall utilization efficiency of the energy storage system.

[0098] This embodiment also provides a distributed photovoltaic energy storage control and regulation system, including: a prediction and analysis module, which obtains light intensity, temperature data and historical load data, and predicts future changes in photovoltaic power generation and load demand; a power grid status analysis module, which analyzes power grid voltage fluctuations and charging requirements of the energy storage system based on the prediction results of photovoltaic power generation and load demand; an energy storage regulation plan module, which formulates a charging and discharging plan for the energy storage system based on the analysis results; a battery management module, which optimizes the charging and discharging depth according to the health status of the energy storage battery and outputs an energy storage regulation plan; a multi-objective optimization module, which uses a multi-objective optimization algorithm to dynamically optimize the charging and discharging plan and reactive power regulation of the energy storage system to generate the optimal reactive power regulation instruction; a coordinated control module, which executes adaptive coordinated control of the substation according to the reactive power regulation instruction to obtain a coordinated control result.

[0099] This embodiment also provides a computer device, which is suitable for the case of a distributed photovoltaic energy storage control and regulation method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the distributed photovoltaic energy storage control and regulation method proposed in the above embodiment.

[0100] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0101] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the energy storage control and regulation method for realizing distributed photovoltaic consumption as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0102] In summary, the present invention combines light intensity, temperature data and historical load data, and uses long short-term memory networks to predict future photovoltaic power generation and load demand, so as to more accurately formulate charging and discharging plans for energy storage systems in uncertain environments; based on the prediction results, by analyzing the voltage fluctuations of the power grid, the charging and discharging strategies of the energy storage system are flexibly adjusted, which not only effectively balances the power difference between photovoltaic power generation and load demand, but also reduces the operating risks caused by grid voltage fluctuations; using a multi-objective optimization algorithm, comprehensively considering voltage stability, photovoltaic absorption, life of the energy storage system and economic factors, dynamically optimizes the charging and discharging plans and reactive power regulation of the energy storage system, and ensures the optimal performance of the system under different operating conditions; through the adaptive collaborative control mechanism of the substation, when the regulation capacity of the local energy storage system is insufficient, the reactive regulation capacity of the adjacent substation is automatically coordinated to further enhance the stability of the power grid and the utilization efficiency of the energy storage system.

[0103] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the energy storage control and regulation method for distributed photovoltaic consumption are provided.

[0104] The experiment selected typical photovoltaic power stations in a certain area and their corresponding load data. The experimental data included historical light intensity, temperature, load data, and the operating status of the energy storage system. The experiment compared the static energy storage control method commonly used in the prior art with the dynamic energy storage adjustment method based on multi-objective optimization proposed in the present invention. The static method of the prior art generally charges and discharges the energy storage system according to a fixed load threshold, without considering the real-time grid voltage fluctuations and the health status of the energy storage system.

[0105] The experiment first collected light intensity and temperature data for a week, and used the long short-term memory network (LSTM) model to predict photovoltaic power generation and load demand.

[0106] Then, based on the predicted PV power generation and load demand, the voltage fluctuation of the power grid was analyzed.

[0107] In addition, the system optimizes the depth of charge and discharge in each period based on the current health status of the energy storage battery.

[0108] The details are shown in Table 1 below:

[0109] Table 1 Comparative experimental data of prior art and the present invention

[0110]

[0111]

[0112] Through the data in the above table, it can be clearly seen that the energy storage control and adjustment method of the present invention has significant advantages over the prior art. First of all, when photovoltaic power generation does not match the load demand, the prior art cannot efficiently use the charging and discharging function of the energy storage system. For example, in the experimental data on the 1st and 3rd days, when the photovoltaic power generation power was insufficient, the prior art failed to make full use of the energy storage system for discharge, resulting in large fluctuations in the grid voltage (the voltage on the 1st day was 220V, lower than the reference voltage). In contrast, the present invention optimizes the charging and discharging plan of the energy storage system, charges during periods of sufficient photovoltaic power generation (such as the 1st day), and reasonably discharges during periods of higher load (such as the 3rd day), successfully stabilizing the grid voltage at a level closer to the reference voltage (such as 221V on the 1st day and 224V on the 3rd day).

[0113] Secondly, the present invention introduces a dynamic adjustment mechanism for the battery health status, avoiding the problem of overcharging and discharging of batteries in traditional energy storage control methods. For example, in the experimental data on the second day, the method of the present invention only arranged a charging power of 15kW instead of full power charging according to the battery health status, which not only extended the battery life but also ensured the stability of the system. Under similar conditions, the existing technology ignored the health status of the battery and failed to make full use of the energy storage system for effective power regulation.

[0114] Finally, by comparison, it can be seen that the present invention is more stable in terms of grid voltage regulation. When the photovoltaic power generation and load demand fluctuate greatly (such as on the third day), the present invention realizes coordinated control between stations by dynamically optimizing reactive power output, and successfully controls the voltage fluctuation range to a level close to the reference voltage, while the voltage fluctuation of the prior art is larger, showing the defect of insufficient grid voltage regulation ability.

[0115] In summary, the present invention can significantly improve the photovoltaic absorption rate, reduce grid voltage fluctuations, and extend the service life of the energy storage system by combining multi-objective optimization algorithms, prediction models, and energy storage battery health management.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling and regulating energy storage for distributed photovoltaic consumption, characterized in that: include, Obtain light intensity, temperature data and historical load data to predict future changes in photovoltaic power generation and load demand; Analyze grid voltage fluctuations and charging requirements of energy storage systems based on the forecast results of photovoltaic power generation and load demand; Based on the analysis results, formulate a charging and discharging plan for the energy storage system; According to the health status of the energy storage battery, optimize the charge and discharge depth and output the energy storage adjustment plan; Use multi-objective optimization algorithms to dynamically optimize the charging and discharging plan and reactive power regulation of the energy storage system and generate optimal reactive power regulation instructions; According to the reactive power regulation instruction, the substation adaptive coordinated control is executed to obtain the coordinated control result.

2. The energy storage control and regulation method for distributed photovoltaic consumption according to claim 1, characterized in that: Obtaining light intensity, temperature data and historical load data, and predicting future changes in photovoltaic power generation and load demand include the following steps: Predict future photovoltaic power generation based on the acquired light intensity and temperature data; Process the acquired historical load data in sections; Build a long short-term memory network model and train it using segmented data to learn the daily, weekly, and seasonal fluctuations of the load; Based on the load data, light intensity and temperature data at the current time point, the load demand in the short term is predicted.

3. The energy storage control and regulation method for distributed photovoltaic consumption according to claim 2 is characterized in that: Based on the forecast results of photovoltaic power generation and load demand, the analysis of grid voltage fluctuations and charging requirements of energy storage systems includes the following steps: Evaluate the feasibility of charging and discharging based on the current state of the energy storage battery.

4. The energy storage control and regulation method for distributed photovoltaic consumption according to claim 3 is characterized in that: Based on the analysis results, the charging and discharging plan of the energy storage system includes the following steps: Set the photovoltaic power generation peak threshold. When the photovoltaic power generation power exceeds the photovoltaic power generation peak threshold, the period is defined as the photovoltaic peak period. Set the load peak threshold. When the load power exceeds the load peak threshold, the period is defined as the load peak period. Formulate a charging and discharging plan for the energy storage system based on the power difference and the feasibility assessment results of charging and discharging; When P sur When (t)>0, it means that the photovoltaic power generation is greater than the load demand, there is a power surplus in the power grid at the current moment, and the energy storage system is charged; When P sur When (t)<0, it means that the load demand is greater than the photovoltaic power generation, there is a power gap in the power grid at the current moment, and the energy storage system discharges.

5. The energy storage control and regulation method for distributed photovoltaic consumption according to claim 4 is characterized in that: According to the health status of the energy storage battery, the charge and discharge depth is optimized, and the output energy storage adjustment plan includes the following steps: Set dynamic upper and lower limits of charging and discharging according to the health status of the battery; During the peak period of photovoltaic power generation, the energy storage system is arranged to charge, and charging is stopped when the battery capacity limit is met; During peak load periods, the energy storage system is scheduled to discharge and stop discharging when the battery reaches its minimum capacity; Dynamically adjust the charging power and discharging power as well as the depth of charging and discharging in each period to generate an energy storage adjustment plan.

6. The energy storage control and regulation method for distributed photovoltaic consumption according to claim 5 is characterized in that: The multi-objective optimization algorithm is used to dynamically optimize the charging and discharging plan and reactive power regulation of the energy storage system. The generation of the optimal reactive power regulation instruction includes the following steps: Determine the optimization objectives of minimizing voltage deviation, maximizing PV absorption rate, maximizing energy storage system life, and minimizing electricity purchase cost; Setting constraints for voltage constraints, energy storage system power limits, and energy storage capacity constraints; Generate an initial population through genetic algorithm, each individual represents a possible energy storage regulation strategy; Evaluate the fitness of each individual based on the optimization goal, select individuals with high fitness for mating based on the fitness evaluation results, and generate new individuals through crossover and mutation operations; The process of fitness evaluation, selection, crossover and mutation is repeated until the optimization convergence condition is met and the optimal reactive power regulation instruction is output.

7. The energy storage control and regulation method for distributed photovoltaic consumption according to claim 6 is characterized in that: According to the reactive power regulation instruction, the adaptive coordinated control of the substation is executed to obtain the coordinated control result, which includes the following steps: Obtain the real-time voltage of each substation, compare it with the reference voltage of the substation, and calculate the voltage deviation; Dynamically adjust the reactive power output of the energy storage system according to the voltage deviation of each substation; When the reactive power output of the energy storage system in a certain substation has reached its limit and the voltage deviation in the substation has not yet returned to the normal range, the substation adaptive collaborative control is performed according to the topology of the power network, and the reactive power regulation capability of the neighboring substation is sought to help the substation regulate the voltage and obtain a collaborative control result.

8. A distributed photovoltaic energy storage control and regulation system, based on the distributed photovoltaic energy storage control and regulation method according to any one of claims 1 to 7, characterized in that: include, Prediction and analysis module, which obtains light intensity, temperature data and historical load data to predict future changes in photovoltaic power generation and load demand; The grid status analysis module analyzes grid voltage fluctuations and charging requirements of the energy storage system based on the forecast results of photovoltaic power generation and load demand; The energy storage adjustment plan module formulates the charging and discharging plan of the energy storage system based on the analysis results; The battery management module optimizes the charge and discharge depth and outputs the energy storage adjustment plan according to the health status of the energy storage battery; The multi-objective optimization module uses a multi-objective optimization algorithm to dynamically optimize the charging and discharging plan and reactive power regulation of the energy storage system and generate the optimal reactive power regulation instructions; The coordinated control module executes the adaptive coordinated control of the substation according to the reactive power regulation instruction and obtains the coordinated control result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the energy storage control and regulation method for distributed photovoltaic consumption according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy storage control and regulation method for distributed photovoltaic consumption as described in any one of claims 1 to 7 are implemented.

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