Scheduling Method and System for Photovoltaic Energy Storage System

Abstract

The present invention provides a scheduling method and system for a photovoltaic energy storage system, relating to the technical field of photovoltaic energy storage. The present invention predicts the future power generation of a photovoltaic panel by obtaining the rated power generation parameters of the photovoltaic panel in the photovoltaic energy storage system and the future light and temperature data of the area where the photovoltaic panel is located, determines the overall scheduling strategy of the scheduling system according to the real-time storage capacity of the battery of the obtained energy storage system and the future power consumption of the equipment and the predicted power generation of the photovoltaic panel. When the future power generation of the photovoltaic panel cannot meet the power consumption demand, power is preferentially scheduled from the power grid, and a pre-scheduling model is established. When the future power generation of the photovoltaic panel can meet the demand, the remaining power generation of the photovoltaic panel is sent to the power grid, and scheduling is carried out between the remaining storage capacity of the energy storage system and the power grid to relieve the peak-valley pressure of the power grid. By establishing a first scheduling model, power is scheduled, and a first scheduling optimization model is established to optimize and adjust the scheduling result of the first scheduling model.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage, and specifically to a scheduling method and system for a photovoltaic energy storage system. Background Art

[0002] In the past few decades, photovoltaic has developed rapidly. A new type of power energy storage system mainly based on photovoltaic has the advantages of being clean, low-carbon, safe, reliable, open, flexible, and highly adaptable, and can meet the needs of future power development. However, the intermittency and volatility of photovoltaic power generation have brought huge challenges to the scheduling and consumption of the power system, and traditional scheduling models are difficult to effectively cope with.

[0003] In the prior art, the publication number CN117578534A discloses a scheduling method, device, equipment, and storage medium for a photovoltaic energy storage system. The scheduling method for the photovoltaic energy storage system includes: generating an energy distribution plan based on the predicted energy output and predicted load demand of the photovoltaic energy storage system under different time periods and different preset conditions; selecting an energy configuration plan from the energy distribution plan, controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the energy configuration plan, and collecting the operation data of the energy storage unit in real time to draw an actual energy output curve. However, the instability of the photovoltaic system itself will lead to a lack of long-term stability and predictability of the system, being easily affected by unexpected events, and a large number of scheduling plans need to be generated based on a large amount of data.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a scheduling method and system for a photovoltaic energy storage system, so as to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A scheduling method and system for a photovoltaic energy storage system, the specific steps include:

[0008] Step 1: Obtain the rated power generation parameters of the photovoltaic panels in the photovoltaic energy storage system, the future light intensity and temperature data of the area where the photovoltaic panels are located, and predict the future power generation of the photovoltaic panels;

[0009] Step 2: Obtain the real-time storage capacity of the battery in the photovoltaic energy storage system, the future equipment power consumption, the battery voltage range, the charge and discharge current, and the continuous charge and discharge time, perform real-time update calculation on the storage capacity through the battery, and determine the overall scheduling strategy of the scheduling system according to the predicted power generation of the photovoltaic panels;

[0010] Step 3: When dispatching from the power grid, preferentially dispatch electricity from the power grid. Determine the dispatching period of the photovoltaic energy storage system according to the full charge and discharge time of the battery, obtain the maximum charge and discharge power of the battery, establish a preliminary dispatching model, and conduct preliminary dispatching of the electricity.

[0011] Step 4: When dispatching to the power grid, establish a first dispatching model based on the predicted photovoltaic power generation, future equipment power consumption, and simultaneously utilize the remaining storage capacity of the battery and the peak-valley price of the power grid to dispatch the electricity.

[0012] Step 5: Based on the real-time power generation of the photovoltaic, real-time equipment power consumption, and real-time power grid price, with the dispatching time period of the first dispatching model as the dispatching period, establish a first dispatching optimization model to optimize and adjust the dispatching result of the first dispatching model.

[0013] Furthermore, the calculation method for predicting the future power generation of the photovoltaic panel is as follows;

[0014]

[0015] Among them, is the predicted power generation of the photovoltaic panel, is the rated power generation of the photovoltaic panel, , are the standard light intensity and temperature under the rated power generation of the photovoltaic panel respectively, is the future light intensity, is the future temperature, is the temperature coefficient.

[0016] Furthermore, the formula for calculating the real-time update of the battery storage capacity is as follows;

[0017]

[0018] Among them, is the battery storage capacity, is the charge and discharge current, is the continuous charge and discharge time, and are the charge and discharge times at the upper and lower limits of the battery voltage range respectively;

[0019] The logic for determining the overall dispatching strategy of the dispatching system based on the future predicted power generation, real-time battery storage, and future equipment power consumption is as follows;

[0020]

[0021] Among them, is the predicted remaining power generation of the photovoltaic panel, is the predicted power generation of the photovoltaic panels, The real-time storage capacity of the battery. For future equipment power consumption, Storage capacity for batteries;

[0022] when When the power generation is predicted to be greater than the remaining battery storage and future equipment power consumption, the grid is mainly dispatched;

[0023] when When the predicted power generation is less than the remaining battery storage and future equipment power consumption, the grid dispatch is the main method to make , and then dispatch to the power grid.

[0024] Furthermore, the method for determining the scheduling period is:

[0025]

[0026] in, is the scheduling period, and are the charge and discharge times for the upper and lower safety limits of the battery voltage respectively;

[0027] The specific method of establishing the pre-scheduling model is:

[0028] With the battery storage capacity, battery release capacity, and power dispatched from the power grid as decision variables, and the goal of minimizing the cost of dispatching from the power grid, the objective function is constructed as follows;

[0029]

[0030] in, is the minimum cost value, for Peak electricity price at the time of for The valley electricity price at the time, for The peak electricity price of electricity dispatched from the power grid at any time, for The amount of electricity at the valley price dispatched from the power grid at any given moment, is the scheduling period, is the time in the model scheduling cycle, are all positive integers;

[0031] The constraints are;

[0032] Battery capacity limitations;

[0033]

[0034] in, The real-time battery power at time, is the battery storage capacity;

[0035] Battery energy balance constraint;

[0036]

[0037] Among them, the real-time battery power at time, is the real-time battery power at time, is the electricity charged into the battery at time, is the electricity discharged from the battery at time;

[0038] Power balance constraint;

[0039]

[0040] Among them, the electricity generated by the photovoltaic panel at time, is the electricity with peak grid price dispatched at time, is the electricity with valley grid price dispatched at time, is the electricity discharged from the battery at time, is the electricity consumption of the equipment at time, is the electricity charged into the battery at time;

[0041] Battery charge and discharge power constraint;

[0042]

[0043]

[0044] Among them, the electricity charged into the battery at time, is the maximum battery charging power, is the electricity charged into the battery at time, is the maximum battery discharge power;

[0045] Equipment electricity consumption constraint condition;

[0046]

[0047] Among them, is the electricity quantity at the peak electricity price dispatched from the power grid at time is the electricity quantity at the valley electricity price dispatched from the power grid at time is the real-time electricity quantity of the battery at time is the power generation of the photovoltaic panel at time is the electricity consumption of the equipment at time;

[0048] Furthermore, the specific method for establishing the first scheduling model is;

[0049] Taking the electricity quantity charged into the battery, the electricity quantity released by the battery, the electricity quantity dispatched to the power grid, and the electricity quantity dispatched from the power grid as decision variables, through the strategy of selling high and buying low, the economic benefit during the system operation is improved. Taking the maximum profit from selling to the power grid as the goal, the objective function is constructed as;

[0050]

[0051] Among them, is the maximum profit value, is the peak electricity price at time is the valley electricity price at time is the electricity quantity dispatched to the power grid at time is the electricity quantity dispatched from the power grid at time is the scheduling period, is the time within the model scheduling period, are all positive integers;

[0052] The constraint conditions are;

[0053] The limitation of the battery capacity;

[0054]

[0055] Among them, is the real-time electricity quantity of the battery at time is the battery storage capacity;

[0056] The battery energy balance constraint;

[0057]

[0058] Among them, is The real-time battery power at a moment is The real-time battery power at a moment is The power charged into the battery at a moment is The power released by the battery at a moment;

[0059] Power balance constraint;

[0060]

[0061] Among them, is The power generation of the photovoltaic panel at a moment Bis The power dispatched from the power grid at a moment is The power released by the battery at a moment is The power consumption of the device at a moment is The power dispatched to the power grid at a moment is The power charged into the battery at a moment;

[0062] Battery charge and discharge power constraint;

[0063]

[0064]

[0065] Among them, is The power charged into the battery at a moment is the maximum charging power of the battery is The power charged into the battery at a moment is the maximum charging power of the battery

[0066] Device power consumption constraint condition;

[0067]

[0068] Among them, is The real-time battery power at a moment is The power generation of the photovoltaic panel at a moment is The power consumption of the device at a moment.

[0069] Furthermore, the specific method for establishing the first scheduling optimization model is;

[0070] Build the objective function as;

[0071]

[0072] where, is the maximum profit value within the time period , is the real-time peak electricity price at time , is the real-time valley electricity price at time is the amount of electricity dispatched to the power grid at time is the amount of electricity dispatched from the power grid at time is the time period within the dispatching cycle of the first dispatching model, is the time within the first dispatching optimization model, and are all positive integers;

[0073] is one hour, is the time within the first dispatching optimization model to facilitate capturing and adjusting unforeseen changes in the first dispatching model;

[0074] The constraint conditions are;

[0075] The limitation of battery capacity;

[0076]

[0077] where, is the real-time battery power at time is the battery storage capacity, is the real-time electricity consumption of the device at time;

[0078] The battery energy balance constraint;

[0079]

[0080] where, is [[ID=7 / 4]]the real-time battery power at time is the real-time battery power at time is the amount of electricity charged into the battery at time is the amount of electricity released from the battery at time;

[0081] The power balance constraint;

[0082]

[0083] Among them, is the real-time power generation of the photovoltaic panel at time is the power quantity dispatched from the power grid at time is the power quantity released by the battery at time is the real-time power consumption of the device at time is the power quantity dispatched to the power grid at time is the power quantity charged into the battery at time;

[0084] Battery charge and discharge power constraint;

[0085]

[0086] Among them, is the power quantity released by the battery at time is the total battery storage;

[0087] Device power consumption constraint condition;

[0088]

[0089] Among them, is the real-time battery power at time is the real-time power generation of the photovoltaic panel at time is the power consumption of the device at time.

[0090] The present invention further provides a dispatching system for a photovoltaic energy storage system. The dispatching system for the photovoltaic energy storage system is used to execute the above-mentioned dispatching method for the photovoltaic energy storage system, including;

[0091] A data acquisition module, which is used to obtain real-time and future light and temperature data in the area where the photovoltaic panel in the photovoltaic energy storage system is located, the real-time battery storage, the future power consumption of the device, the battery voltage range, the charge and discharge current, the continuous charge and discharge time, and the maximum charge and discharge power, and the real-time and future prices of the power grid;

[0092] A dispatching decision module, which calculates and updates the storage capacity of the battery in real time, and determines the overall dispatching strategy of the dispatching system according to the predicted power generation of the photovoltaic panel;

[0093] A pre-scheduling module, which determines the scheduling period of the photovoltaic energy storage system according to the full charge and discharge time of the battery, obtains the maximum charge and discharge power of the battery, establishes a pre-scheduling model, and pre-schedules the power.

[0094] A first scheduling module, which establishes a first scheduling model according to the predicted photovoltaic power generation and future equipment power consumption, and at the same time utilizes the remaining storage capacity of the battery and the peak-valley price of the power grid to schedule the power.

[0095] A scheduling optimization module, which establishes a first scheduling optimization model with the scheduling period of the first scheduling model based on the real-time photovoltaic power generation, real-time equipment power consumption, and real-time power grid price, and optimizes and adjusts the scheduling result of the first scheduling model.

[0096] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0097] The present invention predicts the future power generation of the photovoltaic panel by obtaining the rated power generation parameters of the photovoltaic panel in the photovoltaic energy storage system, the future light and temperature data of the area where the photovoltaic panel is located, determines the overall scheduling strategy of the scheduling system according to the real-time storage capacity of the battery of the energy storage system obtained and the future equipment power consumption and the predicted power generation of the photovoltaic panel. When the future power generation of the photovoltaic panel cannot meet the power consumption demand, power is preferentially scheduled from the power grid, and a pre-scheduling model is established. When the future power generation of the photovoltaic panel can meet the demand, the remaining power generation of the photovoltaic panel is sent to the power grid, and scheduling is carried out between the remaining storage capacity of the energy storage system and the power grid to relieve the peak-valley pressure of the power grid. The power is scheduled by establishing a first scheduling model, and a first scheduling optimization model is established to optimize and adjust the scheduling result of the first scheduling model.

[0098] According to the present invention, by predicting and analyzing the relationship between the power generation of photovoltaic panels during the day, the battery storage capacity, and the device usage, the energy flow of the photovoltaic system can be adjusted more precisely. When the battery storage capacity and the power generation are insufficient, the system preferentially obtains the necessary power from the power grid through a pre-scheduling model to meet the power consumption demand. Then, various problems faced by the photovoltaic energy storage system are converted into the same scheduling problem and are uniformly processed by establishing a first scheduling model. Subsequently, a first scheduling optimization model is established to optimize and adjust the scheduling results of the first scheduling model, ensuring the continuity and reliability of the energy supply, preventing power outages or unstable device operation caused by insufficient energy. During the scheduling process of the photovoltaic energy storage system, various problems are unified into the same scheduling mode, which can effectively reduce the decision-making complexity and conflicts during the execution process, simplify the operation and management of the system, making the system easier to monitor and maintain. Based on multiple time scales, scheduling is carried out in two stages: the scheduling cycle of the photovoltaic energy storage system and the time period within the cycle, enabling the system to flexibly adjust according to actual needs, effectively solving the problems of lack of long-term stability and predictability of the system due to the instability of the photovoltaic system itself, and further resulting in unstable and inaccurate scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0100] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0101] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0102] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms "including" or "comprising" and the like mean that the elements or objects appearing before the term cover the elements or objects listed after the term and their equivalents, without excluding other elements or objects. The terms "connected" or "coupled" and the like are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0103] Embodiment:

[0104] Please refer to Figure 1 , the present invention provides a technical solution:

[0105] A scheduling method for a photovoltaic energy storage system, the specific steps include:

[0106] Step 1: Obtain the rated power generation parameters of the photovoltaic panels in the photovoltaic energy storage system, the future light intensity and temperature data of the area where the photovoltaic panels are located, and predict the future power generation of the photovoltaic panels.

[0107] By predicting the power generation of the photovoltaic panels, the scheduling system can plan the charging and discharging cycles of the battery according to the expected power output. This can not only ensure maximizing battery storage when the power supply is sufficient, but also rationally allocate the stored power when the power supply is insufficient, plan the power scheduling in advance, and thus balance the supply and demand.

[0108] The working principle of the photovoltaic panel is to generate current by absorbing photons in sunlight. Therefore, the light intensity is the most direct and important factor determining the power generation performance of the photovoltaic panel. The higher the light intensity, the more current is generated and the greater the power generation. Temperature also has a significant impact on the performance of the photovoltaic panel. The efficiency of the photovoltaic panel usually decreases at higher temperatures because the internal resistance of the photovoltaic increases with the increase in temperature, thereby reducing the electrical energy output. Accurately predicting the temperature change can help the scheduler understand the output change of the photovoltaic panel under different temperature conditions, and thus more reasonably adjust the power supply and storage plan.

[0109] By fully considering these two key variables of light and temperature, the photovoltaic energy storage system can achieve more efficient and reliable power generation prediction, and then better achieve the dual optimization of energy and economic benefits.

[0110] In this embodiment, the calculation method for predicting the future power generation of the photovoltaic panel is;

[0111]

[0112] Where, is the predicted power generation of the photovoltaic panel, is the rated power generation of the photovoltaic panel, , are the standard light intensity and temperature under the rated power generation of the photovoltaic panel respectively, is the future light intensity, is the future temperature, is the temperature coefficient.

[0113] Step 2: Obtain the real-time storage capacity of the battery in the photovoltaic energy storage system, the future equipment power consumption, the battery voltage range, the charge and discharge current, and the continuous charge and discharge time, calculate the real-time update of the storage capacity through the battery, and determine the overall scheduling strategy of the scheduling system according to the predicted power generation of the photovoltaic panel.

[0114] By predicting and analyzing the relationship between the daytime power generation of the photovoltaic panel, the battery storage capacity, and the device power consumption, the energy flow of the photovoltaic system can be adjusted more precisely, ensuring that every unit of power generation is utilized most effectively, reducing waste. Ignoring the relationship between the daytime power generation of the photovoltaic panel, the battery storage capacity, and the device usage may result in the ineffective utilization of excess power during the photovoltaic power generation period or the failure of the battery to be fully charged during low-demand periods.

[0115] In this embodiment, the formula for calculating the real-time update of the battery storage capacity is as follows;

[0116]

[0117] Where, is the battery storage capacity, is the charge-discharge current, is the continuous charge-discharge time, and are the charge-discharge times at the upper and lower limits of the battery voltage range respectively.

[0118] By calculating the real-time update of the battery storage capacity, it can be determined that the battery will not be affected by the external environment during use, such as the charge-discharge rate, temperature, battery health status, etc. If the battery capacity is not updated in real time, it may lead to overcharging or over-discharging, which not only affects the battery life but may also violate grid regulations or cause economic losses. Updating the battery capacity in real time can ensure that the dispatching system accurately understands the current available battery capacity, which is crucial for making correct charge-discharge decisions.

[0119] The logic for determining the overall dispatching strategy of the dispatching system based on the predicted future power generation, the real-time battery storage, and the future device power consumption is as follows;

[0120]

[0121] Where, is the predicted remaining power generation of the photovoltaic panel, is the predicted power generation of the photovoltaic panel, is the real-time battery storage, is the future device power consumption, is the battery storage capacity;

[0122] When , that is, when the predicted power generation is greater than the remaining battery storage and the future device power consumption, the main dispatching is to the power grid;

[0123] When , that is, when the predicted power generation is less than the remaining battery storage and the future device power consumption, the main dispatching is first from the power grid, making , and then to the power grid dispatching.

[0124] Step 3: When dispatching from the power grid, preferentially dispatch electricity from the power grid. Determine the dispatching cycle of the photovoltaic energy storage system according to the full charge and discharge time of the battery, obtain the maximum charge and discharge power of the battery, establish a pre-dispatch model, and perform pre-dispatch on the electricity.

[0125] In the dispatching of the photovoltaic energy storage system, usually taking a day as the dispatching unit, because the output of photovoltaic power generation is directly affected by sunlight, and this influence shows obvious daily periodicity. During the day, the sunlight is strong and the power generation of the photovoltaic panel is high, while at night there is no sunlight and the power generation is zero. Dispatching the photovoltaic energy storage system in units of days can better match the natural cycle of power generation and consumption.

[0126] If the dispatching cycle does not match the optimal charge and discharge cycle of the battery, it may lead to the battery being full and unable to continue storing electricity during the peak period of photovoltaic power generation, or the battery having insufficient power during the peak demand period and being unable to effectively utilize the stored energy, resulting in energy waste. An unreasonable dispatching cycle will cause the charge and discharge frequency of the battery to often work in a non-ideal state, such as quickly charging from low power to high power frequently, or the battery being unable to be deeply discharged or charged for a long time. This will affect the charging efficiency of the battery, increase energy loss, and at the same time affect the energy balance and stability of the entire system.

[0127] In this embodiment, the method for determining the dispatching cycle is;

[0128]

[0129] Among them, is the dispatching cycle, and are the charge and discharge times of the upper and lower limits of the battery voltage safety respectively;

[0130] The specific method for establishing the pre-dispatch model is;

[0131] Taking the electricity stored in the battery, the electricity released by the battery, and the electricity dispatched from the power grid as decision variables, and taking the minimum cost of dispatching from the power grid as the goal, construct the objective function as;

[0132]

[0133] Among them, is the minimum cost value, is the peak electricity price at time is the valley electricity price at time is the electricity quantity of the peak electricity price dispatched from the power grid at time is The electricity quantity at the valley electricity price dispatched from the power grid at time is the dispatching period, is the time within the model dispatching period, and are all positive integers;

[0134] The constraint conditions are;

[0135] The limitation of the battery capacity;

[0136]

[0137] Among them, is the real-time electricity quantity of the battery at time is the battery storage capacity;

[0138] The battery energy balance constraint;

[0139]

[0140] Among them, is the real-time electricity quantity of the battery at time is the real-time electricity quantity of the battery at time is the electricity quantity charged into the battery at time is the electricity quantity released by the battery at time;

[0141] The power balance constraint;

[0142]

[0143] Among them, is the power generation of the photovoltaic panel at time is the electricity quantity at the peak electricity price dispatched from the power grid at time is the electricity quantity at the valley electricity price dispatched from the power grid at time is the electricity quantity released by the battery at time is the electricity consumption of the equipment at time is the electricity quantity charged into the battery at time;

[0144] The battery charge and discharge power constraint;

[0145]

[0146]

[0147] Among them, is the amount of electricity charged into the battery at time is the maximum charging power of the battery, is the amount of electricity charged into the battery at time is the maximum discharging power of the battery;

[0148] Equipment power consumption constraint condition;

[0149]

[0150] Among them, is the amount of electricity at peak electricity price dispatched from the power grid at time is the amount of electricity at valley electricity price dispatched from the power grid at time is the real-time battery power at time is the power generation of the photovoltaic panel at time is the equipment power consumption at time

[0151] The pre-scheduling model is used to obtain the necessary electricity from the power grid when the battery storage capacity and power generation of the system are insufficient to meet the current electricity demand, which ensures the continuity and reliability of the energy supply and prevents power outages or unstable equipment operation caused by insufficient energy.

[0152] During the scheduling of the photovoltaic energy storage system, various problems faced are unified into the same scheduling mode, which can effectively reduce the decision-making complexity and conflicts during the execution process, simplify the operation and management of the system, make the system easier to monitor and maintain, reduce the management cost and the possibility of errors. At the same time, the system can quickly respond to the fluctuations of photovoltaic panel power generation and power demand. This fast response ability is particularly important for utilizing the instability of photovoltaic power generation and the dynamic changes of power grid demand.

[0153] Step 4: When dispatching to the power grid, according to the predicted photovoltaic power generation, future equipment power consumption, and at the same time using the remaining battery storage capacity and the peak-valley prices of the power grid, establish the first scheduling model to dispatch the electricity.

[0154] Linear programming is a powerful tool for dealing with optimization problems with linear constraints and linear objective functions. In the scheduling problem of photovoltaic energy storage systems, the objective function is usually to maximize profits or minimize costs. This can be expressed through linear relationships such as electricity price, power generation, battery capacity, and battery charge and discharge rate. New constraints (such as battery life, safe operating range, etc.) and objectives can also be easily added to better adapt to complex electricity markets and technological advances. It can also effectively solve the problems caused by the need for photovoltaic energy storage systems to quickly respond to electricity demand and weather changes, ensuring system stability.

[0155] In this embodiment, the specific method of establishing the first scheduling model is:

[0156] The decision variables are the amount of electricity charged into the battery, the amount of electricity discharged from the battery, the amount of electricity dispatched to the grid, and the amount of electricity dispatched from the grid. By adopting the strategy of selling high and buying low, the economic benefits of the system during operation are improved, with the goal of maximizing the profit from selling to the grid. The objective function is constructed as follows:

[0157]

[0158] in, is the maximum profit value, for Peak electricity price at the time of for The valley electricity price at the time, for The amount of electricity dispatched to the grid at any given time, for The amount of electricity dispatched from the power grid at any time, is the scheduling period, is the time in the model scheduling cycle, are all positive integers;

[0159] The constraints are;

[0160] Battery capacity limitations;

[0161]

[0162] in, for Real-time battery power at all times, Storage capacity for batteries;

[0163] The battery does not store more than its maximum capacity.

[0164] Battery energy balance constraints;

[0165]

[0166] in, is the real-time battery power at time is the real-time battery power at time is the power charged into the battery at time is the power released by the battery at time;

[0167] Power balance constraint;

[0168]

[0169] Among them, is the power generation of the photovoltaic panel at time is the power dispatched from the power grid at time is the power released by the battery at time is the power consumption of the equipment at time is the power dispatched to the power grid at time is the power charged into the battery at time;

[0170] Battery charge and discharge power constraint;

[0171]

[0172]

[0173] Among them, is the power charged into the battery at time is the maximum charging power of the battery is the power charged into the battery at time is the maximum charging power of the battery

[0174] Equipment power consumption constraint condition;

[0175]

[0176] Among them, is the real-time battery power at time is the power generation of the photovoltaic panel at time is the power consumption of the equipment at time.

[0177] The first scheduling model makes plans before the battery charging and discharging time as the cycle, which can effectively predict and manage risks. For example, by predicting the photovoltaic power generation and market electricity price after the cycle, the system can optimize the scheduling use among photovoltaic power generation, battery storage and the power grid, avoiding possible losses or shortages.

[0178] By optimizing the use of power generation and energy storage equipment, the first scheduling model helps to increase economic benefits. The system can adjust the purchase and sale of electricity according to the peak-valley electricity price of the power grid to maximize profits. At the same time, it helps the power system operator to predict and adjust the power generation and consumption, ensuring the stability and reliability of the power grid. Through predictive scheduling, the supply and demand can be balanced and the load fluctuation of the power grid can be reduced.

[0179] Step 5: Based on the real-time photovoltaic power generation, real-time equipment power consumption, and real-time power grid price, taking the scheduling time period of the first scheduling model as the scheduling cycle, establish the first scheduling optimization model and optimize and adjust the scheduling result of the first scheduling model.

[0180] The first scheduling model makes plans based on predicted data, but prediction deviations may occur in actual operation. For example, weather changes may cause the photovoltaic power generation to deviate from the predicted value, or the actual power grid demand fluctuates. The first optimization model can adjust the strategy according to real-time data and optimize the system response. By optimizing the photovoltaic power generation and battery charging and discharging strategies in real time, the first optimization model can make decisions on buying and selling electricity according to the real-time fluctuations of photovoltaic real-time power generation, battery storage power, equipment power consumption and electricity price, thus further improving the economic return. The first scheduling model can respond more flexibly to equipment failures, market emergencies or other unexpected events by frequently updating the operation plan, ensuring the stable operation of the system.

[0181] In this embodiment, the specific method for establishing the first scheduling optimization model is as follows;

[0182] Construct the objective function as;

[0183]

[0184] Where, is the maximum profit value within the time period ; is the real-time peak electricity price at time ; is the real-time valley electricity price at time <s ; is the electricity quantity dispatched to the power grid at time ; is the time within the first scheduling optimization model, both are positive integers;

[0185] is one hour, is the time within the first scheduling optimization model to facilitate capturing and adjusting unforeseen changes in the first scheduling model.

[0186] The constraint conditions are;

[0187] The limit of battery capacity;

[0188]

[0189] Among them, is the real-time battery power at time is the battery storage capacity, is the real-time power consumption of the device at time

[0190] The battery energy balance constraint;

[0191]

[0192] Among them, is the real-time battery power at time is the real-time battery power at time is the power charged into the battery at time is the power released from the battery at time

[0193] The power balance constraint;

[0194]

[0195] Among them, is the real-time power generation of the photovoltaic panel at time is the power dispatched from the power grid at time is the power released from the battery at time is the real-time power consumption of the device at time is the power dispatched to the power grid at time is the power charged into the battery at time

[0196] The battery charge and discharge power constraint;

[0197]

[0198] Among them, is the power released by the battery at time is the total battery storage capacity;

[0199] Device power consumption constraint condition;

[0200]

[0201] Among them, is the real-time battery power at time is the real-time power generation of the photovoltaic panel at time is the device power consumption at time

[0202] Please refer to Figure 2 , this invention also provides a scheduling system for a photovoltaic energy storage system, and the scheduling system for the photovoltaic energy storage system is used to execute the above-mentioned scheduling method for the photovoltaic energy storage system, including;

[0203] Data acquisition module, the data acquisition module is used to obtain real-time and future light and temperature data, battery real-time storage capacity, future device power consumption, battery voltage range, charge and discharge current, continuous charge and discharge time, and maximum charge and discharge power in the area where the photovoltaic panel is located in the photovoltaic energy storage system, and real-time and future grid prices; [[ID=4))

[0204] Scheduling decision module, the scheduling decision module calculates and updates the storage capacity of the battery in real time, and determines the overall scheduling strategy of the scheduling system according to the predicted power generation of the photovoltaic panel;

[0205] Pre-scheduling module, the pre-scheduling module determines the scheduling cycle of the photovoltaic energy storage system according to the full charge and discharge time of the battery, obtains the maximum charge and discharge power of the battery, establishes a pre-scheduling model, and pre-schedules the power;

[0206] First scheduling module, the first scheduling module establishes a first scheduling model according to the predicted power generation of the photovoltaic panel and future device power consumption, and at the same time uses the remaining storage capacity of the battery and the peak-valley price of the power grid to schedule the power;

[0207] Scheduling optimization module, the scheduling optimization module establishes a first scheduling optimization model with the scheduling time period of the first scheduling model as the scheduling cycle according to the real-time power generation of the photovoltaic panel, real-time device power consumption, and real-time grid price, and optimizes and adjusts the scheduling result of the first scheduling model.

[0208] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0209] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or by the combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0210] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0211] As mentioned above, the above are only the specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered within the protection scope of this application.

Claims

1. A scheduling method for a photovoltaic energy storage system, characterized in that: The specific steps include: Step 1: Obtain the rated power generation parameters of the photovoltaic panels in the photovoltaic energy storage system, the future light intensity and temperature data of the area where the photovoltaic panels are located, and predict the future power generation of the photovoltaic panels; Step 2: Obtain the real-time battery storage capacity of the photovoltaic energy storage system, future equipment power consumption, battery voltage range, charge and discharge current, and continuous charge and discharge time. By performing real-time updates on the battery storage capacity and determining the overall scheduling strategy of the scheduling system based on the predicted power generation of the photovoltaic panels; Step 3: When dispatching from the grid, prioritize dispatching power from the grid. Determine the dispatch cycle of the PV energy storage system based on the battery's full charge and discharge time, obtain the battery's maximum charge and discharge power, establish a pre-dispatch model, and pre-dispatch power. Step 4: When dispatching to the grid, a first dispatch model is established based on the predicted photovoltaic power generation, future equipment power consumption, and the remaining battery storage capacity and peak and valley prices of the grid to dispatch the power; Step 5: Based on the real-time photovoltaic power generation, real-time equipment power consumption, and real-time grid prices, the first scheduling optimization model is established with the scheduling period of the first scheduling model as the scheduling period, and the scheduling results of the first scheduling model are optimized and adjusted.

2. A scheduling method for a photovoltaic energy storage system according to claim 1, characterized in that: The calculation method for predicting the future power generation of photovoltaic panels is: in, is the predicted power generation of the photovoltaic panels, is the rated power generation of the photovoltaic panel, 、 are the standard light intensity and temperature under the rated power generation of the photovoltaic panel, is the future light intensity, For future temperatures, is the temperature coefficient.

3. The scheduling method of a photovoltaic energy storage system according to claim 1, characterized in that: The formula for calculating the real-time update of the battery storage capacity is: in, is the battery storage capacity, is the charge and discharge current, is the continuous charge and discharge time, and are the charge and discharge time at the upper and lower limits of the battery voltage range respectively; The logic of determining the overall dispatching strategy of the dispatching system based on the future predicted power generation, real-time battery storage capacity and future equipment power consumption is as follows; in, is the predicted remaining power generation of the photovoltaic panels, is the predicted power generation of the photovoltaic panels, The real-time storage capacity of the battery. For future equipment power consumption, Storage capacity for batteries; when When the power generation is predicted to be greater than the remaining battery storage and future equipment power consumption, the grid is mainly dispatched; when When the predicted power generation is less than the remaining battery storage and future equipment power consumption, the grid dispatch is the main method to make , and then dispatch to the power grid.

4. The scheduling method of a photovoltaic energy storage system according to claim 1, characterized in that: The method for determining the scheduling period is: in, is the scheduling period, and are the charge and discharge times for the upper and lower safety limits of the battery voltage respectively; The specific method of establishing the pre-scheduling model is: With the battery storage capacity, battery release capacity, and power dispatched from the power grid as decision variables, and the goal of minimizing the cost of dispatching from the power grid, the objective function is constructed as follows; in, is the minimum cost value, for Peak electricity price at the time of for The valley electricity price at the time, for The peak electricity price of electricity dispatched from the power grid at any time, for The amount of electricity at the valley price dispatched from the power grid at any given moment, is the scheduling period, is the time in the model scheduling cycle, are all positive integers; The constraints are; Battery capacity limitations; in, for Real-time battery power at all times, Storage capacity for batteries; Battery energy balance constraints; in, for Real-time battery power at all times, for Real-time battery power at all times, for The amount of electricity charged to the battery at any moment, for The amount of electricity released by the battery at each moment; Power balance constraints; in, for The power generation of photovoltaic panels at any moment, for The peak electricity price of electricity dispatched from the power grid at any time, for The amount of electricity at the valley price dispatched from the power grid at any given moment, for The amount of electricity released by the battery at any moment, for The power consumption of the device at all times, for The amount of electricity charged to the battery at any moment; Battery charge and discharge power constraints; in, for The amount of electricity charged into the battery at any moment, is the maximum charging power of the battery, for The amount of electricity charged to the battery at any moment, is the maximum discharge power of the battery; Equipment power consumption constraints; in, for The peak electricity price of electricity dispatched from the power grid at any time, for The amount of electricity at the valley price dispatched from the power grid at any given moment, for Real-time battery charge at all times, for The power generation of photovoltaic panels at any moment, for The power consumption of the device at any moment.

5. The scheduling method of a photovoltaic energy storage system according to claim 1, characterized in that: The specific method of establishing the first scheduling model is: The decision variables are the amount of electricity charged into the battery, the amount of electricity discharged from the battery, the amount of electricity dispatched to the grid, and the amount of electricity dispatched from the grid. By adopting the strategy of selling high and buying low, the economic benefits of the system during operation are improved, with the goal of maximizing the profit from selling to the grid. The objective function is constructed as follows: in, is the maximum profit value, for Peak electricity price at the time of for The valley electricity price at the time, for The amount of electricity dispatched to the grid at any given time, for The amount of electricity dispatched from the power grid at any time, is the scheduling period, is the time in the model scheduling cycle, are all positive integers; The constraints are; Battery capacity limitations; in, for Real-time battery power at all times, Storage capacity for batteries; Battery energy balance constraints; in, for Real-time battery power at all times, for Real-time battery power at all times, for The amount of electricity charged in the battery at any moment, for The amount of electricity released by the battery at each moment; Power balance constraints; in, for The power generation of photovoltaic panels at any moment, for The amount of electricity dispatched from the power grid at any time, for The amount of electricity released by the battery at any moment, for The power consumption of the device at all times, for The amount of electricity dispatched to the grid at any given time, for The amount of electricity charged to the battery at any moment; Battery charge and discharge power constraints; in, for The amount of electricity charged in the battery at any moment, is the maximum charging power of the battery, for The amount of electricity charged in the battery at any moment, Maximum charging power for the battery Equipment power consumption constraints; in, for Real-time battery power at all times, for The power generation of photovoltaic panels at any moment, for The power consumption of the device at any moment.

6. The photovoltaic energy storage system scheduling method according to claim 1, characterized in that: The specific method of establishing the first scheduling optimization model is: The objective function is constructed as; in, For time period The maximum profit value within for Real-time peak electricity price at the moment, for Real-time valley electricity price at the moment, for The amount of electricity dispatched to the grid at any given time, for The amount of electricity dispatched from the power grid at any time, is the time period within the scheduling cycle of the first scheduling model, is the moment in the first scheduling optimization model, are all positive integers; For one hour, Optimizing the moments within the model for the first schedule in order to capture and adjust for changes not foreseen in the first schedule model; The constraints are; Battery capacity limitations; in, for Real-time battery power at all times, is the battery storage capacity, for Real-time power consumption of equipment at all times; Battery energy balance constraints; in, for Real-time battery power at all times, for Real-time battery power at all times, for The amount of electricity charged in the battery at any moment, for The amount of electricity released by the battery at each moment; Power balance constraints; in, for The real-time power generation of photovoltaic panels at every moment, for The amount of electricity dispatched from the power grid at any time, for The amount of electricity released by the battery at any moment, for Real-time power consumption of the device at all times, for The amount of electricity dispatched to the grid at any given time, for The amount of electricity charged to the battery at any moment; Battery charge and discharge power constraints; in, for The amount of electricity released by the battery at any moment, is the total amount of battery storage; Equipment power consumption constraints; in, for Real-time battery power at all times, for The real-time power generation of photovoltaic panels at every moment, for The power consumption of the device at any moment.

7. A scheduling system for a photovoltaic energy storage system, characterized by: The scheduling of the photovoltaic energy storage system is used to execute the scheduling method of the photovoltaic energy storage system according to any one of claims 1 to 6, comprising: A data acquisition module, which is used to obtain real-time and future light and temperature data for the area where the photovoltaic panels in the photovoltaic energy storage system are located, real-time battery storage capacity, future equipment power consumption, battery voltage range, charge and discharge current, continuous charge and discharge time, maximum charge and discharge power, and real-time and future grid prices; A scheduling decision module, which calculates the storage capacity of the battery in real time and determines the overall scheduling strategy of the scheduling system based on the predicted power generation of the photovoltaic panels; A pre-scheduling module, which determines the scheduling period of the photovoltaic energy storage system based on the battery's full charge and discharge time, obtains the battery's maximum charge and discharge power, establishes a pre-scheduling model, and pre-schedules the power; a first scheduling module, which establishes a first scheduling model to schedule electricity based on the predicted photovoltaic power generation, future equipment power consumption, and the remaining battery storage capacity and peak and valley prices of the power grid; The scheduling optimization module establishes a first scheduling optimization model based on the real-time photovoltaic power generation, real-time equipment power consumption, and real-time grid prices, with the scheduling time period of the first scheduling model as the scheduling cycle, and optimizes and adjusts the scheduling results of the first scheduling model.

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