Coupling electricity price and photovoltaic power generation day-ahead prediction and arbitrage method

Abstract

The invention discloses a coupling electricity price and photovoltaic power generation day-ahead prediction and arbitrage method, relates to the field of energy storage of photovoltaic project configuration, and can realize the purpose that a power generation power change curve is simulated through a day-ahead real-time electricity price prediction curve and coupling a day-ahead photovoltaic project system. Part of photovoltaic project power generation is stored in project distribution and storage equipment, and low income caused by too low electricity price in the time period is reduced; in the time period of high real-time electricity price prediction, the electricity quantity stored in the energy storage device is subjected to electricity market spot transaction, peak-valley arbitrage of energy storage is increased to obtain additional income, the overall income condition of a photovoltaic storage project is improved, meanwhile, the current situation that the utilization rate of energy storage equipment configured in a current photovoltaic project is low is avoided, and the energy utilization rate of the photovoltaic project is increased. The scheme can be tried out in areas where the electricity trading market develops quickly and the system is complete.

Description

Technical Field

[0001] The present invention relates to the field of energy storage configured in photovoltaic projects, and in particular to a method for coupling electricity price and photovoltaic power generation day-ahead prediction and arbitrage. Background Art

[0002] With the implementation of the "Regulations on the Supervision of Full Guaranteed Purchase of Renewable Energy Electricity" from April 1, 2024, the electricity generated by new energy is no longer all connected to the grid based on the benchmark coal-fired electricity price in its location, and the power supply department purchases it in full. The role of the power market has become increasingly obvious. For example, Hebei, Shandong and other places are promoting local centralized new energy projects to fully participate in market-based transactions. In theory, as time approaches, futures prices will gradually return to spot prices. At the same time, the changes in electricity spot prices within a single day tend to be stable. Combined with the deepening of the exploration of the electricity market and output capacity, the next day's spot prices predicted recently have become more accurate. The overall price shows that the electricity spot transaction price is lower during the daytime photovoltaic period and higher at night.

[0003] With the continuous development of domestic new energy projects, project absorption has become a major problem, and centralized photovoltaic configuration with energy storage has become the norm.

[0004] Therefore, it is necessary to provide a method for coupling electricity price and photovoltaic power generation day-ahead prediction and arbitrage to solve the above problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for coupling electricity price and photovoltaic power generation day-ahead forecasting and arbitrage, comprising the following steps:

[0007] Step 1: Collect the change data of sunshine radiation in the area where the system is located, and calculate the change curve between the daily output power of the photovoltaic system and time through experiments and software simulation; calculate the optimal power that the system needs to maintain according to the power demand, estimate the daily output efficiency of the photovoltaic system through experiments, and calculate the reported power accordingly;

[0008] Step 2: According to the day-ahead real-time electricity price forecast curve provided by the power trading market or secondary auxiliary institutions, the daily output efficiency of the photovoltaic system is coupled, the daily output efficiency curve of the photovoltaic system is matched to the real-time electricity price forecast period, and the real-time electricity price forecast price of each period within the photovoltaic power generation period is combined with the output efficiency of the photovoltaic system during this period;

[0009] Step 3: During the period of the lowest daily real-time electricity price forecast, all or part of the output of the photovoltaic system during this period is stored in the photovoltaic storage project distribution storage equipment until the photovoltaic storage project distribution storage equipment is fully charged;

[0010] Step 4. During the highest period of the daily real-time electricity price prediction, all the electricity stored in the energy storage equipment of the PV energy storage project is released into the power grid and participates in the real-time power trading to achieve peak shaving and valley filling in the power market;

[0011] Step 5. Couple the charging prediction price Cts and its corresponding charging electricity quantity Qs to obtain the charging cost Cin = ΣQs * Cts; couple the discharging prediction price Ctm and its corresponding charging electricity quantity Qm to obtain the charging cost Cout = ΣQm * Ctm. If Cout > Cin, then execute this charge-discharge strategy; if Cout < Cin, then abandon the execution of this charge-discharge strategy.

[0012] Preferably, the specific content of Step 1 is as follows: Use a photovoltaic meteorological monitoring station to monitor the solar radiation data in real time through a solar radiation sensor, including direct radiation, diffuse radiation, and total radiation, as well as the ambient temperature of the area where the system is located; establish a data-driven prediction model by using the historical data accumulated by the photovoltaic meteorological station for a long time, combined with the actual operation data of the photovoltaic power generation system and the data of PVGIS and Meteonorm meteorological software, establish the correlation with the power generation amount, so as to predict the power generation amount in the next period of time, and calculate the change curve between the daily output power of the photovoltaic system and time.

[0013] Preferably, in Step 3, for the daily coupling of the photovoltaic power generation day-ahead prediction, power judgment and electricity quantity judgment are required:

[0014] Power judgment: If PVts > P, then in this period, the input power of the energy storage equipment of the PV energy storage project is P, and the electricity quantity input is Qs = P * ts. If PVts < P, then in this period, the input power of the energy storage equipment is PVts, and the electricity quantity input is Qs = PVts * ts;

[0015] Electricity quantity judgment: Each time the electricity quantity of the energy storage equipment is input, it is necessary to judge whether Qi is greater than the sum of the electricity quantities Qs. If Qi > ΣQs, then loop, and find the next lowest period of the real-time electricity price prediction again. The photovoltaic system re-judges the input power and inputs the electricity quantity of this period until Qi ≤ ΣQs, so as to complete the electricity quantity input to the energy storage during each spot lowest price period within the photovoltaic power generation period;

[0016] Among them, the lowest period of the real-time electricity price prediction within the photovoltaic power generation period is recorded as ts, the prediction price at this time is Cts, the output power of the photovoltaic system in this period is PVts, the rated input power of the energy storage equipment is P, the rated input electricity quantity is Qi, and s = 1, 2, 3...

[0017] Preferably, in step 4, the daily coupled photovoltaic power generation forecast needs to be used to judge the input power and input power: each time the power of the energy storage device is input, it is necessary to judge whether Qo is less than the sum of the output power Qm. If Qo>ΣQm, a cycle is performed to find the next time period with the highest real-time electricity price forecast. The photovoltaic storage project storage device discharges again and inputs the power of this time period until Qo≤ΣQm, so as to achieve the output of energy storage power in each time period with the highest spot price.

[0018] Determine that the highest period of real-time electricity price forecast is tm (m=1, 2, 3...) and the predicted price at this time is Ctm, the rated output power of the photovoltaic storage project's storage equipment is Pm, the rated output power is Qm, and the rated total output power is Qo.

[0019] Preferably, in step 5, the coupling charging prediction price Cts and its corresponding charging power Qs are specifically as follows: the charging prediction power price Cts is the lower power price in the real-time power price period within the photovoltaic power generation period found by the real-time power price prediction curve a day ago, and the charging power Qs needs to be combined with the output efficiency of the photovoltaic system in this real-time period to judge the input efficiency of the energy storage device, and after obtaining the input efficiency Ps of this period, the corresponding charging power Qs of the energy storage in this period is obtained = Ps*ts;

[0020] The coupled discharge prediction price Ctm and its corresponding charging power Qm are specifically as follows: the discharge prediction power price Ctm is the higher power price in the real-time power price period found by the real-time power price prediction curve a day ago, and the discharge power is the output power Pm of the energy storage device. After combining the discharge time, the corresponding discharge power of the energy storage device during this period is obtained as Qm=Pm*tm.

[0021] Beneficial effects of the present invention: By using the day-ahead real-time electricity price forecast curve and coupling the day-ahead photovoltaic project system simulation power generation change curve, the following can be achieved:

[0022] During periods when the real-time electricity price forecast is low, part of the electricity generated by the photovoltaic project will be stored in the project's storage equipment to reduce the low returns caused by the low electricity price during this period;

[0023] During periods when the real-time electricity price forecast is higher, the electricity stored in the energy storage device can be traded in the electricity market spot market, increasing the peak-valley arbitrage of energy storage to obtain additional income, thereby improving the overall profitability of the photovoltaic storage project. At the same time, it avoids the current situation of low utilization rate of energy storage equipment configured in photovoltaic projects. This solution can be piloted in areas where the electricity trading market is developing rapidly and the system is complete. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0025] To make the technical means, creative features, achieved objectives and effects of the present invention easily understood, the present invention will be further described below in conjunction with specific embodiments.

[0026] As Figure 1 shown, a method for coupling day-ahead prediction and arbitrage of electricity price and photovoltaic power generation includes the following steps:

[0027] Step 1: Collect the change data of solar radiation in the area where the system is located, and calculate the change curve between the output power of the photovoltaic system and time every day through experiments and software simulations; calculate the optimized power that the system power supply needs to maintain according to the electricity consumption needs, estimate the daily output efficiency of the photovoltaic system through experiments, and thus calculate the reported power;

[0028] Step 2: Couple the day-ahead real-time electricity price prediction curve provided by the power trading market or the secondary auxiliary institution with the daily output efficiency of the photovoltaic system, match the daily output efficiency curve of the photovoltaic system to the real-time electricity price prediction period, and combine the real-time electricity price prediction electricity price of each period during the photovoltaic power generation period with the output efficiency of the photovoltaic system at this time;

[0029] Step 3: At the lowest period of the daily real-time electricity price prediction, store all or part of the output of the photovoltaic system at this time in the energy storage equipment of the photovoltaic energy storage project until the energy storage equipment of the photovoltaic energy storage project is fully charged;

[0030] Step 4: At the highest period of the daily real-time electricity price prediction, release all the electricity stored in the energy storage equipment of the photovoltaic energy storage project into the power grid and participate in the real-time power trading to achieve peak shaving and valley filling in the power market;

[0031] Step 5: Couple the charging prediction price Cts and its corresponding charging power Qs to obtain the charging cost Cin = ΣQs * Cts; couple the discharging prediction price Ctm and its corresponding charging power Qm to obtain the charging cost Cout = ΣQm * Ctm. If Cout > Cin, execute this charging and discharging strategy; if Cout < Cin, abandon the execution of this charging and discharging strategy.

[0032] In this embodiment, the specific content of Step 1 is as follows: Use a photovoltaic meteorological monitoring station to monitor the solar radiation data in real time through a solar radiation sensor, including direct radiation, scattered radiation and total radiation, as well as the ambient temperature in the area where the system is located; establish a data-driven prediction model by using the historical data accumulated by the photovoltaic meteorological station for a long time, combined with the actual operation data of the photovoltaic power generation system and the data of PVGIS and Meteonorm meteorological software, establish the correlation with the power generation amount, so as to predict the power generation amount in the future for a period of time, and calculate the change curve between the output power of the photovoltaic system and time every day.

[0033] In this embodiment, in step three, for the daily coupled photovoltaic power generation day-ahead prediction, power judgment and electricity quantity judgment are required:

[0034] Power judgment: If PVts > P, then during this period, the input power of the energy storage device of the photovoltaic and energy storage project is P, and the electricity input is Qs = P * ts. If PVts < P, then during this period, the input power of the energy storage device is PVts, and the electricity input is Qs = PVts * ts;

[0035] Electricity quantity judgment: Each time the electricity quantity of the energy storage device is input, it is necessary to judge whether Qi is greater than the sum of the electricity quantities Qs. If Qi > ΣQs, then a loop is performed to find the next period with the lowest real-time electricity price prediction again. The photovoltaic system re-performs the input power judgment and inputs the electricity quantity of this period until Qi ≤ ΣQs, so as to complete the electricity input to the energy storage during each spot lowest price period within the photovoltaic power generation period;

[0036] Among them, the period with the lowest real-time electricity price prediction within the photovoltaic power generation period is recorded as ts, the predicted price at this time is Cts, the output power of the photovoltaic system during this period is PVts, the rated input power of the energy storage device is P, the rated input electricity quantity is Qi, and s = 1, 2, 3...

[0037] In this embodiment, in step four, for the daily coupled photovoltaic power generation day-ahead prediction, input power and input electricity quantity judgment are required: Each time the electricity quantity of the energy storage device is input, it is necessary to judge whether Qo is less than the sum of the output electricity quantities Qm. If Qo > ΣQm, then a loop is performed to find the next period with the highest real-time electricity price prediction again. The energy storage device of the photovoltaic and energy storage project discharges again and inputs the electricity quantity of this period until Qo ≤ ΣQm, so as to complete the energy storage electricity output during each spot highest price period;

[0038] The period with the highest real-time electricity price prediction is judged as tm (m = 1, 2, 3...) and the predicted price at this time is Ctm. The rated output power of the energy storage device of the photovoltaic and energy storage project is Pm, the rated output electricity quantity is Qm, and the rated total output electricity quantity is Qo.

[0039] In this embodiment, the coupled charging prediction price Cts and its corresponding charging electricity quantity Qs in step five are specifically as follows: The charging prediction electricity price Cts is the lower electricity price in the real-time electricity price periods within the photovoltaic power generation period found from the day-ahead real-time electricity price prediction curve. For the charging electricity quantity Qs, it is necessary to combine the output efficiency of the photovoltaic system in this real-time period, perform the input efficiency judgment of the energy storage device, and after obtaining the input efficiency Ps at this time, the corresponding charging electricity quantity Qs of the energy storage at this time is obtained as Qs = Ps * ts;

[0040] The coupled discharge prediction price Ctm and its corresponding charging power Qm are specifically as follows: the discharge prediction power price Ctm is the higher power price in the real-time power price period found by the real-time power price prediction curve a day ago, and the discharge power is the output power Pm of the energy storage device. After combining the discharge time, the corresponding discharge power of the energy storage device during this period is obtained as Qm=Pm*tm.

[0041] By using the day-ahead real-time electricity price forecast curve and coupling it with the day-ahead photovoltaic project system simulation power generation curve, the following can be achieved:

[0042] During periods when the real-time electricity price forecast is low, part of the electricity generated by the photovoltaic project will be stored in the project's storage equipment to reduce the low returns caused by the low electricity price during this period;

[0043] During periods when the real-time electricity price forecast is higher, the electricity stored in the energy storage device can be traded in the electricity market spot market, increasing the peak-valley arbitrage of energy storage to obtain additional income, thereby improving the overall profitability of the photovoltaic storage project. At the same time, it avoids the current situation of low utilization rate of energy storage equipment configured in photovoltaic projects. This solution can be piloted in areas where the electricity trading market is developing rapidly and the system is complete.

[0044] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A method for coupling electricity price and photovoltaic power generation day-ahead forecasting and arbitrage, characterized in that: It includes the following steps: Step 1: Collect the change data of solar radiation in the area where the system is located, and calculate the change curve between the daily output power of the photovoltaic system and time through experiments and software simulations; calculate the optimized power that the system power supply needs to maintain according to the electricity consumption needs, estimate the daily output efficiency of the photovoltaic system through experiments, and thus calculate the reported power; Step 2: Coupling with the daily output efficiency of the photovoltaic system according to the day-ahead real-time electricity price prediction curve provided by the power trading market or the secondary auxiliary agency, matching the daily output efficiency curve of the photovoltaic system to the real-time electricity price prediction period, and combining the real-time electricity price prediction electricity price in each period during the photovoltaic power generation period with the output efficiency of the photovoltaic system at this time; Step 3: At the lowest period of the daily real-time electricity price prediction, all or part of the output of the photovoltaic system at this time is stored in the energy storage device of the photovoltaic energy storage project until the energy storage device of the photovoltaic energy storage project is fully charged; Step 4: At the highest period of the daily real-time electricity price prediction, all the electricity stored in the energy storage device of the photovoltaic energy storage project is released into the power grid and participates in the real-time power trading to achieve peak shaving and valley filling in the power market; Step 5: Coupling the charging prediction price Cts and its corresponding charging power Qs to obtain the charging cost Cin = ΣQs * Cts; coupling the discharging prediction price Ctm and its corresponding charging power Qm to obtain the charging cost Cout = ΣQm * Ctm. If Cout > Cin, then execute this charge-discharge strategy; if Cout < Cin, then abandon the execution of this charge-discharge strategy.

2. A method for coupling electricity price and photovoltaic power generation day-ahead forecasting and arbitrage according to claim 1, characterized in that: Specifically, in Step 1, a photovoltaic meteorological monitoring station is used to monitor the solar radiation data in real time through a solar radiation sensor, including direct radiation, scattered radiation and total radiation, as well as the ambient temperature in the area where the system is located; by using the historical data accumulated by the photovoltaic meteorological station for a long time, combined with the actual operation data of the photovoltaic power generation system and the data of PVGIS and Meteonorm meteorological software, a data-driven prediction model is established, and the correlation with the power generation amount is established, so as to predict the power generation amount in the future for a period of time and calculate the change curve between the daily output power of the photovoltaic system and time.

3. The method for coupling electricity price and photovoltaic power generation day-ahead forecasting and arbitrage according to claim 1, characterized in that: In Step 3, daily coupling with the day-ahead prediction of photovoltaic power generation requires power judgment and electricity judgment: Power judgment: If PVts > P, then in this period, the input power of the energy storage device of the photovoltaic energy storage project is P, and the electricity input is Qs = P * ts. If PVts < P, then in this period, the input power of the energy storage device is PVts, and the electricity input is Qs = PVts * ts; Electricity judgment: Each time the electricity is input into the energy storage device, it is necessary to judge whether Qi is greater than the sum of the electricity Qs. If Qi > ΣQs, then loop, and find the next lowest period of the real-time electricity price prediction again. The photovoltaic system re-judges the input power and inputs the electricity in this period until Qi ≤ ΣQs, realizing the input of electricity into the energy storage during each spot lowest price period during the photovoltaic power generation period; Among them, the lowest period of real-time electricity price forecast during the photovoltaic power generation period is recorded as ts, the predicted price at this time is Cts, the output power of the photovoltaic system at this time period is PVts, the rated input power of the storage equipment is P, the rated input power is Qi, s = 1, 2, 3...

4. The method for coupling electricity price and photovoltaic power generation day-ahead forecasting and arbitrage according to claim 1, characterized in that: In step 4, the daily coupled photovoltaic power generation forecast needs to be used to determine the input power and input power: each time the energy storage device is input, it is necessary to determine whether Qo is less than the sum of the output power Qm. If Qo>ΣQm, the cycle is repeated to find the next time period with the highest real-time electricity price forecast. The photovoltaic storage project's storage equipment is discharged again, and the power of this time period is input until Qo≤ΣQm, so as to achieve the output of energy storage power in each time period with the highest spot price. Determine that the highest period of real-time electricity price forecast is tm (m=1, 2, 3...) and the predicted price at this time is Ctm, the rated output power of the photovoltaic storage project's storage equipment is Pm, the rated output power is Qm, and the rated total output power is Qo.

5. The method for coupling electricity price and photovoltaic power generation day-ahead forecasting and arbitrage according to claim 1, characterized in that: In step 5, the coupling charging prediction price Cts and its corresponding charging power Qs are specifically as follows: the charging prediction power price Cts is the lower power price in the real-time power price period of the photovoltaic power generation period found by the real-time power price prediction curve a day ago. The charging power Qs needs to be combined with the output efficiency of the photovoltaic system in this real-time period to judge the input efficiency of the energy storage device. After obtaining the input efficiency Ps of this period, the corresponding charging power Qs of the energy storage in this period is obtained = Ps*ts; The coupled discharge prediction price Ctm and its corresponding charging power Qm are specifically as follows: the discharge prediction power price Ctm is the higher power price in the real-time power price period found by the real-time power price prediction curve a day ago, and the discharge power is the output power Pm of the energy storage device. After combining the discharge time, the corresponding discharge power of the energy storage device during this period is obtained as Qm=Pm*tm.

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