Scheduling method and system of photovoltaic energy storage system

Abstract

The invention provides a scheduling method and system for a photovoltaic energy storage system, and relates to the technical field of photovoltaic energy storage, and the method comprises the steps: obtaining the rated power generation parameters of a photovoltaic panel in the photovoltaic energy storage system, and the future illumination and temperature data of an area where the photovoltaic panel is located, and predicting the future power generation amount of the photovoltaic panel; the overall scheduling strategy of the scheduling system is determined according to the obtained battery real-time storage capacity of the energy storage system, the future equipment electricity consumption and the predicted generating capacity of the photovoltaic panel, when the future generating capacity of the photovoltaic panel cannot meet the electricity demand, the electricity is scheduled from the power grid preferentially, and a pre-scheduling model is established; when the future generating capacity of the photovoltaic panel can meet the requirement, the residual generating capacity of the photovoltaic panel is sent to the power grid, scheduling is carried out between the residual storage capacity of the energy storage system and the power grid, the peak valley pressure of the power grid is relieved, and by establishing a first scheduling model, the electric quantity is scheduled, and a first scheduling optimization model is established; and optimizing and adjusting 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 in particular to a scheduling method and system for a photovoltaic energy storage system. Background Art

[0002] In the past few decades, photovoltaics have developed rapidly. New power storage systems based on photovoltaics have the advantages of clean and low-carbon, safe and reliable, open and flexible, and strong adaptability, which can meet the needs of future power development. However, the intermittent and volatile nature of photovoltaic power generation has brought huge challenges to the dispatch and consumption of power systems, and traditional dispatching models are difficult to effectively cope with.

[0003] In the prior art, 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 allocation plan based on the predicted energy output and predicted load demand of the photovoltaic energy storage system for different time periods and under different preset conditions; selecting an energy configuration scheme from the energy allocation plan, controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the energy configuration scheme, and collecting the operating 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, which is easily affected by emergencies, and a large number of scheduling plans need to be generated based on a large amount of data.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a scheduling method and system for a photovoltaic energy storage system to solve the problems raised in the above background technology.

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

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

[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 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, calculate the storage capacity in real time through the battery, and determine the overall dispatching strategy of the dispatching system based on the predicted power generation of the photovoltaic panels;

[0010] Step 3: When dispatching from the power grid, the power is dispatched from the power grid first. According to the battery's full charge and discharge time, the dispatch cycle of the photovoltaic energy storage system is determined, the maximum charge and discharge power of the battery is obtained, and a pre-dispatch model is established to pre-dispatch the power.

[0011] Step 4: When dispatching to the grid, a first dispatching model is established to dispatch the electricity based on the predicted photovoltaic power generation, future equipment power consumption, and the remaining storage capacity of the battery and the peak and valley prices of the grid;

[0012] 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 time period of the first scheduling model as the scheduling cycle, and the scheduling results of the first scheduling model are optimized and adjusted.

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

[0014]

[0015] Among them, PGFY is the predicted power generation of the photovoltaic panel, PG is the rated power generation of the photovoltaic panel, G g , T b are the standard light intensity and temperature under the rated power generation of the photovoltaic panel, GZ is the future light intensity, T w is the future temperature, and γ is the temperature coefficient.

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

[0017]

[0018] Wherein, CZ is the battery storage capacity, I is the charge and discharge current, tin is the continuous charge and discharge time, Vmax,tin and Vmin,tin are the charge and discharge time at the upper and lower limits of the battery voltage range respectively;

[0019] The logic of 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] PGS=PGFY―CZ―CS―PSB

[0021] Among them, PGS is the predicted remaining power generation of the photovoltaic panel, PGFY is the predicted power generation of the photovoltaic panel, CS is the real-time storage capacity of the battery, PSB is the future power consumption of the equipment, and CZ is the battery storage capacity;

[0022] When PGS ≥ 0, that is, when the predicted power generation is greater than the remaining battery storage and future equipment power consumption, dispatch to the grid is the main approach;

[0023] When PGS<0, that is, the predicted power generation is less than the remaining battery storage and future equipment power consumption, the grid will be dispatched first to make PGS≥0, and then dispatch to the grid.

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

[0025]

[0026] Among them, T is the scheduling period, Vmax,tin and Vmin,tin are the charging and discharging time of the upper and lower limits of the battery voltage safety respectively;

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

[0028] The power stored in the battery, the power released by the battery, and the power dispatched from the power grid are used as decision variables, and the goal is to minimize the dispatch cost from the power grid. The objective function is constructed as follows;

[0029]

[0030] Among them, MAXQ is the minimum cost value, hq t is the peak electricity price at time t, lq t is the valley electricity price at time t, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the amount of electricity at the valley price dispatched from the power grid at time t, T is the dispatch period, t is the time period in the model, and both T and t are positive integers;

[0031] The constraints are adjusted as;

[0032] Battery capacity limitations;

[0033] 0≤CS t ≤CZ

[0034] Among them, CS t is the real-time battery power at time t, and CZ is the battery storage capacity;

[0035] Battery energy balance constraints;

[0036] CS t+1 =CS t +(PCI t ―PCO t )

[0037] Among them, CS t+1 is the real-time battery power at time t+1, CSt is the real-time battery power at time t, PCI t is the amount of electricity charged into the battery at time t, PCO t is the amount of electricity released by the battery at time t;

[0038] Power balance constraints;

[0039] PGF t +PH t +PK t +PCO t =PSB t +PCI t

[0040] Among them, PGF t is the power generation of the photovoltaic panel at time t, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the amount of electricity at the valley price dispatched from the power grid at time t, PCO t is the amount of electricity released by the battery at time t, PSB t is the power consumption of the device at time t, PCI t is the amount of electricity charged into the battery at time t;

[0041] Battery charge and discharge power constraints;

[0042]

[0043] Among them, PCI t is the amount of power charged into the battery at time t, PCI v is the maximum charging power of the battery, PCO t is the amount of electricity charged into the battery at time t, PCO v is the maximum discharge power of the battery;

[0044] Equipment power consumption constraints;

[0045] PH t +PK t +CS t +PGF t >PSB t

[0046] Among them, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the valley price electricity quantity dispatched from the power grid at time t, CS t is the real-time battery power at time t, PGF t is the power generation of the photovoltaic panel at time t, PSB t is the power consumption of the equipment at time t;.

[0047] Furthermore, the specific method of establishing the first scheduling model is:

[0048] The amount of electricity charged into the battery, the amount of electricity released by the battery, the amount of electricity dispatched to the power grid, and the amount of electricity dispatched from the power grid are used as decision variables. The economic benefits of the system during operation are improved through the strategy of selling high and buying low. The goal is to maximize the profit from selling to the power grid. The objective function is constructed as follows;

[0049]

[0050] Among them, MAXQ is the maximum profit value, hq t is the peak electricity price at time t, lq t is the valley electricity price at time t, PM t is the amount of electricity dispatched to the grid at time t, PN t is the amount of electricity dispatched from the power grid at time t, T is the dispatch period, t is the time period in the model, and both T and t are positive integers;

[0051] The constraints are adjusted as;

[0052] Battery capacity limitations;

[0053] 0≤CS t ≤CZ

[0054] Among them, CS t is the real-time battery power at time t, and CZ is the battery storage capacity;

[0055] Battery energy balance constraints;

[0056] CS t+1 =CS t +(PCI t ―PCO t )

[0057] Among them, CS t+1 is the real-time battery power at time t+1, CS t is the real-time battery power at time t, PCI t is the amount of electricity charged into the battery at time t, PCO t is the amount of electricity released by the battery at time t;

[0058] Power balance constraints;

[0059] PGF t +PN t +PCO t =PSB t +PM t +PCI t

[0060] Equipment power consumption constraints;

[0061] Among them, PGF t is the power generation of the photovoltaic panel at time t, PN t is the amount of electricity dispatched from the grid at time t, PCO t is the amount of electricity released by the battery at time t, PSB t is the power consumption of the equipment at time t, PM t is the amount of electricity dispatched to the grid at time t, PCI t is the amount of electricity charged into the battery at time t;

[0062] Battery charge and discharge power constraints;

[0063]

[0064] Among them, PCI t is the amount of power charged into the battery at time t, PCI v is the maximum charging power of the battery, PCO t is the amount of electricity charged into the battery at time t, PCO v Maximum charging power for the battery

[0065] Equipment power consumption constraints;

[0066] CS t +PGF t >PSB t

[0067] Among them, CS t is the real-time battery power at time t, PGF t is the power generation of the photovoltaic panel at time t, PSB t is the power consumption of the equipment at time t.

[0068] Furthermore, the specific method of establishing the scheduling optimization model is:

[0069] The objective function is constructed as;

[0070]

[0071] Among them, MAXQ t is the maximum profit value, hq i is the real-time peak electricity price at time i, lq i is the real-time valley electricity price at time i, PM i is the amount of electricity dispatched to the grid at time i, PN i is the amount of electricity dispatched from the power grid at time i, t is the time period in the model, i is the time period in the optimal dispatch model, and both i and t are positive integers;

[0072] t is one hour, i is the time period within the scheduling cycle, and the specific time length is 15 minutes, so as to capture and adjust the changes that were not foreseen in the first scheduling model;

[0073] The constraints are adjusted as;

[0074] Battery capacity limitations;

[0075] PSB i ≤CS i ≤CZ

[0076] Among them, CS i is the real-time battery power at time i, CZ is the battery storage capacity, PSB i The real-time power consumption of the device at time i;

[0077] Battery energy balance constraints;

[0078] CS i+1 =CS i +(PCI i ―PCO i )

[0079] Among them, CS i+1 is the real-time battery power at time i+1, CS i is the real-time battery power at time i, PCI i is the amount of electricity charged into the battery at time i, PCO i is the amount of electricity released by the battery at moment i;

[0080] Power balance constraints;

[0081] PGF i +PN i +PCO i =PSB i +PM i +PCI i

[0082] Among them, PGF i is the real-time power generation of the photovoltaic panel at time i, PN i is the amount of electricity dispatched from the power grid at time i, PCO i is the amount of electricity released by the battery at time i, PSB i is the real-time power consumption of the device at time i, PM i is the amount of electricity dispatched to the grid at time i, PCI i is the amount of electricity charged into the battery at time i;

[0083] Battery charging and discharging power constraints;

[0084]

[0085] Among them, PCO i is the amount of electricity released by the battery at time i, and CZ is the total amount of battery storage;

[0086] Equipment power consumption constraints;

[0087] CS i +PGF i >PSB i

[0088] Among them, CS i is the real-time battery power at time i, PGF i is the real-time power generation of the photovoltaic panel at time i, PSB i is the power consumption of the device at time i.

[0089] The present invention further provides a scheduling system for a photovoltaic energy storage system, wherein the scheduling system for a photovoltaic energy storage system is used to execute the above-mentioned scheduling method for a photovoltaic energy storage system, including:

[0090] A data acquisition module, which is used to obtain real-time and future light and temperature data of the area where the photovoltaic panels in the photovoltaic energy storage system are located, the real-time storage capacity of the battery, the future power consumption of the equipment, 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;

[0091] A scheduling decision module, which performs real-time update calculations on the storage capacity through the battery and determines the overall scheduling strategy of the scheduling system according to the predicted power generation of the photovoltaic panels;

[0092] 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;

[0093] A first scheduling module, wherein the first scheduling module establishes a first scheduling model to schedule electricity according to the predicted photovoltaic power generation and future equipment power consumption, and utilizes the remaining storage capacity of the battery and the peak and valley prices of the power grid;

[0094] 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 price, and takes 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.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] The present invention predicts the future power generation of photovoltaic panels by acquiring the rated power generation parameters of photovoltaic panels in the photovoltaic energy storage system and the future light and temperature data of the area where the photovoltaic panels are located. The overall scheduling strategy of the scheduling system is determined based on the real-time storage capacity of the battery of the energy storage system and the future power consumption of the equipment and the predicted power generation of the photovoltaic panels. When the future power generation of the photovoltaic panels cannot meet the power demand, power is preferentially scheduled from the power grid and a pre-scheduling model is established. When the future power generation of the photovoltaic panels can meet the demand, the remaining power generation of the photovoltaic panels is sent to the power grid, and the remaining storage capacity of the energy storage system is used to schedule with the power grid to alleviate the peak and valley pressure of the power grid. By establishing a first scheduling model, the power is scheduled, and a first scheduling optimization model is established to optimize and adjust the scheduling results of the first scheduling model.

[0097] The present invention can more accurately adjust the energy flow of the photovoltaic system based on the prediction and analysis of the relationship between the power generation of the photovoltaic panel during the day and the battery storage capacity and the equipment usage. Through the pre-scheduling model, the system will give priority to obtaining the necessary electricity from the power grid to meet the electricity demand when the battery storage capacity and power generation are insufficient. Then, the various problems faced by the photovoltaic energy storage system are converted into the same scheduling problem, and unified processing is performed by establishing a first scheduling model, and then a first scheduling optimization model is established to optimize and adjust the scheduling results of the first scheduling model, thereby ensuring the continuity and reliability of energy supply, preventing power outages or unstable equipment operation caused by insufficient energy, and unifying the various problems faced in the process of scheduling the photovoltaic energy storage system into the same scheduling mode, which can effectively reduce the complexity of decision-making and conflicts in the execution process, simplify the operation and management of the system, and make 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, so that the system can be flexibly adjusted according to actual needs, effectively solving the problem of lack of long-term stability and predictability of the system due to the instability of the photovoltaic system itself, which leads to unstable and inaccurate scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0099] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0100] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0101] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0102] Example:

[0103] See also Figure 1 , the present invention provides a technical solution:

[0104] A scheduling method for a photovoltaic energy storage system, the specific steps comprising:

[0105] 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.

[0106] By predicting the power generation of photovoltaic panels, the dispatch system can plan the battery charging and discharging cycle according to the expected power output. This not only ensures that the battery storage is maximized when the power supply is sufficient, but also reasonably allocates the stored power when the power supply is insufficient, and plans the power dispatch in advance to balance supply and demand.

[0107] Photovoltaic panels work by generating electric current by absorbing photons in sunlight. Therefore, light intensity is the most direct and important factor that determines the power generation performance of photovoltaic panels. 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 photovoltaic panels. The efficiency of photovoltaic panels usually decreases at higher temperatures. This is because the internal resistance of photovoltaics increases with increasing temperature, thereby reducing the output of electrical energy. Accurately predicting temperature changes can help schedulers understand the output changes of photovoltaic panels under different temperature conditions, so as to more reasonably adjust the power supply and storage plan.

[0108] By fully considering the two key variables of light and temperature, the photovoltaic energy storage system can achieve more efficient and reliable power generation forecasts, thereby better realizing the dual optimization of energy and economic benefits.

[0109] In this embodiment, the calculation method for predicting the future power generation of the photovoltaic panel is:

[0110]

[0111] Among them, PGFY is the predicted power generation of the photovoltaic panel, PG is the rated power generation of the photovoltaic panel, G g , T b are the standard light intensity and temperature under the rated power generation of the photovoltaic panel, GZ is the future light intensity, T w is the future temperature, and γ is the temperature coefficient.

[0112] 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, calculate the storage capacity in real time through the battery, and determine the overall scheduling strategy of the scheduling system based on the predicted power generation of the photovoltaic panels.

[0113] By predicting and analyzing the relationship between the power generation of photovoltaic panels during the day and the battery storage and equipment power, the energy flow of the photovoltaic system can be adjusted more accurately to ensure that each unit of power generation is used most effectively and waste is reduced. Failure to consider the relationship between the power generation of photovoltaic panels during the day and the battery storage and equipment usage may result in excess electricity during photovoltaic power generation periods not being effectively utilized, or the battery not being fully charged during low-demand periods.

[0114] In this embodiment, the formula for calculating the real-time update of the battery storage capacity is:

[0115]

[0116] Wherein, CZ is the battery storage capacity, I is the charge and discharge current, tin is the continuous charge and discharge time, Vmax,tin and Vmin,tin are the charge and discharge time at the upper and lower limits of the battery voltage range respectively.

[0117] By calculating the battery storage capacity in real time, it can be determined that the battery will not be affected by the external environment when in use, such as charge and discharge rate, temperature, battery health, etc. If the battery capacity is not updated in real time, it may cause overcharging or discharging, which not only affects the battery life, but may also violate grid regulations or cause economic losses. Real-time updating of battery capacity can ensure that the dispatch system accurately understands the current available battery capacity, which is crucial to making correct charging and discharging decisions.

[0118] The logic of 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;

[0119] PGS=PGFY―CZ―CS―PSB

[0120] Among them, PGS is the predicted remaining power generation of the photovoltaic panel, PGFY is the predicted power generation of the photovoltaic panel, CS is the real-time storage capacity of the battery, PSB is the future power consumption of the equipment, and CZ is the battery storage capacity;

[0121] When PGS ≥ 0, that is, when the predicted power generation is greater than the remaining battery storage and future equipment power consumption, dispatch to the grid is the main approach;

[0122] When PGS<0, that is, the predicted power generation is less than the remaining battery storage and future equipment power consumption, the grid is dispatched first to make PGS≥0, and then dispatched to the grid.

[0123] Step 3: When dispatching from the power grid, priority is given to dispatching electricity from the power grid. According to the full charge and discharge time of the battery, the dispatch cycle of the photovoltaic energy storage system is determined, the maximum charge and discharge power of the battery is obtained, and a pre-dispatch model is established to pre-dispatch the electricity.

[0124] In the scheduling of photovoltaic energy storage systems, days are usually used as the scheduling unit. This is because the output of photovoltaic power generation is directly affected by sunlight. This influence shows an obvious daily periodicity. During the day, the sunlight is strong and the photovoltaic panels generate high power. At night, there is no sunlight and the power generation is zero. Scheduling photovoltaic energy storage systems on a daily basis can better match the natural cycle of power generation and consumption.

[0125] If the scheduling cycle does not match the optimal charging and discharging cycle of the battery, the battery may be full and unable to continue storing electricity during the peak period of photovoltaic power generation, or the battery may be insufficient during the peak period of demand, and the stored energy cannot be effectively utilized, resulting in energy waste. An unreasonable scheduling cycle will cause the battery charging and discharging frequency to often operate under non-ideal conditions, such as frequent rapid charging from low power to high power, or the battery cannot be deeply discharged or charged for a long time, which will affect the battery charging efficiency, increase energy loss, and at the same time affect the energy balance and stability of the entire system.

[0126] In this embodiment, the method for determining the scheduling period is:

[0127]

[0128] Among them, T is the scheduling period, Vmax,tin and Vmin,tin are the charging and discharging time of the upper and lower limits of the battery voltage safety respectively;

[0129] The specific method of establishing the pre-scheduling model is:

[0130] The power stored in the battery, the power released by the battery, and the power dispatched from the power grid are used as decision variables, and the goal is to minimize the dispatch cost from the power grid. The objective function is constructed as follows;

[0131]

[0132] Among them, MAXQ is the minimum cost value, hq t is the peak electricity price at time t, lq t is the valley electricity price at time t, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the amount of electricity at the valley price dispatched from the power grid at time t, T is the dispatch period, t is the time period in the model, and both T and t are positive integers;

[0133] The constraints are adjusted as;

[0134] Battery capacity limitations;

[0135] 0≤CS t ≤CZ

[0136] Among them, CS t is the real-time battery power at time t, and CZ is the battery storage capacity;

[0137] Battery energy balance constraints;

[0138] CS t+1 =CS t +(PCI t ―PCO t )

[0139] Among them, CS t+1 is the real-time battery power at time t+1, CS t is the real-time battery power at time t, PCI t is the amount of electricity charged into the battery at time t, PCO t is the amount of electricity released by the battery at time t;

[0140] Power balance constraints;

[0141] PGF t +PH t +PK t +PCO t =PSB t +PCI t

[0142] Among them, PGF t is the power generation of the photovoltaic panel at time t, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the amount of electricity at the valley price dispatched from the power grid at time t, PCO t is the amount of electricity released by the battery at time t, PSB t is the power consumption of the device at time t, PCI tis the amount of electricity charged into the battery at time t;

[0143] Battery charge and discharge power constraints;

[0144]

[0145] Among them, PCI t is the amount of power charged into the battery at time t, PCI v is the maximum charging power of the battery, PCO t is the amount of electricity charged into the battery at time t, PCO v is the maximum discharge power of the battery;

[0146] Equipment power consumption constraints;

[0147] PH t +PK t +CS t +PGF t >PSB t

[0148] Among them, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the valley price electricity quantity dispatched from the power grid at time t, CS t is the real-time battery power at time t, PGF t is the power generation of the photovoltaic panel at time t, PSB t is the power consumption of the equipment at time t.

[0149] The pre-dispatch model is used to enable the system to obtain necessary electricity from the grid when the battery storage and power generation are insufficient to meet the current electricity demand. This ensures the continuity and reliability of energy supply and prevents power outages or unstable equipment operation due to insufficient energy.

[0150] Unifying the various problems faced in the process of scheduling photovoltaic energy storage systems into the same scheduling mode can effectively reduce the complexity of decision-making and conflicts in the execution process, simplify the operation and management of the system, which makes the system easier to monitor and maintain, reduces management costs and the possibility of errors. At the same time, the system can quickly respond to fluctuations in photovoltaic panel power generation and power demand. This rapid response capability is particularly important for utilizing the instability of photovoltaic power generation and the dynamic changes in grid demand.

[0151] Step 4: When dispatching to the grid, the first dispatching model is established to dispatch 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 grid.

[0152] 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, which can be expressed through linear relationships such as electricity prices, power generation, battery capacity, and battery charge and discharge rates. New constraints (such as battery life, safe operating range, etc.) and goals can also be easily added to better adapt to complex electricity markets and technological advances. It can also efficiently solve the problems caused by the need for photovoltaic energy storage systems to quickly respond to electricity demand and weather changes, and ensure the stability of the system.

[0153] In this embodiment, the specific method of establishing the first scheduling model is:

[0154] The amount of electricity charged into the battery, the amount of electricity released by the battery, the amount of electricity dispatched to the power grid, and the amount of electricity dispatched from the power grid are used as decision variables. The economic benefits of the system during operation are improved through the strategy of selling high and buying low. The goal is to maximize the profit from selling to the power grid. The objective function is constructed as follows;

[0155]

[0156] Among them, MAXQ is the maximum profit value, hq t is the peak electricity price at time t, lq t is the valley electricity price at time t, PM t is the amount of electricity dispatched to the grid at time t, PN t is the amount of electricity dispatched from the power grid at time t, T is the dispatch period, t is the time period in the model, and both T and t are positive integers;

[0157] The constraints are adjusted as;

[0158] Battery capacity limitations;

[0159] 0≤CS t ≤CZ

[0160] Among them, CS t is the real-time battery power at time t, and CZ is the battery storage capacity;

[0161] The battery does not store more than its maximum capacity.

[0162] Battery energy balance constraints;

[0163] CS t+1 =CS t +(PCI t ―PCO t )

[0164] Among them, CS t+1 is the real-time battery power at time t+1, CS tis the real-time battery power at time t, PCI t is the amount of electricity charged into the battery at time t, PCO t is the amount of electricity released by the battery at time t;

[0165] Power balance constraints;

[0166] PGF t +PN t +PCO t =PSB t +PM t +PCI t

[0167] Equipment power consumption constraints;

[0168] Among them, PGF t is the power generation of the photovoltaic panel at time t, PN t is the amount of electricity dispatched from the grid at time t, PCO t is the amount of electricity released by the battery at time t, PSB t is the power consumption of the equipment at time t, PM t is the amount of electricity dispatched to the grid at time t, PCI t is the amount of electricity charged into the battery at time t;

[0169] Battery charge and discharge power constraints;

[0170]

[0171] Among them, PCI t is the amount of power charged into the battery at time t, PCI v is the maximum charging power of the battery, PCO t is the amount of electricity charged into the battery at time t, PCO v Maximum charging power for the battery

[0172] Equipment power consumption constraints;

[0173] CS t +PGF t >PSB t

[0174] Among them, CS t is the real-time battery power at time t, PGF t is the power generation of the photovoltaic panel at time t, PSB t is the power consumption of the equipment at time t.

[0175] The first scheduling model plans ahead of the battery charging and discharging cycle, which can effectively predict and manage risks. For example, by predicting the photovoltaic power generation and market electricity prices after the cycle, the system can optimize the scheduling and use between photovoltaic power generation, battery storage and the power grid to avoid possible losses or shortages.

[0176] The first dispatch model helps increase economic benefits by optimizing the use of power generation and energy storage equipment. The system can adjust the purchase and sale of electricity according to the peak and valley electricity prices of the power grid to maximize profits. At the same time, it helps power system operators predict and adjust power generation and consumption to ensure the stability and reliability of the power grid. Through predictive dispatch, supply and demand can be balanced and load fluctuations in the power grid can be reduced.

[0177] 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 time period of the first scheduling model as the scheduling cycle, and the scheduling results of the first scheduling model are optimized and adjusted.

[0178] The first dispatch model makes plans based on forecast data, but forecast deviations may occur in actual operation. For example, weather changes may cause photovoltaic power generation to be inconsistent with the forecast value, or actual grid demand may fluctuate. The first optimization model can adjust strategies based on real-time data to optimize system response. By optimizing photovoltaic power generation and battery charging and discharging strategies in real time, the first optimization model can make decisions on buying and selling electricity based on real-time photovoltaic power generation, battery storage power, equipment power consumption, and real-time fluctuations in electricity prices, thereby further improving economic returns. By frequently updating the operation plan, the first dispatch model can respond more flexibly to equipment failures, market emergencies, or other emergencies to ensure the stable operation of the system.

[0179] In this embodiment, the specific method of establishing the scheduling optimization model is:

[0180] The objective function is constructed as;

[0181]

[0182] Among them, MAXQ t is the maximum profit value, hq i is the real-time peak electricity price at time i, lq i is the real-time valley electricity price at time i, PM i is the amount of electricity dispatched to the grid at time i, PN i is the amount of electricity dispatched from the power grid at time i, t is the time period in the model, i is the time period in the optimal dispatch model, and both i and t are positive integers;

[0183] t is one hour, i is the time period within the scheduling cycle, and the specific time length is 15 minutes, so as to capture and adjust the changes that were not foreseen in the first scheduling model.

[0184] The constraints are adjusted as;

[0185] Battery capacity limitations;

[0186] PSB i ≤CS i ≤CZ

[0187] Among them, CS i is the real-time battery power at time i, CZ is the battery storage capacity, PSB i The real-time power consumption of the device at time i;

[0188] Battery energy balance constraints;

[0189] CS i+1 =CS i +(PCI i ―PCO i )

[0190] Among them, CS i+1 is the real-time battery power at time i+1, CS i is the real-time battery power at time i, PCI i is the amount of electricity charged into the battery at time i, PCO i is the amount of electricity released by the battery at moment i;

[0191] Power balance constraints;

[0192] PGF i +PN i +PCO i =PSB i +PM i +PCI i

[0193] Among them, PGF i is the real-time power generation of the photovoltaic panel at time i, PN i is the amount of electricity dispatched from the power grid at time i, PCO i is the amount of electricity released by the battery at time i, PSB i is the real-time power consumption of the device at time i, PM i is the amount of electricity dispatched to the grid at time i, PCI i is the amount of electricity charged into the battery at time i;

[0194] Battery charging and discharging power constraints;

[0195]

[0196] Among them, PCO i is the amount of electricity released by the battery at time i, and CZ is the total amount of battery storage;

[0197] Equipment power consumption constraints;

[0198] CS i +PGF i >PSB i

[0199] Among them, CS i is the real-time battery power at time i, PGF i is the real-time power generation of the photovoltaic panel at time i, PSB i is the power consumption of the device at time i.

[0200] See also Figure 2 The present invention further provides a scheduling system for a photovoltaic energy storage system, wherein the scheduling system for a photovoltaic energy storage system is used to execute the above-mentioned scheduling method for a photovoltaic energy storage system, including:

[0201] A data acquisition module, which is used to obtain real-time and future light and temperature data of the area where the photovoltaic panels in the photovoltaic energy storage system are located, the real-time storage capacity of the battery, the future power consumption of the equipment, 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;

[0202] A scheduling decision module, which performs real-time update calculations on the storage capacity through the battery and determines the overall scheduling strategy of the scheduling system according to the predicted power generation of the photovoltaic panels;

[0203] 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;

[0204] A first scheduling module, wherein the first scheduling module establishes a first scheduling model to schedule electricity according to the predicted photovoltaic power generation and future equipment power consumption, and utilizes the remaining storage capacity of the battery and the peak and valley prices of the power grid;

[0205] 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 price, and takes 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.

[0206] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0207] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0208] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0209] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present 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, calculate the storage capacity in real time through the battery, and determine the overall dispatching strategy of the dispatching system based on the predicted power generation of the photovoltaic panels; Step 3: When dispatching from the power grid, the power is dispatched from the power grid first. According to the battery's full charge and discharge time, the dispatch cycle of the photovoltaic energy storage system is determined, the maximum charge and discharge power of the battery is obtained, and a pre-dispatch model is established to pre-dispatch the power. Step 4: When dispatching to the grid, a first dispatching model is established to dispatch the electricity based on the predicted photovoltaic power generation, future equipment power consumption, and the remaining storage capacity of the battery and the peak and valley prices of the grid; 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 time period of the first scheduling model as the scheduling cycle, 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; Among them, PGFY is the predicted power generation of the photovoltaic panel, PG is the rated power generation of the photovoltaic panel, G g , T b are the standard light intensity and temperature under the rated power generation of the photovoltaic panel, GZ is the future light intensity, T w is the future temperature, and γ is the temperature coefficient.

3. The scheduling method of a photovoltaic energy storage system according to claim 1, characterized in that: The formula for real-time updating and calculating the battery storage capacity is: Wherein, CZ is the battery storage capacity, I is the charge and discharge current, tin is the continuous charge and discharge time, Vmax,tin and Vmin,tin 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 and future equipment power consumption is as follows; PGS=PGFY―CZ―CS―PSB Among them, PGS is the predicted remaining power generation of the photovoltaic panel, PGFY is the predicted power generation of the photovoltaic panel, CS is the real-time storage capacity of the battery, PSB is the future power consumption of the equipment, and CZ is the battery storage capacity; When PGS ≥ 0, that is, when the predicted power generation is greater than the remaining battery storage and future equipment power consumption, dispatch to the grid is the main approach; When PGS<0, that is, the predicted power generation is less than the remaining battery storage and future equipment power consumption, the grid will be dispatched first to make PGS≥0, and then dispatch to the grid.

4. A scheduling method for a photovoltaic energy storage system according to claim 1, characterized in that: The method for determining the scheduling period is: Among them, T is the scheduling period, Vmax,tin and Vmin,tin are the charging and discharging time of the upper and lower limits of the battery voltage safety respectively; The specific method of establishing the pre-scheduling model is: The power stored in the battery, the power released by the battery, and the power dispatched from the power grid are used as decision variables, and the goal is to minimize the dispatch cost from the power grid. The objective function is constructed as follows; Among them, MAXQ is the minimum cost value, hq t is the peak electricity price at time t, lq t is the valley electricity price at time t, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the amount of electricity at the valley price dispatched from the power grid at time t, T is the dispatch period, t is the time period in the model, and both T and t are positive integers; The constraints are adjusted as; Battery capacity limitations; 0≤CS t ≤CZ Among them, CS t is the real-time battery power at time t, and CZ is the battery storage capacity; Battery energy balance constraints; CS t+1 =CS t +(PCI t ―PCO t ) Among them, CS t+1 is the real-time battery power at time t+1, CS t is the real-time battery power at time t, PCI t is the amount of electricity charged into the battery at time t, PCO t is the amount of electricity released by the battery at time t; Power balance constraints; PGF t +PH t +PK t +PCO t =PSB t +PCI t Among them, PGF t is the power generation of the photovoltaic panel at time t, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the amount of electricity at the valley price dispatched from the power grid at time t, PCO t is the amount of electricity released by the battery at time t, PSB t is the power consumption of the device at time t, PCI t is the amount of electricity charged into the battery at time t; Battery charging and discharging power constraints; Among them, PCI t is the amount of power charged into the battery at time t, PCI v is the maximum charging power of the battery, PCO t is the amount of electricity charged into the battery at time t, PCO v is the maximum discharge power of the battery; Equipment power consumption constraints; PH t +PK t +CS t +PGF t >PSB t Among them, PK t is the peak electricity price dispatched from the power grid at time t, PH t is the valley price electricity quantity dispatched from the power grid at time t, CS t is the real-time battery power at time t, PGF t is the power generation of the photovoltaic panel at time t, PSB t is the power consumption of the equipment at time t.

5. A scheduling method for a photovoltaic energy storage system according to claim 4, characterized in that: The specific method of establishing the first scheduling model is: The amount of electricity charged into the battery, the amount of electricity released by the battery, the amount of electricity dispatched to the power grid, and the amount of electricity dispatched from the power grid are used as decision variables. The economic benefits of the system during operation are improved through the strategy of selling high and buying low. The goal is to maximize the profit from selling to the power grid. The objective function is constructed as follows; Among them, MAXQ is the maximum profit value, hq t is the peak electricity price at time t, lq t is the valley electricity price at time t, PM t is the amount of electricity dispatched to the grid at time t, PN t is the amount of electricity dispatched from the power grid at time t, T is the dispatch period, t is the time period in the model, and both T and t are positive integers; The constraints are adjusted as; Battery capacity limitations; 0≤CS t ≤CZ Among them, CS t is the real-time battery power at time t, and CZ is the battery storage capacity; Battery energy balance constraints; CS t+1 =CS t +(PCI t ―PCO t ) Among them, CS t+1 is the real-time battery power at time t+1, CS t is the real-time battery power at time t, PCI t is the amount of electricity charged into the battery at time t, PCO t is the amount of electricity released by the battery at time t; Power balance constraints; PGF t +PN t +PCO t =PSB t +PM t +PCI t Equipment power consumption constraints; Among them, PGF t is the power generation of the photovoltaic panel at time t, PN t is the amount of electricity dispatched from the grid at time t, PCO t is the amount of electricity released by the battery at time t, PSB t is the power consumption of the equipment at time t, PM t is the amount of electricity dispatched to the grid at time t, PCI t is the amount of electricity charged into the battery at time t; Battery charge and discharge power constraints; Among them, PCI t is the amount of power charged into the battery at time t, PCI v is the maximum charging power of the battery, PCO t is the amount of electricity charged into the battery at time t, PCO v Maximum charging power for the battery Equipment power consumption constraints; CS t +PGF t >PSB t Among them, CS t is the real-time battery power at time t, PGF t is the power generation of the photovoltaic panel at time t, PSB t is the power consumption of the equipment at time t.

6. A scheduling method for a photovoltaic energy storage system according to claim 5, characterized in that: The specific method of establishing the scheduling optimization model is: The objective function is constructed as; Among them, MAXQ t is the maximum profit value, hq i is the real-time peak electricity price at time i, lq i is the real-time valley electricity price at time i, PM i is the amount of electricity dispatched to the grid at time i, PN i is the amount of electricity dispatched from the power grid at time i, t is the time period in the model, i is the time period in the optimal dispatch model, and both i and t are positive integers; t is one hour, i is the time period within the scheduling cycle, and the specific time length is 15 minutes, so as to capture and adjust the changes that were not foreseen in the first scheduling model; The constraints are adjusted as; Battery capacity limitations; PSB i ≤CS i ≤CZ Among them, CS i is the real-time battery power at time i, CZ is the battery storage capacity, PSB i The real-time power consumption of the device at time i; Battery energy balance constraints; CS i+1 =CS i +(PCI i ―PCO i ) Among them, CS i+1 is the real-time battery power at time i+1, CS i is the real-time battery power at time i, PCI i is the amount of electricity charged into the battery at time i, PCO i is the amount of electricity released by the battery at moment i; Power balance constraints; PGF i +PN i +PCO i =PSB i +PM i +PCI i Among them, PGF i is the real-time power generation of the photovoltaic panel at time i, PN i is the amount of electricity dispatched from the power grid at time i, PCO i is the amount of electricity released by the battery at time i, PSB i is the real-time power consumption of the device at time i, PM i is the amount of electricity dispatched to the grid at time i, PCI i is the amount of electricity charged into the battery at time i; Battery charge and discharge power constraints; Among them, PCO i is the amount of electricity released by the battery at time i, and CZ is the total amount of battery storage; Equipment power consumption constraints; CS i +PGF i >PSB i Among them, CS i is the real-time battery power at time i, PGF i is the real-time power generation of the photovoltaic panel at time i, PSB i is the power consumption of the device at time i.

7. A dispatching system for a photovoltaic energy storage system, characterized in that: 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, including: A data acquisition module, which is used to obtain real-time and future light and temperature data of the area where the photovoltaic panels in the photovoltaic energy storage system are located, the real-time storage capacity of the battery, the future power consumption of the equipment, 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; A scheduling decision module, which performs real-time update calculations on the storage capacity through the battery and determines the overall scheduling strategy of the scheduling system according to the predicted power generation of the photovoltaic panels; 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; A first scheduling module, wherein the first scheduling module establishes a first scheduling model to schedule electricity according to the predicted photovoltaic power generation and future equipment power consumption, and utilizes the remaining storage capacity of the battery and the 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 price, and takes 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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