Control method of energy storage photovoltaic energy system, photovoltaic energy storage device and energy storage photovoltaic energy system

Abstract

This invention discloses a control method, photovoltaic energy storage device, and energy storage photovoltaic energy system for an energy storage system. The control method predicts electricity consumption and power generation over a future period, establishes a curve of electricity prices over that period, identifies all peaks and troughs in the curve, and establishes a set of peak and trough objects. It then selects peaks and troughs to be processed. If the price of a peak to be processed is greater than the price of a trough, it performs trough processing logic, updating the purchased electricity volume and net power generation of the entity corresponding to the index of the peak to be processed in the peak and trough object sets. This process continues until all peaks are processed. This control method moves users' electricity demand step-by-step to locations with lower electricity prices for pre-storage. Through multiple iterations of peak shaving and valley filling and electricity demand transfer, it moves high-priced electricity demand to low-priced electricity demand, maximizing economic efficiency.

Description

Technical Field

[0001] This invention relates to a control method for an energy storage photovoltaic energy system, particularly an intelligent control method based on the "Three Monks" AI big data model, as well as a photovoltaic energy storage device and an energy storage photovoltaic energy system using this control method. Background Technology

[0002] In recent years, the rapid development of artificial intelligence (AI) technology has profoundly changed various industries, especially in the field of energy management, where it has shown unprecedented potential. Particularly in the inverter market, with the increasing demand for green energy and sustainable development, intelligent management has become a core driving force for improving system efficiency and optimizing energy allocation. For the energy storage application segment, users are increasingly demanding intelligent and efficient utilization of energy storage resources to reduce energy costs.

[0003] Currently, for energy storage photovoltaic energy systems, the industry's algorithms and applications mainly focus on simple control based on the highest and lowest electricity prices. This involves charging during the lowest price range and discharging during the highest, failing to maximize economic benefits. When the electricity price curve fluctuates multiple times or is not a simple sine function, it will result in significant economic discrepancies from the optimal solution. Existing control methods have not fully utilized the deep learning capabilities of big data and AI algorithms. For example, they may ignore complex factors such as the specific electricity price policies in the user's region, the impact of geographical location on solar power generation, and historical electricity consumption patterns in the surrounding environment. This limitation leads to low prediction accuracy, insufficiently refined allocation of energy storage resources, and an inability to fully meet the user's optimal cost-saving goals within a specific time period.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a control method for an energy storage photovoltaic energy system, which addresses the shortcomings of energy storage inverter users in efficiently utilizing energy storage resources. It ensures that users maximize the potential of the energy storage photovoltaic energy system, efficiently and optimally copes with the uncertainty and drastic fluctuations of electricity prices, and shifts electricity demand from high-priced electricity to low-priced electricity, thereby maximizing economic efficiency.

[0006] The present invention also provides a photovoltaic energy storage device and a photovoltaic energy storage system employing the control method.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A control method for an energy storage photovoltaic energy system includes the following steps:

[0009] S100. Predict the electricity consumption and power generation in the future period, establish a curve of electricity price over time in the period, find all peaks and troughs in the curve, and establish a set of peak and trough objects.

[0010] S200: Set the peak to be processed. During the first processing, set the last peak as the peak to be processed.

[0011] S300, the trough that is the closest to the peak time to be processed is the trough to be processed;

[0012] S400. If the electricity price of the peak to be processed is greater than the electricity price of the valley to be processed, perform valley processing logic and update the purchased electricity volume and net power generation of the peak to be processed object information corresponding to the index of the peak and valley object in the peak and valley object set.

[0013] S500, Determine whether the net power generation of the wave peak to be processed is 0;

[0014] If the result is yes, then mark the peak to be processed as used, and take the peaks with higher electricity prices on the left and right sides of the peak as new peaks to be processed, and execute the following step S600.

[0015] If the result is negative, the lower electricity prices on the left and right sides of the valley to be processed are taken as new valleys to be processed; if the index of the new valley to be processed in the set of peaks and valleys is not equal to the index of the peak to be processed in the set of peaks and valleys, then repeat step S400; if the index of the new valley to be processed is equal to the index of the peak to be processed, then execute the following step S600.

[0016] S600: Determine whether the new peak to be processed is the first peak;

[0017] If the result is yes, then the program ends;

[0018] If the result is negative, repeat step S200.

[0019] In a preferred embodiment, in step S100, the current power of the energy storage photovoltaic energy system is used as the initial power and placed into the earliest time data in the peak and trough object set.

[0020] In a preferred embodiment, in step S100, information is initialized based on the predicted data, and the initialized information is sorted according to time. The initialized information includes the expected power generation, power consumption, net power generation, maximum hourly charging capacity of the battery, available power, excess net power generation, available discharge capacity, initial power, initial available power and battery capacity for each time interval; wherein, if the current power is less than or equal to 0, the initial power is 0.

[0021] In a more preferred embodiment, in step S100, the peak and trough object sets use different memory addresses. The peak and trough object sets include multiple values ​​such as power station ID, inverter serial number, capacity, time, electricity price, expected power generation, expected power consumption, net power generation, power generation exceeding the range, available power, available discharge capacity, initial battery SOC, available power, expected battery capacity, maximum charging capacity per hour, charge / discharge plan (inactive, charging, discharging), cost, and initial cost. The indices of all peaks and troughs are found based on the peak and trough object sets and placed into the peak index set and trough index set.

[0022] In a preferred embodiment, step S400 includes the following steps:

[0023] S401. Determine whether the electricity price of the peak to be processed is greater than the electricity price of the valley to be processed, and whether the valley status is normal; if the result is no, then find the peak and valley to be processed again; if the result is yes, then execute the following steps S410 to S411.

[0024] S410. Find the peaks on both sides of the peak to be processed that are closest to it and are unused;

[0025] S411. Determine whether the net power generation of the wave peak to be processed is greater than or equal to 0.

[0026] If the result is yes, then mark the peak as used, and compare the peaks on both sides found in step S410 to see which one has the highest electricity price. Set the peak with the highest electricity price as the new current processing peak, and repeat step S401; if the result is no, then perform the valley processing logic.

[0027] In a more preferred embodiment, step S400 includes the following steps:

[0028] S420. Should trough processing be performed? If trough processing is performed, proceed to step S421 below.

[0029] S421. Determine whether the net power generation of the trough processed in the current processing is greater than 0; if the result is yes, execute the following step S422; if the result is no, jump to the following step S424.

[0030] S422. Determine whether the net power generation of the valley to be processed is greater than the net power generation required for the peak to be processed; if the result is yes, then proceed to steps S423 to S424.

[0031] S423. Update the data for trough net power generation and peak power generation to be processed. Peak net power generation is 0.

[0032] S424. If the peak to be processed still needs power, determine whether there is available capacity for the trough to be processed; if the result is yes, then proceed to the following step S425.

[0033] S425. Determine whether the available capacity of the valley to be processed can meet the power demand of the peak to be processed.

[0034] If the result is yes, then proceed to step S426: Set the net power generation of the peak to be processed to 0, and update the data of the available power for purchase in the valley, the net power generation in the valley, the power purchase power for the peak, and the power to be processed in the peak.

[0035] If the result is negative, proceed to step S427: Set the available electricity for the valley to be processed to 0, and update the data of peak net power generation, valley net power generation, valley purchased electricity, and peak processing electricity.

[0036] In a more preferred embodiment, step S400 includes the following steps following step S426 or step S427:

[0037] S430. Determine if the following conditions are met: the power generation is negative, there is purchased electricity and it is at a peak; if the result is yes, proceed to step S431 below; if the result is no, proceed to step 435 below.

[0038] S431. Determine whether the peak net power generation has changed. If it has changed, proceed to step S432 below. If it has not changed, proceed to step S434 below.

[0039] S432. Determine whether the peak change value is greater than the peak purchase volume; if the result is yes, then execute the following step S433; if the result is no, then update the sum of the peak purchase volume and the peak change value to the new peak purchase volume, and execute the following step S434.

[0040] S433: Clear the peak battery purchase;

[0041] S434. Update the purchase volume of the entity corresponding to the peak index in the peak and trough object sets;

[0042] S435, Update the set of peak and trough objects, net power generation in troughs, available electricity in troughs, available electricity in troughs, and net power generation at peaks;

[0043] S436. If the amount of electricity purchased during the trough is not 0, then record it as the net power generation value to be processed during the peak.

[0044] In a further preferred embodiment, step S400 includes the following steps following step S436:

[0045] S440, update initial battery level and expected battery level information;

[0046] S441, Pass in the current set of peak and trough objects and the current trough index;

[0047] S442. If the index of the currently processed valley is greater than or equal to 0, start updating the data in a loop from the valley index.

[0048] S443. Determine if the purchased power is greater than 0; if it is greater than 0, proceed to step S444; if it is equal to 0, update the expected battery power = expected battery power = current battery power + purchased power + estimated power generation - estimated purchased power, and then proceed to step S450.

[0049] S444. If the difference between the expected power generation and the expected power purchase is greater than 0, then update the expected battery capacity = current battery capacity + power purchase + expected power generation - expected power purchase, and then execute step S450; if it is not greater than 0, then update the expected battery capacity = current battery capacity + power purchase, and then execute step S450.

[0050] S450: Determine whether the desired battery charge is greater than the battery capacity.

[0051] If the result is negative, then update the expected battery capacity to 0;

[0052] If the result is yes, and the processed data is the current trough or data next to the trough, then mark the current round of beam processing as complete and set the expected battery capacity to be equal to the battery capacity.

[0053] S451. Determine whether the current battery level plus the available battery level is greater than the battery capacity. If the result is yes, proceed to step S452. If the result is no, proceed to step S457.

[0054] S452, Set Excess Capacity = Current Battery Level + Available Battery Level - Battery Capacity;

[0055] S453. Determine whether the excess capacity is greater than the available electricity. If the result is yes, clear the available electricity and proceed to step S457. If the result is no, execute the following step S454.

[0056] S454: Subtract the excess capacity from the available battery capacity and update it to the available battery capacity;

[0057] S455. Determine whether the original purchased battery capacity is greater than the battery capacity minus the current battery capacity; if the result is positive, proceed to step S456; if the result is negative, proceed to step S457.

[0058] S456, Update purchased battery capacity = Battery capacity - Current battery capacity;

[0059] S457. Update the expected battery level and the initial battery level of the object with the current index + 1 to the expected battery level of the current data.

[0060] In a more preferred embodiment, in step S422, if the result is negative, the data is updated, the net power generation at the trough after the update is 0, the net power generation at the peak after the update is equal to the net power generation at the peak before the update plus the net power generation at the trough, the power generation at the peak before the update is equal to the power generation at the peak after the update minus the net power generation at the trough, and then the process jumps to step S424.

[0061] In a more preferred embodiment, in step S423, the updated trough net power generation = the previous trough net power generation + the peak net power generation; the updated peak power generation = the previous peak power generation + the peak net power generation.

[0062] In a preferred embodiment, in step S400, the expected power is updated, and the expected power is used as the initial power of the peak and trough object information of the current index + 1.

[0063] In a preferred embodiment, before step S600, the trough processing logic is exited and the next peak processing logic is entered.

[0064] In a preferred embodiment, the future period is the next 1 to 7 days, and the future period is divided into multiple time segments, each segment lasting 10 to 60 minutes. More preferably, each time segment lasts 10 to 30 minutes. In a specific and preferred embodiment, each time segment lasts 15 minutes.

[0065] Another technical solution adopted in this invention is as follows:

[0066] A photovoltaic energy storage device, comprising:

[0067] A controller is used to receive prediction data generated by the server according to the control method, and to control the charging and discharging time and charging and discharging power of the battery according to the prediction data.

[0068] In a preferred embodiment, instructions to turn smart control on or off are input via a user interface;

[0069] After receiving the instruction, the user terminal or the controller of the photovoltaic energy storage device generates an instruction data packet to be sent to the server. The instruction data packet includes the user ID, the device ID, and the intelligent control enabled or disabled status.

[0070] Another technical solution adopted in this invention is as follows:

[0071] An energy storage photovoltaic energy system, comprising:

[0072] The server is used to receive instruction data packets sent by photovoltaic energy storage devices. The instruction data packets include user ID, device ID, and intelligent control enabled or disabled status.

[0073] The server also obtains instructions from the instruction data packet. After confirming the activation of intelligent control, it generates predictive data based on the device ID, the device's geographical location, historical power generation and consumption data, and regional electricity price policies, using the control method described above, and sends the predictive data to the photovoltaic energy storage device. The predictive data includes power generation and consumption, charging and discharging time, and charging and discharging power for a future period of time.

[0074] The above-mentioned solution adopted in this invention has the following advantages:

[0075] The control method for the energy storage photovoltaic energy system of this invention utilizes a "water-carrying" mode in the algorithm to progressively shift users' electricity demand to locations with lower electricity prices for pre-storage. Through multiple iterations of peak shaving and valley filling and electricity demand shifting, it can efficiently and optimally cope with the uncertainty and severe fluctuations of electricity price models, smoothly handling multiple peaks and troughs sequentially, and shifting electricity demand from high-priced areas to low-priced areas, thereby maximizing economic efficiency. This control method achieves intelligent electricity cost optimization, significant cost savings; accurate prediction and efficient energy storage management; and personalized strategies with strong adaptability. Attached Figure Description

[0076] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used 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 those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] Figures 1 to 8 A flowchart of a control method according to an embodiment of the present invention is shown.

[0078] Figure 9 The diagram illustrates electricity cost statistics for the control method according to an embodiment of the present invention and a conventional method. Detailed Implementation

[0079] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art. It should be noted that the description of these embodiments is for the purpose of aiding understanding the present invention, but does not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0080] This embodiment aims to construct a highly intelligent photovoltaic energy storage management system. This system includes photovoltaic energy storage devices or controls one or more photovoltaic energy storage devices. The photovoltaic energy storage devices can be power equipment such as photovoltaic inverters and converters. The batteries within these devices have an energy storage function, storing electricity from the photovoltaic panels. When needed, electricity is purchased from the grid and stored in the batteries for later use. The energy storage photovoltaic energy management system also includes a server used to predict the power generation and consumption of the photovoltaic energy storage devices over a specific period based on a "three monks" AI model, and to customize control strategies. These control strategies include, but are not limited to, optimal charging and discharging times and charging and discharging power.

[0081] This energy storage photovoltaic energy management system integrates the user's country's electricity pricing policy, the precise latitude and longitude information of the deployed equipment, and historical electricity consumption and generation data of the surrounding environment. Through machine learning models, it comprehensively analyzes this diverse data to achieve high-precision predictions of electricity demand for the next 1-7 days. Based on this, it further provides users with personalized electricity consumption and energy storage strategy suggestions, ensuring that the stored energy is utilized most efficiently at the most critical moments, thereby achieving significant cost savings in subtle ways and improving user experience and satisfaction.

[0082] This control method utilizes the "water-carrying" mode in the algorithm to move users' electricity demand step by step to a lower electricity price for pre-storage. Through multiple iterations of peak shaving and valley filling and electricity demand transfer, it can efficiently and optimally cope with the uncertainty and severe fluctuations of the electricity price model, smoothly handle multiple peaks and valleys one after another, and move the electricity demand at high electricity prices to low electricity prices, thereby maximizing economic efficiency.

[0083] The initial information used in this control method (one record per hour):

[0084] Projected power generation: Calculated by large model, projected power generation * discharge loss rate;

[0085] Electricity consumption: Electricity consumption is calculated using a large-scale model;

[0086] Net generation: Estimated generation - Estimated electricity consumption;

[0087] Maximum charging capacity per hour: If it is less than or equal to 0, it is considered to be 0;

[0088] Available electricity for purchase within a given period: When the maximum hourly charging capacity is greater than 0, (maximum hourly charging capacity * charging loss rate) - net power generation (if net power generation is less than or equal to 0, the net power generation is not reduced); when the maximum hourly charging capacity is 0, the available electricity for purchase within the given period is 0.

[0089] Exceeding Net Power Generation: If the net power generation exceeds the maximum charging capacity per hour, the excess value should be included in the "Exceeding Net Power Generation" category.

[0090] Dischargeable capacity: Maximum power consumption per hour * Discharge loss rate;

[0091] Initial battery level: 0;

[0092] Initial battery purchase: 0;

[0093] Battery capacity: Find the result based on the model number.

[0094] Prediction Logic: Based on the electricity pricing policy of the user's country, the precise latitude and longitude information of the deployed equipment, and historical electricity consumption and generation data of the surrounding environment, the system can comprehensively analyze this diverse data through machine learning models to achieve high-precision predictions of electricity demand for the next 1-7 days (one record every 15 minutes).

[0095] 1. Create two dimensions, peaks and troughs, based on the predicted data (peak: a price higher than both sides of the electricity price is a peak; trough: a price lower than both sides of the electricity price is a trough); find all peak and trough information based on the predicted data. Place the current electricity consumption obtained by the heavy equipment as the initial electricity consumption into the earliest time data in the peak and trough object set.

[0096] 2. Set the peak information for this processing to the last one within this time range, and at the same time, obtain the closest trough with a time smaller than the peak time as the trough for this round of processing.

[0097] 2.1 Determine if the price peak is greater than the trough, and if the trough is in a normal state:

[0098] a) If the conditions are not met, find the peak and trough to process again, that is, the peak closest to the left of the current peak and the trough with a time smaller than the peak are the troughs to process in this round.

[0099] b) Find the prediction data that is closest in time to the peak and is unused (by default, all are unused).

[0100] c) Determine if the current peak net power generation is greater than or equal to 0. If it is, mark the peak as used and compare the unused peaks on both sides to see which has a higher electricity price. Obtain the corresponding detailed information and set it as the new peak. Simultaneously, re-trigger 2.1. If it is less than 0, enter the trough processing logic.

[0101] 2.2 Determine whether to enter the trough processing flow:

[0102] a) If the trough processing logic is not entered, the next peak and trough are searched again. That is, the nearest peak to the left of the current peak and the nearest trough with a time smaller than that peak are obtained as the troughs for this round of processing and the logic of 2.0 is re-entered.

[0103] b) Enter the trough data processing flow

[0104] Current trough net power generation is greater than 0:

[0105] i. Does the net power generation during the trough exceed the net power generation required during the peak?

[0106] Yes, update the data:

[0107] Trough net power generation = Trough net power generation + Peak net power generation

[0108] Peak power generation = Peak power generation + Net peak power generation

[0109] Peak net power generation = 0

[0110] No, update the data and:

[0111] Peak net power generation = Peak net power generation + Valley net power generation;

[0112] Peak power generation = Peak power generation - Off-peak net power generation

[0113] Net power generation during trough = 0

[0114] d) If the current net power generation during the trough is less than or equal to 0, or if the data for net power generation during the trough is greater than 0 has been updated, determine if there is available capacity available during the trough and if electricity is still needed during the peak:

[0115] Yes, and the available capacity during off-peak periods can address the need to update data during peak periods:

[0116] Off-peak electricity purchase capacity = Off-peak electricity purchase capacity + Peak net power generation

[0117] Trough net power generation = Trough net power generation + Peak net power generation

[0118] Peak electricity purchase = Peak electricity purchase + |Peak net power generation|

[0119] Peak power generation = Peak power generation + Net peak power generation = 0;

[0120] No, and the available capacity during off-peak periods cannot meet the demand for updating data during peak periods:

[0121] Peak net power generation = Peak net power generation + Off-peak available power for purchase

[0122] Off-peak net power generation = Off-peak net power generation - Off-peak available electricity for purchase

[0123] Off-peak electricity purchases = Off-peak electricity purchases + |Off-peak available electricity purchases|

[0124] Peak-hour processing volume = Peak-hour processing volume - Off-peak hour available volume

[0125] Off-peak electricity available for purchase = 0.00.

[0126] e) Scenarios where there is available capacity during the trough but electricity is still needed during the peak, or scenarios where available capacity has been processed but electricity is still needed during the peak:

[0127] Net power generation is negative and there is purchased electricity at a peak:

[0128] Yes, has the peak net power generation changed?

[0129] The peak change value is greater than the peak electricity purchase value:

[0130] Yes: Peak-time purchase clears battery.

[0131] No: Peak power purchase = Peak power purchase (negative) + Peak change value. No, has the net peak power generation changed? Or, the net power generation is negative and there is power purchase, and the peak processing is complete:

[0132] Update the purchase volume of the entity corresponding to the peak index in the peak and trough sets, update the peak and trough sets, the net power generation of the trough, the available power generation of the trough, the power purchase volume of the trough, and the net power generation of the peak.

[0133] If the purchased electricity for peak periods is not zero, record in the trough information how much net electricity generation was processed for that peak period.

[0134] f) Update initial battery level, desired battery level, and other information.

[0135] Retrieve the current peak and trough object set and the current trough index (only update the trough index and all subsequent initial battery information).

[0136] i. Check if the trough index is greater than or equal to 0: Start updating data in a loop with re-trough indexes.

[0137] ii. Is the purchased battery capacity greater than 0?

[0138] Greater than 0, the expected power generation minus the expected power purchase is greater than 0;

[0139] Expected battery capacity = Current battery capacity + Purchased power + Expected power generation - Expected purchased power.

[0140] If the value is greater than 0, the expected power generation minus the expected power purchase is less than or equal to 0.

[0141] Desired battery capacity = Current battery capacity + Purchased battery capacity.

[0142] iii. Purchased electricity equals 0:

[0143] Expected battery capacity = Current battery capacity + Purchased power + Expected power generation - Expected purchased power

[0144] g) Is the expected battery capacity greater than the expected battery capacity? Is the expected battery capacity less than 0?

[0145] Update expected battery capacity = 0;

[0146] Expected battery capacity to be greater than battery level:

[0147] If the data being processed happens to be the current trough or the data next to the trough, then mark the current trough as processed.

[0148] Set desired battery capacity = battery capacity.

[0149] h) Current battery level + available battery capacity is greater than battery capacity

[0150] Excess capacity = Current battery level + Available battery level - Battery capacity

[0151] Exceeding capacity by more than the available battery capacity:

[0152] Yes: Clear available battery power.

[0153] No: Available battery capacity = Available battery capacity - Excess capacity

[0154] Original purchased battery capacity greater than battery capacity minus current battery capacity:

[0155] Yes: Purchased battery capacity = Battery capacity - Current battery capacity.

[0156] i) Update the expected power, and set the initial power of the object at the current index + 1 to the expected power of the current data. j) If the net power generation at the trough is less than or equal to 0 and the available power for purchase is less than or equal to 0, mark the trough as used. k) Check if the net power generation at the peak is 0.

[0157] A value of 0 indicates that the peak has been used. Compare the previously found indexes on the left and right sides to determine which has a higher electricity price, and use that as the new peak index. Update the peak index to reflect the higher price.

[0158] Other: Find the unused indexes on the left and right sides of the trough, and find the one with the lower price as the next trough, and update the trough index to the index with the lower price.

[0159] i) If a new trough occurs, re-enter logic 2.2;

[0160] j) If the new trough index equals the peak index, exit the trough logic, enter the next peak, update the peak and trough information, and re-enter logic 2.

[0161] In other cases, the logic exits the trough and enters the next peak, updating the peak and trough information before re-entering logic 2, until all peaks have been processed.

[0162] Combination Figures 1 to 8 As shown, the control method of the energy storage photovoltaic energy system in this embodiment includes the following steps:

[0163] S100. Predict the electricity consumption and power generation in the future period, establish a curve of electricity price over time in the period, find all peaks and troughs in the curve, and establish a set of peak and trough objects.

[0164] S200: Set the peak to be processed. During the first processing, set the last peak as the peak to be processed.

[0165] S300, the trough that is the closest to the peak time to be processed is the trough to be processed;

[0166] S400. If the electricity price of the peak to be processed is greater than the electricity price of the valley to be processed, perform valley processing logic and update the purchased electricity volume and net power generation of the peak to be processed object information corresponding to the index of the peak and valley object in the peak and valley object set.

[0167] S500, Determine whether the net power generation of the wave peak to be processed is 0;

[0168] If the result is yes, then mark the peak to be processed as used, and take the peaks with higher electricity prices on the left and right sides of the peak as new peaks to be processed, and execute the following step S600.

[0169] If the result is negative, the lower electricity prices on the left and right sides of the valley to be processed are taken as new valleys to be processed; if the index of the new valley to be processed in the set of peaks and valleys is not equal to the index of the peak to be processed in the set of peaks and valleys, then repeat step S400; if the index of the new valley to be processed is equal to the index of the peak to be processed, then execute the following step S600.

[0170] S600: Determine whether the new peak to be processed is the first peak;

[0171] If the result is yes, then the program ends;

[0172] If the result is negative, repeat step S200.

[0173] The specific process of step S100 is as follows: Determine the initial battery capacity of the photovoltaic energy storage device. If it is less than or equal to 0, modify the input initial battery capacity to 0; if it is greater than 0, initialize other information according to the prediction data set, and update the data according to time. The prediction data set includes: expected power generation, power consumption, net power generation, maximum hourly charging capacity, available power within the specified range, excess net power generation, discharge capacity, initial battery capacity, initial available power, and battery capacity.

[0174] Peak and trough object sets are created based on the predicted data set, each using a different memory address. The elements of each peak and trough object set specifically include: power station ID, inverter serial number, capacity, time, electricity price, projected power generation, projected power consumption, net power generation, power generation exceeding the range, available power purchase, available power discharge, initial battery SOC, available power purchase, expected battery capacity, maximum hourly charging, charge / discharge plan (stationary, charging, discharging), cost, and initial cost.

[0175] Based on the peak and trough object sets, find all peak and trough indices and add them to the peak index set and trough index set, respectively, corresponding to the peak and trough positions in the peak and trough object sets. The elements of the peak index set specifically include: peak power station ID, inverter serial number, capacity, time, electricity price, estimated power generation, estimated power consumption, net power generation, power generation exceeding the range, available power purchase, available power discharge, initial battery SOC, purchased power, expected battery capacity, maximum charging per hour, charge / discharge plan (stationary, charging, discharging), cost, and initial cost. The elements of the trough index set specifically include: trough power station ID, inverter serial number, capacity, time, electricity price, estimated power generation, estimated power consumption, net power generation, power generation exceeding the range, available power purchase, available power discharge, initial battery SOC, purchased power, expected battery capacity, maximum charging per hour, charge / discharge plan (stationary, charging, discharging), cost, and initial cost.

[0176] The initial charge is placed into the earliest time data in the peak and trough object set.

[0177] In step S200, the set of peak and trough objects is obtained, along with the peak index in the currently processed peak index set, the peak index set, and the trough index combination. It is then confirmed whether the input peak index exists. If it does not exist, the program ends; if it does exist, the process jumps to step S300. Specifically, during the initial processing, the index of the last peak is used as the index of the peak to be processed.

[0178] In step S300, based on the input peak index, the data of the peak in the peak and trough object set is found. The nearest trough index on the left is found based on the peak index, and then the data of the trough is found in the peak and trough object set based on that trough index. The peak and trough indices to be processed are initialized to the indices corresponding to the peaks and troughs found above, and the process proceeds to step S400.

[0179] Step S400 specifically includes:

[0180] S401. Determine whether the electricity price of the peak to be processed (currently processed peak) is greater than that of the valley to be processed (currently processed valley) and whether the valley is in a normal state. If the result is not, then pass the index of the current index set minus 1 as the index of the peak to be processed, and then jump to step S200.

[0181] If the result is yes, then find the unused indices on both sides of the peak to be processed (by default, all indices are unused).

[0182] Step S411: Determine if the net power generation of the current peak being processed is greater than 0. If the result is yes, mark the peak as used, compare the electricity prices of the unused peaks on both sides, obtain their corresponding index and set it as the new peak index to be processed, update the peak, and then jump to step S401. If the result is no, the default is to process, enter the valley loop processing logic, i.e., jump to step S420.

[0183] S420. Determine if there is a cyclic trough. If not, use the index of the current index set minus 1 as the index of the peak to be processed, and then jump to step S200. If it is entered, jump to step S421.

[0184] S421. Determine whether the net power of the current processing trough is greater than 0; if the result is yes, execute the following step S422; if the result is no, jump to the following step S424.

[0185] S422. Determine whether the net power generation of the trough to be processed is greater than the net power generation required for the peak to be processed.

[0186] S423. If the result is yes, update the data as follows:

[0187] Trough net power generation = Trough net power generation + Peak net power generation

[0188] Peak power generation = Peak power generation + Net peak power generation

[0189] Peak net power generation = 0

[0190] If the result is negative, update the data as follows:

[0191] Peak net power generation = Peak net power generation + Valley net power generation

[0192] Peak power generation = Peak power generation - Off-peak net power generation

[0193] Net power generation during trough = 0.

[0194] S424. If the peak to be processed still needs power, determine whether there is available capacity for the trough to be processed; if the result is yes, then proceed to the following step S425.

[0195] S425. Determine whether the available capacity of the valley to be processed can meet the power demand of the peak to be processed.

[0196] If the result is yes, then proceed to step S426, which means that the available capacity during the trough can meet the demand during the peak, and update the data as follows:

[0197] Off-peak electricity purchase capacity = Off-peak electricity purchase capacity + Peak net power generation

[0198] Trough net power generation = Trough net power generation + Peak net power generation

[0199] Peak electricity purchase = Peak electricity purchase + |Peak net power generation|

[0200] Peak power generation = Peak power generation + Net peak power generation

[0201] Peak net power generation = 0.

[0202] If the result is negative, proceed to step S427, which indicates that the available capacity during the trough cannot meet the demand during the peak. Update the data as follows:

[0203] Peak net power generation = Peak net power generation + Off-peak available power for purchase

[0204] Off-peak net power generation = Off-peak net power generation - Off-peak available electricity for purchase

[0205] Off-peak electricity purchases = Off-peak electricity purchases + |Off-peak available electricity purchases|

[0206] Peak-hour processing volume = Peak-hour processing volume - Off-peak hour available volume

[0207] Off-peak electricity available for purchase = 0.00.

[0208] After step S421, step S426, or step S427, perform the following steps:

[0209] S430. Determine if the following conditions are met: The net power generation of the data in the peak index set processed this time is negative, there is purchased electricity and it is a peak; if the result is yes, execute the following step S431; if the result is no, execute the following step 435.

[0210] S431. Determine whether the peak net power generation has changed. If it has changed, proceed to step S432 below. If it has not changed, proceed to step S434 below.

[0211] S432. Determine whether the peak change value is greater than the peak purchase volume; if the result is yes, then execute the following step S433; if the result is no, then update the sum of the peak purchase volume (negative number) and the peak change value to the new peak purchase volume, and execute the following step S434.

[0212] S433: Clear the peak battery purchase;

[0213] S434. Update the purchase volume of the information corresponding to the peak index in the peak and trough object sets;

[0214] S435, Update the set of peak and trough objects, net power generation in troughs, available electricity in troughs, available electricity in troughs, and net power generation at peaks;

[0215] S436. If the amount of electricity purchased during the trough is not 0, then record it as the net power generation value to be processed during the peak.

[0216] If the purchased electricity volume for peak processing is not zero, the net power generation processed during that peak is recorded in the trough information. This is only for future storage purposes and is not currently in use.

[0217] Next, update the initial battery level, expected battery level, and other information as follows:

[0218] S440, update initial battery level and desired battery level information.

[0219] S441, pass in the current peak and trough object set and the current trough index; just update the trough index and all subsequent initial power information.

[0220] S442. If the valley index to be processed is greater than or equal to 0, start updating the data in a loop from the valley index.

[0221] S443. Determine if the purchased power is greater than 0; if it is greater than 0, proceed to step S444; if it is equal to 0, update the expected battery power = expected battery power = current battery power + purchased power + estimated power generation - estimated purchased power, and then proceed to step S450.

[0222] S444. If the difference between the expected power generation and the expected power purchase is greater than 0, then update the expected battery capacity = current battery capacity + power purchase + expected power generation - expected power purchase, and then execute step S450; if it is not greater than 0, then update the expected battery capacity = current battery capacity + power purchase, and then execute step S450.

[0223] S450: Determine whether the desired battery charge is greater than the battery capacity.

[0224] If the result is negative, then update the expected battery capacity to 0;

[0225] If the result is yes, and the processed data is the current trough or data next to the trough, then mark the current round of beam processing as complete and set the expected battery capacity to be equal to the battery capacity.

[0226] S451. Determine whether the current battery level plus the available battery level is greater than the battery capacity. If the result is yes, proceed to step S452. If the result is no, proceed to step S457.

[0227] S452, Set Excess Capacity = Current Battery Level + Available Battery Level - Battery Capacity;

[0228] S453. Determine whether the excess capacity is greater than the available electricity. If the result is yes, clear the available electricity and proceed to step S457. If the result is no, execute the following step S454.

[0229] S454: Subtract the excess capacity from the available battery capacity and update it to the available battery capacity;

[0230] S455. Determine whether the original purchased battery capacity is greater than the battery capacity minus the current battery capacity; if the result is positive, proceed to step S456; if the result is negative, proceed to step S457.

[0231] S456, Update purchased battery capacity = Battery capacity - Current battery capacity;

[0232] S457. Update the expected power in the information corresponding to the peak and trough index of the current processing, that is, the expected power processed by logic such as S443 and S444, and the initial power of the object with the current index + 1 as the expected power of the current data.

[0233] In step S600, if the judgment result is negative, first jump to step S401. When the price of the peak is not greater than that of the trough or the trough state is abnormal, pass in the index of the current encapsulation index set minus 1, and then enter step S200.

[0234] The specific operation process of the above-mentioned energy storage photovoltaic energy system is as follows:

[0235] Step 1: User Preference Configuration Reception

[0236] User Interface: Users can choose whether to enable the "Three Monks" AI big data model intelligent control algorithm through the user interface, such as the app's settings. This operation generates a command data packet containing the user ID, device ID, and the algorithm's enabled / disabled status.

[0237] Processing procedure: The user interface collects the user's selections, encrypts the instruction data packet, and sends it to the user preference processing module on the server through a secure channel.

[0238] Step 2: Server-side processing and prediction task startup

[0239] Encrypted instruction data packets received by the server's user preference processing module and prediction algorithm module

[0240] Processing steps: The user preference processing module decrypts the data packet, verifies the user's identity, and extracts the instruction content. After confirming that the intelligent control algorithm is enabled, it forwards the device identifier and related parameters to the prediction algorithm module. Based on diverse data such as the device's geographical location, historical power generation and consumption data, and regional electricity pricing policies, the prediction algorithm module initiates power generation and consumption predictions for 1 to 7 days.

[0241] Step 3: Data Analysis and Strategy Generation

[0242] The server's prediction algorithm module and intelligent optimization module analyze and process the prediction data (future power generation and power consumption predictions).

[0243] Processing: The future power supply and demand forecast data generated by the prediction algorithm module is sent to the intelligent optimization module. Based on the forecast results, combined with the current energy storage status, equipment characteristics, and user preferences, this module optimizes the energy storage charging and discharging strategy. The strategy includes, but is not limited to, optimal charging and discharging time and power.

[0244] The controller of the photovoltaic energy storage device receives the above-mentioned energy storage charging and discharging strategy and controls the charging and discharging time and charging and discharging power of the battery according to the predicted data.

[0245] Through the above steps, from user settings to encrypted data transmission, cloud-based intelligent analysis and processing, to device-side policy implementation and user feedback, a closed-loop, high-efficiency management process is formed, ensuring the security of user data and the intelligence of device operation.

[0246] Simulation Example

[0247] The control method described above in the embodiment specifies the energy storage charging and discharging strategy of the photovoltaic energy storage device. The data for 24 hours of operation are shown in Table 1 below.

[0248] Table 1

[0249]

[0250] Traditional control strategies are simple, based on the highest and lowest electricity prices, charging only during the lowest price range and discharging only during the highest price range. For example... Figure 9As shown, the curve in the initial mode represents the electricity cost and electricity consumption statistics of the traditional control strategy, while the curve in the smart mode represents the electricity cost and electricity consumption statistics of the control strategy in the embodiment.

[0251] The control method in this embodiment can optimize electricity costs and significantly save costs; it can make accurate predictions and achieve efficient energy storage management; and it can customize control strategies for photovoltaic energy storage equipment, making it highly adaptable.

[0252] Current market systems fail to fully leverage the potential of big data and AI algorithms, particularly neglecting the specific impacts of regional electricity pricing policies, geographical location on solar power generation, and historical electricity consumption patterns in the surrounding environment. This means the key to improvement lies in more deeply integrating and analyzing these complex factors. The control method in this embodiment improves predictive accuracy: by introducing a more advanced machine learning model, the new system aims to significantly improve the accuracy of future electricity demand forecasts, covering short-term forecasts of 1-7 days. This improvement directly relates to the efficient planning and allocation of energy storage resources. The control method in this embodiment enables personalized strategy formulation: the system will provide more personalized electricity consumption and energy storage strategy suggestions, based on each user's unique needs and the specific conditions of their environment, to achieve optimal use of energy storage capacity at critical moments. This is a significant breakthrough from the traditional "one-size-fits-all" strategy. The control method in this embodiment achieves refined resource allocation: the improved system is committed to achieving refined management of energy storage resources, ensuring maximum cost savings during the periods when users need them most, thereby improving overall energy efficiency and economic benefits. The control method in this embodiment improves user experience and satisfaction: Through the above improvements, the ultimate goal is to enhance user experience and satisfaction in energy conservation and cost reduction, allowing users to intuitively perceive cost savings while strengthening their trust and reliance on the intelligent energy management system. In summary, the key improvements in the control method of this embodiment focus on deepening data application, improving prediction accuracy, implementing personalized strategies, achieving refined management, and ultimately improving user satisfaction.

[0253] As indicated in this specification and claims, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, and these steps and elements do not constitute an exclusive list; the method or apparatus may also include other steps or elements. The term "and / or" as used herein includes any combination of one or more of the associated listed items.

[0254] It should be noted that, unless otherwise specified, when a feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. Furthermore, the descriptions of "up," "down," "left," and "right" used in this invention are only relative to the relative positional relationships of the various components of the invention in the accompanying drawings.

[0255] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are preferred embodiments. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and they should not be construed as limiting the scope of protection of the present invention. All equivalent transformations or modifications made according to the principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A control method for an energy storage photovoltaic energy system, characterized in that, The steps S100, S200, S300, S400, S500, and S600 are as follows: S100 includes: predicting electricity consumption and power generation in the future period, establishing a curve of electricity price over time in the period, finding all peaks and troughs in the curve, and establishing a set of peak and trough objects. S200 includes: setting peaks to be processed, and setting the last peak as the peak to be processed during the first processing; S300 includes: setting the trough that is smaller and closest to the peak time to be processed as the trough to be processed; The S400 includes: Step S401: Determine whether the electricity price of the peak to be processed is greater than the electricity price of the valley to be processed, and whether the valley status is normal; if the result is no, then find the peak and valley to be processed again; if the result is yes, then execute the following steps S410 to S411. Step S410: Find the closest unused peaks on both sides of the peak to be processed; Step S411: Determine whether the net power generation of the wave peak to be processed is greater than or equal to 0; If the result is yes, then mark the peak as used, and compare which of the two peaks found in step S410 has the highest electricity price. Set the peak with the highest electricity price as the new current peak to be processed, and repeat step S401; if the result is no, then perform the valley processing logic; after performing the valley processing logic, S400 also includes updating the purchased electricity volume and net peak power generation of the peak and valley object corresponding to the index of the peak to be processed in the peak and valley object set. S500 includes: determining whether the net power generation of the wave peak to be processed is 0; If the result is yes, then mark the peak to be processed as used, and take the peaks with higher electricity prices on the left and right sides of the peak as new peaks to be processed, and execute the following step S600. If the result is negative, the lower electricity prices on the left and right sides of the valley to be processed are taken as new valleys to be processed; if the index of the new valley to be processed in the set of peaks and valleys is not equal to the index of the peak to be processed in the set of peaks and valleys, then repeat step S400; if the index of the new valley to be processed is equal to the index of the peak to be processed, then execute the following step S600. S600 includes: determining whether a new peak to be processed is the first peak; If the result is yes, then the program ends; If the result is negative, repeat step S200; Step S400 further includes the following steps: S420. Should the trough processing logic be performed? If the trough processing logic is performed, execute the following step S421. S421. Determine whether the net power generation of the current trough is greater than 0. If the result is yes, proceed to step S422. If the result is no, proceed to step S424. S422. Determine whether the net power generation of the valley to be processed is greater than the net power generation required for the peak to be processed; if the result is yes, then proceed to steps S423 to S424. S423. Update the data for trough net power generation and peak power generation to be processed. Peak net power generation is 0. S424. If the peak to be processed still needs electricity, determine whether there is any electricity available for purchase in the trough to be processed; if the result is yes, then proceed to the following step S425. S425. Determine whether the available electricity for the valley to be processed can meet the electricity demand of the peak to be processed. If the result is yes, then proceed to step S426: Set the net power generation of the peak to be processed to 0, and update the data of the available power for purchase in the valley, the net power generation in the valley, the power purchase power for the peak, and the power to be processed in the peak. If the result is negative, proceed to step S427: Set the available electricity for the valley to be processed to 0, and update the data of peak net power generation, valley net power generation, valley purchased electricity, and peak processing electricity.

2. The control method according to claim 1, characterized in that, In step S100, the current power of the energy storage photovoltaic energy system is used as the initial power and placed into the earliest time data in the peak and trough object set.

3. The control method according to claim 2, characterized in that, In step S100, information is initialized based on the predicted data, and the initialized information is sorted according to time. The initialized information includes the expected power generation, power consumption, net power generation, maximum hourly charging capacity of the battery, available power, excess net power generation, available discharge capacity, initial power, initial available power and battery capacity for each time interval; wherein, if the current power is less than or equal to 0, the initial power is 0.

4. The control method according to claim 3, characterized in that, In step S100, the peak and trough object sets use different memory addresses. The peak and trough object sets include multiple items such as power station ID, inverter serial number, capacity, time, electricity price, expected power generation, expected power consumption, net power generation, power generation exceeding the range, available power purchase, available power discharge, initial battery SOC, available power purchase, expected battery power, maximum charging per hour, charging and discharging plan, cost, and initial cost. Based on the peak and trough object sets, all peak and trough indices are found and placed into the peak index set and trough index set.

5. The control method according to claim 1, characterized in that, Step S400 includes the following steps following step S426 or step S427: S430. Determine if the following conditions are met: The net power generation in the peak index set processed this time is negative, there is purchased electricity and it is a peak; if the result is yes, execute the following step S431. If the result is negative, proceed to step 435 below; S431. Determine whether the peak net power generation has changed. If it has changed, proceed to step S432 below. If it has not changed, proceed to step S434 below. S432. Determine whether the peak change value is greater than the peak purchase volume. If the result is yes, then proceed to step S433 below; if the result is no, then update the sum of the peak purchase volume and the peak change value to the new peak purchase volume, and proceed to step S434 below. S433: Clear the peak battery purchase; S434. Update the purchase volume of the entity corresponding to the peak index in the peak and trough object sets; S435, Update the set of peak and trough objects, net power generation in troughs, available electricity in troughs, available electricity in troughs, and net power generation at peaks; S436. If the amount of electricity purchased during the trough is not 0, then record the net power generation value of the peak to be processed.

6. The control method according to claim 5, characterized in that, Step S400 includes the following steps following step S436: S440, update initial battery level and expected battery level information; S441, Pass in the current set of peak and trough objects and the current trough index; S442. If the index of the valley to be processed is greater than or equal to 0, start updating the data in a loop from the valley index. S443. Determine if the purchased power is greater than 0; if it is greater than 0, proceed to step S444; if it is equal to 0, update the expected battery power = current battery power + purchased power + expected power generation - expected purchased power, and then proceed to step S450. S444. If the difference between the expected power generation and the expected power purchase is greater than 0, then update the expected battery capacity = current battery capacity + power purchase + expected power generation - expected power purchase, and then proceed to step S450; if it is not greater than 0, then update the expected battery capacity = current battery capacity + power purchase, and then proceed to step S450. S450: Determine whether the desired battery charge is greater than the battery capacity. If the result is negative, then update the expected battery capacity to 0; If the result is yes, and the data being processed is the current trough or data next to the trough, then mark the current trough processing as complete and set the expected battery capacity to be equal to the battery capacity. S451. Determine whether the current battery level plus the available battery level is greater than the battery capacity. If the result is yes, then proceed to step S452 below; If the result is negative, proceed to step S457; S452, Set Excess Capacity = Current Battery Level + Available Battery Level - Battery Capacity; S453. Determine whether the excess capacity is greater than the available electricity volume; If the result is yes, clear the available battery capacity and proceed to step S457; If the result is negative, proceed to step S454 below; S454: Subtract the excess capacity from the available battery capacity and update it to the available battery capacity; S455. Determine whether the original purchased battery capacity is greater than the battery capacity minus the current battery capacity. If the result is yes, proceed to step S456 below; If the result is negative, proceed to step S457; S456, Update Battery Purchase Rate = Battery Capacity - Current Battery Capacity; S457. Update the expected battery level. Update the initial battery level of the object at the current index + 1 to the expected battery level of the current data.

7. The control method according to claim 1, characterized in that, In step S422, if the result is negative, the data is updated. After the update, the net power generation at the trough is 0, the net power generation at the peak after the update is equal to the net power generation at the peak before the update plus the net power generation at the trough, and the power generation processed at the peak this time after the update is equal to the power generation processed at the peak this time before the update minus the net power generation at the trough. Then, the process jumps to step S424.

8. The control method according to claim 1 or 7, characterized in that, In step S423, the updated trough net power generation = the original trough net power generation + the original peak net power generation; the updated peak power generation = the original peak power generation + the peak net power generation.

9. The control method according to claim 1, characterized in that, In step S400, the expected power level is updated, and the expected power level is used as the initial power level of the peak and trough object with the current index +1.

10. The control method according to claim 1, characterized in that, Before step S600, exit the trough processing logic and enter the next peak processing logic.

11. The control method according to claim 1, characterized in that, The future period refers to the next 1 to 7 days, and the future period is divided into multiple time segments, each lasting 10 to 60 minutes.

12. A photovoltaic energy storage device, characterized in that, include: A controller is configured to receive prediction data generated by the server according to any one of claims 1 to 11, and control the charging and discharging time and charging and discharging power of the battery based on the prediction data.

13. An energy storage photovoltaic energy system, characterized in that, include: The server is used to receive user instruction data packets, which include user ID, device ID, and smart control enabled or disabled status. The server also obtains instructions from the instruction data packet, confirms the activation of intelligent control, and generates predictive data based on the device ID, the device's geographical location, historical power generation and consumption data, and the local electricity price, using the control method described in any one of claims 1 to 11, and sends the predictive data to the photovoltaic energy storage device. The predictive data includes power generation and consumption, charging and discharging time, and charging and discharging power for a future period of time.

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