Method and device for operating a photovoltaic storage system based on a residual demand curve

Abstract

The present invention proposes a photovoltaic storage system operation method and device based on a residual demand curve, including: considering the operation constraints of energy storage and the coupling relationship between the electric energy and the reserve market, establishing an electric energy-reserve residual demand curve; based on the electric energy-reserve residual demand curve, adjusting the day-ahead output plan and the reserve capacity of the energy storage with the goal of maximizing the profit of the photovoltaic storage power station, and considering the uncertainty of the real-time market, reducing the real-time deviation penalty of the photovoltaic storage power station by calling the reserve capacity of the energy storage.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic storage system operation, and in particular to a photovoltaic storage system operation method and device based on a residual demand curve. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In order to build a clean and low-carbon energy system, photovoltaic and other renewable energy power generation has developed rapidly. However, due to the difficulty in predicting and controlling the output, photovoltaic power stations face greater profit risks when participating in market bidding.

[0004] Energy storage has strong regulation capabilities and can smooth out photovoltaic fluctuations. Therefore, the combined operation of photovoltaic and storage is receiving more and more attention, and new energy sites are encouraged to build their own energy storage. Considering the operating characteristics of energy storage and formulating a reasonable bidding strategy are of great significance for the participation of photovoltaic and storage combined power generation systems in the power market.

[0005] However, the existing photovoltaic storage has the following problems during operation, namely: when the existing photovoltaic storage system is in operation, it only considers the impact of its bidding strategy on the clearing price of the energy market, and believes that the clearing price in the reserve market is a fixed value; or it only considers the impact of its bidding strategy on the clearing price of the reserve market, and believes that the clearing price in the energy market is a fixed value. This leads to the photovoltaic storage system only considering the maximization of the interests of a single market when formulating an operation plan, and cannot maximize the overall benefits of the photovoltaic storage system in the energy and reserve markets. Summary of the invention

[0006] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a method for operating a photovoltaic storage system based on a residual demand curve, which can improve the rational operation of the photovoltaic storage system.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0008] In a first aspect, a method for operating a photovoltaic storage system based on a residual demand curve is disclosed, comprising:

[0009] Considering the operation constraints of energy storage and the coupling relationship between the power energy and reserve market, the power energy and reserve surplus demand curve is established;

[0010] Based on the electricity-reserve surplus demand curve, the day-ahead output plan and reserve capacity of the energy storage are adjusted with the goal of maximizing the benefits of the PV-storage power station. At the same time, the uncertainty of the real-time market is taken into account, and the real-time deviation penalty of the PV-storage power station is reduced by calling the reserve capacity of the energy storage.

[0011] As a further technical solution, considering the operating constraints of energy storage and the coupling relationship between the electricity energy and reserve markets, a feasible bidding combination of electricity energy and reserve is generated within the day-ahead bidding range of the photovoltaic energy storage power station, and the corresponding market clearing electricity price is calculated based on neural network prediction.

[0012] As a further technical solution, the day-ahead electricity bid of the photovoltaic power station is the sum of the photovoltaic day-ahead forecast value and the energy storage day-ahead charging and discharging plan, and the reserve bid is the sum of the photovoltaic day-ahead maximum forecast deviation and the energy storage increase / decrease in reserve capacity.

[0013] As a further technical solution, a power-reserve surplus demand curve is established, specifically:

[0014] Obtain historical data of the photovoltaic and energy storage system, use the day-ahead load forecast information and the day-ahead photovoltaic and energy storage power station bidding information as input, and use the electricity volume, electricity price, and reserve electricity price as output to train the neural network;

[0015] Set the number of intervals of each remaining demand curve to N;

[0016] Randomly sort the integers 1 to N twice;

[0017] Check whether the bidding combination of PV and energy storage power stations meets the maximum charging and discharging power constraints of energy storage;

[0018] If the conditions are met, the day-ahead load forecast information and the day-ahead PV power station bidding information are used as inputs, and the electricity quantity, electricity price and reserve electricity price are obtained through the neural network prediction model;

[0019] Calculate the electricity prices corresponding to all bidding combinations of photovoltaic and energy storage power stations;

[0020] The remaining demand curve of the photovoltaic power station electricity bidding and the remaining demand curve of the reserve bidding at time t are obtained.

[0021] As a further technical solution, by establishing a power-reserve surplus demand curve, the surplus demand curves in the day-ahead power market and the reserve market are jointly generated, wherein each hour includes a day-ahead power bid surplus demand curve and a reserve bid surplus demand curve.

[0022] As a further technical solution, the day-ahead output plan and reserve capacity of energy storage are adjusted with the goal of maximizing the benefits of the photovoltaic and storage power station. The objective function takes into account the day-ahead scenario, the real-time photovoltaic power random scenario, the probability of the real-time electricity price scenario, the part of the photovoltaic and storage power station's actual output that is greater than the day-ahead bid power at time t, the penalty power of the photovoltaic and storage power station at time t, and the charging and discharging cost of energy storage at time t.

[0023] As a further technical solution, the constraint conditions of the objective function include:

[0024] The bidding quantity constraints of photovoltaic and storage power stations, the day-ahead charging and discharging plan constraints of energy storage, the reserve capacity constraints of energy storage, the real-time adjustment power constraints of energy storage, the real-time electricity sales constraints of photovoltaic and storage power stations, and the real-time penalty quantity constraints of photovoltaic and storage power stations.

[0025] In a second aspect, a photovoltaic storage system operation device based on a residual demand curve is disclosed, comprising:

[0026] The curve establishment module is configured to: consider the operation constraints of energy storage and the coupling relationship between the electric energy and the reserve market, and establish the electric energy-reserve surplus demand curve;

[0027] The adjustment module is configured to: adjust the day-ahead output plan and reserve capacity of the energy storage based on the power-reserve surplus demand curve with the goal of maximizing the profit of the photovoltaic and storage power station, while considering the uncertainty of the real-time market and reducing the real-time deviation penalty of the photovoltaic and storage power station by calling the reserve capacity of the energy storage.

[0028] One or more of the above technical solutions have the following beneficial effects:

[0029] The operation plan of the photovoltaic storage system based on the residual demand curve takes into account the operation constraints of energy storage and the coupling relationship between the electric energy and reserve markets, and establishes a residual demand curve. Based on this curve, a feasible bidding combination of electricity and reserve is generated within the day-ahead bidding range of the photovoltaic storage power station. The corresponding market clearing electricity price is calculated based on neural network prediction. The day-ahead output plan and reserve capacity of the energy storage are adjusted with the goal of maximizing the profit of the photovoltaic storage power station. At the same time, considering the uncertainty of the real-time market, the real-time deviation penalty of the photovoltaic storage power station is reduced by calling the reserve capacity of energy storage. This operation plan can take into account the profits of the photovoltaic storage system in the electric energy market and the reserve market, and can maximize the utilization of the power regulation capability and reserve support capability of the energy storage, and ultimately make the photovoltaic storage system operate in the optimal state.

[0030] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0032] Figure 1 It is a schematic diagram of the residual demand curve and bidding results of the day-ahead electric energy market according to an embodiment of the present invention;

[0033] Figure 2 It is a schematic diagram of the remaining demand curve and bidding results of the standby market according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0035] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0036] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0037] Embodiment 1

[0038] This embodiment discloses a method for operating a photovoltaic storage system based on a residual demand curve, including:

[0039] Considering the operation constraints of energy storage and the coupling relationship between the power energy and reserve market, the power energy and reserve surplus demand curve is established;

[0040] Based on the electricity-reserve surplus demand curve, the day-ahead output plan and reserve capacity of the energy storage are adjusted with the goal of maximizing the benefits of the PV-storage power station. At the same time, the uncertainty of the real-time market is taken into account, and the real-time deviation penalty of the PV-storage power station is reduced by calling the reserve capacity of the energy storage.

[0041] In this implementation example, the remaining demand curve is jointly modeled:

[0042] The day-ahead electricity bid of the photovoltaic power station is the day-ahead forecast value of photovoltaic power station. and energy storage day-ahead charging and discharging plan ( and ), the reserve bid is the maximum forecast deviation of photovoltaic day With energy storage up / down reserve capacity ( and ) and:

[0043]

[0044]

[0045] The above electricity bid represents the amount of electricity sold by the photovoltaic power station at the corresponding time, in MWh. In this example, the time scale is 1 hour, so the amount of electricity and power are equal in value, so it can also be understood as the power supply at time t.

[0046] The reserve bid indicates the reserve power that the PV-storage power station needs to be provided by the external power grid at time t.

[0047] The proposed joint modeling method for residual demand curves takes into account the operating constraints of energy storage and the coupling relationship between the electricity energy-reserve market, generates a feasible bidding portfolio of electricity quantity-reserve within the day-ahead bidding range of the PV-ESS, and calculates the corresponding market clearing electricity price based on neural network prediction.

[0048] The specific steps are as follows:

[0049] 1) Collect historical data, use {day-ahead load forecast information, day-ahead PV-ESS bidding information} as input, and {electricity price, reserve electricity price} as output to train the neural network. Let t = 1, and set the number of intervals of each residual demand curve to N.

[0050] 2) Considering the maximum adjustment range of the charging and discharging power of the energy storage, divide the electricity quantity and reserve bids of the PV-ESS at time t into N points:

[0051]

[0052]

[0053] where i = 1, 2, …, N; and are the maximum charging and discharging powers of the energy storage.

[0054] 3) Randomly sort the integers from 1 to N twice, and record them in order as and {σ(1), σ(2), …, σ(N)}. Let represent the possible bidding combinations of the electricity quantity and reserve of the PV-ESS.

[0055] 4) Check whether the bidding combination of the PV-ESS satisfies the maximum charging and discharging power constraint of the energy storage:

[0056]

[0057] If equation (5) is satisfied, proceed to the next step and let i = 1; otherwise, return to step 3).

[0058] 5) Use {day-ahead load forecast information, day-ahead PV-ESS bidding information} as input, and obtain the electricity price and reserve electricity price through the neural network prediction model, denoted as and

[0059] 6) If i < N, let i = i + 1, return to step 5), and calculate the electricity prices corresponding to all bidding combinations of the PV-ESS; otherwise, proceed to the next step.

[0060] 7) Connect to obtain the residual demand curve of the electricity quantity bid of the PV-ESS at time t, and connect The residual demand curve of the reserve bidding of the photovoltaic and storage power station at time t is obtained by connecting them.

[0061] In step 2), the electricity bid of the photovoltaic power station at time t is divided into N points. The value of i in formula 3 starts from 1 and the maximum value is N. For each value of i, there is a corresponding bid electricity and clearing price In the coordinate axis, the price is the vertical axis and the quantity is the horizontal axis, then is a point in the coordinate system, and connecting all the points under the value of i can obtain the electricity bidding residual demand curve.

[0062] 8) If t<24, set t=t+1, return to step 2), and generate the remaining demand curve for the next period; if t=T, end.

[0063] After steps 1)-8), the joint generation of the remaining demand curves in the day-ahead electricity energy and reserve market is achieved, including a day-ahead electricity bidding remaining demand curve and a reserve bidding remaining demand curve for the photovoltaic power station within each hour.

[0064] Since the day-ahead bidding for the PV storage system requires a bidding curve for the next 24 hours, a power bidding remaining demand curve and a standby bidding remaining demand curve are required every hour.

[0065] About the joint bidding of day-ahead power and reserve:

[0066] Based on the generated electricity-reserve surplus demand curve, an optimization model is established with the goal of maximizing the profit of the photovoltaic and energy storage power station. Based on this model, the day-ahead output plan and reserve capacity of the energy storage are adjusted. At the same time, the uncertainty of the real-time market is taken into account, and the real-time deviation penalty of the photovoltaic and energy storage power station is reduced by calling the reserve capacity of the energy storage.

[0067] Objective function:

[0068]

[0069] In the formula, ρ ω , ξ , ψ are the probabilities of the day-ahead scenario, the real-time PV power random scenario, and the real-time electricity price scenario, respectively. ω, ξ, and ψ represent the corresponding scenario numbers; represents the real-time electricity price under scenario ψ at time t; It represents the part of the actual output of the photovoltaic power station under the scenario (ω,ξ,ψ) at time t that is greater than the bid power a day ago; β * The output deviation penalty coefficient of the PV-storage power station in the real-time market; represents the penalty power of the photovoltaic power station under the scenario (ω,ξ,ψ) at time t; is the charging and discharging cost of energy storage under the scenario (ω,ξ,ψ) at time t.

[0070] Constraints:

[0071] 1) Bidding volume constraints

[0072] The bidding amount of the photovoltaic power station can be expressed as:

[0073]

[0074]

[0075] 2) Constraints on the day-ahead charging and discharging plan for energy storage

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] In the formula, Indicates the charging and discharging state of energy storage under the scenario ω at time t. When it is 1, it indicates discharging, and when it is 0, it indicates charging; η c and η d Indicates the charge and discharge efficiency; Indicates the remaining energy after arranging the charging and discharging plan on the previous day; E min and E max Indicates the minimum and maximum values ​​of the remaining energy.

[0082] 3) Reserve capacity constraints of energy storage

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] In the formula, It represents the reserve capacity status of energy storage under the scenario ω at time t. When it is 1 or 0, it indicates providing upward reserve and downward reserve respectively. It indicates the remaining energy after considering the day-ahead charging and discharging plan and the backup capacity.

[0091] 4) Real-time adjustment of power constraints of energy storage

[0092] The PV power station uses energy storage to reduce the penalty of real-time deviation, which needs to meet the following constraints:

[0093]

[0094] In the formula, and They represent the part where the actual output of the PV-storage power station is greater than or less than the lower boundary of the day-ahead bid of the PV-storage power station in the scenario (ω,ξ) when no real-time adjustment of energy storage is performed; represents the real-time photovoltaic output under the scenario ξ at time t; A binary variable indicating whether the actual output of the photovoltaic power station is greater than the lower limit of the bidding of the photovoltaic power station when no real-time adjustment of energy storage is performed. 1 represents yes and 0 represents no. M is a sufficiently large positive real number. Represents the actual surplus energy of energy storage in the real-time market.

[0095] 5) Real-time power sales constraints of PV power stations

[0096]

[0097] 6) Real-time penalty constraints for solar-storage power stations

[0098] The real-time penalty amount of the photovoltaic power station can be expressed as:

[0099]

[0100] The above optimization model is a mixed integer linear programming problem, which can be solved directly using commercial software.

[0101] Regarding the example verification:

[0102] The installed capacity data of a photovoltaic-energy storage integrated power station is selected for simulation to verify the effectiveness of the proposed method. The photovoltaic capacity is 400MW, the rated power and capacity of the energy storage are 100MW and 100MWh respectively, the charging and discharging efficiency is 90%, and the upper and lower limits of the remaining energy are 90MWh and 10MWh respectively. The charging and discharging cost of the energy storage is 5$ / MWh.

[0103] Taking the 19th hour as an example, the remaining demand curve scenario and the bidding results of the photovoltaic power station are as follows: Figure 1 and Figure 2As shown in the figure. It can be seen from the figure that the residual demand curve of the photovoltaic power station bidding in the day-ahead electricity energy and reserve market generally shows a downward trend. However, in some scenarios, the price corresponding to the smaller bid amount is lower than the price corresponding to the larger bid amount. For example, the electricity price in the third interval of the fourth electricity bidding residual demand curve scenario is lower than the electricity price in the fourth interval. This is because the proposed residual demand curve modeling method takes into account the coupling relationship between the electricity energy market and the reserve market. The electricity and reserve bids of the photovoltaic power station may be small at the same time. Due to the joint clearing of the electricity energy market and the reserve market, the smaller reserve demand bid further reduces the electricity price in the electricity energy market. Therefore, compared with the larger bid electricity, the smaller bid electricity may produce a lower electricity market clearing price.

[0104] Set up the following three strategy combinations.

[0105] Combination 1: The bidding method proposed in the present invention.

[0106] Combination 2: The PV power station acts as a price determiner in the day-ahead electricity energy market and as a price taker in the reserve market.

[0107] Combination 3: The PV power station acts as a price taker in the day-ahead electricity energy market and as a price determiner in the reserve market.

[0108] The results of energy storage operation and the bidding results of photovoltaic power stations are shown in Tables 1 and 2. In combination 2, the energy storage has the largest day-ahead charging and discharging plan, so the day-ahead electricity sales revenue is the highest; the energy storage provides the least backup capacity, so the photovoltaic power station has the highest backup expenditure. Combination 3 is the opposite of combination 2. It can be seen that the photovoltaic power station is more active in the market where it is the price determiner, and increases the corresponding bidding amount to obtain higher benefits. When the photovoltaic power station acts as a price determiner in both markets, the photovoltaic power station will balance its bids in the day-ahead electricity energy and backup markets, and obtain more benefits than the other two strategy combinations.

[0109] Table 1 Energy storage operation results

[0110]

[0111] Table 2 PV and energy storage power station bidding results ($)

[0112]

[0113] Embodiment 2

[0114] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0115] Embodiment 3

[0116] The purpose of this embodiment is to provide a computer-readable storage medium.

[0117] A computer-readable storage medium stores a computer program, which executes the steps of the above method when executed by a processor.

[0118] Embodiment 4

[0119] The purpose of this embodiment is to provide a photovoltaic storage system operation device based on a residual demand curve, including:

[0120] The curve establishment module is configured to: consider the operation constraints of energy storage and the coupling relationship between the electric energy and the reserve market, and establish the electric energy-reserve surplus demand curve;

[0121] The adjustment module is configured to: adjust the day-ahead output plan and reserve capacity of the energy storage based on the power-reserve surplus demand curve with the goal of maximizing the profit of the photovoltaic and storage power station, while considering the uncertainty of the real-time market and reducing the real-time deviation penalty of the photovoltaic and storage power station by calling the reserve capacity of the energy storage.

[0122] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0123] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0124] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for operating a photovoltaic storage system based on a residual demand curve, characterized in that: include: Considering the operation constraints of energy storage and the coupling relationship between the power energy and reserve market, the power energy and reserve surplus demand curve is established; Establish the power-reserve surplus demand curve, specifically: Obtain historical data of the photovoltaic and energy storage system, use the day-ahead load forecast information and the day-ahead photovoltaic and energy storage power station bidding information as input, and use the electricity volume, electricity price, and reserve electricity price as output to train the neural network; Set the number of intervals for each remaining demand curve to N ; For integers Perform two random sorts; Check whether the bidding combination of PV and energy storage power stations meets the maximum charging and discharging power constraints of energy storage; If the conditions are met, the day-ahead load forecast information and the day-ahead PV power station bidding information are used as inputs, and the electricity quantity, electricity price and reserve electricity price are obtained through the neural network prediction model; Calculate the electricity prices corresponding to all bidding combinations of photovoltaic and energy storage power stations; get t The remaining demand curve of the photovoltaic power plant electricity bidding and the remaining demand curve of the standby bidding at that moment; Based on the electricity-reserve surplus demand curve, the day-ahead output plan and reserve capacity of the energy storage are adjusted with the goal of maximizing the benefits of the PV-storage power station. At the same time, the uncertainty of the real-time market is taken into account, and the real-time deviation penalty of the PV-storage power station is reduced by calling the reserve capacity of the energy storage.

2. The method for operating a photovoltaic storage system based on a residual demand curve according to claim 1, characterized in that: Considering the operation constraints of energy storage and the coupling relationship between the electricity energy and reserve market, a feasible bidding combination of electricity energy and reserve is generated within the day-ahead bidding range of the photovoltaic energy storage power station, and the corresponding market clearing electricity price is calculated based on neural network prediction.

3. The method for operating a photovoltaic storage system based on a residual demand curve according to claim 1, characterized in that: The day-ahead electricity bid of a photovoltaic power station with energy storage is the sum of the photovoltaic day-ahead forecast value and the energy storage day-ahead charging and discharging plan. The reserve bid is the sum of the photovoltaic day-ahead maximum forecast deviation and the energy storage increase / decrease in reserve capacity.

4. The method for operating a photovoltaic storage system based on a residual demand curve according to claim 1, characterized in that: By establishing the electricity-reserve surplus demand curve, the surplus demand curves in the day-ahead electricity energy and reserve markets are jointly generated, wherein each hour includes a day-ahead electricity bidding surplus demand curve and a reserve bidding surplus demand curve.

5. The method for operating a photovoltaic storage system based on a residual demand curve according to claim 1, characterized in that: The day-ahead output plan and reserve capacity of energy storage are adjusted with the goal of maximizing the benefits of the photovoltaic power station. The objective function takes into account the probability of the day-ahead scenario, the real-time photovoltaic power random scenario, and the real-time electricity price scenario. t The actual output of the photovoltaic power station under the time scenario is greater than the bid power a day ago, t The penalty power of the photovoltaic power station under the time scenario and t The charging and discharging cost of energy storage in real-time scenarios.

6. The method for operating a photovoltaic storage system based on a residual demand curve according to claim 5, characterized in that: The constraints of the objective function include: The bidding quantity constraints of photovoltaic and storage power stations, the day-ahead charging and discharging plan constraints of energy storage, the reserve capacity constraints of energy storage, the real-time adjustment power constraints of energy storage, the real-time electricity sales constraints of photovoltaic and storage power stations, and the real-time penalty quantity constraints of photovoltaic and storage power stations.

7. A photovoltaic storage system operation device based on a residual demand curve, characterized in that: include: The curve establishment module is configured to: consider the operation constraints of energy storage and the coupling relationship between the electric energy and the reserve market, and establish the electric energy-reserve surplus demand curve; Establish the power-reserve surplus demand curve, specifically: Obtain historical data of the photovoltaic and energy storage system, use the day-ahead load forecast information and the day-ahead photovoltaic and energy storage power station bidding information as input, and use the electricity volume, electricity price, and reserve electricity price as output to train the neural network; Set the number of intervals for each remaining demand curve to N ; For integers Perform two random sorts; Check whether the bidding combination of PV and energy storage power stations meets the maximum charging and discharging power constraints of energy storage; If the conditions are met, the day-ahead load forecast information and the day-ahead PV power station bidding information are used as inputs, and the electricity quantity, electricity price and reserve electricity price are obtained through the neural network prediction model; Calculate the electricity prices corresponding to all bidding combinations of photovoltaic and energy storage power stations; get t The remaining demand curve of the photovoltaic power plant electricity bidding and the remaining demand curve of the standby bidding at that moment; The adjustment module is configured to: adjust the day-ahead output plan and reserve capacity of the energy storage based on the power-reserve surplus demand curve with the goal of maximizing the profit of the photovoltaic and storage power station, while considering the uncertainty of the real-time market and reducing the real-time deviation penalty of the photovoltaic and storage power station by calling the reserve capacity of the energy storage.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are performed.

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